\\n\\nBuilt by [James](https://jamslevy.github.io/) (james@poolside.finance) and [Julia](https://juliawu.me) (julia@poolside.finance) at the MarketMake hackathon. \\n\\n## About the Project\\nThe first version of Poolside offers the following core features:\\n\\n1. New listings from Uniswap with scam filtering (automated and manual curation)\\n2. Trending tokens (top movers based on volume) and quotes from 1inch\\n3. Basic NFT data (Aavegotchi)\\n4. Search\\n\\nUsers come to Poolside to discover the latest and promising tokens listed on Uniswap. As we fetch and index new listings, we also apply scam-filtering logic to eliminate the obvious fakes and clones. We also allow admins to manually flag tokens as scams. We believe that this form of curation can later become incentivized through PLSD tokens.\\n\\nThe top movers are ordered by trade volume. For each token, one can see the daily and all-time volumes, current price, and change in the past day. The detailed view provides links to the token's website, whitepaper, and etherscan. \\n\\n<img src=\\\"static/images/poolside-details-screenshot.png\\\" alt=\\\"alt text\\\" width=\\\"500px\\\">\\n\\n\\nWhat differentiates Poolside from existing landing pages such as Defi Pulse? \\n\\n1. Data is updated on regular intervals and ranked dynamically, not static\\n2. Scam-filtering\\n3. Search functionality\\n4. Wallet integration for personalized results.\\n\\n## What's Next? \\n\\nWe plan to take Poolside further with personalization, integrations with news and scam-filtering token lists, and detection of noteworthy events in the markets. We also plan to continue indexing data from even more sources and types (NFT, Token Sets).\\n\\n\\n# Run Locally\\n\\n1. Clone this repo\\n2. Install `gcloud` if it is not already installed.\\n3. In the parent directory containing this project, run `dev_appserver.py poolside`\\n4. Go to http://localhost:8080/update-data to load initial data into the database.\\n5. Go to http://localhost:8080/ to view previously loaded data\\n\"\n },\n {\n \"alpha_fraction\": 0.739130437374115,\n \"alphanum_fraction\": 0.739130437374115,\n \"avg_line_length\": 19.44444465637207,\n \"blob_id\": \"fddff27f92d2c4e7b06844553a1f2a0d3d1978a6\",\n \"content_id\": \"09579bf5dcf70516f21b81729abfc6a7325184e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 184,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 9,\n \"path\": \"/admin.py\",\n \"repo_name\": \"jw122/poolside\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from google.appengine.api import users\\nimport logging\\n\\ndef is_admin():\\n\\tuser = users.get_current_user()\\n\\tif user and users.is_current_user_admin():\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn False\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2975792,"cells":{"repo_name":{"kind":"string","value":"Npasin/thoughts_flask"},"repo_url":{"kind":"string","value":"https://github.com/Npasin/thoughts_flask"},"snapshot_id":{"kind":"string","value":"b961d6945419c0f554381df6e8f2548342600f7b"},"revision_id":{"kind":"string","value":"8a4c775a06b0e25aa71aef190a49589a84407acd"},"directory_id":{"kind":"string","value":"f5e69340d6dfc4937c0525c00ad74b4d5a7ec3bb"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-12T23:59:37.525169","string":"2020-08-12T23:59:37.525169"},"revision_date":{"kind":"timestamp","value":"2019-10-13T19:51:00","string":"2019-10-13T19:51:00"},"committer_date":{"kind":"timestamp","value":"2019-10-13T19:51:00","string":"2019-10-13T19:51:00"},"github_id":{"kind":"number","value":214869679,"string":"214,869,679"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6281940937042236,"alphanum_fraction":0.6304909586906433,"avg_line_length":37.916202545166016,"blob_id":"b0f01f3be3b255a22c9b3468d5f4aaa99403f889","content_id":"9ba89ed10022dfef370652a3d6c9472e9d48b456","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6966,"license_type":"no_license","max_line_length":325,"num_lines":179,"path":"/server.py","repo_name":"Npasin/thoughts_flask","src_encoding":"UTF-8","text":"from flask import Flask, render_template, request, redirect, flash, session\nfrom mysqlconnection import connectToMySQL\nfrom flask_bcrypt import Bcrypt\nimport re\napp = Flask(__name__)\napp.secret_key=\"itsasecrettoeverybody\"\nEMAIL_REGEX = re.compile(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\\.[a-zA-Z]+$')\nbcrypt = Bcrypt(app)\n\n\n@app.route(\"/\")\ndef login_or_reg():\n return render_template(\"index.html\")\n\n@app.route(\"/register\", methods=[\"POST\"])\ndef register():\n is_valid = True\n if not request.form[\"fname\"].isalpha() or not len(request.form[\"fname\"]) >=2:\n is_valid = False\n flash(\"First name can only contain letters and must be at least 2 characters long\")\n if not request.form['lname'].isalpha() or not len(request.form['lname']) >= 2:\n is_valid = False\n flash(\"Last name can only contain letters and must be at least 2 characters long\")\n if not EMAIL_REGEX.match(request.form[\"email\"]):\n is_valid = False\n flash(\"Invalid Email Address\")\n if len(request.form[\"password\"]) < 8:\n is_valid = False\n flash(\"Password must be at least 8 characters long\")\n if request.form[\"password\"] != request.form[\"confirmpass\"]:\n is_valid = False\n flash(\"Passwords do not match\")\n if is_valid:\n pw_hash = bcrypt.generate_password_hash(request.form[\"password\"])\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"INSERT INTO users (first_name, last_name, email, password, created_at, updated_at) VALUES (%(fname)s, %(lname)s, %(email)s, %(password)s, NOW(), NOW())\"\n data = {\n \"fname\": request.form[\"fname\"],\n \"lname\": request.form[\"lname\"],\n \"email\": request.form[\"email\"],\n \"password\": pw_hash\n }\n user_id = mysql.query_db(query, data)\n session[\"user_id\"] = user_id\n print(user_id)\n return redirect(\"/thoughts\")\n\n return redirect(\"/\")\n\n@app.route(\"/login\", methods=[\"POST\"])\ndef login():\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT * FROM users WHERE users.email = %(email)s\"\n data = {\n \"email\": request.form[\"email\"]\n }\n user = mysql.query_db(query, data)\n if user:\n hashed_pw = user[0][\"password\"]\n if bcrypt.check_password_hash(hashed_pw, request.form['pass']):\n session[\"user_id\"] = user[0][\"user_id\"]\n return redirect(\"/thoughts\")\n else:\n flash(\"Invalid Password\")\n return redirect(\"/\")\n else:\n flash(\"Email not in Database\")\n return redirect(\"/\")\n\n@app.route(\"/logout\")\ndef logout():\n session.clear()\n return redirect(\"/\")\n\n@app.route(\"/thoughts\")\ndef user_landing():\n if \"user_id\" not in session:\n return redirect(\"/\")\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT * FROM users WHERE users.user_id = %(id)s\"\n data = {'id': session['user_id']}\n user = mysql.query_db(query, data)\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT thoughts.thought_id, thoughts.author, thoughts.content, thoughts.created_at, users.first_name, COUNT(user_likes.thought_like) as like_count FROM thoughts JOIN users ON thoughts.author = users.user_id LEFT JOIN user_likes on thoughts.thought_id = thought_like GROUP BY thought_id ORDER BY like_count desc\"\n thoughts_data = mysql.query_db(query)\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT thought_like, COUNT(thought_like) as like_count FROM user_likes GROUP BY thought_like\"\n like_count = mysql.query_db(query)\n\n return render_template(\"user_landing.html\", user_data = user[0], thoughts_data=thoughts_data, like_count = like_count)\n\n@app.route(\"/create_thought\", methods=[\"POST\"])\ndef create_thought():\n if \"user_id\" not in session:\n return redirect(\"/\")\n is_valid = True\n if len(request.form[\"thought_content\"]) < 6:\n is_valid = False\n flash(\"Thoughts must be longer than 5 characters.\", \"false\")\n if is_valid:\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"INSERT INTO thoughts (content, author, created_at, updated_at) VALUES (%(content)s, %(author)s, NOW(), NOW())\"\n data = {\n \"content\": request.form[\"thought_content\"],\n \"author\": session[\"user_id\"]\n }\n mysql.query_db(query, data)\n flash(\"Thought added!\", \"true\")\n return redirect(\"/thoughts\")\n\n@app.route(\"/delete_thought/<thought_id>\")\ndef delete_thought(thought_id):\n if \"user_id\" not in session:\n return redirect(\"/\")\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"DELETE FROM thoughts WHERE author = %(user_id)s and thought_id = %(thought_id)s\"\n data = {\n \"user_id\": session[\"user_id\"],\n \"thought_id\": thought_id\n }\n mysql.query_db(query, data)\n return redirect(\"/thoughts\")\n\n@app.route(\"/<thought_id>\")\ndef thought_details(thought_id):\n if \"user_id\" not in session:\n return redirect(\"/\")\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT thoughts.author, thoughts.thought_id, thoughts.content, users.first_name FROM thoughts JOIN users ON thoughts.author = users.user_id WHERE thoughts.thought_id = %(thought_id)s\"\n data = {\"thought_id\": thought_id}\n thought_data = mysql.query_db(query,data)\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT thought_like from user_likes WHERE user_like= %(user_id)s\"\n data = {\"user_id\": session[\"user_id\"]}\n liked_thoughts = [thought[\"thought_like\"] for thought in mysql.query_db(query, data)]\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"SELECT users.user_id, users.first_name, users.last_name FROM user_likes JOIN users ON user_likes.user_like = users.user_id WHERE thought_like = %(thought_id)s\"\n data = {\n \"thought_id\": thought_id\n }\n users_liked = mysql.query_db(query,data)\n\n return render_template(\"thought_details.html\", thought = thought_data[0], liked_thoughts = liked_thoughts, users_liked = users_liked)\n\n@app.route(\"/like/<thought_id>\")\ndef like_thought(thought_id):\n if \"user_id\" not in session:\n return redirect(\"/\")\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"INSERT INTO user_likes (user_like, thought_like) VALUES (%(user_id)s, %(thought_id)s)\"\n data = {\n \"user_id\": session[\"user_id\"],\n \"thought_id\": thought_id\n }\n mysql.query_db(query, data)\n # return redirect(f\"/{thought_id}\")\n return redirect(\"/{}\".format(thought_id))\n\n@app.route(\"/unlike/<thought_id>\")\ndef unlike_thought(thought_id):\n if \"user_id\" not in session:\n return redirect(\"/\")\n\n mysql = connectToMySQL(\"thoughts_db\")\n query = \"DELETE FROM user_likes WHERE user_like = %(user_id)s and thought_like = %(thought_id)s\"\n data = {\n \"user_id\": session[\"user_id\"],\n \"thought_id\": thought_id\n }\n mysql.query_db(query, data)\n # return redirect(f\"/{thought_id}\")\n return redirect(\"/{}\".format(thought_id))\nif __name__ == \"__main__\":\n app.run(debug=True)\n"},{"alpha_fraction":0.5447561740875244,"alphanum_fraction":0.5511022210121155,"avg_line_length":34.5121955871582,"blob_id":"7cd793b104a6c5dc28fdd7fb66cbad34d54b12c7","content_id":"9bafe3abb4769484d868467ac22f2800a41e642b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":2994,"license_type":"no_license","max_line_length":159,"num_lines":82,"path":"/thoughts_db.sql","repo_name":"Npasin/thoughts_flask","src_encoding":"UTF-8","text":"-- MySQL Workbench Forward Engineering\r\n\r\nSET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0;\r\nSET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0;\r\nSET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='ONLY_FULL_GROUP_BY,STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_ENGINE_SUBSTITUTION';\r\n\r\n-- -----------------------------------------------------\r\n-- Schema thoughts_db\r\n-- -----------------------------------------------------\r\nDROP SCHEMA IF EXISTS `thoughts_db` ;\r\n\r\n-- -----------------------------------------------------\r\n-- Schema thoughts_db\r\n-- -----------------------------------------------------\r\nCREATE SCHEMA IF NOT EXISTS `thoughts_db` DEFAULT CHARACTER SET utf8 ;\r\nUSE `thoughts_db` ;\r\n\r\n-- -----------------------------------------------------\r\n-- Table `thoughts_db`.`users`\r\n-- -----------------------------------------------------\r\nDROP TABLE IF EXISTS `thoughts_db`.`users` ;\r\n\r\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`users` (\r\n `user_id` INT NOT NULL AUTO_INCREMENT,\r\n `first_name` VARCHAR(255) NULL,\r\n `last_name` VARCHAR(255) NULL,\r\n `email` VARCHAR(255) NULL,\r\n `password` VARCHAR(255) NULL,\r\n `created_at` DATETIME NULL,\r\n `updated_at` DATETIME NULL,\r\n PRIMARY KEY (`user_id`))\r\nENGINE = InnoDB;\r\n\r\n\r\n-- -----------------------------------------------------\r\n-- Table `thoughts_db`.`thoughts`\r\n-- -----------------------------------------------------\r\nDROP TABLE IF EXISTS `thoughts_db`.`thoughts` ;\r\n\r\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`thoughts` (\r\n `thought_id` INT NOT NULL AUTO_INCREMENT,\r\n `content` TEXT NULL,\r\n `created_at` DATETIME NULL,\r\n `updated_at` DATETIME NULL,\r\n `author` INT NOT NULL,\r\n PRIMARY KEY (`thought_id`),\r\n INDEX `fk_thoughts_users_idx` (`author` ASC) VISIBLE,\r\n CONSTRAINT `fk_thoughts_users`\r\n FOREIGN KEY (`author`)\r\n REFERENCES `thoughts_db`.`users` (`user_id`)\r\n ON DELETE NO ACTION\r\n ON UPDATE NO ACTION)\r\nENGINE = InnoDB;\r\n\r\n\r\n-- -----------------------------------------------------\r\n-- Table `thoughts_db`.`user_likes`\r\n-- -----------------------------------------------------\r\nDROP TABLE IF EXISTS `thoughts_db`.`user_likes` ;\r\n\r\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`user_likes` (\r\n `user_like` INT NOT NULL,\r\n `thought_like` INT NOT NULL,\r\n PRIMARY KEY (`user_like`, `thought_like`),\r\n INDEX `fk_users_has_thoughts_thoughts1_idx` (`thought_like` ASC) VISIBLE,\r\n INDEX `fk_users_has_thoughts_users1_idx` (`user_like` ASC) VISIBLE,\r\n CONSTRAINT `fk_users_has_thoughts_users1`\r\n FOREIGN KEY (`user_like`)\r\n REFERENCES `thoughts_db`.`users` (`user_id`)\r\n ON DELETE NO ACTION\r\n ON UPDATE NO ACTION,\r\n CONSTRAINT `fk_users_has_thoughts_thoughts1`\r\n FOREIGN KEY (`thought_like`)\r\n REFERENCES `thoughts_db`.`thoughts` (`thought_id`)\r\n ON DELETE NO ACTION\r\n ON UPDATE NO ACTION)\r\nENGINE = InnoDB;\r\n\r\n\r\nSET SQL_MODE=@OLD_SQL_MODE;\r\nSET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS;\r\nSET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS;\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6281940937042236,\n \"alphanum_fraction\": 0.6304909586906433,\n \"avg_line_length\": 37.916202545166016,\n \"blob_id\": \"b0f01f3be3b255a22c9b3468d5f4aaa99403f889\",\n \"content_id\": \"9ba89ed10022dfef370652a3d6c9472e9d48b456\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6966,\n \"license_type\": \"no_license\",\n \"max_line_length\": 325,\n \"num_lines\": 179,\n \"path\": \"/server.py\",\n \"repo_name\": \"Npasin/thoughts_flask\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask, render_template, request, redirect, flash, session\\nfrom mysqlconnection import connectToMySQL\\nfrom flask_bcrypt import Bcrypt\\nimport re\\napp = Flask(__name__)\\napp.secret_key=\\\"itsasecrettoeverybody\\\"\\nEMAIL_REGEX = re.compile(r'^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\\\\.[a-zA-Z]+$')\\nbcrypt = Bcrypt(app)\\n\\n\\n@app.route(\\\"/\\\")\\ndef login_or_reg():\\n return render_template(\\\"index.html\\\")\\n\\n@app.route(\\\"/register\\\", methods=[\\\"POST\\\"])\\ndef register():\\n is_valid = True\\n if not request.form[\\\"fname\\\"].isalpha() or not len(request.form[\\\"fname\\\"]) >=2:\\n is_valid = False\\n flash(\\\"First name can only contain letters and must be at least 2 characters long\\\")\\n if not request.form['lname'].isalpha() or not len(request.form['lname']) >= 2:\\n is_valid = False\\n flash(\\\"Last name can only contain letters and must be at least 2 characters long\\\")\\n if not EMAIL_REGEX.match(request.form[\\\"email\\\"]):\\n is_valid = False\\n flash(\\\"Invalid Email Address\\\")\\n if len(request.form[\\\"password\\\"]) < 8:\\n is_valid = False\\n flash(\\\"Password must be at least 8 characters long\\\")\\n if request.form[\\\"password\\\"] != request.form[\\\"confirmpass\\\"]:\\n is_valid = False\\n flash(\\\"Passwords do not match\\\")\\n if is_valid:\\n pw_hash = bcrypt.generate_password_hash(request.form[\\\"password\\\"])\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"INSERT INTO users (first_name, last_name, email, password, created_at, updated_at) VALUES (%(fname)s, %(lname)s, %(email)s, %(password)s, NOW(), NOW())\\\"\\n data = {\\n \\\"fname\\\": request.form[\\\"fname\\\"],\\n \\\"lname\\\": request.form[\\\"lname\\\"],\\n \\\"email\\\": request.form[\\\"email\\\"],\\n \\\"password\\\": pw_hash\\n }\\n user_id = mysql.query_db(query, data)\\n session[\\\"user_id\\\"] = user_id\\n print(user_id)\\n return redirect(\\\"/thoughts\\\")\\n\\n return redirect(\\\"/\\\")\\n\\n@app.route(\\\"/login\\\", methods=[\\\"POST\\\"])\\ndef login():\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT * FROM users WHERE users.email = %(email)s\\\"\\n data = {\\n \\\"email\\\": request.form[\\\"email\\\"]\\n }\\n user = mysql.query_db(query, data)\\n if user:\\n hashed_pw = user[0][\\\"password\\\"]\\n if bcrypt.check_password_hash(hashed_pw, request.form['pass']):\\n session[\\\"user_id\\\"] = user[0][\\\"user_id\\\"]\\n return redirect(\\\"/thoughts\\\")\\n else:\\n flash(\\\"Invalid Password\\\")\\n return redirect(\\\"/\\\")\\n else:\\n flash(\\\"Email not in Database\\\")\\n return redirect(\\\"/\\\")\\n\\n@app.route(\\\"/logout\\\")\\ndef logout():\\n session.clear()\\n return redirect(\\\"/\\\")\\n\\n@app.route(\\\"/thoughts\\\")\\ndef user_landing():\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT * FROM users WHERE users.user_id = %(id)s\\\"\\n data = {'id': session['user_id']}\\n user = mysql.query_db(query, data)\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT thoughts.thought_id, thoughts.author, thoughts.content, thoughts.created_at, users.first_name, COUNT(user_likes.thought_like) as like_count FROM thoughts JOIN users ON thoughts.author = users.user_id LEFT JOIN user_likes on thoughts.thought_id = thought_like GROUP BY thought_id ORDER BY like_count desc\\\"\\n thoughts_data = mysql.query_db(query)\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT thought_like, COUNT(thought_like) as like_count FROM user_likes GROUP BY thought_like\\\"\\n like_count = mysql.query_db(query)\\n\\n return render_template(\\\"user_landing.html\\\", user_data = user[0], thoughts_data=thoughts_data, like_count = like_count)\\n\\n@app.route(\\\"/create_thought\\\", methods=[\\\"POST\\\"])\\ndef create_thought():\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n is_valid = True\\n if len(request.form[\\\"thought_content\\\"]) < 6:\\n is_valid = False\\n flash(\\\"Thoughts must be longer than 5 characters.\\\", \\\"false\\\")\\n if is_valid:\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"INSERT INTO thoughts (content, author, created_at, updated_at) VALUES (%(content)s, %(author)s, NOW(), NOW())\\\"\\n data = {\\n \\\"content\\\": request.form[\\\"thought_content\\\"],\\n \\\"author\\\": session[\\\"user_id\\\"]\\n }\\n mysql.query_db(query, data)\\n flash(\\\"Thought added!\\\", \\\"true\\\")\\n return redirect(\\\"/thoughts\\\")\\n\\n@app.route(\\\"/delete_thought/<thought_id>\\\")\\ndef delete_thought(thought_id):\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"DELETE FROM thoughts WHERE author = %(user_id)s and thought_id = %(thought_id)s\\\"\\n data = {\\n \\\"user_id\\\": session[\\\"user_id\\\"],\\n \\\"thought_id\\\": thought_id\\n }\\n mysql.query_db(query, data)\\n return redirect(\\\"/thoughts\\\")\\n\\n@app.route(\\\"/<thought_id>\\\")\\ndef thought_details(thought_id):\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT thoughts.author, thoughts.thought_id, thoughts.content, users.first_name FROM thoughts JOIN users ON thoughts.author = users.user_id WHERE thoughts.thought_id = %(thought_id)s\\\"\\n data = {\\\"thought_id\\\": thought_id}\\n thought_data = mysql.query_db(query,data)\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT thought_like from user_likes WHERE user_like= %(user_id)s\\\"\\n data = {\\\"user_id\\\": session[\\\"user_id\\\"]}\\n liked_thoughts = [thought[\\\"thought_like\\\"] for thought in mysql.query_db(query, data)]\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"SELECT users.user_id, users.first_name, users.last_name FROM user_likes JOIN users ON user_likes.user_like = users.user_id WHERE thought_like = %(thought_id)s\\\"\\n data = {\\n \\\"thought_id\\\": thought_id\\n }\\n users_liked = mysql.query_db(query,data)\\n\\n return render_template(\\\"thought_details.html\\\", thought = thought_data[0], liked_thoughts = liked_thoughts, users_liked = users_liked)\\n\\n@app.route(\\\"/like/<thought_id>\\\")\\ndef like_thought(thought_id):\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"INSERT INTO user_likes (user_like, thought_like) VALUES (%(user_id)s, %(thought_id)s)\\\"\\n data = {\\n \\\"user_id\\\": session[\\\"user_id\\\"],\\n \\\"thought_id\\\": thought_id\\n }\\n mysql.query_db(query, data)\\n # return redirect(f\\\"/{thought_id}\\\")\\n return redirect(\\\"/{}\\\".format(thought_id))\\n\\n@app.route(\\\"/unlike/<thought_id>\\\")\\ndef unlike_thought(thought_id):\\n if \\\"user_id\\\" not in session:\\n return redirect(\\\"/\\\")\\n\\n mysql = connectToMySQL(\\\"thoughts_db\\\")\\n query = \\\"DELETE FROM user_likes WHERE user_like = %(user_id)s and thought_like = %(thought_id)s\\\"\\n data = {\\n \\\"user_id\\\": session[\\\"user_id\\\"],\\n \\\"thought_id\\\": thought_id\\n }\\n mysql.query_db(query, data)\\n # return redirect(f\\\"/{thought_id}\\\")\\n return redirect(\\\"/{}\\\".format(thought_id))\\nif __name__ == \\\"__main__\\\":\\n app.run(debug=True)\\n\"\n },\n {\n \"alpha_fraction\": 0.5447561740875244,\n \"alphanum_fraction\": 0.5511022210121155,\n \"avg_line_length\": 34.5121955871582,\n \"blob_id\": \"7cd793b104a6c5dc28fdd7fb66cbad34d54b12c7\",\n \"content_id\": \"9bafe3abb4769484d868467ac22f2800a41e642b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 2994,\n \"license_type\": \"no_license\",\n \"max_line_length\": 159,\n \"num_lines\": 82,\n \"path\": \"/thoughts_db.sql\",\n \"repo_name\": \"Npasin/thoughts_flask\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"-- MySQL Workbench Forward Engineering\\r\\n\\r\\nSET @OLD_UNIQUE_CHECKS=@@UNIQUE_CHECKS, UNIQUE_CHECKS=0;\\r\\nSET @OLD_FOREIGN_KEY_CHECKS=@@FOREIGN_KEY_CHECKS, FOREIGN_KEY_CHECKS=0;\\r\\nSET @OLD_SQL_MODE=@@SQL_MODE, SQL_MODE='ONLY_FULL_GROUP_BY,STRICT_TRANS_TABLES,NO_ZERO_IN_DATE,NO_ZERO_DATE,ERROR_FOR_DIVISION_BY_ZERO,NO_ENGINE_SUBSTITUTION';\\r\\n\\r\\n-- -----------------------------------------------------\\r\\n-- Schema thoughts_db\\r\\n-- -----------------------------------------------------\\r\\nDROP SCHEMA IF EXISTS `thoughts_db` ;\\r\\n\\r\\n-- -----------------------------------------------------\\r\\n-- Schema thoughts_db\\r\\n-- -----------------------------------------------------\\r\\nCREATE SCHEMA IF NOT EXISTS `thoughts_db` DEFAULT CHARACTER SET utf8 ;\\r\\nUSE `thoughts_db` ;\\r\\n\\r\\n-- -----------------------------------------------------\\r\\n-- Table `thoughts_db`.`users`\\r\\n-- -----------------------------------------------------\\r\\nDROP TABLE IF EXISTS `thoughts_db`.`users` ;\\r\\n\\r\\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`users` (\\r\\n `user_id` INT NOT NULL AUTO_INCREMENT,\\r\\n `first_name` VARCHAR(255) NULL,\\r\\n `last_name` VARCHAR(255) NULL,\\r\\n `email` VARCHAR(255) NULL,\\r\\n `password` VARCHAR(255) NULL,\\r\\n `created_at` DATETIME NULL,\\r\\n `updated_at` DATETIME NULL,\\r\\n PRIMARY KEY (`user_id`))\\r\\nENGINE = InnoDB;\\r\\n\\r\\n\\r\\n-- -----------------------------------------------------\\r\\n-- Table `thoughts_db`.`thoughts`\\r\\n-- -----------------------------------------------------\\r\\nDROP TABLE IF EXISTS `thoughts_db`.`thoughts` ;\\r\\n\\r\\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`thoughts` (\\r\\n `thought_id` INT NOT NULL AUTO_INCREMENT,\\r\\n `content` TEXT NULL,\\r\\n `created_at` DATETIME NULL,\\r\\n `updated_at` DATETIME NULL,\\r\\n `author` INT NOT NULL,\\r\\n PRIMARY KEY (`thought_id`),\\r\\n INDEX `fk_thoughts_users_idx` (`author` ASC) VISIBLE,\\r\\n CONSTRAINT `fk_thoughts_users`\\r\\n FOREIGN KEY (`author`)\\r\\n REFERENCES `thoughts_db`.`users` (`user_id`)\\r\\n ON DELETE NO ACTION\\r\\n ON UPDATE NO ACTION)\\r\\nENGINE = InnoDB;\\r\\n\\r\\n\\r\\n-- -----------------------------------------------------\\r\\n-- Table `thoughts_db`.`user_likes`\\r\\n-- -----------------------------------------------------\\r\\nDROP TABLE IF EXISTS `thoughts_db`.`user_likes` ;\\r\\n\\r\\nCREATE TABLE IF NOT EXISTS `thoughts_db`.`user_likes` (\\r\\n `user_like` INT NOT NULL,\\r\\n `thought_like` INT NOT NULL,\\r\\n PRIMARY KEY (`user_like`, `thought_like`),\\r\\n INDEX `fk_users_has_thoughts_thoughts1_idx` (`thought_like` ASC) VISIBLE,\\r\\n INDEX `fk_users_has_thoughts_users1_idx` (`user_like` ASC) VISIBLE,\\r\\n CONSTRAINT `fk_users_has_thoughts_users1`\\r\\n FOREIGN KEY (`user_like`)\\r\\n REFERENCES `thoughts_db`.`users` (`user_id`)\\r\\n ON DELETE NO ACTION\\r\\n ON UPDATE NO ACTION,\\r\\n CONSTRAINT `fk_users_has_thoughts_thoughts1`\\r\\n FOREIGN KEY (`thought_like`)\\r\\n REFERENCES `thoughts_db`.`thoughts` (`thought_id`)\\r\\n ON DELETE NO ACTION\\r\\n ON UPDATE NO ACTION)\\r\\nENGINE = InnoDB;\\r\\n\\r\\n\\r\\nSET SQL_MODE=@OLD_SQL_MODE;\\r\\nSET FOREIGN_KEY_CHECKS=@OLD_FOREIGN_KEY_CHECKS;\\r\\nSET UNIQUE_CHECKS=@OLD_UNIQUE_CHECKS;\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2975793,"cells":{"repo_name":{"kind":"string","value":"nisheeth84/mock_test_aws"},"repo_url":{"kind":"string","value":"https://github.com/nisheeth84/mock_test_aws"},"snapshot_id":{"kind":"string","value":"a294e45966fbc6f2b9fd06494259a43ee3ccd99d"},"revision_id":{"kind":"string","value":"7be25317e6e5ecc6cb111f9da5f76401535d5178"},"directory_id":{"kind":"string","value":"d10d56262c030051d1594b039b034bbad9b82870"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-12-31T00:39:33.187704","string":"2022-12-31T00:39:33.187704"},"revision_date":{"kind":"timestamp","value":"2020-09-11T15:54:29","string":"2020-09-11T15:54:29"},"committer_date":{"kind":"timestamp","value":"2020-09-11T15:54:29","string":"2020-09-11T15:54:29"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5494158267974854,"alphanum_fraction":0.5898326635360718,"avg_line_length":39.34394836425781,"blob_id":"dcda5e666c91d492e89c77cedec2de8a4a1960f4","content_id":"2d2695b633b0190d61b29dfa21f65a80f2f233e2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6334,"license_type":"no_license","max_line_length":263,"num_lines":157,"path":"/tests/test_my_handler.py","repo_name":"nisheeth84/mock_test_aws","src_encoding":"UTF-8","text":"import sys\nsys.path.append(\"..\")\nimport boto3\nfrom package.moto import mock_sns, mock_cloudwatch\n# from moto import mock_sns, mock_cloudwatch\nfrom src.my_handler import *\nimport time\nfrom os import getenv\nfrom datetime import datetime, timedelta, tzinfo, timezone\nfrom aws_lambda_context import LambdaContext\n\nSUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\n\n@mock_sns\ndef test_get_subs_endpoint():\n client = boto3.client(\"sns\", region_name=\"us-east-1\")\n client.create_topic(Name=\"my-topic\")\n resp = client.create_topic(Name=\"my-topic\")\n arn = resp[\"TopicArn\"]\n # print(arn)\n resp = client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda1@mailinator.com\")\n resp = client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda2@mailinator.com\")\n assert get_subs_endpoint(arn) == ['testlambda1@mailinator.com','testlambda2@mailinator.com']\n print(\"=========================================\")\n print(\"test_get_subs_endpoint ran successful\")\n print(\"=========================================\")\n\n\n@mock_cloudwatch\n@mock_sns\ndef test_get_alarms():\n client = boto3.client(\"cloudwatch\", region_name=\"us-east-1\")\n client = boto3.client(\"sns\", region_name=\"us-east-1\")\n client.create_topic(Name=\"my-topic\")\n resp = client.create_topic(Name=\"my-topic\")\n arn = resp[\"TopicArn\"]\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda1@mailinator.com\")\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda2@mailinator.com\")\n start_date = '2020-06-24T18:59:06.442Z'\n resp = get_alarms(start_date, end_date = None, offset = None)\n assert resp['myalarm']['end_points'] == ['testlambda1@mailinator.com', 'testlambda2@mailinator.com']\n assert resp['myalarm']['count'] == 3\n\n offset = 2\n resp = get_alarms(start_date, end_date = None, offset = offset)\n assert resp['myalarm']['end_points'] == ['testlambda1@mailinator.com', 'testlambda2@mailinator.com']\n assert resp['myalarm']['count'] == 3\n\n print(\"=========================================\")\n print(\"test_get_alarms ran successful\")\n print(\"=========================================\")\n\n\n@mock_cloudwatch\n@mock_sns\ndef test_get_alarm_to_report():\n client = boto3.client(\"cloudwatch\", region_name=\"us-east-1\")\n client = boto3.client(\"sns\", region_name=\"us-east-1\")\n client.create_topic(Name=\"my-topic\")\n resp = client.create_topic(Name=\"my-topic\")\n arn = resp[\"TopicArn\"]\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda1@mailinator.com\")\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda2@mailinator.com\")\n start_date = '2020-06-24T18:59:06.442Z'\n\n event = {'date': '2020-06-24T18:59:06.442Z',\n 'date_start': '2020-06-25 18:00:00',\n 'date_end': '2020-06-25 19:00:00.123'}\n\n dt = datetime.now() - timedelta(1) # yesterday\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\n date_start = date_end = None\n\n if event and 'date' in event:\n date = event['date']\n print(\"Date: \", date)\n if event and 'date_start' in event:\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\n print(\"Date Ini: \", date_start)\n if event and 'date_end' in event:\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\n print(\"Date End: \", date_end)\n\n\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\" \", \"\").lower().split(\",\")), date)\n assert resp[0]['Alarm'] == 'myalarm'\n assert resp[0]['# Events'] == 3\n assert resp[0]['Description'] == 'Test Description'\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\n\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\" \", \"\").lower().split(\",\")), date_start, date_end)\n assert resp[0]['Alarm'] == 'myalarm'\n assert resp[0]['# Events'] == 1\n assert resp[0]['Description'] == 'Test Description'\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\n\n\n offset = 2\n event = {'date': '2020-06-24T20:59:06.442Z',\n 'date_start': '2020-06-25 20:00:00',\n 'date_end': '2020-06-25 21:00:00.123'}\n\n dt = datetime.now() - timedelta(1) # yesterday\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\n date_start = date_end = None\n\n if event and 'date' in event:\n date = event['date']\n print(\"Date: \", date)\n if event and 'date_start' in event:\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\n print(\"Date Ini: \", date_start)\n if event and 'date_end' in event:\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\n print(\"Date End: \", date_end)\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\" \", \"\").lower().split(\",\")), date_start, date_end, offset)\n\n assert resp[0]['Alarm'] == 'myalarm'\n assert resp[0]['# Events'] == 1\n assert resp[0]['Description'] == 'Test Description'\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\n\n\n print(\"=========================================\")\n print(\"test_get_alarm_to_report ran successful\")\n print(\"=========================================\")\n\n@mock_cloudwatch\n@mock_sns\ndef test_lambda_handler():\n client = boto3.client(\"cloudwatch\", region_name=\"us-east-1\")\n client = boto3.client(\"sns\", region_name=\"us-east-1\")\n client.create_topic(Name=\"my-topic\")\n resp = client.create_topic(Name=\"my-topic\")\n arn = resp[\"TopicArn\"]\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda1@mailinator.com\")\n client.subscribe(TopicArn=arn, Protocol=\"email\", Endpoint=\"testlambda2@mailinator.com\")\n start_date = '2020-06-24T18:59:06.442Z'\n\n event = {'date': '2020-06-24T18:59:06.442Z',\n 'date_start': '2020-06-25 12:00:00',\n 'date_end': '2020-06-25 13:00:00.123',\n 'format': 'html'}\n\n resp = lambda_handler(event, LambdaContext)\n\n assert resp['statusCode'] == 200\n assert resp['body'] == '<table border=\"1\"><thead><tr><th>Alarm</th><th># Events</th><th>Description</th><th>Status / JIRA Ticket</th></tr></thead><tbody><tr><td>myalarm</td><td>1</td><td>Test Description</td><td>Test Jira Ticket 123</td></tr></tbody></table>'\n\n print(\"=========================================\")\n print(\"test_lambda_handler ran successful\")\n print(\"=========================================\")\n\n# def run_all_test():\n# test_get_subs_endpoint()\n# test_get_alarms()\n# test_get_alarm_to_report()\n"},{"alpha_fraction":0.5775312185287476,"alphanum_fraction":0.6798890233039856,"avg_line_length":55.328125,"blob_id":"548776631444115f19886b2354d93cd26cbf355e","content_id":"c5137c31ad16e21e0ba53146a0d7a551c0ac239c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3609,"license_type":"no_license","max_line_length":560,"num_lines":64,"path":"/README.md","repo_name":"nisheeth84/mock_test_aws","src_encoding":"UTF-8","text":"# Steps to run this:\n\ngo to terminal and go to inside this folder\n\n### Setup Python Virtual Environment\n> pip install virtualenv\n\n> virtualenv myvirtualevn\n\nFor Mac OS / Linux\n> source myvirtualevn/bin/activate\n\nFor Windows\n> myvirtualevn\\Scripts\\activate\n\n\n### then below commands in sequence:\npip install -r requirements.txt\n\npytest test/test_my_handler.py\n\n### check coverage:\npytest --cov=src tests/\n\nNote: There are couple of things to note when you run/test this code.\n\n- As 'describe_alarm_history' method was not implemented in actual moto library\nwe implemented this in the code with the sample alarms provide.\nThe sample alarms are given below:\n```python\nalarms = [{\n \"Timestamp\": \"2020-06-25T18:59:06.442Z\",\n \"HistoryItemType\": \"StateUpdate\",\n \"AlarmName\": \"myalarm\",\n \"HistoryData\": \"{\\\"version\\\":\\\"1.0\\\",\\\"oldState\\\":{\\\"stateValue\\\":\\\"ALARM\\\",\\\"stateReason\\\":\\\"testing purposes\\\"},\\\"newState\\\":{\\\"stateValue\\\":\\\"OK\\\",\\\"stateReason\\\":\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.958, 40.292].\\\",\\\"stateReasonData\\\":{\\\"version\\\":\\\"1.0\\\",\\\"queryDate\\\":\\\"2014-04-09T18:59:06.419+0000\\\",\\\"startDate\\\":\\\"2014-04-09T18:44:00.000+0000\\\",\\\"statistic\\\":\\\"Average\\\",\\\"period\\\":300,\\\"recentDatapoints\\\":[38.958,40.292],\\\"threshold\\\":70.0}}}\",\n \"HistorySummary\": \"Alarm updated from ALARM to OK arn:aws:sns:us-east-1:123456789012:my-topic\"\n },\n {\n \"Timestamp\": \"2020-06-25T19:59:06.442Z\",\n \"HistoryItemType\": \"StateUpdate\",\n \"AlarmName\": \"myalarm\",\n \"HistoryData\": \"{\\\"version\\\":\\\"1.0\\\",\\\"oldState\\\":{\\\"stateValue\\\":\\\"OK\\\",\\\"stateReason\\\":\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.839999999999996, 39.714].\\\",\\\"stateReasonData\\\":{\\\"version\\\":\\\"1.0\\\",\\\"queryDate\\\":\\\"2014-03-11T22:45:41.569+0000\\\",\\\"startDate\\\":\\\"2014-03-11T22:30:00.000+0000\\\",\\\"statistic\\\":\\\"Average\\\",\\\"period\\\":300,\\\"recentDatapoints\\\":[38.839999999999996,39.714],\\\"threshold\\\":70.0}},\\\"newState\\\":{\\\"stateValue\\\":\\\"ALARM\\\",\\\"stateReason\\\":\\\"testing purposes\\\"}}\",\n \"HistorySummary\": \"Alarm updated from OK to ALARM arn:aws:sns:us-east-1:123456789012:my-topic\"\n },\n {\n \"Timestamp\": \"2020-06-25T20:59:06.442Z\",\n \"HistoryItemType\": \"StateUpdate\",\n \"AlarmName\": \"myalarm\",\n \"HistoryData\": \"{\\\"version\\\":\\\"1.0\\\",\\\"oldState\\\":{\\\"stateValue\\\":\\\"OK\\\",\\\"stateReason\\\":\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.839999999999996, 39.714].\\\",\\\"stateReasonData\\\":{\\\"version\\\":\\\"1.0\\\",\\\"queryDate\\\":\\\"2014-03-11T22:45:41.569+0000\\\",\\\"startDate\\\":\\\"2014-03-11T22:30:00.000+0000\\\",\\\"statistic\\\":\\\"Average\\\",\\\"period\\\":300,\\\"recentDatapoints\\\":[38.839999999999996,39.714],\\\"threshold\\\":70.0}},\\\"newState\\\":{\\\"stateValue\\\":\\\"ALARM\\\",\\\"stateReason\\\":\\\"testing purposes\\\"}}\",\n \"HistorySummary\": \"Alarm updated from OK to ALARM arn:aws:sns:us-east-1:123456789012:my-topic\"\n }]\n```\n\nThis is just for testing purpose, so if you want to modify it you can go below\nand modify it according to your alarms and need to modify the test cases accordingly:\n> package -> moto -> cloudwatch -> responses.py -> line 142\n\n- In the testing we use environment variable SUBSCRIPTORS_TO_REPORT.\nIf there is no data get found in that variable then by default it will take 'testlambda1@mailinator.com' as a value.\n\n\n### Deactivate the virtual environment\nTo decativate the virtual environment and use your original Python environment, simply type ‘deactivate’.\n> deactivate\n"},{"alpha_fraction":0.4821782112121582,"alphanum_fraction":0.694059431552887,"avg_line_length":15.557376861572266,"blob_id":"45fc3ce3ebe50e0de71ad93e991b2cccf4138fe8","content_id":"d4ec7043e69d0b80dbc20a2e79cf607fa5214645","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":1010,"license_type":"no_license","max_line_length":26,"num_lines":61,"path":"/requirements.txt","repo_name":"nisheeth84/mock_test_aws","src_encoding":"UTF-8","text":"attrs==19.3.0\naws-lambda-context==1.1.0\naws-sam-translator==1.25.0\naws-xray-sdk==2.6.0\nboto==2.49.0\nboto3==1.14.11\nbotocore==1.17.11\ncertifi==2020.6.20\ncffi==1.14.0\ncfn-lint==0.33.2\nchardet==3.0.4\ncoverage==5.1\ncryptography==2.9.2\ndecorator==4.4.2\ndocker==4.2.1\ndocutils==0.15.2\necdsa==0.15\nfuture==0.18.2\nidna==2.8\nimportlib-metadata==1.6.1\nJinja2==2.11.2\njmespath==0.10.0\njson2html==1.3.0\njsondiff==1.1.2\njsonpatch==1.26\njsonpickle==1.4.1\njsonpointer==2.0\njsonschema==3.2.0\njunit-xml==1.9\nMarkupSafe==1.1.1\nmock==4.0.2\nmore-itertools==8.4.0\nmoto==1.3.14\nnetworkx==2.4\npackaging==20.4\npip-autoremove==0.9.1\npluggy==0.13.1\npy==1.8.2\npyasn1==0.4.8\npycparser==2.20\npyparsing==2.4.7\npyrsistent==0.16.0\npytest==5.4.3\npytest-cov==2.10.0\npython-dateutil==2.8.1\npython-jose==3.1.0\npytz==2020.1\nPyYAML==5.3.1\nrequests==2.24.0\nresponses==0.10.15\nrsa==4.6\ns3transfer==0.3.3\nsix==1.15.0\nsshpubkeys==3.1.0\nurllib3==1.25.9\nwcwidth==0.2.5\nwebsocket-client==0.57.0\nWerkzeug==1.0.1\nwrapt==1.12.1\nxmltodict==0.12.0\nzipp==3.1.0\n"},{"alpha_fraction":0.5766996741294861,"alphanum_fraction":0.5804578065872192,"avg_line_length":26.744075775146484,"blob_id":"778fa226bbcc9040c1fdf6b05a58bbf4375f550e","content_id":"440b0c5d48d5a1d6a1a2b1b4cd4d6848a50cee6f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5854,"license_type":"no_license","max_line_length":178,"num_lines":211,"path":"/src/my_handler.py","repo_name":"nisheeth84/mock_test_aws","src_encoding":"UTF-8","text":"import boto3\nimport json\nfrom json2html import *\n#from jsonconv import *\nimport time\nfrom os import getenv\nfrom datetime import datetime, timedelta, tzinfo, timezone\n\n# SUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\nSUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\n\ndef get_subs_endpoint(topic):\n end_points = []\n snsclient = boto3.client('sns', region_name=\"us-east-1\")\n paginator = snsclient.get_paginator('list_subscriptions_by_topic')\n #print(\"Topic: \", topic)\n try:\n for response in paginator.paginate(TopicArn=topic):\n #print(\"Response: \", response)\n if 'Subscriptions' in response:\n #print(\"Subscriptions: \", response['Subscriptions'])\n for subscription in response['Subscriptions']:\n #print(\"EndPoint: \", subscription['Endpoint'])\n end_points.append(subscription['Endpoint'].lower())\n except Exception as e:\n print(\"Error getting topic: \", topic)\n print(e)\n # print (\"End Points\", end_points)\n return end_points\n\n\n\ndef get_alarms(start_date, end_date = None, offset = None):\n time_diff = None\n if offset:\n time_diff = timedelta(hours=offset)\n print(\"Timediff=>\", time_diff)\n alarms = {}\n cloudwatch = boto3.client('cloudwatch', region_name=\"us-east-1\")\n\n paginator = cloudwatch.get_paginator('describe_alarm_history')\n\n # print(\"Alarms Date: \", start_date)\n\n if end_date:\n # print(\"Alarms from: \", end_date)\n page_iterator = paginator.paginate(HistoryItemType='Action',\n StartDate=start_date,\n EndDate=end_date)\n else:\n page_iterator = paginator.paginate(HistoryItemType='Action',\n StartDate=start_date)\n\n #central = time.timezone('US/Central')\n\n for response in page_iterator:\n # print(response)\n\n for alarm in response['AlarmHistoryItems']:\n\n # print(\"AlarmName: [\", alarm['AlarmName'], \"]\")\n\n # print(\"HistorySummary: \", alarm['HistorySummary'])\n\n alarm['Timestamp'] = alarm['Timestamp'].replace(tzinfo=None)\n\n if time_diff and alarm['Timestamp']: # Adjust time\n\n alarm['Timestamp'] = alarm['Timestamp'] + time_diff\n\n print(\"Timestamp CT: \", alarm['Timestamp'])\n\n else:\n\n print(\"Timestamp UTC: \", alarm['Timestamp'])\n\n add_alarm = True\n\n if end_date and (alarm['Timestamp'] < start_date or alarm['Timestamp'] > end_date):\n\n # print(\"Alarm outside time frame.... discarded.\")\n\n add_alarm = False\n\n if add_alarm:\n\n if alarm['AlarmName'] in alarms:\n\n #print(\"Alarms from Old: \", alarms)\n\n alarms[alarm['AlarmName']]['count'] += 1\n\n #print(\"Alarm Name Duplicated: \", alarm['AlarmName'])\n\n else:\n\n #print(\"Alarms from New: \", alarms)\n\n alarms.update( { alarm['AlarmName'] : {'end_points' : get_subs_endpoint(alarm['HistorySummary'][alarm['HistorySummary'].find('arn'):]), 'count' : 1} } )\n\n #print(\"Alarm Name New: \", alarm['AlarmName'])\n\n\n\n # print(\"Alarms: \", alarms)\n\n return alarms\n\n\n\ndef get_alarm_to_report(subscriptors, start_date, end_date = None, offset = None):\n\n alarms_to_report = []\n\n alarms = get_alarms(start_date, end_date, offset)\n print(\"Alarms=>: \", alarms)\n print(\"Subscriptors=>\", subscriptors)\n\n for alarm in sorted(alarms):\n\n # print(\"EndPoint=>: \", alarms[alarm]['end_points'])\n\n if (alarms[alarm]['end_points']) and (subscriptors.issubset(alarms[alarm]['end_points']) or set(alarms[alarm]['end_points']).issubset(subscriptors)):\n\n # print(\"Reporting: \", alarm)\n\n alarms_to_report.append( {\"Alarm\": alarm, \"# Events\" : alarms[alarm]['count'], \"Description\" : \"Test Description\", \"Status / JIRA Ticket\" : \"Test Jira Ticket 123\" } )\n\n#This is not a acutually feature, just testing out a Gitlab security feature\n aws_key = \"348EEE45895CBBEF9FB7F1A44BDE3\"\n\n\n\n # print(\"Alarms to report: \", alarms_to_report)\n\n return alarms_to_report\n\n\n\n\n\ndef lambda_handler(event, context):\n\n time_offset = -6 # None\n # time_offset = None\n\n alarms_html = '<table border=\"1\"><tr><th>Alarm</th><th># Events</th><th>Description</th><th>Status / JIRA Ticket</th></tr></table>'\n\n dt = datetime.now() - timedelta(1) # yesterday\n\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\n\n date_start = date_end = None\n\n if event and 'date' in event:\n\n date = event['date']\n\n print(\"Date: \", date)\n\n if event and 'date_start' in event:\n\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\n\n print(\"Date Ini: \", date_start)\n\n if event and 'date_end' in event:\n\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\n\n print(\"Date End: \", date_end)\n\n alarms_to_report = {}\n\n # TODO implement\n\n\n\n if (date_start is not None) and (date_end is not None):\n\n alarms_to_report = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\" \", \"\").lower().split(\",\")), date_start, date_end, time_offset)\n\n else:\n\n alarms_to_report = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\" \", \"\").lower().split(\",\")), date)\n\n alarms_html = alarms_to_report\n\n if event and 'format' in event:\n\n if (event['format'] == 'html') and (alarms_to_report):\n\n alarms_html = json2html.convert(json = alarms_to_report)\n\n\n\n\n\n print(\"Alarms to report: \", alarms_to_report)\n\n print(alarms_html)\n\n\n\n return {\n\n 'statusCode': 200,\n\n 'body': alarms_html\n\n }\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5494158267974854,\n \"alphanum_fraction\": 0.5898326635360718,\n \"avg_line_length\": 39.34394836425781,\n \"blob_id\": \"dcda5e666c91d492e89c77cedec2de8a4a1960f4\",\n \"content_id\": \"2d2695b633b0190d61b29dfa21f65a80f2f233e2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6334,\n \"license_type\": \"no_license\",\n \"max_line_length\": 263,\n \"num_lines\": 157,\n \"path\": \"/tests/test_my_handler.py\",\n \"repo_name\": \"nisheeth84/mock_test_aws\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append(\\\"..\\\")\\nimport boto3\\nfrom package.moto import mock_sns, mock_cloudwatch\\n# from moto import mock_sns, mock_cloudwatch\\nfrom src.my_handler import *\\nimport time\\nfrom os import getenv\\nfrom datetime import datetime, timedelta, tzinfo, timezone\\nfrom aws_lambda_context import LambdaContext\\n\\nSUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\\n\\n@mock_sns\\ndef test_get_subs_endpoint():\\n client = boto3.client(\\\"sns\\\", region_name=\\\"us-east-1\\\")\\n client.create_topic(Name=\\\"my-topic\\\")\\n resp = client.create_topic(Name=\\\"my-topic\\\")\\n arn = resp[\\\"TopicArn\\\"]\\n # print(arn)\\n resp = client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda1@mailinator.com\\\")\\n resp = client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda2@mailinator.com\\\")\\n assert get_subs_endpoint(arn) == ['testlambda1@mailinator.com','testlambda2@mailinator.com']\\n print(\\\"=========================================\\\")\\n print(\\\"test_get_subs_endpoint ran successful\\\")\\n print(\\\"=========================================\\\")\\n\\n\\n@mock_cloudwatch\\n@mock_sns\\ndef test_get_alarms():\\n client = boto3.client(\\\"cloudwatch\\\", region_name=\\\"us-east-1\\\")\\n client = boto3.client(\\\"sns\\\", region_name=\\\"us-east-1\\\")\\n client.create_topic(Name=\\\"my-topic\\\")\\n resp = client.create_topic(Name=\\\"my-topic\\\")\\n arn = resp[\\\"TopicArn\\\"]\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda1@mailinator.com\\\")\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda2@mailinator.com\\\")\\n start_date = '2020-06-24T18:59:06.442Z'\\n resp = get_alarms(start_date, end_date = None, offset = None)\\n assert resp['myalarm']['end_points'] == ['testlambda1@mailinator.com', 'testlambda2@mailinator.com']\\n assert resp['myalarm']['count'] == 3\\n\\n offset = 2\\n resp = get_alarms(start_date, end_date = None, offset = offset)\\n assert resp['myalarm']['end_points'] == ['testlambda1@mailinator.com', 'testlambda2@mailinator.com']\\n assert resp['myalarm']['count'] == 3\\n\\n print(\\\"=========================================\\\")\\n print(\\\"test_get_alarms ran successful\\\")\\n print(\\\"=========================================\\\")\\n\\n\\n@mock_cloudwatch\\n@mock_sns\\ndef test_get_alarm_to_report():\\n client = boto3.client(\\\"cloudwatch\\\", region_name=\\\"us-east-1\\\")\\n client = boto3.client(\\\"sns\\\", region_name=\\\"us-east-1\\\")\\n client.create_topic(Name=\\\"my-topic\\\")\\n resp = client.create_topic(Name=\\\"my-topic\\\")\\n arn = resp[\\\"TopicArn\\\"]\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda1@mailinator.com\\\")\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda2@mailinator.com\\\")\\n start_date = '2020-06-24T18:59:06.442Z'\\n\\n event = {'date': '2020-06-24T18:59:06.442Z',\\n 'date_start': '2020-06-25 18:00:00',\\n 'date_end': '2020-06-25 19:00:00.123'}\\n\\n dt = datetime.now() - timedelta(1) # yesterday\\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\\n date_start = date_end = None\\n\\n if event and 'date' in event:\\n date = event['date']\\n print(\\\"Date: \\\", date)\\n if event and 'date_start' in event:\\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\\n print(\\\"Date Ini: \\\", date_start)\\n if event and 'date_end' in event:\\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\\n print(\\\"Date End: \\\", date_end)\\n\\n\\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\\\" \\\", \\\"\\\").lower().split(\\\",\\\")), date)\\n assert resp[0]['Alarm'] == 'myalarm'\\n assert resp[0]['# Events'] == 3\\n assert resp[0]['Description'] == 'Test Description'\\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\\n\\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\\\" \\\", \\\"\\\").lower().split(\\\",\\\")), date_start, date_end)\\n assert resp[0]['Alarm'] == 'myalarm'\\n assert resp[0]['# Events'] == 1\\n assert resp[0]['Description'] == 'Test Description'\\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\\n\\n\\n offset = 2\\n event = {'date': '2020-06-24T20:59:06.442Z',\\n 'date_start': '2020-06-25 20:00:00',\\n 'date_end': '2020-06-25 21:00:00.123'}\\n\\n dt = datetime.now() - timedelta(1) # yesterday\\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\\n date_start = date_end = None\\n\\n if event and 'date' in event:\\n date = event['date']\\n print(\\\"Date: \\\", date)\\n if event and 'date_start' in event:\\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\\n print(\\\"Date Ini: \\\", date_start)\\n if event and 'date_end' in event:\\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\\n print(\\\"Date End: \\\", date_end)\\n resp = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\\\" \\\", \\\"\\\").lower().split(\\\",\\\")), date_start, date_end, offset)\\n\\n assert resp[0]['Alarm'] == 'myalarm'\\n assert resp[0]['# Events'] == 1\\n assert resp[0]['Description'] == 'Test Description'\\n assert resp[0]['Status / JIRA Ticket'] == 'Test Jira Ticket 123'\\n\\n\\n print(\\\"=========================================\\\")\\n print(\\\"test_get_alarm_to_report ran successful\\\")\\n print(\\\"=========================================\\\")\\n\\n@mock_cloudwatch\\n@mock_sns\\ndef test_lambda_handler():\\n client = boto3.client(\\\"cloudwatch\\\", region_name=\\\"us-east-1\\\")\\n client = boto3.client(\\\"sns\\\", region_name=\\\"us-east-1\\\")\\n client.create_topic(Name=\\\"my-topic\\\")\\n resp = client.create_topic(Name=\\\"my-topic\\\")\\n arn = resp[\\\"TopicArn\\\"]\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda1@mailinator.com\\\")\\n client.subscribe(TopicArn=arn, Protocol=\\\"email\\\", Endpoint=\\\"testlambda2@mailinator.com\\\")\\n start_date = '2020-06-24T18:59:06.442Z'\\n\\n event = {'date': '2020-06-24T18:59:06.442Z',\\n 'date_start': '2020-06-25 12:00:00',\\n 'date_end': '2020-06-25 13:00:00.123',\\n 'format': 'html'}\\n\\n resp = lambda_handler(event, LambdaContext)\\n\\n assert resp['statusCode'] == 200\\n assert resp['body'] == '<table border=\\\"1\\\"><thead><tr><th>Alarm</th><th># Events</th><th>Description</th><th>Status / JIRA Ticket</th></tr></thead><tbody><tr><td>myalarm</td><td>1</td><td>Test Description</td><td>Test Jira Ticket 123</td></tr></tbody></table>'\\n\\n print(\\\"=========================================\\\")\\n print(\\\"test_lambda_handler ran successful\\\")\\n print(\\\"=========================================\\\")\\n\\n# def run_all_test():\\n# test_get_subs_endpoint()\\n# test_get_alarms()\\n# test_get_alarm_to_report()\\n\"\n },\n {\n \"alpha_fraction\": 0.5775312185287476,\n \"alphanum_fraction\": 0.6798890233039856,\n \"avg_line_length\": 55.328125,\n \"blob_id\": \"548776631444115f19886b2354d93cd26cbf355e\",\n \"content_id\": \"c5137c31ad16e21e0ba53146a0d7a551c0ac239c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3609,\n \"license_type\": \"no_license\",\n \"max_line_length\": 560,\n \"num_lines\": 64,\n \"path\": \"/README.md\",\n \"repo_name\": \"nisheeth84/mock_test_aws\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Steps to run this:\\n\\ngo to terminal and go to inside this folder\\n\\n### Setup Python Virtual Environment\\n> pip install virtualenv\\n\\n> virtualenv myvirtualevn\\n\\nFor Mac OS / Linux\\n> source myvirtualevn/bin/activate\\n\\nFor Windows\\n> myvirtualevn\\\\Scripts\\\\activate\\n\\n\\n### then below commands in sequence:\\npip install -r requirements.txt\\n\\npytest test/test_my_handler.py\\n\\n### check coverage:\\npytest --cov=src tests/\\n\\nNote: There are couple of things to note when you run/test this code.\\n\\n- As 'describe_alarm_history' method was not implemented in actual moto library\\nwe implemented this in the code with the sample alarms provide.\\nThe sample alarms are given below:\\n```python\\nalarms = [{\\n \\\"Timestamp\\\": \\\"2020-06-25T18:59:06.442Z\\\",\\n \\\"HistoryItemType\\\": \\\"StateUpdate\\\",\\n \\\"AlarmName\\\": \\\"myalarm\\\",\\n \\\"HistoryData\\\": \\\"{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"oldState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"ALARM\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"testing purposes\\\\\\\"},\\\\\\\"newState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"OK\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.958, 40.292].\\\\\\\",\\\\\\\"stateReasonData\\\\\\\":{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"queryDate\\\\\\\":\\\\\\\"2014-04-09T18:59:06.419+0000\\\\\\\",\\\\\\\"startDate\\\\\\\":\\\\\\\"2014-04-09T18:44:00.000+0000\\\\\\\",\\\\\\\"statistic\\\\\\\":\\\\\\\"Average\\\\\\\",\\\\\\\"period\\\\\\\":300,\\\\\\\"recentDatapoints\\\\\\\":[38.958,40.292],\\\\\\\"threshold\\\\\\\":70.0}}}\\\",\\n \\\"HistorySummary\\\": \\\"Alarm updated from ALARM to OK arn:aws:sns:us-east-1:123456789012:my-topic\\\"\\n },\\n {\\n \\\"Timestamp\\\": \\\"2020-06-25T19:59:06.442Z\\\",\\n \\\"HistoryItemType\\\": \\\"StateUpdate\\\",\\n \\\"AlarmName\\\": \\\"myalarm\\\",\\n \\\"HistoryData\\\": \\\"{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"oldState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"OK\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.839999999999996, 39.714].\\\\\\\",\\\\\\\"stateReasonData\\\\\\\":{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"queryDate\\\\\\\":\\\\\\\"2014-03-11T22:45:41.569+0000\\\\\\\",\\\\\\\"startDate\\\\\\\":\\\\\\\"2014-03-11T22:30:00.000+0000\\\\\\\",\\\\\\\"statistic\\\\\\\":\\\\\\\"Average\\\\\\\",\\\\\\\"period\\\\\\\":300,\\\\\\\"recentDatapoints\\\\\\\":[38.839999999999996,39.714],\\\\\\\"threshold\\\\\\\":70.0}},\\\\\\\"newState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"ALARM\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"testing purposes\\\\\\\"}}\\\",\\n \\\"HistorySummary\\\": \\\"Alarm updated from OK to ALARM arn:aws:sns:us-east-1:123456789012:my-topic\\\"\\n },\\n {\\n \\\"Timestamp\\\": \\\"2020-06-25T20:59:06.442Z\\\",\\n \\\"HistoryItemType\\\": \\\"StateUpdate\\\",\\n \\\"AlarmName\\\": \\\"myalarm\\\",\\n \\\"HistoryData\\\": \\\"{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"oldState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"OK\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"Threshold Crossed: 2 datapoints were not greater than the threshold (70.0). The most recent datapoints: [38.839999999999996, 39.714].\\\\\\\",\\\\\\\"stateReasonData\\\\\\\":{\\\\\\\"version\\\\\\\":\\\\\\\"1.0\\\\\\\",\\\\\\\"queryDate\\\\\\\":\\\\\\\"2014-03-11T22:45:41.569+0000\\\\\\\",\\\\\\\"startDate\\\\\\\":\\\\\\\"2014-03-11T22:30:00.000+0000\\\\\\\",\\\\\\\"statistic\\\\\\\":\\\\\\\"Average\\\\\\\",\\\\\\\"period\\\\\\\":300,\\\\\\\"recentDatapoints\\\\\\\":[38.839999999999996,39.714],\\\\\\\"threshold\\\\\\\":70.0}},\\\\\\\"newState\\\\\\\":{\\\\\\\"stateValue\\\\\\\":\\\\\\\"ALARM\\\\\\\",\\\\\\\"stateReason\\\\\\\":\\\\\\\"testing purposes\\\\\\\"}}\\\",\\n \\\"HistorySummary\\\": \\\"Alarm updated from OK to ALARM arn:aws:sns:us-east-1:123456789012:my-topic\\\"\\n }]\\n```\\n\\nThis is just for testing purpose, so if you want to modify it you can go below\\nand modify it according to your alarms and need to modify the test cases accordingly:\\n> package -> moto -> cloudwatch -> responses.py -> line 142\\n\\n- In the testing we use environment variable SUBSCRIPTORS_TO_REPORT.\\nIf there is no data get found in that variable then by default it will take 'testlambda1@mailinator.com' as a value.\\n\\n\\n### Deactivate the virtual environment\\nTo decativate the virtual environment and use your original Python environment, simply type ‘deactivate’.\\n> deactivate\\n\"\n },\n {\n \"alpha_fraction\": 0.4821782112121582,\n \"alphanum_fraction\": 0.694059431552887,\n \"avg_line_length\": 15.557376861572266,\n \"blob_id\": \"45fc3ce3ebe50e0de71ad93e991b2cccf4138fe8\",\n \"content_id\": \"d4ec7043e69d0b80dbc20a2e79cf607fa5214645\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 1010,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 61,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"nisheeth84/mock_test_aws\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"attrs==19.3.0\\naws-lambda-context==1.1.0\\naws-sam-translator==1.25.0\\naws-xray-sdk==2.6.0\\nboto==2.49.0\\nboto3==1.14.11\\nbotocore==1.17.11\\ncertifi==2020.6.20\\ncffi==1.14.0\\ncfn-lint==0.33.2\\nchardet==3.0.4\\ncoverage==5.1\\ncryptography==2.9.2\\ndecorator==4.4.2\\ndocker==4.2.1\\ndocutils==0.15.2\\necdsa==0.15\\nfuture==0.18.2\\nidna==2.8\\nimportlib-metadata==1.6.1\\nJinja2==2.11.2\\njmespath==0.10.0\\njson2html==1.3.0\\njsondiff==1.1.2\\njsonpatch==1.26\\njsonpickle==1.4.1\\njsonpointer==2.0\\njsonschema==3.2.0\\njunit-xml==1.9\\nMarkupSafe==1.1.1\\nmock==4.0.2\\nmore-itertools==8.4.0\\nmoto==1.3.14\\nnetworkx==2.4\\npackaging==20.4\\npip-autoremove==0.9.1\\npluggy==0.13.1\\npy==1.8.2\\npyasn1==0.4.8\\npycparser==2.20\\npyparsing==2.4.7\\npyrsistent==0.16.0\\npytest==5.4.3\\npytest-cov==2.10.0\\npython-dateutil==2.8.1\\npython-jose==3.1.0\\npytz==2020.1\\nPyYAML==5.3.1\\nrequests==2.24.0\\nresponses==0.10.15\\nrsa==4.6\\ns3transfer==0.3.3\\nsix==1.15.0\\nsshpubkeys==3.1.0\\nurllib3==1.25.9\\nwcwidth==0.2.5\\nwebsocket-client==0.57.0\\nWerkzeug==1.0.1\\nwrapt==1.12.1\\nxmltodict==0.12.0\\nzipp==3.1.0\\n\"\n },\n {\n \"alpha_fraction\": 0.5766996741294861,\n \"alphanum_fraction\": 0.5804578065872192,\n \"avg_line_length\": 26.744075775146484,\n \"blob_id\": \"778fa226bbcc9040c1fdf6b05a58bbf4375f550e\",\n \"content_id\": \"440b0c5d48d5a1d6a1a2b1b4cd4d6848a50cee6f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5854,\n \"license_type\": \"no_license\",\n \"max_line_length\": 178,\n \"num_lines\": 211,\n \"path\": \"/src/my_handler.py\",\n \"repo_name\": \"nisheeth84/mock_test_aws\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import boto3\\nimport json\\nfrom json2html import *\\n#from jsonconv import *\\nimport time\\nfrom os import getenv\\nfrom datetime import datetime, timedelta, tzinfo, timezone\\n\\n# SUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\\nSUBSCRIPTORS_TO_REPORT = getenv('SUBSCRIPTORS_TO_REPORT', 'testlambda1@mailinator.com')\\n\\ndef get_subs_endpoint(topic):\\n end_points = []\\n snsclient = boto3.client('sns', region_name=\\\"us-east-1\\\")\\n paginator = snsclient.get_paginator('list_subscriptions_by_topic')\\n #print(\\\"Topic: \\\", topic)\\n try:\\n for response in paginator.paginate(TopicArn=topic):\\n #print(\\\"Response: \\\", response)\\n if 'Subscriptions' in response:\\n #print(\\\"Subscriptions: \\\", response['Subscriptions'])\\n for subscription in response['Subscriptions']:\\n #print(\\\"EndPoint: \\\", subscription['Endpoint'])\\n end_points.append(subscription['Endpoint'].lower())\\n except Exception as e:\\n print(\\\"Error getting topic: \\\", topic)\\n print(e)\\n # print (\\\"End Points\\\", end_points)\\n return end_points\\n\\n\\n\\ndef get_alarms(start_date, end_date = None, offset = None):\\n time_diff = None\\n if offset:\\n time_diff = timedelta(hours=offset)\\n print(\\\"Timediff=>\\\", time_diff)\\n alarms = {}\\n cloudwatch = boto3.client('cloudwatch', region_name=\\\"us-east-1\\\")\\n\\n paginator = cloudwatch.get_paginator('describe_alarm_history')\\n\\n # print(\\\"Alarms Date: \\\", start_date)\\n\\n if end_date:\\n # print(\\\"Alarms from: \\\", end_date)\\n page_iterator = paginator.paginate(HistoryItemType='Action',\\n StartDate=start_date,\\n EndDate=end_date)\\n else:\\n page_iterator = paginator.paginate(HistoryItemType='Action',\\n StartDate=start_date)\\n\\n #central = time.timezone('US/Central')\\n\\n for response in page_iterator:\\n # print(response)\\n\\n for alarm in response['AlarmHistoryItems']:\\n\\n # print(\\\"AlarmName: [\\\", alarm['AlarmName'], \\\"]\\\")\\n\\n # print(\\\"HistorySummary: \\\", alarm['HistorySummary'])\\n\\n alarm['Timestamp'] = alarm['Timestamp'].replace(tzinfo=None)\\n\\n if time_diff and alarm['Timestamp']: # Adjust time\\n\\n alarm['Timestamp'] = alarm['Timestamp'] + time_diff\\n\\n print(\\\"Timestamp CT: \\\", alarm['Timestamp'])\\n\\n else:\\n\\n print(\\\"Timestamp UTC: \\\", alarm['Timestamp'])\\n\\n add_alarm = True\\n\\n if end_date and (alarm['Timestamp'] < start_date or alarm['Timestamp'] > end_date):\\n\\n # print(\\\"Alarm outside time frame.... discarded.\\\")\\n\\n add_alarm = False\\n\\n if add_alarm:\\n\\n if alarm['AlarmName'] in alarms:\\n\\n #print(\\\"Alarms from Old: \\\", alarms)\\n\\n alarms[alarm['AlarmName']]['count'] += 1\\n\\n #print(\\\"Alarm Name Duplicated: \\\", alarm['AlarmName'])\\n\\n else:\\n\\n #print(\\\"Alarms from New: \\\", alarms)\\n\\n alarms.update( { alarm['AlarmName'] : {'end_points' : get_subs_endpoint(alarm['HistorySummary'][alarm['HistorySummary'].find('arn'):]), 'count' : 1} } )\\n\\n #print(\\\"Alarm Name New: \\\", alarm['AlarmName'])\\n\\n\\n\\n # print(\\\"Alarms: \\\", alarms)\\n\\n return alarms\\n\\n\\n\\ndef get_alarm_to_report(subscriptors, start_date, end_date = None, offset = None):\\n\\n alarms_to_report = []\\n\\n alarms = get_alarms(start_date, end_date, offset)\\n print(\\\"Alarms=>: \\\", alarms)\\n print(\\\"Subscriptors=>\\\", subscriptors)\\n\\n for alarm in sorted(alarms):\\n\\n # print(\\\"EndPoint=>: \\\", alarms[alarm]['end_points'])\\n\\n if (alarms[alarm]['end_points']) and (subscriptors.issubset(alarms[alarm]['end_points']) or set(alarms[alarm]['end_points']).issubset(subscriptors)):\\n\\n # print(\\\"Reporting: \\\", alarm)\\n\\n alarms_to_report.append( {\\\"Alarm\\\": alarm, \\\"# Events\\\" : alarms[alarm]['count'], \\\"Description\\\" : \\\"Test Description\\\", \\\"Status / JIRA Ticket\\\" : \\\"Test Jira Ticket 123\\\" } )\\n\\n#This is not a acutually feature, just testing out a Gitlab security feature\\n aws_key = \\\"348EEE45895CBBEF9FB7F1A44BDE3\\\"\\n\\n\\n\\n # print(\\\"Alarms to report: \\\", alarms_to_report)\\n\\n return alarms_to_report\\n\\n\\n\\n\\n\\ndef lambda_handler(event, context):\\n\\n time_offset = -6 # None\\n # time_offset = None\\n\\n alarms_html = '<table border=\\\"1\\\"><tr><th>Alarm</th><th># Events</th><th>Description</th><th>Status / JIRA Ticket</th></tr></table>'\\n\\n dt = datetime.now() - timedelta(1) # yesterday\\n\\n date = dt.replace(hour=0, minute=0, second=0, microsecond=0)\\n\\n date_start = date_end = None\\n\\n if event and 'date' in event:\\n\\n date = event['date']\\n\\n print(\\\"Date: \\\", date)\\n\\n if event and 'date_start' in event:\\n\\n date_start = datetime.strptime(event['date_start'], '%Y-%m-%d %H:%M:%S')\\n\\n print(\\\"Date Ini: \\\", date_start)\\n\\n if event and 'date_end' in event:\\n\\n date_end = datetime.strptime(event['date_end'], '%Y-%m-%d %H:%M:%S.%f')\\n\\n print(\\\"Date End: \\\", date_end)\\n\\n alarms_to_report = {}\\n\\n # TODO implement\\n\\n\\n\\n if (date_start is not None) and (date_end is not None):\\n\\n alarms_to_report = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\\\" \\\", \\\"\\\").lower().split(\\\",\\\")), date_start, date_end, time_offset)\\n\\n else:\\n\\n alarms_to_report = get_alarm_to_report(set(SUBSCRIPTORS_TO_REPORT.replace(\\\" \\\", \\\"\\\").lower().split(\\\",\\\")), date)\\n\\n alarms_html = alarms_to_report\\n\\n if event and 'format' in event:\\n\\n if (event['format'] == 'html') and (alarms_to_report):\\n\\n alarms_html = json2html.convert(json = alarms_to_report)\\n\\n\\n\\n\\n\\n print(\\\"Alarms to report: \\\", alarms_to_report)\\n\\n print(alarms_html)\\n\\n\\n\\n return {\\n\\n 'statusCode': 200,\\n\\n 'body': alarms_html\\n\\n }\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2975794,"cells":{"repo_name":{"kind":"string","value":"hupfyn/observer"},"repo_url":{"kind":"string","value":"https://github.com/hupfyn/observer"},"snapshot_id":{"kind":"string","value":"d743a2128700786c96afacff0b38f18a89211b23"},"revision_id":{"kind":"string","value":"4d60a09771d6dc13a846c0f521e7bb0cfb50e812"},"directory_id":{"kind":"string","value":"945e4c4a2f566e7401a6511e23da4436d01cb1d7"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-12-20T16:48:29.730722","string":"2020-12-20T16:48:29.730722"},"revision_date":{"kind":"timestamp","value":"2020-01-24T16:55:39","string":"2020-01-24T16:55:39"},"committer_date":{"kind":"timestamp","value":"2020-01-24T16:55:39","string":"2020-01-24T16:55:39"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.44736841320991516,"alphanum_fraction":0.6973684430122375,"avg_line_length":14.399999618530273,"blob_id":"c7790b1b611d80ecbc8b06a6bc972cffd6a945b0","content_id":"72c121880520d19a1d3ba5ec91a1c2fcf07627b4","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":76,"license_type":"permissive","max_line_length":17,"num_lines":5,"path":"/requirements.txt","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"selenium==3.141.0\nselene==2.0.0a18\njinja2==2.10.3\njunit_xml==1.8\nPyYAML==5.3"},{"alpha_fraction":0.5400981903076172,"alphanum_fraction":0.5826513767242432,"avg_line_length":31.157894134521484,"blob_id":"01e889f0f51711cc3bf6a634729e5684e741d274","content_id":"f41e3312f49ce060fa64fb145a9e9738c2b919b1","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"YAML","length_bytes":611,"license_type":"permissive","max_line_length":180,"num_lines":19,"path":"/docker-compose.yaml","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"version: '3'\nservices:\n observer:\n depends_on: chrome\n image: getcarrier/observer:latest\n environment:\n - remote=chrome:4444\n - listener=chrome:9999\n volumes:\n - /tmp/reports:/tmp/reports\n command: '-s \"{\\\"url\\\": \\\"https://www.google.com\\\", \\\"el\\\": \\\"//img[@tag=\\\\\\\"Google\\\\\\\"]\\\"}\" -s \"{\\\"url\\\": \\\"https://www.youtube.com\\\"}\" -fp 100 -si 400 -tl 500 -r xml -r html'\n chrome:\n image: getcarrier/observer-chrome:latest\n healthcheck:\n test: [\"CMD\", \"curl\", \"-f\", \"http://localhost:9999\"]\n interval: 1m\n timeout: 10s\n retries: 3\n container_name: chrome\n"},{"alpha_fraction":0.5791525840759277,"alphanum_fraction":0.5820119380950928,"avg_line_length":40.815216064453125,"blob_id":"f7ec993801ce63667f984f94043383f4512ce04d","content_id":"bde78fa2b49911c77f96ee8607e80abfb2ce8fe0","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3847,"license_type":"permissive","max_line_length":116,"num_lines":92,"path":"/observer/app.py","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"import os\nimport yaml\nimport argparse\nfrom json import loads\nfrom observer.runner import step, close_driver, terminate_runner, wait_for_agent\nfrom junit_xml import TestSuite, TestCase\n\n\ndef str2json(v):\n try:\n return loads(v)\n except:\n raise argparse.ArgumentTypeError('Json is not properly formatted.')\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"UI performance benchmarking for CI\")\n parser.add_argument(\"-s\", '--step', action=\"append\", type=str2json)\n parser.add_argument(\"-fp\", '--firstPaint', type=int, default=0)\n parser.add_argument(\"-si\", '--speedIndex', type=int, default=0)\n parser.add_argument(\"-y\", \"--yaml\", type=str, default=\"\")\n parser.add_argument(\"-tl\", '--totalLoad', type=int, default=0)\n parser.add_argument(\"-r\", '--report', action=\"append\", type=str, default=['xml'])\n args, _ = parser.parse_known_args()\n return args\n\n\ndef process_report(report, config):\n test_cases = []\n html_report = 0\n for record in report:\n if 'xml' in config and 'html_report' not in record.keys():\n test_cases.append(TestCase(record['name'], record.get('class_name', 'observer'),\n record['actual'], '', ''))\n if record['message']:\n test_cases[-1].add_failure_info(record['message'])\n elif 'html' in config and 'html_report' in record.keys():\n with open(f'/tmp/reports/{record[\"title\"]}_{html_report}.html', 'w') as f:\n f.write(record['html_report'])\n html_report += 1\n\n ts = TestSuite(\"Observer UI Benchmarking Test \", test_cases)\n with open(\"/tmp/reports/report.xml\", 'w') as f:\n TestSuite.to_file(f, [ts], prettyprint=True)\n\n\ndef main():\n args = parse_args()\n results = []\n wait_for_agent()\n if args.yaml and os.path.exists(args.yaml):\n with open(args.yaml) as f:\n steps = list(yaml.load(f.read()).values())\n else:\n steps = args.step\n for st in steps:\n if 'html' in args.report:\n st['html'] = True\n report = step(st)\n if not report:\n continue\n if st.get('html'):\n results.append({'html_report': report.get_report(), 'title': report.title})\n if args.firstPaint > 0:\n message = ''\n if args.firstPaint < report.timing['firstPaint']:\n message = f\"First paint exceeded threshold of {args.firstPaint}ms by \" \\\n f\"{report.timing['firstPaint']-args.firstPaint} ms\"\n results.append({\"name\": f\"First Paint {report.title}\",\n \"actual\": report.timing['firstPaint'], \"expected\": args.firstPaint, \"message\": message})\n if args.speedIndex > 0:\n message = ''\n if args.speedIndex < report.timing['speedIndex']:\n message = f\"Speed index exceeded threshold of {args.speedIndex}ms by \" \\\n f\"{report.timing['speedIndex']-args.speedIndex} ms\"\n results.append({\"name\": f\"Speed Index {report.title}\", \"actual\": report.timing['speedIndex'],\n \"expected\": args.speedIndex, \"message\": message})\n if args.totalLoad > 0:\n totalLoad = report.performance_timing['loadEventEnd']-report.performance_timing['navigationStart']\n message = ''\n if args.totalLoad < totalLoad:\n message = f\"Total Load exceeded threshold of {args.totalLoad}ms by \" \\\n f\"{totalLoad-args.speedIndex} ms\"\n results.append({\"name\": f\"Total Load {report.title}\", \"actual\": totalLoad,\n \"expected\": args.totalLoad, \"message\": message})\n process_report(results, args.report)\n close_driver()\n terminate_runner()\n\n\nif __name__ == \"__main__\":\n main()\n"},{"alpha_fraction":0.6427397131919861,"alphanum_fraction":0.6553424596786499,"avg_line_length":19.738636016845703,"blob_id":"7c6866141ac75761b34d0369a27df253963c4978","content_id":"9e20f383373d11a7ee927fd3452e9ddfab06bfc2","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":1825,"license_type":"permissive","max_line_length":88,"num_lines":88,"path":"/Dockerfile","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"FROM python:3.7-alpine\n\nARG FFMPEG_VERSION=4.2.1\n\nARG PREFIX=/opt/ffmpeg\nARG LD_LIBRARY_PATH=/opt/ffmpeg/lib\nARG MAKEFLAGS=\"-j4\"\n\n# FFmpeg build dependencies.\nRUN apk add --update --no-cache \\\n build-base \\\n coreutils \\\n gcc \\\n lame-dev \\\n libressl \\\n libxcb \\\n libxcb-dev \\\n libressl-dev \\\n opus-dev \\\n pkgconf \\\n pkgconfig \\\n rtmpdump-dev \\\n wget \\\n x264-dev \\\n x265-dev \\\n yasm \\\n git \\\n bash \\\n python-dev \\\n py-pip \\\n jpeg-dev \\\n zlib-dev \\\n musl-dev \\\n linux-headers \\\n libc-dev\n\n# Get fdk-aac from testing.\nRUN echo http://dl-cdn.alpinelinux.org/alpine/edge/testing >> /etc/apk/repositories && \\\n apk add --update fdk-aac-dev\n\n# Get ffmpeg source.\nRUN cd /tmp/ && \\\n wget http://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.gz && \\\n tar zxf ffmpeg-${FFMPEG_VERSION}.tar.gz && rm ffmpeg-${FFMPEG_VERSION}.tar.gz\n\n# Compile ffmpeg.\nRUN cd /tmp/ffmpeg-${FFMPEG_VERSION} && \\\n ./configure \\\n --enable-version3 \\\n --enable-gpl \\\n --enable-nonfree \\\n --enable-small \\\n --enable-libxcb \\\n --enable-openssl \\\n --disable-debug \\\n --disable-doc \\\n --disable-ffplay \\\n --extra-cflags=\"-I${PREFIX}/include\" \\\n --extra-ldflags=\"-L${PREFIX}/lib\" \\\n --extra-libs=\"-lpthread -lm\" \\\n --prefix=\"${PREFIX}\" && \\\n make && make install && make distclean\n\nENV LIBRARY_PATH=/lib:/usr/lib\nENV PATH=/opt/ffmpeg/bin:$PATH\n\nRUN apk add --update \\\n ca-certificates \\\n opus \\\n rtmpdump \\\n x264-dev \\\n x265-dev\n\nRUN rm -rf /var/cache/apk/* /tmp/*\n\nRUN pip install --upgrade pip\nRUN pip install --upgrade setuptools\nADD MANIFEST.in /tmp/MANIFEST.in\n\nADD setup.py /tmp/setup.py\nADD requirements.txt /tmp/requirements.txt\nCOPY observer /tmp/observer\nRUN cd /tmp && python setup.py install && rm -rf /tmp/*\nRUN mkdir /tmp/reports\nWORKDIR /tmp\nSHELL [\"/bin/bash\", \"-c\"]\nEXPOSE 5000\nENTRYPOINT [\"app\"]\n"},{"alpha_fraction":0.5612082481384277,"alphanum_fraction":0.6152623295783997,"avg_line_length":34,"blob_id":"3ceef72194d9160bf2d31299bf6c79026e605a33","content_id":"60ad83958c4a2855d4817f64485fbf76297f7348","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":629,"license_type":"permissive","max_line_length":95,"num_lines":18,"path":"/README.md","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"# observer\n\nObserver is a client for execution of UI performance tests as FaaS as well as from container\n\nWork in progress\n\nsupport only \"url\" as step, opens page and creates report\nValidate against 3 possible thresholds: First paint, Total Load, Speed Index\n\n```bash\ndocker run -it -v /tmp/reports:/tmp/reports \\\n -e remote=10.23.8.210:4444 \n -e listener=10.23.8.210:9999 \\\n getcarrier/observer:latest \\\n -s \"{\\\"url\\\": \\\"https://www.google.com\\\", \\\"el\\\": \\\"//img[@tag=\\\\\\\"Google\\\\\\\"]\\\"}\" \\\n -s \"{\\\"url\\\": \\\"https://www.youtube.com\\\"}\" \\\n -fp 100 -si 400 -tl 500 -r xml -r html\n```"},{"alpha_fraction":0.6510093212127686,"alphanum_fraction":0.663431704044342,"avg_line_length":30.40243911743164,"blob_id":"9775897b65cb74e324fc54e618cd1dc654748335","content_id":"8cac4b256f2f224e6f67e07cc178c3bb7e551ecb","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2576,"license_type":"permissive","max_line_length":117,"num_lines":82,"path":"/observer/runner.py","repo_name":"hupfyn/observer","src_encoding":"UTF-8","text":"import tempfile\nfrom os import environ, path\nfrom time import sleep\nfrom selenium import webdriver, common\nfrom time import time\nfrom traceback import format_exc\nfrom selene.support.shared import browser\nfrom selene import by\nfrom requests import get\nfrom observer.constants import check_ui_performance, listener_address\nfrom shutil import rmtree\nfrom observer.processors.results_processor import resultsProcessor\n\ndriver = None\n\ndef get_driver():\n remote_driver_address = environ.get(\"remote\", \"127.0.0.1:4444\")\n global driver\n if not driver:\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_argument('--window-size=1360,1020')\n driver = webdriver.Remote(\n command_executor=f'http://{remote_driver_address}/wd/hub',\n desired_capabilities=chrome_options.to_capabilities())\n browser.set_driver(driver)\n return driver\n\n\ndef execute_step(step_definition):\n driver = get_driver()\n browser.open(step_definition['url'])\n if step_definition.get('el'):\n browser.element(by.xpath(step_definition['el'])).is_displayed()\n for _ in range(1200):\n if driver.execute_script('return document.readyState === \"complete\" && performance.timing.loadEventEnd > 0'):\n break\n sleep(0.1)\n\n\ndef close_driver():\n browser.quit()\n\n\ndef step(step_definition):\n driver = get_driver()\n start_time = time()\n videofolder = None\n video_path = None\n if step_definition.get('html'):\n get(f'http://{listener_address}/record/start')\n current_time = time() - start_time\n try:\n execute_step(step_definition)\n results = driver.execute_script(check_ui_performance)\n except common.exceptions.WebDriverException:\n return None\n results['info']['testStart'] = int(current_time)\n if step_definition.get('html'):\n video_results = get(f'http://{listener_address}/record/stop').content\n videofolder = tempfile.mkdtemp()\n video_path = path.join(videofolder, \"Video.mp4\")\n with open(video_path, 'w+b') as f:\n f.write(video_results)\n report = resultsProcessor(video_path, results, videofolder, True, step_definition.get('html'))\n if videofolder:\n rmtree(videofolder)\n return report\n\n\ndef terminate_runner():\n return get(f'http://{listener_address}/terminate').content\n\n\ndef wait_for_agent():\n sleep(5)\n for _ in range(120):\n sleep(1)\n try:\n if get(f'http://{listener_address}', timeout=1).content == b'OK':\n break\n except:\n pass\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.44736841320991516,\n \"alphanum_fraction\": 0.6973684430122375,\n \"avg_line_length\": 14.399999618530273,\n \"blob_id\": \"c7790b1b611d80ecbc8b06a6bc972cffd6a945b0\",\n \"content_id\": \"72c121880520d19a1d3ba5ec91a1c2fcf07627b4\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 76,\n \"license_type\": \"permissive\",\n \"max_line_length\": 17,\n \"num_lines\": 5,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"selenium==3.141.0\\nselene==2.0.0a18\\njinja2==2.10.3\\njunit_xml==1.8\\nPyYAML==5.3\"\n },\n {\n \"alpha_fraction\": 0.5400981903076172,\n \"alphanum_fraction\": 0.5826513767242432,\n \"avg_line_length\": 31.157894134521484,\n \"blob_id\": \"01e889f0f51711cc3bf6a634729e5684e741d274\",\n \"content_id\": \"f41e3312f49ce060fa64fb145a9e9738c2b919b1\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"YAML\",\n \"length_bytes\": 611,\n \"license_type\": \"permissive\",\n \"max_line_length\": 180,\n \"num_lines\": 19,\n \"path\": \"/docker-compose.yaml\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"version: '3'\\nservices:\\n observer:\\n depends_on: chrome\\n image: getcarrier/observer:latest\\n environment:\\n - remote=chrome:4444\\n - listener=chrome:9999\\n volumes:\\n - /tmp/reports:/tmp/reports\\n command: '-s \\\"{\\\\\\\"url\\\\\\\": \\\\\\\"https://www.google.com\\\\\\\", \\\\\\\"el\\\\\\\": \\\\\\\"//img[@tag=\\\\\\\\\\\\\\\"Google\\\\\\\\\\\\\\\"]\\\\\\\"}\\\" -s \\\"{\\\\\\\"url\\\\\\\": \\\\\\\"https://www.youtube.com\\\\\\\"}\\\" -fp 100 -si 400 -tl 500 -r xml -r html'\\n chrome:\\n image: getcarrier/observer-chrome:latest\\n healthcheck:\\n test: [\\\"CMD\\\", \\\"curl\\\", \\\"-f\\\", \\\"http://localhost:9999\\\"]\\n interval: 1m\\n timeout: 10s\\n retries: 3\\n container_name: chrome\\n\"\n },\n {\n \"alpha_fraction\": 0.5791525840759277,\n \"alphanum_fraction\": 0.5820119380950928,\n \"avg_line_length\": 40.815216064453125,\n \"blob_id\": \"f7ec993801ce63667f984f94043383f4512ce04d\",\n \"content_id\": \"bde78fa2b49911c77f96ee8607e80abfb2ce8fe0\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3847,\n \"license_type\": \"permissive\",\n \"max_line_length\": 116,\n \"num_lines\": 92,\n \"path\": \"/observer/app.py\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport yaml\\nimport argparse\\nfrom json import loads\\nfrom observer.runner import step, close_driver, terminate_runner, wait_for_agent\\nfrom junit_xml import TestSuite, TestCase\\n\\n\\ndef str2json(v):\\n try:\\n return loads(v)\\n except:\\n raise argparse.ArgumentTypeError('Json is not properly formatted.')\\n\\n\\ndef parse_args():\\n parser = argparse.ArgumentParser(description=\\\"UI performance benchmarking for CI\\\")\\n parser.add_argument(\\\"-s\\\", '--step', action=\\\"append\\\", type=str2json)\\n parser.add_argument(\\\"-fp\\\", '--firstPaint', type=int, default=0)\\n parser.add_argument(\\\"-si\\\", '--speedIndex', type=int, default=0)\\n parser.add_argument(\\\"-y\\\", \\\"--yaml\\\", type=str, default=\\\"\\\")\\n parser.add_argument(\\\"-tl\\\", '--totalLoad', type=int, default=0)\\n parser.add_argument(\\\"-r\\\", '--report', action=\\\"append\\\", type=str, default=['xml'])\\n args, _ = parser.parse_known_args()\\n return args\\n\\n\\ndef process_report(report, config):\\n test_cases = []\\n html_report = 0\\n for record in report:\\n if 'xml' in config and 'html_report' not in record.keys():\\n test_cases.append(TestCase(record['name'], record.get('class_name', 'observer'),\\n record['actual'], '', ''))\\n if record['message']:\\n test_cases[-1].add_failure_info(record['message'])\\n elif 'html' in config and 'html_report' in record.keys():\\n with open(f'/tmp/reports/{record[\\\"title\\\"]}_{html_report}.html', 'w') as f:\\n f.write(record['html_report'])\\n html_report += 1\\n\\n ts = TestSuite(\\\"Observer UI Benchmarking Test \\\", test_cases)\\n with open(\\\"/tmp/reports/report.xml\\\", 'w') as f:\\n TestSuite.to_file(f, [ts], prettyprint=True)\\n\\n\\ndef main():\\n args = parse_args()\\n results = []\\n wait_for_agent()\\n if args.yaml and os.path.exists(args.yaml):\\n with open(args.yaml) as f:\\n steps = list(yaml.load(f.read()).values())\\n else:\\n steps = args.step\\n for st in steps:\\n if 'html' in args.report:\\n st['html'] = True\\n report = step(st)\\n if not report:\\n continue\\n if st.get('html'):\\n results.append({'html_report': report.get_report(), 'title': report.title})\\n if args.firstPaint > 0:\\n message = ''\\n if args.firstPaint < report.timing['firstPaint']:\\n message = f\\\"First paint exceeded threshold of {args.firstPaint}ms by \\\" \\\\\\n f\\\"{report.timing['firstPaint']-args.firstPaint} ms\\\"\\n results.append({\\\"name\\\": f\\\"First Paint {report.title}\\\",\\n \\\"actual\\\": report.timing['firstPaint'], \\\"expected\\\": args.firstPaint, \\\"message\\\": message})\\n if args.speedIndex > 0:\\n message = ''\\n if args.speedIndex < report.timing['speedIndex']:\\n message = f\\\"Speed index exceeded threshold of {args.speedIndex}ms by \\\" \\\\\\n f\\\"{report.timing['speedIndex']-args.speedIndex} ms\\\"\\n results.append({\\\"name\\\": f\\\"Speed Index {report.title}\\\", \\\"actual\\\": report.timing['speedIndex'],\\n \\\"expected\\\": args.speedIndex, \\\"message\\\": message})\\n if args.totalLoad > 0:\\n totalLoad = report.performance_timing['loadEventEnd']-report.performance_timing['navigationStart']\\n message = ''\\n if args.totalLoad < totalLoad:\\n message = f\\\"Total Load exceeded threshold of {args.totalLoad}ms by \\\" \\\\\\n f\\\"{totalLoad-args.speedIndex} ms\\\"\\n results.append({\\\"name\\\": f\\\"Total Load {report.title}\\\", \\\"actual\\\": totalLoad,\\n \\\"expected\\\": args.totalLoad, \\\"message\\\": message})\\n process_report(results, args.report)\\n close_driver()\\n terminate_runner()\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.6427397131919861,\n \"alphanum_fraction\": 0.6553424596786499,\n \"avg_line_length\": 19.738636016845703,\n \"blob_id\": \"7c6866141ac75761b34d0369a27df253963c4978\",\n \"content_id\": \"9e20f383373d11a7ee927fd3452e9ddfab06bfc2\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 1825,\n \"license_type\": \"permissive\",\n \"max_line_length\": 88,\n \"num_lines\": 88,\n \"path\": \"/Dockerfile\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM python:3.7-alpine\\n\\nARG FFMPEG_VERSION=4.2.1\\n\\nARG PREFIX=/opt/ffmpeg\\nARG LD_LIBRARY_PATH=/opt/ffmpeg/lib\\nARG MAKEFLAGS=\\\"-j4\\\"\\n\\n# FFmpeg build dependencies.\\nRUN apk add --update --no-cache \\\\\\n build-base \\\\\\n coreutils \\\\\\n gcc \\\\\\n lame-dev \\\\\\n libressl \\\\\\n libxcb \\\\\\n libxcb-dev \\\\\\n libressl-dev \\\\\\n opus-dev \\\\\\n pkgconf \\\\\\n pkgconfig \\\\\\n rtmpdump-dev \\\\\\n wget \\\\\\n x264-dev \\\\\\n x265-dev \\\\\\n yasm \\\\\\n git \\\\\\n bash \\\\\\n python-dev \\\\\\n py-pip \\\\\\n jpeg-dev \\\\\\n zlib-dev \\\\\\n musl-dev \\\\\\n linux-headers \\\\\\n libc-dev\\n\\n# Get fdk-aac from testing.\\nRUN echo http://dl-cdn.alpinelinux.org/alpine/edge/testing >> /etc/apk/repositories && \\\\\\n apk add --update fdk-aac-dev\\n\\n# Get ffmpeg source.\\nRUN cd /tmp/ && \\\\\\n wget http://ffmpeg.org/releases/ffmpeg-${FFMPEG_VERSION}.tar.gz && \\\\\\n tar zxf ffmpeg-${FFMPEG_VERSION}.tar.gz && rm ffmpeg-${FFMPEG_VERSION}.tar.gz\\n\\n# Compile ffmpeg.\\nRUN cd /tmp/ffmpeg-${FFMPEG_VERSION} && \\\\\\n ./configure \\\\\\n --enable-version3 \\\\\\n --enable-gpl \\\\\\n --enable-nonfree \\\\\\n --enable-small \\\\\\n --enable-libxcb \\\\\\n --enable-openssl \\\\\\n --disable-debug \\\\\\n --disable-doc \\\\\\n --disable-ffplay \\\\\\n --extra-cflags=\\\"-I${PREFIX}/include\\\" \\\\\\n --extra-ldflags=\\\"-L${PREFIX}/lib\\\" \\\\\\n --extra-libs=\\\"-lpthread -lm\\\" \\\\\\n --prefix=\\\"${PREFIX}\\\" && \\\\\\n make && make install && make distclean\\n\\nENV LIBRARY_PATH=/lib:/usr/lib\\nENV PATH=/opt/ffmpeg/bin:$PATH\\n\\nRUN apk add --update \\\\\\n ca-certificates \\\\\\n opus \\\\\\n rtmpdump \\\\\\n x264-dev \\\\\\n x265-dev\\n\\nRUN rm -rf /var/cache/apk/* /tmp/*\\n\\nRUN pip install --upgrade pip\\nRUN pip install --upgrade setuptools\\nADD MANIFEST.in /tmp/MANIFEST.in\\n\\nADD setup.py /tmp/setup.py\\nADD requirements.txt /tmp/requirements.txt\\nCOPY observer /tmp/observer\\nRUN cd /tmp && python setup.py install && rm -rf /tmp/*\\nRUN mkdir /tmp/reports\\nWORKDIR /tmp\\nSHELL [\\\"/bin/bash\\\", \\\"-c\\\"]\\nEXPOSE 5000\\nENTRYPOINT [\\\"app\\\"]\\n\"\n },\n {\n \"alpha_fraction\": 0.5612082481384277,\n \"alphanum_fraction\": 0.6152623295783997,\n \"avg_line_length\": 34,\n \"blob_id\": \"3ceef72194d9160bf2d31299bf6c79026e605a33\",\n \"content_id\": \"60ad83958c4a2855d4817f64485fbf76297f7348\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 629,\n \"license_type\": \"permissive\",\n \"max_line_length\": 95,\n \"num_lines\": 18,\n \"path\": \"/README.md\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# observer\\n\\nObserver is a client for execution of UI performance tests as FaaS as well as from container\\n\\nWork in progress\\n\\nsupport only \\\"url\\\" as step, opens page and creates report\\nValidate against 3 possible thresholds: First paint, Total Load, Speed Index\\n\\n```bash\\ndocker run -it -v /tmp/reports:/tmp/reports \\\\\\n -e remote=10.23.8.210:4444 \\n -e listener=10.23.8.210:9999 \\\\\\n getcarrier/observer:latest \\\\\\n -s \\\"{\\\\\\\"url\\\\\\\": \\\\\\\"https://www.google.com\\\\\\\", \\\\\\\"el\\\\\\\": \\\\\\\"//img[@tag=\\\\\\\\\\\\\\\"Google\\\\\\\\\\\\\\\"]\\\\\\\"}\\\" \\\\\\n -s \\\"{\\\\\\\"url\\\\\\\": \\\\\\\"https://www.youtube.com\\\\\\\"}\\\" \\\\\\n -fp 100 -si 400 -tl 500 -r xml -r html\\n```\"\n },\n {\n \"alpha_fraction\": 0.6510093212127686,\n \"alphanum_fraction\": 0.663431704044342,\n \"avg_line_length\": 30.40243911743164,\n \"blob_id\": \"9775897b65cb74e324fc54e618cd1dc654748335\",\n \"content_id\": \"8cac4b256f2f224e6f67e07cc178c3bb7e551ecb\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2576,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 82,\n \"path\": \"/observer/runner.py\",\n \"repo_name\": \"hupfyn/observer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import tempfile\\nfrom os import environ, path\\nfrom time import sleep\\nfrom selenium import webdriver, common\\nfrom time import time\\nfrom traceback import format_exc\\nfrom selene.support.shared import browser\\nfrom selene import by\\nfrom requests import get\\nfrom observer.constants import check_ui_performance, listener_address\\nfrom shutil import rmtree\\nfrom observer.processors.results_processor import resultsProcessor\\n\\ndriver = None\\n\\ndef get_driver():\\n remote_driver_address = environ.get(\\\"remote\\\", \\\"127.0.0.1:4444\\\")\\n global driver\\n if not driver:\\n chrome_options = webdriver.ChromeOptions()\\n chrome_options.add_argument('--window-size=1360,1020')\\n driver = webdriver.Remote(\\n command_executor=f'http://{remote_driver_address}/wd/hub',\\n desired_capabilities=chrome_options.to_capabilities())\\n browser.set_driver(driver)\\n return driver\\n\\n\\ndef execute_step(step_definition):\\n driver = get_driver()\\n browser.open(step_definition['url'])\\n if step_definition.get('el'):\\n browser.element(by.xpath(step_definition['el'])).is_displayed()\\n for _ in range(1200):\\n if driver.execute_script('return document.readyState === \\\"complete\\\" && performance.timing.loadEventEnd > 0'):\\n break\\n sleep(0.1)\\n\\n\\ndef close_driver():\\n browser.quit()\\n\\n\\ndef step(step_definition):\\n driver = get_driver()\\n start_time = time()\\n videofolder = None\\n video_path = None\\n if step_definition.get('html'):\\n get(f'http://{listener_address}/record/start')\\n current_time = time() - start_time\\n try:\\n execute_step(step_definition)\\n results = driver.execute_script(check_ui_performance)\\n except common.exceptions.WebDriverException:\\n return None\\n results['info']['testStart'] = int(current_time)\\n if step_definition.get('html'):\\n video_results = get(f'http://{listener_address}/record/stop').content\\n videofolder = tempfile.mkdtemp()\\n video_path = path.join(videofolder, \\\"Video.mp4\\\")\\n with open(video_path, 'w+b') as f:\\n f.write(video_results)\\n report = resultsProcessor(video_path, results, videofolder, True, step_definition.get('html'))\\n if videofolder:\\n rmtree(videofolder)\\n return report\\n\\n\\ndef terminate_runner():\\n return get(f'http://{listener_address}/terminate').content\\n\\n\\ndef wait_for_agent():\\n sleep(5)\\n for _ in range(120):\\n sleep(1)\\n try:\\n if get(f'http://{listener_address}', timeout=1).content == b'OK':\\n break\\n except:\\n pass\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2975795,"cells":{"repo_name":{"kind":"string","value":"Meg1211/GamestonkTerminal"},"repo_url":{"kind":"string","value":"https://github.com/Meg1211/GamestonkTerminal"},"snapshot_id":{"kind":"string","value":"917fccd8fa97b002b0cd744a080c8be5309c46b9"},"revision_id":{"kind":"string","value":"9e8b9fb631df1778795236b3ec2aa66725266c15"},"directory_id":{"kind":"string","value":"baaa0e1caa34796d7e010ec277d94940c8b7ee72"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-09T23:35:33.450603","string":"2023-07-09T23:35:33.450603"},"revision_date":{"kind":"timestamp","value":"2021-08-08T16:31:44","string":"2021-08-08T16:31:44"},"committer_date":{"kind":"timestamp","value":"2021-08-08T16:31:44","string":"2021-08-08T16:31:44"},"github_id":{"kind":"number","value":393953964,"string":"393,953,964"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_created_at":{"kind":"timestamp","value":"2021-08-08T12:27:42","string":"2021-08-08T12:27:42"},"gha_updated_at":{"kind":"timestamp","value":"2021-08-08T12:27:41","string":"2021-08-08T12:27:41"},"gha_pushed_at":{"kind":"timestamp","value":"2021-08-08T12:27:18","string":"2021-08-08T12:27:18"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5313138961791992,"alphanum_fraction":0.5361996293067932,"avg_line_length":26.95806884765625,"blob_id":"aa2a34288681e0c74c25c8cb30b483aa77fc7dfe","content_id":"e1989dcd56ec2d16a70a82c74f619bf09ed43fac","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":44006,"license_type":"permissive","max_line_length":121,"num_lines":1574,"path":"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"CoinPaprika View\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport difflib\nfrom tabulate import tabulate\nfrom pandas.plotting import register_matplotlib_converters\nimport matplotlib.pyplot as plt\nimport mplfinance as mpf\nimport pandas as pd\n\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, check_positive\nfrom gamestonk_terminal.helper_funcs import plot_autoscale\nfrom gamestonk_terminal.feature_flags import USE_ION as ion\nimport gamestonk_terminal.cryptocurrency.coinpaprika.coinpaprika_model as paprika\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import (\n long_number_format_with_type_check,\n)\n\n\nregister_matplotlib_converters()\n\n# pylint: disable=inconsistent-return-statements\n# pylint: disable=C0302, too-many-lines\n\nCURRENCIES = [\n \"BTC\",\n \"ETH\",\n \"USD\",\n \"EUR\",\n \"PLN\",\n \"KRW\",\n \"GBP\",\n \"CAD\",\n \"JPY\",\n \"RUB\",\n \"TRY\",\n \"NZD\",\n \"AUD\",\n \"CHF\",\n \"UAH\",\n \"HKD\",\n \"SGD\",\n \"NGN\",\n \"PHP\",\n \"MXN\",\n \"BRL\",\n \"THB\",\n \"CLP\",\n \"CNY\",\n \"CZK\",\n \"DKK\",\n \"HUF\",\n \"IDR\",\n \"ILS\",\n \"INR\",\n \"MYR\",\n \"NOK\",\n \"PKR\",\n \"SEK\",\n \"TWD\",\n \"ZAR\",\n \"VND\",\n \"BOB\",\n \"COP\",\n \"PEN\",\n \"ARS\",\n \"ISK\",\n]\n\n# see https://github.com/GamestonkTerminal/GamestonkTerminal/pull/562#issuecomment-887842888\n# EXCHANGES = paprika.get_list_of_exchanges()\n\n\ndef global_market(other_args: List[str]):\n \"\"\"Return data frame with most important global crypto statistics like:\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\n market_cap_change_24h, volume_24h_change_24h, last_updated,\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"global\",\n description=\"\"\"Show most important global crypto statistics like:\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\n market_cap_change_24h, volume_24h_change_24h, last_updated.\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_global_market()\n df[\"Value\"] = df[\"Value\"].apply(lambda x: long_number_format_with_type_check(x))\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef coins(other_args: List[str]):\n \"\"\"Shows list of all available coins on CoinPaprika\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"coins\",\n description=\"\"\"Shows list of all available coins on CoinPaprika.\n You can display top N number of coins with --top N flag,\n You can search by starting letters with -l/--letter flag like `coins -l M`\n And you can also specify by which column you are searching for coin with --key\n Displays columns like:\n rank, id, name, type\"\"\",\n )\n parser.add_argument(\n \"-s\",\n \"--skip\",\n default=0,\n dest=\"skip\",\n help=\"Skip n of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=30,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\"-l\", \"--letter\", dest=\"letter\", help=\"First letters\", type=str)\n parser.add_argument(\n \"-k\",\n \"--key\",\n dest=\"key\",\n help=\"Search in column symbol, name, id\",\n type=str,\n choices=[\"id\", \"symbol\", \"name\"],\n default=\"symbol\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_list_of_coins()\n\n letter = ns_parser.letter\n if letter and isinstance(letter, str):\n df = df[\n df[ns_parser.key].str.match(f\"^({letter.lower()}|{letter.upper()})\")\n ]\n\n try:\n df = df[ns_parser.skip : ns_parser.skip + ns_parser.top]\n except Exception:\n df = paprika.get_list_of_coins()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef all_coins_market_info(other_args: List[str]):\n \"\"\"Displays basic market information for all coins from CoinPaprika API\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"markets\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Show market related (price, supply, volume) coin information for all coins on CoinPaprika.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, volume_24h, mcap_change_24h, pct_change_1h, pct_change_24h,\n ath_price, pct_from_ath, --sort parameter and also with --descend flag to sort descending.\n Displays:\n rank, name, symbol, price, volume_24h, mcap_change_24h,\n pct_change_1h, pct_change_24h, ath_price, pct_from_ath,\n \"\"\",\n )\n parser.add_argument(\n \"--vs\",\n help=\"Quoted currency. Default USD\",\n dest=\"vs\",\n default=\"USD\",\n type=str,\n choices=CURRENCIES,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"volume_24h\",\n \"mcap_change_24h\",\n \"pct_change_1h\",\n \"pct_change_24h\",\n \"ath_price\",\n \"pct_from_ath\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_coins_market_info(quotes=ns_parser.vs).sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return\n\n cols = [col for col in df.columns if col != \"rank\"]\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\n print(\"\")\n print(f\"Displaying data vs {ns_parser.vs}\")\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".3f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef all_coins_info(other_args: List[str]):\n \"\"\"Displays basic coin information for all coins from CoinPaprika API\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"info\",\n description=\"\"\"Show basic coin information for all coins from CoinPaprika API\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, volume_24h, circulating_supply, total_supply, max_supply,\n market_cap, beta_value, ath_price --sort parameter and also with --descend flag to sort descending.\n Displays:\n rank, name, symbol, price, volume_24h, circulating_supply,\n total_supply, max_supply, market_cap, beta_value, ath_price\n \"\"\",\n )\n parser.add_argument(\n \"--vs\",\n help=\"Quoted currency. Default USD\",\n dest=\"vs\",\n default=\"USD\",\n type=str,\n choices=CURRENCIES,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"volume_24h\",\n \"circulating_supply\",\n \"total_supply\",\n \"max_supply\",\n \"ath_price\",\n \"market_cap\",\n \"beta_value\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_coins_info(quotes=ns_parser.vs).sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if df.empty:\n print(\"Not data found\", \"\\n\")\n return\n\n cols = [col for col in df.columns if col != \"rank\"]\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\n\n print(\"\")\n print(f\"Displaying data vs {ns_parser.vs}\")\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".3f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef all_exchanges(other_args: List[str]):\n \"\"\"List exchanges from CoinPaprika API\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"exchanges\",\n description=\"\"\"Show all exchanges from CoinPaprika\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, currencies, markets, fiats, confidence,\n volume_24h,volume_7d ,volume_30d, sessions_per_month --sort parameter\n and also with --descend flag to sort descending.\n Displays:\n rank, name, currencies, markets, fiats, confidence, volume_24h,\n volume_7d ,volume_30d, sessions_per_month\"\"\",\n )\n parser.add_argument(\n \"--vs\",\n help=\"Quoted currency. Default USD\",\n dest=\"vs\",\n default=\"USD\",\n type=str,\n choices=CURRENCIES,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"currencies\",\n \"markets\",\n \"fiats\",\n \"confidence\",\n \"volume_24h\",\n \"volume_7d\",\n \"volume_30d\",\n \"sessions_per_month\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_list_of_exchanges(quotes=ns_parser.vs).sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return\n\n cols = [col for col in df.columns if col != \"rank\"]\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\n print(\"\")\n print(f\"Displaying data vs {ns_parser.vs}\")\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef search(other_args: List[str]):\n \"\"\"Search in CoinPaprika\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"search\",\n description=\"\"\"Search over CoinPaprika API\n You can display only top N number of results with --top parameter.\n You can sort data by id, name , category --sort parameter and also with --descend flag to sort descending.\n To choose category in which you are searching for use --cat/-c parameter. Available categories:\n currencies|exchanges|icos|people|tags|all\n Displays:\n id, name, category\"\"\",\n )\n parser.add_argument(\n \"-q\",\n \"--query\",\n help=\"phrase for search\",\n dest=\"query\",\n type=str,\n required=\"-h\" not in other_args,\n )\n parser.add_argument(\n \"-c\",\n \"--cat\",\n help=\"Categories to search: currencies|exchanges|icos|people|tags|all. Default: all\",\n dest=\"category\",\n default=\"all\",\n type=str,\n choices=[\n \"currencies\",\n \"exchanges\",\n \"icos\",\n \"people\",\n \"tags\",\n \"all\",\n ],\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: id\",\n default=\"id\",\n choices=[\"category\", \"id\", \"name\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n\n if other_args:\n if not other_args[0][0] == \"-\":\n other_args.insert(0, \"-q\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n category = ns_parser.category\n if category.lower() == \"all\":\n category = \"currencies,exchanges,icos,people,tags\"\n\n df = paprika.search(q=ns_parser.query, c=category)\n\n if df.empty:\n print(\n f\"No results for search query '{ns_parser.query}' in category '{ns_parser.category}'\"\n )\n print(\"\")\n return\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef exchange_markets(other_args: List[str]):\n \"\"\"Get all markets for given exchange\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"ex_markets\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Get all exchange markets found for given exchange\n You can display only top N number of records with --top parameter.\n You can sort data by pair, base_currency_name, quote_currency_name, market_url, category,\n reported_volume_24h_share, trust_score --sort parameter and also with --descend flag to sort descending.\n You can use additional flag --links to see urls for each market\n Displays:\n exchange_id, pair, base_currency_name, quote_currency_name, market_url,\n category, reported_volume_24h_share, trust_score,\"\"\",\n )\n parser.add_argument(\n \"-e\",\n \"--exchange\",\n help=\"Identifier of exchange e.g for Binance Exchange -> binance\",\n dest=\"exchange\",\n default=\"binance\",\n type=str,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: reported_volume_24h_share\",\n default=\"reported_volume_24h_share\",\n choices=[\n \"pair\",\n \"base_currency_name\",\n \"quote_currency_name\",\n \"category\",\n \"reported_volume_24h_share\",\n \"trust_score\",\n \"market_url\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=False,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"\"\"Flag to show urls. If you will use that flag you will see only:\n exchange, pair, trust_score, market_url columns\"\"\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_exchanges_market(exchange_id=ns_parser.exchange)\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n if ns_parser.links is True:\n df = df[[\"exchange_id\", \"pair\", \"trust_score\", \"market_url\"]]\n else:\n df.drop(\"market_url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef all_platforms(other_args: List[str]):\n \"\"\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"platforms\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_all_contract_platforms()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef contracts(other_args: List[str]):\n \"\"\"Gets all contract addresses for given platform\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"contracts\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Gets all contract addresses for given platform.\n Provide platform id with -p/--platform parameter\n You can display only top N number of smart contracts with --top parameter.\n You can sort data by id, type, active, address --sort parameter\n and also with --descend flag to sort descending.\n\n Displays:\n id, type, active, address\n \"\"\",\n )\n parser.add_argument(\n \"-p\",\n \"--platform\",\n help=\"Blockchain platform like eth-ethereum\",\n dest=\"platform\",\n default=\"eth-ethereum\",\n type=str,\n choices=paprika.get_all_contract_platforms()[\"platform_id\"].tolist(),\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column\",\n default=\"id\",\n choices=[\"id\", \"type\", \"active\", \"address\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_contract_platform(ns_parser.platform)\n\n if df.empty:\n print(f\"Nothing found for platform: {ns_parser.platform}\", \"\\n\")\n return\n\n df = df.sort_values(ns_parser.sortby, ascending=ns_parser.descend)\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\n# Coin Related\n\n\ndef find(other_args: List[str]):\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"find\",\n description=\"\"\"\n Find similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinPaprika,\n you can use this command to display coins with similar name, symbol or id to your search query.\n Example of usage: coin name is something like \"kusama\". So : find -c kusama -k name -t 25\n It will search for coin that has similar name to kusama and display top 25 matches.\n -c, --coin stands for coin - you provide here your search query\n -k, --key it's a searching key. You can search by symbol, id or name of coin\n -t, --top it displays top N number of records.\n \"\"\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n help=\"Coin name or id, or symbol\",\n dest=\"coin\",\n required=\"-h\" not in other_args,\n type=str,\n )\n parser.add_argument(\n \"-k\",\n \"--key\",\n dest=\"key\",\n help=\"Specify by which column you would like to search: symbol, name, id\",\n type=str,\n choices=[\"id\", \"symbol\", \"name\"],\n default=\"name\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n\n try:\n\n if other_args:\n if not other_args[0][0] == \"-\":\n other_args.insert(0, \"-c\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if not ns_parser.coin or ns_parser.coin is None:\n print(\n \"You didn't provide coin. Please use param -c/--coin <coin name>\", \"\\n\"\n )\n return\n\n coins_df = paprika.get_list_of_coins()\n coins_list = coins_df[ns_parser.key].to_list()\n\n keys = {\"name\": \"title\", \"symbol\": \"upper\", \"id\": \"lower\"}\n\n key = keys.get(ns_parser.key)\n coin = getattr(ns_parser.coin, str(key))()\n\n sim = difflib.get_close_matches(coin, coins_list, ns_parser.top)\n df = pd.Series(sim).to_frame().reset_index()\n df.columns = [\"index\", ns_parser.key]\n df = df.merge(coins_df, on=ns_parser.key)\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef twitter(coin_id: str, other_args: List[str]):\n \"\"\"Get twitter timeline for given coin id. Not more than last 50 tweets\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"twitter\",\n description=\"\"\"Show last 10 tweets for given coin.\n You can display only top N number of tweets with --top parameter.\n You can sort data by date, user_name, status, retweet_count, like_count --sort parameter\n and also with --descend flag to sort descending.\n Displays:\n date, user_name, status, retweet_count, like_count\n \"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: date\",\n default=\"date\",\n choices=[\"date\", \"user_name\", \"status\", \"retweet_count\", \"like_count\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_coin_twitter_timeline(coin_id)\n\n if df.empty:\n print(f\"Couldn't find any tweets for coin {coin_id}\", \"\\n\")\n return\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n # Remove unicode chars (it breaks pretty tables)\n df[\"status\"] = df[\"status\"].apply(\n lambda text: \"\".join(i if ord(i) < 128 else \"\" for i in text)\n )\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef events(coin_id: str, other_args: List[str]):\n \"\"\"Get all events for given coin id.\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"events\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Show information about most important coins events. Most of coins doesn't have any events.\n You can display only top N number of events with --top parameter.\n You can sort data by id, date , date_to, name, description, is_conference --sort parameter\n and also with --descend flag to sort descending.\n You can use additional flag --links to see urls for each event\n Displays:\n date , date_to, name, description, is_conference, link, proof_image_link\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: date\",\n default=\"date\",\n choices=[\"date\", \"date_to\", \"name\", \"description\", \"is_conference\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=False,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will see only date, name, link columns\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_coin_events_by_id(coin_id)\n\n if df.empty:\n print(f\"Couldn't find any events for coin {coin_id}\")\n print(\"\")\n return\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n if ns_parser.links is True:\n df = df[[\"date\", \"name\", \"link\"]]\n else:\n df.drop(\"link\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef exchanges(coin_id: str, other_args: List[str]):\n \"\"\"Get all exchanges for given coin id.\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ex\",\n description=\"\"\"Get all exchanges found for given coin.\n You can display only top N number of exchanges with --top parameter.\n You can sort data by id, name, adjusted_volume_24h_share, fiats --sort parameter\n and also with --descend flag to sort descending.\n Displays:\n id, name, adjusted_volume_24h_share, fiats\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: date\",\n default=\"adjusted_volume_24h_share\",\n choices=[\"id\", \"name\", \"adjusted_volume_24h_share\", \"fiats\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_coin_exchanges_by_id(coin_id)\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef markets(coin_id: str, other_args: List[str]):\n \"\"\"Get all markets for given coin id.\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"mkt\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Get all markets found for given coin.\n You can display only top N number of markets with --top parameter.\n You can sort data by pct_volume_share, exchange, pair, trust_score, volume, price --sort parameter\n and also with --descend flag to sort descending.\n You can use additional flag --links to see urls for each market\n Displays:\n exchange, pair, trust_score, volume, price, pct_volume_share,\"\"\",\n )\n parser.add_argument(\n \"--vs\",\n help=\"Quoted currency. Default USD\",\n dest=\"vs\",\n default=\"USD\",\n type=str,\n choices=CURRENCIES,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=20,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: pct_volume_share\",\n default=\"pct_volume_share\",\n choices=[\n \"pct_volume_share\",\n \"exchange\",\n \"pair\",\n \"trust_score\",\n \"volume\",\n \"price\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=False,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"\"\"Flag to show urls. If you will use that flag you will see only:\n exchange, pair, trust_score, market_url columns\"\"\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n vs = ns_parser.vs\n sort = ns_parser.sortby\n if sort in [\"volume\", \"price\"]:\n sort = f\"{str(vs).lower()}_{sort}\"\n\n df = paprika.get_coin_markets_by_id(coin_id, vs)\n\n if df.empty:\n print(\"There is no data\")\n return\n\n df = df.sort_values(by=sort, ascending=ns_parser.descend)\n\n if ns_parser.links is True:\n df = df[[\"exchange\", \"pair\", \"trust_score\", \"market_url\"]]\n else:\n df.drop(\"market_url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef chart(coin_id: str, other_args: List[str]):\n \"\"\"Plots chart for loaded cryptocurrency\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"chart\",\n description=\"\"\"Display chart for loaded coin. You can specify currency vs which you want\n to show chart and also number of days to get data for.\n By default currency: usd and days: 90.\n E.g. if you loaded in previous step Ethereum and you want to see it's price vs bitcoin\n in last 90 days range use `chart --vs btc --days 90`\n Available quoted currencies are only btc and usd\"\"\",\n )\n parser.add_argument(\n \"--vs\",\n default=\"usd\",\n dest=\"vs\",\n help=\"Currency to display vs coin\",\n choices=[\"usd\", \"btc\", \"BTC\", \"USD\"],\n )\n parser.add_argument(\n \"-d\",\n \"--days\",\n default=30,\n dest=\"days\",\n type=check_positive,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = paprika.get_ohlc_historical(coin_id, ns_parser.vs.upper(), ns_parser.days)\n\n if df.empty:\n print(\"There is not data to plot chart\\n\")\n return\n\n df.drop([\"time_close\", \"market_cap\"], axis=1, inplace=True)\n df.columns = [\n \"Time0\",\n \"Open\",\n \"High\",\n \"Low\",\n \"Close\",\n \"Volume\",\n ]\n df = df.set_index(pd.to_datetime(df[\"Time0\"])).drop(\"Time0\", axis=1)\n title = (\n f\"\\n{coin_id}/{ns_parser.vs} from {df.index[0].strftime('%Y/%m/%d')} to {df.index[-1].strftime('%Y/%m/%d')}\",\n )\n df[\"Volume\"] = df[\"Volume\"] / 1_000_000\n mpf.plot(\n df,\n type=\"candle\",\n volume=True,\n ylabel_lower=\"Volume [1M]\",\n title=str(title[0]) if isinstance(title, tuple) else title,\n xrotation=20,\n style=\"binance\",\n figratio=(10, 7),\n figscale=1.10,\n figsize=(plot_autoscale()),\n update_width_config=dict(\n candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0\n ),\n )\n\n if ion:\n plt.ion()\n plt.show()\n print(\"\")\n except SystemExit:\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef price_supply(coin_id: str, other_args: List[str]):\n \"\"\"Get ticker information for single coin\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"ps\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Get price and supply related metrics for given coin.\"\"\",\n )\n parser.add_argument(\n \"--vs\",\n help=\"Quoted currency. Default USD\",\n dest=\"vs\",\n default=\"USD\",\n type=str,\n choices=CURRENCIES,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.get_tickers_info_for_coin(coin_id, ns_parser.vs)\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return\n\n df = df.applymap(lambda x: long_number_format_with_type_check(x))\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef load(other_args: List[str]):\n \"\"\"Select coin from CoinPaprika\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n Returns\n -------\n coin: str\n Coin that is defined on binance\n df_coin : pd.DataFrame\n Dataframe of prices for selected coin\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"load\",\n description=\"Define the coin to be used from CoinPaprika and get data\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n help=\"Coin to get\",\n dest=\"coin\",\n type=str,\n required=\"-h\" not in other_args,\n )\n\n try:\n\n if other_args:\n if not other_args[0][0] == \"-\":\n other_args.insert(0, \"-c\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n COINS = paprika.get_list_of_coins()\n COINS_DCT = dict(zip(COINS.id, COINS.symbol))\n coin_id, _ = paprika.validate_coin(ns_parser.coin, COINS_DCT)\n\n if coin_id:\n print(\"\")\n return coin_id\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef ta(coin_id: str, other_args: List[str]):\n \"\"\"Load data for Technical Analysis\n\n Parameters\n ----------\n coin_id: Identifier of coin for CoinPaprika\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ta\",\n description=\"\"\"Loads data for technical analysis. You can specify currency vs which you want\n to show chart and also number of days to get data for.\n By default currency: usd and days: 30.\n E.g. if you loaded in previous step Ethereum and you want to see it's price vs btc\n in last 90 days range use `ta --vs btc --days 90`\"\"\",\n )\n parser.add_argument(\n \"--vs\",\n default=\"usd\",\n dest=\"vs\",\n help=\"Currency to display vs coin\",\n choices=[\"usd\", \"btc\", \"BTC\", \"USD\"],\n type=str,\n )\n parser.add_argument(\n \"-d\",\n \"--days\",\n default=30,\n dest=\"days\",\n help=\"Number of days to get data for\",\n type=check_positive,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return None, None\n\n df = paprika.get_ohlc_historical(coin_id, ns_parser.vs.upper(), ns_parser.days)\n\n if df.empty:\n print(\"No data found\", \"\\n\")\n return None, None\n\n df.drop([\"time_close\", \"market_cap\"], axis=1, inplace=True)\n df.columns = [\n \"Time0\",\n \"Open\",\n \"High\",\n \"Low\",\n \"Close\",\n \"Volume\",\n ]\n df = df.set_index(pd.to_datetime(df[\"Time0\"])).drop(\"Time0\", axis=1)\n return df, ns_parser.vs\n\n except SystemExit:\n print(\"\")\n return None, None\n\n except Exception as e:\n print(e, \"\\n\")\n return None, None\n\n\ndef basic(coin_id: str, other_args: List[str]):\n \"\"\"Get basic information for coin. Like:\n name, symbol, rank, type, description, platform, proof_type,\n contract, tags, parent\n\n Parameters\n ----------\n coin_id: str\n Identifier of coin for CoinPaprika API\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"basic\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Get basic information for coin. Like:\n name, symbol, rank, type, description, platform, proof_type,\n contract, tags, parent\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = paprika.basic_coin_info(coin_id)\n\n if df.empty:\n print(\"No data available\\n\")\n return\n\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5048558115959167,"alphanum_fraction":0.5055915117263794,"avg_line_length":32.810943603515625,"blob_id":"8365c269a6fa1b9a6fb8cd3757430fb46cf60a91","content_id":"99c2a1da539c97f9f34b5eca6926ce3a8cb87dbe","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6796,"license_type":"permissive","max_line_length":116,"num_lines":201,"path":"/gamestonk_terminal/etf/screener_model.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Screener model\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport os\nimport configparser\nimport pandas as pd\nfrom tabulate import tabulate\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, export_data\n\n\ndef etf_screener(other_args: List[str]):\n \"\"\"\n Screens the etfs pulled from my repo, which is updated daily at midnight EST\n Parameters\n ----------\n other_args:\n List or argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"etfscr\",\n add_help=False,\n description=\"Screens ETFS from a personal scraping github repository. Data scraped from stockanalysis.com\",\n )\n\n parser.add_argument(\n \"-p\", \"--min_price\", help=\"min price\", dest=\"min_price\", default=False\n )\n parser.add_argument(\n \"-P\", \"--max_price\", help=\"max price\", dest=\"max_price\", default=False\n )\n parser.add_argument(\n \"-a\", \"--min_assets\", help=\"min assets ($M)\", dest=\"min_assets\", default=False\n )\n parser.add_argument(\n \"-A\", \"--max_assets\", help=\"max assets ($M)\", dest=\"max_assets\", default=False\n )\n parser.add_argument(\n \"-n\",\n \"--min_nav\",\n help=\"min nav (net asset value)\",\n dest=\"min_nav\",\n default=False,\n )\n\n parser.add_argument(\n \"-N\",\n \"--max_nav\",\n help=\"max nav (net asset value)\",\n dest=\"max_nav\",\n default=False,\n )\n parser.add_argument(\n \"-e\", \"--min_exp\", help=\"min expense ratio (%%)\", dest=\"min_exp\", default=False\n )\n\n parser.add_argument(\n \"-E\", \"--max_exp\", help=\"max expense ratio (%%)\", dest=\"max_exp\", default=False\n )\n\n parser.add_argument(\n \"-r\", \"--min_pe\", help=\"min pe ratio\", dest=\"min_pe\", default=False\n )\n parser.add_argument(\n \"-R\", \"--max_pe\", help=\"max pe ratio\", dest=\"max_pe\", default=False\n )\n\n parser.add_argument(\n \"-d\", \"--min_div\", help=\"min dividend yield (%%)\", dest=\"min_div\", default=False\n )\n parser.add_argument(\n \"-D\", \"--max_div\", help=\"max dividend yield (%%)\", dest=\"max_div\", default=False\n )\n parser.add_argument(\n \"-b\", \"--min_beta\", help=\"min 5Y beta\", dest=\"min_beta\", default=False\n )\n parser.add_argument(\n \"-B\", \"--max_beta\", help=\"max beta\", dest=\"max_beta\", default=False\n )\n parser.add_argument(\n \"--num\", type=int, help=\"Number of etfs to show\", dest=\"num\", default=20\n )\n\n parser.add_argument(\n \"--config\",\n help=\"Load options from config file\",\n dest=\"config\",\n action=\"store_true\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n # pylint: disable=no-member\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = pd.read_csv(\n \"https://raw.githubusercontent.com/jmaslek/etf_scraper/main/etf_overviews.csv\",\n index_col=0,\n )\n print(\"ETFs downloaded\\n\")\n param_string = \"etf_screener\"\n if ns_parser.config:\n cf = configparser.ConfigParser()\n cf.read(\"gamestonk_terminal/etf/etf_config.ini\")\n cols = cf.sections()\n\n for col in cols:\n if cf[col][\"Min\"] != \"None\":\n query = f\"{col} > {cf[col]['Min']} \"\n df = df.query(query)\n if cf[col][\"Max\"] != \"None\":\n query = f\"{col} < {cf[col]['Max']} \"\n df = df.query(query)\n param_string += \"_from_config\"\n else:\n\n if ns_parser.min_price:\n df = df.query(f\"Price > {ns_parser.min_price}\")\n param_string += f\"_p_{ns_parser.min_price}\".replace(\".\", \"p\")\n\n if ns_parser.max_price:\n df = df.query(f\"Price < {ns_parser.max_price}\")\n param_string += f\"_P_{ns_parser.max_price}\".replace(\".\", \"p\")\n\n if ns_parser.min_assets:\n df = df.query(f\"Assets > {ns_parser.min_assets}\")\n param_string += f\"_a_{ns_parser.min_assets}\".replace(\".\", \"p\")\n\n if ns_parser.max_assets:\n df = df.query(f\"Assets < {ns_parser.max_assets}\")\n param_string += f\"_A_{ns_parser.max_assets}\".replace(\".\", \"p\")\n\n if ns_parser.min_nav:\n df = df.query(f\"NAV > {ns_parser.min_nav}\")\n param_string += f\"_n_{ns_parser.min_nav}\".replace(\".\", \"p\")\n\n if ns_parser.max_nav:\n df = df.query(f\"NAV < {ns_parser.max_nav}\")\n param_string += f\"_N_{ns_parser.max_nav}\".replace(\".\", \"p\")\n\n if ns_parser.min_exp:\n df = df.query(f\"Expense > {ns_parser.min_exp}\")\n param_string += f\"_e_{ns_parser.min_exp}\".replace(\".\", \"p\")\n\n if ns_parser.max_exp:\n df = df.query(f\"Expense < {ns_parser.max_exp}\")\n param_string += f\"_E_{ns_parser.max_exp}\".replace(\".\", \"p\")\n\n if ns_parser.min_pe:\n df = df.query(f\"PE > {ns_parser.min_pe}\")\n param_string += f\"_r_{ns_parser.min_pe}\".replace(\".\", \"p\")\n if ns_parser.max_pe:\n df = df.query(f\"PE < {ns_parser.max_pe}\")\n param_string += f\"_R_{ns_parser.max_pe}\".replace(\".\", \"p\")\n\n if ns_parser.min_div:\n df = df.query(f\"DivYield > {ns_parser.min_div}\")\n param_string += f\"_d_{ns_parser.min_div}\".replace(\".\", \"p\")\n if ns_parser.max_div:\n df = df.query(f\"DivYield < {ns_parser.max_div}\")\n param_string += f\"_D_{ns_parser.max_div}\".replace(\".\", \"p\")\n\n if ns_parser.min_beta:\n df = df.query(f\"Beta > {ns_parser.min_beta}\")\n param_string += f\"_b_{ns_parser.min_beta}\".replace(\".\", \"p\").replace(\n \"-\", \"neg\"\n )\n\n if ns_parser.max_beta:\n df = df.query(f\"Beta < {ns_parser.max_beta}\")\n param_string += f\"_B_{ns_parser.max_beta}\".replace(\".\", \"p\").replace(\n \"-\", \"neg\"\n )\n\n export_data(\n ns_parser.export,\n os.path.join(os.path.dirname(os.path.abspath(__file__)), \"screeners\"),\n param_string,\n df,\n )\n\n if df.shape[0] > int(ns_parser.num):\n df = df.sample(ns_parser.num)\n print(\n tabulate(\n df.fillna(\"\"), tablefmt=\"fancy_grid\", headers=df.columns, showindex=True\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5245620608329773,"alphanum_fraction":0.5257045030593872,"avg_line_length":22.446428298950195,"blob_id":"76b558d52c4a1383f88c2b914da6a22caac154b6","content_id":"196487d0feed94a562f9ad79784b690da9cdb461","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5252,"license_type":"permissive","max_line_length":69,"num_lines":224,"path":"/gamestonk_terminal/economy/wsj_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"WSJ view \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom tabulate import tabulate\n\nfrom gamestonk_terminal.economy.wsj_model import (\n market_overview,\n us_bonds,\n us_indices,\n global_bonds,\n global_currencies,\n top_commodities,\n)\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\n\n\ndef display_overview(other_args: List[str]):\n \"\"\"Display market overview\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"overview\",\n description=\"WSJ market overview\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n data = market_overview()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef display_indices(other_args: List[str]):\n \"\"\"Display us indices\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"indices\",\n description=\"WSJ US Indices\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n data = us_indices()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef display_futures(other_args: List[str]):\n \"\"\"Display futures/commodities\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"futures\",\n description=\"WSJ futures/commodities\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n data = top_commodities()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef display_usbonds(other_args: List[str]):\n \"\"\"Display us bonds overview\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"usbonds\",\n description=\"WSJ US Bonds overview\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n data = us_bonds()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef display_glbonds(other_args: List[str]):\n \"\"\"Display global bond overview\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"glbond\",\n description=\"WSJ global bond overview\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n data = global_bonds()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef display_currencies(other_args: List[str]):\n \"\"\"Display global currencies\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"currencies\",\n description=\"WSJ currency overview\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n data = global_currencies()\n print(\n tabulate(\n data,\n showindex=False,\n headers=data.columns,\n floatfmt=\".2f\",\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.4917961359024048,"alphanum_fraction":0.5026862025260925,"avg_line_length":27.695833206176758,"blob_id":"40b6cb89f5c09c5b022ac1bc5da6b4466fa7f28b","content_id":"409fe69dcdc46334de886f8058551cf1617ef2fc","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6887,"license_type":"permissive","max_line_length":98,"num_lines":240,"path":"/gamestonk_terminal/stocks/discovery/stockgrid_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" Stockgrid View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport os\nfrom typing import List\nimport requests\nimport pandas as pd\nfrom tabulate import tabulate\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n check_positive,\n export_data,\n)\n\n\ndef darkshort(other_args: List[str]):\n \"\"\"Get dark pool short positions.\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"darkshort\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Get dark pool short positions. [Source: Stockgrid]\",\n )\n parser.add_argument(\n \"-n\",\n \"--number\",\n help=\"Number of top tickers to show\",\n type=check_positive,\n default=10,\n dest=\"num\",\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n help=\"Field for which to sort by, where 'sv': Short Vol. (1M), \"\n \"'sv_pct': Short Vol. %%, 'nsv': Net Short Vol. (1M), \"\n \"'nsv_dollar': Net Short Vol. ($100M), 'dpp': DP Position (1M), \"\n \"'dpp_dollar': DP Position ($1B)\",\n choices=[\"sv\", \"sv_pct\", \"nsv\", \"nsv_dollar\", \"dpp\", \"dpp_dollar\"],\n default=\"dpp_dollar\",\n dest=\"sort_field\",\n )\n parser.add_argument(\n \"-a\",\n \"--ascending\",\n action=\"store_true\",\n default=False,\n dest=\"ascending\",\n help=\"Data in ascending order\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n d_fields_endpoints = {\n \"sv\": \"Short+Volume\",\n \"sv_pct\": \"Short+Volume+%25\",\n \"nsv\": \"Net+Short+Volume\",\n \"nsv_dollar\": \"Net+Short+Volume+$\",\n \"dpp\": \"Dark+Pools+Position\",\n \"dpp_dollar\": \"Dark+Pools+Position+$\",\n }\n\n field = d_fields_endpoints[ns_parser.sort_field]\n\n if ns_parser.ascending:\n order = \"asc\"\n else:\n order = \"desc\"\n\n link = f\"https://stockgridapp.herokuapp.com/get_dark_pool_data?top={field}&minmax={order}\"\n\n response = requests.get(link)\n df = pd.DataFrame(response.json()[\"data\"])\n\n df = df[\n [\n \"Ticker\",\n \"Date\",\n \"Short Volume\",\n \"Short Volume %\",\n \"Net Short Volume\",\n \"Net Short Volume $\",\n \"Dark Pools Position\",\n \"Dark Pools Position $\",\n ]\n ]\n dp_date = df[\"Date\"].values[0]\n df = df.drop(columns=[\"Date\"])\n df[\"Net Short Volume $\"] = df[\"Net Short Volume $\"] / 100_000_000\n df[\"Short Volume\"] = df[\"Short Volume\"] / 1_000_000\n df[\"Net Short Volume\"] = df[\"Net Short Volume\"] / 1_000_000\n df[\"Short Volume %\"] = df[\"Short Volume %\"] * 100\n df[\"Dark Pools Position $\"] = df[\"Dark Pools Position $\"] / (1_000_000_000)\n df[\"Dark Pools Position\"] = df[\"Dark Pools Position\"] / 1_000_000\n df.columns = [\n \"Ticker\",\n \"Short Vol. (1M)\",\n \"Short Vol. %\",\n \"Net Short Vol. (1M)\",\n \"Net Short Vol. ($100M)\",\n \"DP Position (1M)\",\n \"DP Position ($1B)\",\n ]\n\n # Assuming that the datetime is the same, which from my experiments seems to be the case\n print(f\"The following data corresponds to the date: {dp_date}\")\n print(\n tabulate(\n df.iloc[: ns_parser.num],\n tablefmt=\"fancy_grid\",\n floatfmt=\".2f\",\n headers=list(df.columns),\n showindex=False,\n )\n )\n print(\"\")\n\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n \"darkshort\",\n df,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef shortvol(other_args: List[str]):\n \"\"\"Print short interest and days to cover\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"shortvol\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Print short interest and days to cover. [Source: Stockgrid]\",\n )\n parser.add_argument(\n \"-n\",\n \"--number\",\n help=\"Number of top tickers to show\",\n type=check_positive,\n default=10,\n dest=\"num\",\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n help=\"Field for which to sort by, where 'float': Float Short %%, \"\n \"'dtc': Days to Cover, 'si': Short Interest\",\n choices=[\"float\", \"dtc\", \"si\"],\n default=\"float\",\n dest=\"sort_field\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n link = \"https://stockgridapp.herokuapp.com/get_short_interest?top=days\"\n r = requests.get(link)\n df = pd.DataFrame(r.json()[\"data\"])\n df.head()\n\n d_fields = {\n \"float\": \"%Float Short\",\n \"dtc\": \"Days To Cover\",\n \"si\": \"Short Interest\",\n }\n\n df = df[\n [\"Ticker\", \"Date\", \"%Float Short\", \"Days To Cover\", \"Short Interest\"]\n ].sort_values(\n by=d_fields[ns_parser.sort_field],\n ascending=bool(ns_parser.sort_field == \"dtc\"),\n )\n dp_date = df[\"Date\"].values[0]\n df = df.drop(columns=[\"Date\"])\n df[\"Short Interest\"] = df[\"Short Interest\"] / 1_000_000\n df.head()\n df.columns = [\n \"Ticker\",\n \"Float Short %\",\n \"Days to Cover\",\n \"Short Interest (1M)\",\n ]\n\n # Assuming that the datetime is the same, which from my experiments seems to be the case\n print(f\"The following data corresponds to the date: {dp_date}\")\n print(\n tabulate(\n df.iloc[: ns_parser.num],\n tablefmt=\"fancy_grid\",\n floatfmt=\".2f\",\n headers=list(df.columns),\n showindex=False,\n )\n )\n print(\"\")\n\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n \"shortvol\",\n df,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.4743128716945648,"alphanum_fraction":0.48047778010368347,"avg_line_length":32.13191604614258,"blob_id":"ed0482c00a481556fa6e614f9138e6e99882ebeb","content_id":"45eebe5946503837d6cfdd240e5765bed10830c5","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7786,"license_type":"permissive","max_line_length":86,"num_lines":235,"path":"/gamestonk_terminal/stocks/due_diligence/sec_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" SEC View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport os\nfrom typing import List\nfrom datetime import datetime, timedelta\nfrom tabulate import tabulate\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom matplotlib import pyplot as plt\nimport matplotlib.dates as mdates\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import (\n get_user_agent,\n check_positive,\n parse_known_args_and_warn,\n valid_date,\n export_data,\n)\n\n\ndef fails_to_deliver(other_args: List[str], ticker: str, stock: pd.DataFrame):\n \"\"\"Display fails-to-deliver data for a given ticker\n\n Parameters\n ----------\n other_args : List[str]\n argparse other args - [\"-n\", \"10\"]\n ticker : str\n Stock ticker\n stock : pd.DataFrame\n Stock data\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n prog=\"ftd\",\n description=\"\"\"Prints latest fails-to-deliver data. [Source: SEC]\"\"\",\n )\n parser.add_argument(\n \"-s\",\n \"--start\",\n action=\"store\",\n dest=\"start\",\n type=valid_date,\n default=(datetime.now() - timedelta(days=60)).strftime(\"%Y-%m-%d\"),\n help=\"start of datetime to see FTD\",\n )\n parser.add_argument(\n \"-e\",\n \"--end\",\n action=\"store\",\n dest=\"end\",\n type=valid_date,\n default=datetime.now().strftime(\"%Y-%m-%d\"),\n help=\"end of datetime to see FTD\",\n )\n parser.add_argument(\n \"-n\",\n \"--num\",\n action=\"store\",\n dest=\"n_num\",\n type=check_positive,\n default=0,\n help=\"number of latest fails-to-deliver being printed\",\n )\n parser.add_argument(\n \"--raw\",\n action=\"store_true\",\n default=False,\n dest=\"raw\",\n help=\"Print raw data.\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n ftds_data = pd.DataFrame()\n\n # Filter by number of last FTD\n if ns_parser.n_num > 0:\n url_ftds = \"https://www.sec.gov/data/foiadocsfailsdatahtm\"\n text_soup_ftds = BeautifulSoup(\n requests.get(url_ftds, headers={\"User-Agent\": get_user_agent()}).text,\n \"lxml\",\n )\n\n table = text_soup_ftds.find(\"table\", {\"class\": \"list\"})\n links = table.findAll(\"a\")\n link_idx = 0\n\n while len(ftds_data) < ns_parser.n_num:\n if link_idx > len(links):\n break\n link = links[link_idx]\n url = \"https://www.sec.gov\" + link[\"href\"]\n all_ftds = pd.read_csv(\n url,\n compression=\"zip\",\n sep=\"|\",\n engine=\"python\",\n skipfooter=2,\n usecols=[0, 2, 3, 5],\n dtype={\"QUANTITY (FAILS)\": \"int\"},\n )\n tmp_ftds = all_ftds[all_ftds[\"SYMBOL\"] == ticker]\n del tmp_ftds[\"PRICE\"]\n del tmp_ftds[\"SYMBOL\"]\n # merge the data from this archive\n ftds_data = pd.concat([ftds_data, tmp_ftds], ignore_index=True)\n link_idx += 1\n\n # clip away extra rows\n ftds_data = ftds_data.sort_values(\"SETTLEMENT DATE\")[-ns_parser.n_num :]\n\n ftds_data[\"SETTLEMENT DATE\"] = ftds_data[\"SETTLEMENT DATE\"].apply(\n lambda x: datetime.strptime(str(x), \"%Y%m%d\")\n )\n\n # Filter by start and end dates for FTD\n else:\n\n base_url = \"https://www.sec.gov/files/data/fails-deliver-data/cnsfails\"\n ftd_dates = list()\n\n for y in range(ns_parser.start.year, ns_parser.end.year + 1):\n if y < ns_parser.end.year:\n for m in range(ns_parser.start.month, 13):\n month = \"%02d\" % m\n if m == ns_parser.start.month and y == ns_parser.start.year:\n if ns_parser.start.day < 16:\n ftd_dates.append(str(y) + month + \"a\")\n ftd_dates.append(str(y) + month + \"b\")\n else:\n ftd_dates.append(str(y) + month + \"a\")\n ftd_dates.append(str(y) + month + \"b\")\n\n else:\n for m in range(1, ns_parser.end.month):\n month = \"%02d\" % m\n if m == ns_parser.end.month - 1:\n ftd_dates.append(str(y) + month + \"a\")\n if ns_parser.end.day > 15:\n ftd_dates.append(str(y) + month + \"b\")\n else:\n ftd_dates.append(str(y) + month + \"a\")\n ftd_dates.append(str(y) + month + \"b\")\n\n ftd_urls = [base_url + ftd_date + \".zip\" for ftd_date in ftd_dates]\n\n for ftd_link in ftd_urls:\n all_ftds = pd.read_csv(\n ftd_link,\n compression=\"zip\",\n sep=\"|\",\n engine=\"python\",\n skipfooter=2,\n usecols=[0, 2, 3, 5],\n dtype={\"QUANTITY (FAILS)\": \"int\"},\n )\n tmp_ftds = all_ftds[all_ftds[\"SYMBOL\"] == ticker]\n del tmp_ftds[\"PRICE\"]\n del tmp_ftds[\"SYMBOL\"]\n # merge the data from this archive\n ftds_data = pd.concat([ftds_data, tmp_ftds], ignore_index=True)\n\n ftds_data[\"SETTLEMENT DATE\"] = ftds_data[\"SETTLEMENT DATE\"].apply(\n lambda x: datetime.strptime(str(x), \"%Y%m%d\")\n )\n\n ftds_data = ftds_data[ftds_data[\"SETTLEMENT DATE\"] > ns_parser.start]\n ftds_data = ftds_data[ftds_data[\"SETTLEMENT DATE\"] < ns_parser.end]\n\n plt.bar(\n ftds_data[\"SETTLEMENT DATE\"],\n ftds_data[\"QUANTITY (FAILS)\"] / 1000,\n )\n plt.ylabel(\"Shares [K]\")\n plt.title(f\"Fails-to-deliver Data for {ticker}\")\n plt.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\", alpha=0.2)\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(\"%Y/%m/%d\"))\n plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7))\n plt.gcf().autofmt_xdate()\n plt.xlabel(\"Days\")\n\n _ = plt.gca().twinx()\n\n if ns_parser.n_num > 0:\n stock_ftd = stock[\n stock.index > (datetime.now() - timedelta(days=ns_parser.n_num + 31))\n ]\n else:\n stock_ftd = stock[stock.index > ns_parser.start]\n stock_ftd = stock_ftd[stock_ftd.index < ns_parser.end]\n plt.plot(stock_ftd.index, stock_ftd[\"Adj Close\"], color=\"tab:orange\")\n plt.ylabel(\"Share Price [$]\")\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.show()\n print(\"\")\n\n if ns_parser.raw:\n print(\n tabulate(\n ftds_data,\n headers=ftds_data.columns,\n tablefmt=\"fancy_grid\",\n stralign=\"right\",\n showindex=False,\n )\n )\n print(\"\")\n\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n \"ftd\",\n ftds_data.reset_index(),\n )\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5628488659858704,"alphanum_fraction":0.5655437707901001,"avg_line_length":28.02234649658203,"blob_id":"4277dd5f0d5975912177e42fe2e33159701c516d","content_id":"cd4b5ed4473fe8ab616e46c3d45a40166bf9b5af","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5195,"license_type":"permissive","max_line_length":112,"num_lines":179,"path":"/gamestonk_terminal/cryptocurrency/crypto_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Cryptocurrency Controller\"\"\"\n__docformat__ = \"numpy\"\n# pylint: disable=R0904, C0302, R1710\nimport argparse\nimport os\nimport matplotlib.pyplot as plt\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.cryptocurrency.coinmarketcap import coinmarketcap_controller\nfrom gamestonk_terminal.cryptocurrency.binance import binance_controller\nfrom gamestonk_terminal.cryptocurrency.coingecko import pycoingecko_controller\nfrom gamestonk_terminal.cryptocurrency import finbrain_crypto_view\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_controller\nfrom gamestonk_terminal.cryptocurrency.report import report_controller\n\n\nclass CryptoController:\n\n CHOICES = [\n \"?\",\n \"cls\",\n \"help\",\n \"q\",\n \"quit\",\n ]\n\n CHOICES_COMMAND = [\n \"finbrain\",\n ]\n\n CHOICES_MENUS = [\n \"cg\",\n \"bin\",\n \"cmc\",\n \"cp\",\n \"report\",\n ]\n\n CHOICES += CHOICES_COMMAND\n CHOICES += CHOICES_MENUS\n\n def __init__(self):\n \"\"\"CONSTRUCTOR\"\"\"\n\n self.crypto_parser = argparse.ArgumentParser(add_help=False, prog=\"crypto\")\n self.crypto_parser.add_argument(\"cmd\", choices=self.CHOICES)\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\n \"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/cryptocurrency\"\n )\n print(\"\")\n print(\">> CRYPTO <<\")\n print(\"\")\n print(\"What do you want to do?\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\")\n print(\" finbrain Crypto sentiment from 15+ major news headlines\")\n print(\"\")\n print(\"> cg CoinGecko overview (market statistics) and coin menu\")\n print(\"> cmc Coinmarketcap menu\")\n print(\"> bin Binance menu with order book, candles, ta.. \")\n print(\"> cp CoinPaprika menu\")\n print(\"> report generate automatic report\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self.crypto_parser.parse_known_args(an_input.split())\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu\"\"\"\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program\"\"\"\n return True\n\n def call_cg(self, _):\n if pycoingecko_controller.menu():\n return True\n print(\"\")\n\n def call_bin(self, _):\n \"\"\"Process bin command\"\"\"\n if binance_controller.menu():\n return True\n print(\"\")\n\n def call_cmc(self, _):\n \"\"\"Process top command\"\"\"\n if coinmarketcap_controller.menu():\n return True\n print(\"\")\n\n def call_finbrain(self, other_args):\n \"\"\"Process sentiment command\"\"\"\n finbrain_crypto_view.crypto_sentiment_analysis(other_args=other_args)\n\n def call_cp(self, _):\n \"\"\"Process cp command\"\"\"\n if coinpaprika_controller.menu():\n return True\n print(\"\")\n\n def call_report(self, _):\n \"\"\"Process report command\"\"\"\n ret = report_controller.menu()\n\n if ret is False:\n self.print_help()\n else:\n return True\n\n\ndef menu():\n crypto_controller = CryptoController()\n crypto_controller.print_help()\n plt.close(\"all\")\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in crypto_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (crypto)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (crypto)> \")\n\n try:\n process_input = crypto_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.6077635884284973,"alphanum_fraction":0.6106604933738708,"avg_line_length":27.766666412353516,"blob_id":"969c4018c4aa2dc9756dc3ef0b73d7de74a34e96","content_id":"dc003098c6d000ccd26812c259be94c9e693c6a1","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1726,"license_type":"permissive","max_line_length":90,"num_lines":60,"path":"/gamestonk_terminal/options/barchart_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Helper functions for scraping options data\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nfrom tabulate import tabulate\n\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\n\n\ndef print_options_data(stock: str, other_args: List[str]):\n \"\"\"Scrapes Barchart.com for the options information\n\n Parameters\n ----------\n stock: str\n Ticker to get options info for\n other_args: List[str]\n Other arguments. Currently just accepts a browser flag for selenium\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"info\",\n description=\"Display option data [Source: Barchart.com]\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n page = f\"https://www.barchart.com/stocks/quotes/{stock}/overview\"\n\n r = requests.get(page, headers={\"User-Agent\": \"Mozilla/5.0\"})\n soup = BeautifulSoup(r.text, \"html.parser\")\n tags = soup.find(\n \"div\",\n attrs={\n \"class\": \"barchart-content-block symbol-fundamentals bc-cot-table-wrapper\"\n },\n )\n data = tags.find_all(\"li\")\n labels = []\n values = []\n for row in data:\n labels.append(row.find_all(\"span\")[0].getText())\n values.append(row.find_all(\"span\")[1].getText())\n\n df = pd.DataFrame(data=[labels, values]).T\n print(tabulate(df, tablefmt=\"fancy_grid\", showindex=False))\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.7263843417167664,"alphanum_fraction":0.7361563444137573,"avg_line_length":37.375,"blob_id":"28446a10ffe5ffc5d34047567004aa0fb746eb81","content_id":"3aa06d181430b0c0d5748154b594c07a8d79f4ec","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":307,"license_type":"permissive","max_line_length":102,"num_lines":8,"path":"/docker/gst-poetry-deps.dockerfile","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"FROM gst/gst-python:1.0.0\n\nCOPY --chown=python:python pyproject.toml poetry.lock /home/python/\n\nRUN mkdir /home/python/gamestonk_terminal && \\\n touch gamestonk_terminal/__init__.py && \\\n chown python:python /home/python/gamestonk_terminal /home/python/gamestonk_terminal/__init__.py && \\\n poetry install\n"},{"alpha_fraction":0.5646258592605591,"alphanum_fraction":0.5659863948822021,"avg_line_length":27.269229888916016,"blob_id":"5f6cd1b98c4a99736a7ff5c1983b32f1fe702ec0","content_id":"38b3a4d503f3df91d121978e7d5d282cf57b4217","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2205,"license_type":"permissive","max_line_length":93,"num_lines":78,"path":"/gamestonk_terminal/stocks/due_diligence/financial_modeling_prep_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" Financial Modeling Prep View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom tabulate import tabulate\nimport FundamentalAnalysis as fa\nfrom gamestonk_terminal import config_terminal as cfg\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, check_positive\n\n\ndef rating(other_args: List[str], ticker: str):\n \"\"\"Display ratings for a given ticker\n\n Parameters\n ----------\n other_args : List[str]\n argparse other args\n ticker : str\n Stock ticker\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n prog=\"rating\",\n description=\"\"\"\n Based on specific ratios, prints information whether the company\n is a (strong) buy, neutral or a (strong) sell. The following fields are expected:\n P/B, ROA, DCF, P/E, ROE, and D/E. [Source: Financial Modeling Prep]\n \"\"\",\n )\n parser.add_argument(\n \"-n\",\n \"--num\",\n action=\"store\",\n dest=\"n_num\",\n type=check_positive,\n default=10,\n help=\"number of regions to plot that show highest interest.\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df_fa = fa.rating(ticker, cfg.API_KEY_FINANCIALMODELINGPREP)\n\n # TODO: This could be displayed in a nice rating plot over time\n\n l_recoms = [col for col in df_fa.columns if \"Recommendation\" in col]\n l_recoms_show = [\n recom.replace(\"rating\", \"\")\n .replace(\"Details\", \"\")\n .replace(\"Recommendation\", \" Recommendation\")\n for recom in l_recoms\n ]\n print(\n tabulate(\n df_fa[l_recoms].head(ns_parser.n_num),\n headers=l_recoms_show,\n floatfmt=\".2f\",\n showindex=True,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except KeyError:\n print(\n f\"Financialmodelingprep.com is returning empty response the ticker {ticker}.\"\n )\n print(\"\")\n return\n\n except Exception as e:\n print(e)\n print(\"\")\n return\n"},{"alpha_fraction":0.5406919121742249,"alphanum_fraction":0.5413509011268616,"avg_line_length":27.904762268066406,"blob_id":"243163cf4fb8c8243312dc197f5d65940c4bbb6f","content_id":"c0f122f2a9bb1f38d92d94100bf8432af13bd871","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3035,"license_type":"permissive","max_line_length":93,"num_lines":105,"path":"/gamestonk_terminal/economy/cnn_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" Fred View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom fear_greed_index.CNNFearAndGreedIndex import CNNFearAndGreedIndex\nfrom pandas.plotting import register_matplotlib_converters\nimport matplotlib.pyplot as plt\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import feature_flags as gtff\n\nregister_matplotlib_converters()\n\n\ndef fear_and_greed_index(other_args: List[str]):\n \"\"\"Display CNN Fear And Greed Index.\n\n Parameters\n ----------\n other_args : List[str]\n argparse other args\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n prog=\"feargreed\",\n description=\"\"\"\n Display CNN Fear And Greed Index from https://money.cnn.com/data/fear-and-greed/.\n \"\"\",\n )\n\n parser.add_argument(\n \"-i\",\n \"--indicator\",\n dest=\"indicator\",\n required=False,\n type=str,\n choices=[\"jbd\", \"mv\", \"pco\", \"mm\", \"sps\", \"spb\", \"shd\", \"index\"],\n help=\"\"\"\n CNN Fear And Greed indicator or index. From Junk Bond Demand, Market Volatility,\n Put and Call Options, Market Momentum Stock Price Strength, Stock Price Breadth,\n Safe Heaven Demand, and Index.\n \"\"\",\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-i\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n cnn_fg = CNNFearAndGreedIndex()\n\n fig = plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\n\n if ns_parser.indicator:\n if ns_parser.indicator == \"index\":\n print(cnn_fg.index_summary)\n\n im = cnn_fg.index_chart\n plt.imshow(im)\n\n else:\n d_indicator_match = {\n \"jbd\": \"Junk Bond Demand\",\n \"mv\": \"Market Volatility\",\n \"pco\": \"Put and Call Options\",\n \"mm\": \"Market Momentum\",\n \"sps\": \"Stock Price Strength\",\n \"spb\": \"Stock Price Breadth\",\n \"shd\": \"Safe Heaven Demand\",\n }\n indicator_name = d_indicator_match[ns_parser.indicator]\n\n for indicator in cnn_fg.all_indicators:\n if indicator_name == indicator.type_indicator:\n print(indicator.get_report())\n im = indicator.chart\n plt.imshow(im)\n\n else:\n # print Fear and Greed complete report\n print(cnn_fg.get_complete_report())\n\n # plot Fear and Greed charts\n cnn_fg.plot_all_charts(fig)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.show()\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n return\n"},{"alpha_fraction":0.6605504751205444,"alphanum_fraction":0.6880733966827393,"avg_line_length":20.799999237060547,"blob_id":"40000596d52544c4a2a7ef8bd39fb0ee508c9b2b","content_id":"212ac7ff6a8dcb47e204fcb4788528d89843e173","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":109,"license_type":"permissive","max_line_length":44,"num_lines":5,"path":"/docker/gst-poetry.dockerfile","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"FROM gst/gst-poetry-deps:1.0.0\n\nCOPY --chown=python:python . .\n\nCMD [\"bash\", \"docker/entry-point-poetry.sh\"]\n"},{"alpha_fraction":0.6982758641242981,"alphanum_fraction":0.6982758641242981,"avg_line_length":26.066667556762695,"blob_id":"27c80ceea9ee6fab48aecf26810940d7b8662ae3","content_id":"fa0cf64964debce734a29fd8652b4e44b8ef9499","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":812,"license_type":"permissive","max_line_length":84,"num_lines":30,"path":"/tests/test_fa/test_fa_fmp_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" fundamental_analysis/financial_modeling_prep/fmp_view.py tests \"\"\"\n# Not testing these tests further. I do not have a fmp key\nfrom contextlib import contextmanager\nimport unittest\nimport io\nimport sys\n\n# pylint: disable=unused-import\nfrom gamestonk_terminal.stocks.fundamental_analysis.financial_modeling_prep import (\n fmp_view,\n)\n\n\n@contextmanager\ndef replace_stdin(target):\n orig = sys.stdin\n sys.stdin = target\n yield\n sys.stdin = orig\n\n\nclass TestFMPView(unittest.TestCase):\n def test_fmp_valinvest_score(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n fmp_view.valinvest_score([], \"GME\")\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n print(capt)\n self.assertIn(\"Ticker should be a NASDAQ\", capt)\n"},{"alpha_fraction":0.45219168066978455,"alphanum_fraction":0.4670678973197937,"avg_line_length":27.590909957885742,"blob_id":"cbd805d459d3a41800c65bdd160c4d8c56d9f2be","content_id":"2b417c9c870a17d595fdf010752a6b1c11298e0a","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8806,"license_type":"permissive","max_line_length":98,"num_lines":308,"path":"/gamestonk_terminal/stocks/due_diligence/stockgrid_dd_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Stockgrid DD View\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport os\nfrom typing import List\nfrom datetime import timedelta\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal.feature_flags import USE_ION\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n plot_autoscale,\n check_positive,\n export_data,\n)\n\n\ndef shortview(ticker: str, other_args: List[str]):\n \"\"\"Plot price vs short interest volume\n\n Parameters\n ----------\n ticker: str\n Stock to plot for\n other_args : List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"shortvol\",\n add_help=False,\n description=\"Shows price vs short interest volume. [Source: Stockgrid]\",\n )\n parser.add_argument(\n \"-n\",\n \"--number\",\n help=\"Number of last open market days to show\",\n type=check_positive,\n default=10 if \"-r\" in other_args else 120,\n dest=\"num\",\n )\n parser.add_argument(\n \"-r\",\n action=\"store_true\",\n default=False,\n help=\"Flag to print raw data instead\",\n dest=\"raw\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n link = f\"https://stockgridapp.herokuapp.com/get_dark_pool_individual_data?ticker={ticker}\"\n response = requests.get(link)\n\n df = pd.DataFrame(response.json()[\"individual_short_volume_table\"][\"data\"])\n df[\"date\"] = pd.to_datetime(df[\"date\"])\n\n if ns_parser.raw:\n df = df.sort_values(by=\"date\", ascending=False)\n\n df[\"Short Vol. (1M)\"] = df[\"short_volume\"] / 1_000_000\n df[\"Short Vol. %\"] = df[\"short_volume%\"] * 100\n df[\"Short Exempt Vol. (1K)\"] = df[\"short_exempt_volume\"] / 1_000\n df[\"Total Vol. (1M)\"] = df[\"total_volume\"] / 1_000_000\n\n df = df[\n [\n \"date\",\n \"Short Vol. (1M)\",\n \"Short Vol. %\",\n \"Short Exempt Vol. (1K)\",\n \"Total Vol. (1M)\",\n ]\n ]\n\n df.date = df.date.dt.date\n\n print(\n tabulate(\n df.iloc[: ns_parser.num],\n tablefmt=\"fancy_grid\",\n floatfmt=\".2f\",\n headers=list(df.columns),\n showindex=False,\n )\n )\n else:\n prices = response.json()[\"prices\"][\"prices\"]\n\n _, axes = plt.subplots(\n 2,\n 1,\n figsize=(plot_autoscale()),\n dpi=PLOT_DPI,\n gridspec_kw={\"height_ratios\": [2, 1]},\n )\n\n axes[0].bar(\n df[\"date\"],\n df[\"total_volume\"] / 1_000_000,\n width=timedelta(days=1),\n color=\"b\",\n alpha=0.4,\n label=\"Total Volume\",\n )\n axes[0].bar(\n df[\"date\"],\n df[\"short_volume\"] / 1_000_000,\n width=timedelta(days=1),\n color=\"r\",\n alpha=0.4,\n label=\"Short Volume\",\n )\n\n axes[0].set_ylabel(\"Volume (1M)\")\n ax2 = axes[0].twinx()\n ax2.plot(\n df[\"date\"].values, prices[len(prices) - len(df) :], c=\"k\", label=\"Price\"\n )\n ax2.set_ylabel(\"Price ($)\")\n\n lines, labels = axes[0].get_legend_handles_labels()\n lines2, labels2 = ax2.get_legend_handles_labels()\n ax2.legend(lines + lines2, labels + labels2, loc=\"upper left\")\n\n axes[0].set_xlim(\n df[\"date\"].values[max(0, len(df) - ns_parser.num)],\n df[\"date\"].values[len(df) - 1],\n )\n\n axes[0].grid()\n axes[0].ticklabel_format(style=\"plain\", axis=\"y\")\n plt.title(f\"Price vs Short Volume Interest for {ticker}\")\n plt.gcf().autofmt_xdate()\n\n axes[1].plot(\n df[\"date\"].values,\n 100 * df[\"short_volume%\"],\n c=\"green\",\n label=\"Short Vol. %\",\n )\n\n axes[1].set_xlim(\n df[\"date\"].values[max(0, len(df) - ns_parser.num)],\n df[\"date\"].values[len(df) - 1],\n )\n axes[1].set_ylabel(\"Short Vol. %\")\n\n axes[1].grid(axis=\"y\")\n lines, labels = axes[1].get_legend_handles_labels()\n axes[1].legend(lines, labels, loc=\"upper left\")\n axes[1].set_ylim([0, 100])\n\n if USE_ION:\n plt.ion()\n\n plt.show()\n print(\"\")\n\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n \"shortview\",\n df,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef darkpos(ticker: str, other_args: List[str]):\n \"\"\"Plot dark pool position\n\n Parameters\n ----------\n ticker: str\n Stock to plot for\n other_args : List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"darkpos\",\n add_help=False,\n description=\"Shows Net Short Vol. vs Position. [Source: Stockgrid]\",\n )\n parser.add_argument(\n \"-n\",\n \"--number\",\n help=\"Number of last open market days to show\",\n type=check_positive,\n default=10 if \"-r\" in other_args else 120,\n dest=\"num\",\n )\n parser.add_argument(\n \"-r\",\n action=\"store_true\",\n default=False,\n help=\"Flag to print raw data instead\",\n dest=\"raw\",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n link = f\"https://stockgridapp.herokuapp.com/get_dark_pool_individual_data?ticker={ticker}\"\n response = requests.get(link)\n\n df = pd.DataFrame(response.json()[\"individual_dark_pool_position_data\"])\n df[\"dates\"] = pd.to_datetime(df[\"dates\"])\n\n if ns_parser.raw:\n df = df.sort_values(by=\"dates\", ascending=False)\n\n df[\"Net Short Vol. (1k $)\"] = df[\"dollar_net_volume\"] / 1_000\n df[\"Position (1M $)\"] = df[\"dollar_dp_position\"]\n\n df = df[\n [\n \"dates\",\n \"Net Short Vol. (1k $)\",\n \"Position (1M $)\",\n ]\n ]\n\n df[\"dates\"] = df[\"dates\"].dt.date\n\n print(\n tabulate(\n df.iloc[: ns_parser.num],\n tablefmt=\"fancy_grid\",\n floatfmt=\".2f\",\n headers=list(df.columns),\n showindex=False,\n )\n )\n\n else:\n fig = plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\n\n ax = fig.add_subplot(111)\n ax.bar(\n df[\"dates\"],\n df[\"dollar_net_volume\"] / 1_000,\n color=\"r\",\n alpha=0.4,\n label=\"Net Short Vol. (1k $)\",\n )\n ax.set_ylabel(\"Net Short Vol. (1k $)\")\n\n ax2 = ax.twinx()\n ax2.plot(\n df[\"dates\"].values,\n df[\"dollar_dp_position\"],\n c=\"tab:blue\",\n label=\"Position (1M $)\",\n )\n ax2.set_ylabel(\"Position (1M $)\")\n\n lines, labels = ax.get_legend_handles_labels()\n lines2, labels2 = ax2.get_legend_handles_labels()\n ax2.legend(lines + lines2, labels + labels2, loc=\"upper left\")\n\n ax.set_xlim(\n df[\"dates\"].values[max(0, len(df) - ns_parser.num)],\n df[\"dates\"].values[len(df) - 1],\n )\n\n ax.grid()\n plt.title(f\"Net Short Vol. vs Position for {ticker}\")\n plt.gcf().autofmt_xdate()\n\n if USE_ION:\n plt.ion()\n\n plt.show()\n print(\"\")\n\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n \"darkpos\",\n df,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5976826548576355,"alphanum_fraction":0.5983592867851257,"avg_line_length":29.24040985107422,"blob_id":"800322ff8ce8f2867770b304f081e632bdf2b472","content_id":"729a130b0a7ac1f8a3162dc72c4fe98e91ab1fcb","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11824,"license_type":"permissive","max_line_length":111,"num_lines":391,"path":"/tests/test_cryptocurrency/test_coingecko_crypto.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"from unittest import mock, TestCase\nimport json\nimport sys\nimport io\n\nfrom pycoingecko import CoinGeckoAPI\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view import (\n load,\n ta,\n chart,\n info,\n web,\n social,\n dev,\n ath,\n atl,\n score,\n bc,\n market,\n holdings_overview,\n holdings_companies_list,\n gainers,\n losers,\n discover,\n news,\n categories,\n recently_added,\n stablecoins,\n yfarms,\n top_volume_coins,\n top_defi_coins,\n top_dex,\n top_nft,\n nft_of_the_day,\n nft_market_status,\n exchanges,\n platforms,\n products,\n indexes,\n derivatives,\n exchange_rates,\n global_market_info,\n global_defi_info,\n coin_list,\n find,\n)\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_coin_model import Coin\n\n# pylint: disable=unused-import\n\n\n@mock.patch(\n \"gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view.CoinGeckoAPI.get_coin_market_chart_by_id\"\n)\ndef get_bitcoin(mock_load):\n # pylint: disable=unused-argument\n with open(\"tests/data/btc_usd_test_data.json\") as f:\n sample_return = json.load(f)\n mock_load.return_value = sample_return\n return load([\"-c\", \"bitcoin\"])\n\n\n# pylint: disable=R0904\nclass TestCoinGeckoAPI(TestCase):\n # pylint: disable = no-value-for-parameter\n coin = get_bitcoin()\n\n def test_coin_api_load(self):\n \"\"\"\n Mock load function through get_coin_market_chart_by_id.\n Mock returns a dict saved as .json\n\n \"\"\"\n self.assertEqual(self.coin.coin_symbol, \"bitcoin\")\n self.assertIsInstance(self.coin, Coin)\n\n @mock.patch(\n \"gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view.CoinGeckoAPI.get_coin_market_chart_by_id\"\n )\n def test_coin_api_load_df_for_ta(self, mock_load):\n \"\"\"\n Mock load function through get_coin_market_chart_by_id.\n Mock returns a dict saved as .json\n \"\"\"\n\n with open(\"tests/data/btc_usd_test_data.json\") as f:\n sample_return = json.load(f)\n\n mock_load.return_value = sample_return\n coin = load([\"-c\", \"bitcoin\"])\n mock_return, vs = ta(coin, [\"--vs\", \"usd\"])\n self.assertTrue(mock_return.shape == (722, 2))\n self.assertTrue(vs == \"usd\")\n\n def test_get_coins(self):\n \"\"\"Test that pycoingecko retrieves the major coins\"\"\"\n coins = CoinGeckoAPI().get_coins()\n bitcoin_list = [coin[\"id\"] for coin in coins]\n test_coins = [\"bitcoin\", \"ethereum\", \"dogecoin\"]\n for test in test_coins:\n self.assertIn(test, bitcoin_list)\n\n @mock.patch(\"matplotlib.pyplot.show\")\n def test_coin_chart(self, mock_matplot):\n # pylint: disable=unused-argument\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n chart(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertEqual(\"\\n\", capt)\n\n def test_coin_info(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n info(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"asset_platform_id\", capt)\n\n def test_coin_web(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n web(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"homepage\", capt)\n\n def test_coin_social(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n social(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"telegram\", capt)\n\n def test_coin_dev(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n dev(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"forks\", capt)\n\n def test_coin_ath(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n ath(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"ath_date_btc\", capt)\n\n def test_coin_atl(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n atl(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"atl_date_btc\", capt)\n\n def test_coin_score(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n score(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"twitter_followers\", capt)\n\n def test_coin_bc(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n bc(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Metric\", capt)\n\n def test_coin_market(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n market(self.coin, [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"max_supply\", capt)\n\n def test_coin_holdings_overview(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n holdings_overview([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Total Bitcoin Holdings\", capt)\n\n def test_coin_holdings_companies_list(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n holdings_companies_list([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"country\", capt)\n\n def test_coin_gainers(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n gainers([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"rank\", capt)\n\n def test_coin_losers(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n losers([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"rank\", capt)\n\n def test_coin_discover(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n discover(\"trending\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"CryptoBlades\", capt)\n\n def test_coin_news(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n news([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"author\", capt)\n\n def test_coin_categories(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n categories([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Decentralized Finance\", capt)\n\n def test_coin_recently_added(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n recently_added([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"rank\", capt)\n\n def test_coin_stablecoins(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n stablecoins([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_yfarms(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n yfarms([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_top_volume_coins(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n top_volume_coins([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_top_defi_coins(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n top_defi_coins([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_top_dex(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n top_dex([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_top_nft(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n top_nft([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_nft_of_the_day(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n nft_of_the_day([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Metric\", capt)\n\n def test_coin_nft_market_status(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n nft_market_status([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Metric\", capt)\n\n def test_coin_exchanges(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n exchanges([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_platforms(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n platforms([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_products(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n products([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"platform\", capt)\n\n def test_coin_indexes(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n indexes([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_derivatives(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n derivatives([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"price\", capt)\n\n def test_coin_exchange_rates(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n exchange_rates([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_global_market_info(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n global_market_info([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Metric\", capt)\n\n def test_coin_global_defi_info(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n global_defi_info([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_coin_list(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coin_list([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_coin_find(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n find([\"-c\", \"bitcoin\"])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n"},{"alpha_fraction":0.6554973721504211,"alphanum_fraction":0.7157068252563477,"avg_line_length":35.730770111083984,"blob_id":"2ed1831c2acf5abfda8e1e7d1937aef5985bc9e0","content_id":"347b9c39c817924ce1fc89af0a0e0d5468707e17","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1910,"license_type":"permissive","max_line_length":333,"num_lines":52,"path":"/gamestonk_terminal/cryptocurrency/binance/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# BINANCE\n\nBinance is the largest cryptocurrency exchange in the world in terms of trading volume.\nThis menu aims to explore Binance API and display data like order book, candles chart, account balance.\n\n## load <a name=\"load\"></a>\n````\nusage: load [-c --coin] [-q --quote] [-i --interval] [-l --limit]\n````\n\nSelect a coin/currency to the current object and load in the price dataframe. Note that in binance, the exchange \"ticker\" is usually COINCURR, such as BTCEUR for BTC to EURO. There is no USD, but it uses a coin tethered to the USD (USDT), which is the default. Some symbols are a combination of coins, as `ETHBTC` is a valid symbol\n* -c/--coin Coin to load. If not specified, BTC will be loaded to prevent errors later.\n* -q/--quote Quote currency. Defaults to `USDT` (which is 1-to-1 with USD)\n* -i/--interval. Interval for candles. One of [1day,3day,1hour,2hour,4hour,6hour,8hour,12hour,1week,1min,3min,5min,15min,30min,1month]. Defaults to 1day.\n* -l/--limit. Number of candles to get. Defaults to 100.\n\n## book <a name=\"book\"></a>\n````\nusage: book -l/--limit\n````\n\nGets and shows the order book for the given coin/currency. Shows the cumulative amount of orders.\n\n* -l/--limit Number of orders to get on each side. One of [5,10,20,50,100,500,1000,5000]. Defaults to 100.\n\n\n\n\n## candle <a name=\"candle\"></a>\n````\nusage: candle\n````\n\nShow candle chart for loaded coin/currency.\n\n\n\n\n## balance <a name=\"balance\"></a>\n````\nusage: balance\n````\n\nShows the current holding balance in your account.\n\n## ta <a name=\"ta\"></a>\n\n````\nusage: ta\n````\n\nOpen Technical Analysis menu for loaded coin. [technical analysis](/gamestonk_terminal/cryptocurrency/coinmarketcap/)\n"},{"alpha_fraction":0.5166915655136108,"alphanum_fraction":0.5313901305198669,"avg_line_length":27.069929122924805,"blob_id":"467edfe574162b83d39addae0bb785eaa5cd0269","content_id":"6015cfe38ff15b0917046a0587dd7ccd08e3a1d3","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4014,"license_type":"permissive","max_line_length":104,"num_lines":143,"path":"/gamestonk_terminal/options/calculator_model.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Calculator Model\"\"\"\n___docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, plot_autoscale\nfrom gamestonk_terminal import config_plot as cfp\nfrom gamestonk_terminal import feature_flags as gtff\n\n\ndef pnl_calculator(other_args: List[str]):\n \"\"\"Plot profit/loss for different option variables\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"calc\",\n description=\"Calculate profit or loss for given option settings.\",\n )\n\n parser.add_argument(\n \"--put\",\n action=\"store_true\",\n default=False,\n dest=\"put\",\n help=\"Flag to calculate put option\",\n )\n\n parser.add_argument(\n \"--sell\",\n action=\"store_true\",\n default=False,\n dest=\"sell\",\n help=\"Flag to get profit chart of selling contract\",\n )\n\n parser.add_argument(\n \"-s\",\n \"--strike\",\n type=float,\n dest=\"strike\",\n help=\"Option strike price\",\n default=10,\n )\n\n parser.add_argument(\n \"-p\", \"--premium\", type=float, dest=\"premium\", help=\"Premium price\", default=1\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n strike = ns_parser.strike\n premium = ns_parser.premium\n\n price_at_expiry = np.linspace(ns_parser.strike / 2, 1.5 * ns_parser.strike, 301)\n\n sell = [1, -1][ns_parser.sell]\n if ns_parser.put:\n break_even = strike - sell * premium\n pnl = strike - premium - price_at_expiry\n pnl = sell * 100 * np.where(price_at_expiry < strike, pnl, -premium)\n\n else:\n break_even = strike + sell * premium\n pnl = price_at_expiry - strike - premium\n pnl = sell * 100 * np.where(price_at_expiry > strike, pnl, -premium)\n\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\n ax.plot(price_at_expiry, pnl, alpha=0.1, c=\"k\")\n ax.fill_between(\n price_at_expiry, 0, pnl, where=(pnl > 0), facecolor=\"green\", alpha=0.5\n )\n ax.fill_between(\n price_at_expiry, 0, pnl, where=(pnl < 0), facecolor=\"red\", alpha=0.5\n )\n ax.axvline(\n x=break_even, c=\"black\", lw=3, alpha=0.6, label=f\"Breakeven: ${break_even}\"\n )\n ax.axvline(\n x=strike, c=\"dodgerblue\", lw=3, alpha=0.6, label=f\"Strike: ${strike}\"\n )\n if ns_parser.sell:\n ax.axhline(\n y=100 * premium,\n c=\"seagreen\",\n lw=3,\n alpha=0.6,\n label=f\"Max Profit: ${100*premium}\",\n )\n else:\n ax.axhline(\n y=-100 * premium,\n c=\"firebrick\",\n lw=3,\n alpha=0.6,\n label=f\"Max Loss: ${-100 * premium}\",\n )\n\n ax.set_xlabel(\"Price at Expiry\")\n\n ax.set_ylabel(\"Profit\")\n ax.set_title(\n f\"Profit for {['Buying','Selling'][ns_parser.sell]} {['Call', 'Put'][ns_parser.put]} option\"\n )\n ax.grid(True)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.legend(loc=0)\n fig.tight_layout(pad=1)\n plt.show()\n print(f\"Strike: ${strike}\")\n print(f\"Premium: ${premium}\")\n print(f\"Breakeven price: ${break_even}\")\n if ns_parser.sell:\n print(f\"Max profit: ${100 * premium}\")\n print(\"Max loss: Unlimited\")\n else:\n print(\"Max profit: Unlimited\")\n print(f\"Max loss: ${-100*premium}\")\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n return\n\n except SystemExit:\n print(\"\")\n return\n"},{"alpha_fraction":0.655235767364502,"alphanum_fraction":0.6619718074798584,"avg_line_length":31.65999984741211,"blob_id":"ef7857fa24d2a5a4b6024df8cbbbc286f0ffbc07","content_id":"9b82fc82c84e054c03e5b6924c2be06a71e3abb5","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1633,"license_type":"permissive","max_line_length":100,"num_lines":50,"path":"/tests/test_fa/test_fa_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" fundamental_analysis/fa_controller.py tests \"\"\"\nfrom datetime import datetime, timedelta\nfrom contextlib import contextmanager\nimport unittest\nimport io\nimport sys\n\n# pylint: disable=unused-import\nfrom gamestonk_terminal.stocks.fundamental_analysis import fa_controller\n\n\n@contextmanager\ndef replace_stdin(target):\n orig = sys.stdin\n sys.stdin = target\n yield\n sys.stdin = orig\n\n\nclass TestFaController(unittest.TestCase):\n start = datetime.now() - timedelta(days=200)\n menuClass = fa_controller.FundamentalAnalysisController(\"GME\", start, \"1440min\") # type: ignore\n choices = fa_controller.FundamentalAnalysisController.CHOICES\n\n def test_fa_controller_help(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n self.menuClass.print_help()\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n for item in self.choices:\n self.assertIn(item, capt)\n\n def key_metrics_explained(self):\n # Is this base function ever even used?\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n fa_controller.key_metrics_explained([\"\"])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"RETURN ON EQUITY\", capt)\n\n def test_menu(self):\n with replace_stdin(io.StringIO(\"q\")):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n fa_controller.menu(\"GME\", self.start, \"1440min\")\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"fraud\", capt)\n"},{"alpha_fraction":0.6150252819061279,"alphanum_fraction":0.6150252819061279,"avg_line_length":34.19178009033203,"blob_id":"6000013d7f37418a3e3bf727ab28c95a06774b8c","content_id":"f5f087c81e0a4a39b02ea074c4fae06fdddd4abb","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2569,"license_type":"permissive","max_line_length":83,"num_lines":73,"path":"/tests/test_portfolio_optimization/test_optimizer_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"from unittest import TestCase, mock\nimport sys\nimport io\n\nfrom gamestonk_terminal.portfolio.portfolio_optimization import optimizer_view\n\n\nclass TestOptimizerView(TestCase):\n def test_equal_weights(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.equal_weight([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n def test_property_weighting(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.property_weighting([\"TSLA\", \"GME\"], [\"-p\", \"previousClose\"])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n def test_max_sharpe(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.max_sharpe([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n def test_min_volatility(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.min_volatility([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n def test_max_quadratic_utility(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.max_quadratic_utility([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n def test_efficient_risk(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.efficient_risk([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"The minimum volatility\", capt)\n\n def test_efficient_return(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.efficient_return([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"TSLA\", capt)\n\n @mock.patch(\"matplotlib.pyplot.show\")\n def test_show_ef(self, mock_mlp):\n # pylint: disable=unused-argument\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n optimizer_view.show_ef([\"TSLA\", \"GME\"], [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"\\n\", capt)\n"},{"alpha_fraction":0.7211174368858337,"alphanum_fraction":0.7452651262283325,"avg_line_length":53.153846740722656,"blob_id":"5f42bb0cca8a08e620480f0a8eb8974b2a0bbf3a","content_id":"2d8a9237705623dce768302f15b3c7ace5b0104a","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2112,"license_type":"permissive","max_line_length":381,"num_lines":39,"path":"/gamestonk_terminal/cryptocurrency/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# CRYPTOCURRENCY\n\nThis menu aims to explore crypto world, and the usage of the following commands along with an example will be exploited below.\n\nNote that we have added the ability to look at technical analysis in the ta menu. Data loaded from CoinGecko has no candle data,\nso indicators that rely on anything other than close will fail with an error.\n\n\n* [finbrain](#finbrain)\n * crypto sentiment from 15+ major news headlines for more then 100 coins\n\n[COINGECKO standalone menu](/gamestonk_terminal/cryptocurrency/coingecko/)\n\n[COINMARKETCAP standalone menu](/gamestonk_terminal/cryptocurrency/coinmarketcap/)\n\n[BINANCE standalone menu](/gamestonk_terminal/cryptocurrency/binance/)\n\n[COINPAPRIKA standalone menu](/gamestonk_terminal/cryptocurrency/coinpaprika/)\n\n## finbrain <a name=\"finbrain\"></a>\n\n```bash\nusage: finbrain [-c --coin]\n```\n\nFinBrain collects the news headlines from 15+ major financial news sources on a daily basis and analyzes them to generate sentiment scores for more than 4500 US stocks. FinBrain Technologies develops deep learning algorithms for financial analysis and prediction, which currently serves traders from more than 150 countries all around the world. [Source: See https://finbrain.tech]\n\nCurrently all sentiment is gathered for `COIN-USD` pairs. Please use upper case symbols of coins.\n\n* -c/--coin - Symbol of the Coin for which you want to analyse sentiment. Currently available coins are:\n```\nAAVE, ADA, ADX, AE, ANT, ARDR, ARK, ATOM, BAT, BCCOIN, BCH, BCN, BLOCK, BNB, BNT, BTC, BTCD, BTG, BTM, BTS, CVC, DASH,\nDCN, DCR, DGB, DGD, DNT, DOGE, DOT, EDG, EOS, ETH, ETP, FAIR, FCT, FUN, GAME, GAS, GBYTE, GNO, GNT, HSR, ICX, IOC, KIN,\nKMD, KNC, LINK, LKK, LRC, LSK, LTC, MAID, MCAP, MCO, MGO, MKR, MLN, MONA, MTL, NAV, NEBL, NEO, NLC2, NXS, NXT, OMG, PAY,\nPIVX, PPT, QASH, QRL, QTUM, REP, RLC, SALT, SC, SMART, SNGLS, STEEM, STORJ, SUB, SYS, TAAS, TRX, UBQ, UNI, USDT, VEN,\nVERI, VTC, WAVES, WINGS, WTC, XCP, XLM, XMR, XRP, XTZ, XVG, YFI, ZEC, ZEN, ZRX\n```\n\n\n"},{"alpha_fraction":0.47911280393600464,"alphanum_fraction":0.5174853801727295,"avg_line_length":35.929649353027344,"blob_id":"a6b6dcd197ca8a39f85699b1736b149625eacd62","content_id":"84023883c9d35da6d72d291ea1ffc9e54be08f82","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":10083,"license_type":"permissive","max_line_length":226,"num_lines":199,"path":"/gamestonk_terminal/etf/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# ETF\nMenu for Exchange Traded Funds.\n\nData is currently scraped from StockAnalysis.com/etf\n\n* [web](#web)\n * open webbroswer to stockanalysis.com\n* [search](#search)\n * search for ETFs matching an input\n* [overview](#overview)\n * get ETF overview\n* [holdings](#holdings)\n * show ETF holdings\n* [compare](#compare)\n * compare multiple ETFs overview\n* [screener](#screener)\n * screen ETFs\n\n[WSJ](#WSJ)\n* [gainers](#gainers)\n * show top gainers\n* [decliners](#decliners)\n * show top decliners\n* [active](#active)\n * show most active\n\n## web <a name=\"web\"></a>\n```python\nusage: web\n```\nOpens StockAnalysis.com/etf. This site shows all avalaiable ETFs (currently > 2800).\n\n## search <a name=\"search\"></a>\n```python\nusage: search [-n --name]\n```\nSearch for ETFs matching\n* -n/--name : Name to search for. Can be a company or other search term\n\nExample Usage: Search for all ETFs that deal with AI\n```python\n(✨) (etf)> search artificial intelligence\nBOTZ - Global X Robotics & Artificial Intelligence ETF\nIRBO - iShares Robotics and Artificial Intelligence ETF\nROBT - First Trust Nasdaq Artificial Intelligence and Robotics ETF\nTHNQ - ROBO Global Artificial Intelligence ETF\nUBOT - Daily Robotics, Artificial Intelligence & Automation Index Bull 3X Shares\n```\nNote that this searches for exact strings in the ETF name. Searching for SPY will return nothing.\n\n## overview <a name=\"overview\"></a>\n```python\nusage: overview [-n --name]\n```\n* -n/--name : ETF to get data for\n\nReturns the overview of the ETF. Shows Last Price, Assets, Net Asset Value (NAV), Expense Ratio, PE Ratio, 5Year Beta, Total Dividend and Dividend Yield.\n\nExample will be a single column from the [compare](#compare) example.\n\n## holdings <a name=\"holdings\"></a>\n```python\nusage: overview [-n --name] [-l --limit]\n```\n* -n/--name : Name of ETF to get data for.\n* -l/--limit : Number of assets to show. Max 200. Defaults to 20.\n\nReturns the top holdings in the ETF. Shows percentage and total shares held.\n\nExample:\n```\n(✨) (etf)> holdings spy -l 5\n╒════╤══════════╤════════════╤═════════════╕\n│ │ Ticker │ % of ETF │ Shares │\n╞════╪══════════╪════════════╪═════════════╡\n│ 0 │ AAPL │ 5.66% │ 162,078,640 │\n├────┼──────────┼────────────┼─────────────┤\n│ 1 │ MSFT │ 5.26% │ 77,463,250 │\n├────┼──────────┼────────────┼─────────────┤\n│ 2 │ AMZN │ 3.89% │ 4,396,091 │\n├────┼──────────┼────────────┼─────────────┤\n│ 3 │ FB │ 2.11% │ 24,705,450 │\n├────┼──────────┼────────────┼─────────────┤\n│ 4 │ GOOGL │ 1.93% │ 3,088,769 │\n╘════╧══════════╧════════════╧═════════════╛\n```\n\n## compare <a name=\"compare\"></a>\n```python\nusage: compare [-n --names]\n```\n* -n/--names : ETFs to compare. Input as comma separated (ETF1,ETF2,..)\n\nCampares the overview of different ETFs.\n\nExample:\n\n```\n(✨) (etf)> compare spy,qqq,voo,doge\nDOGE not found\n╒════════════════╤══════════╤══════════╤══════════╕\n│ │ SPY │ QQQ │ VOO │\n╞════════════════╪══════════╪══════════╪══════════╡\n│ Last Price │ $414.94 │ $327.01 │ $381.47 │\n├────────────────┼──────────┼──────────┼──────────┤\n│ Assets │ $353.25B │ $157.51B │ $222.38B │\n├────────────────┼──────────┼──────────┼──────────┤\n│ NAV │ $415.21 │ $328.91 │ $381.70 │\n├────────────────┼──────────┼──────────┼──────────┤\n│ Expense Ratio │ 0.09% │ 0.20% │ 0.03% │\n├────────────────┼──────────┼──────────┼──────────┤\n│ PE Ratio │ 25.16 │ 35.32 │ 27.60 │\n├────────────────┼──────────┼──────────┼──────────┤\n│ Beta (5Y) │ 0.99 │ 1.04 │ 0.99 │\n├────────────────┼──────────┼──────────┼──────────┤\n│ Dividend (ttm) │ $5.56 │ $1.77 │ $5.39 │\n├────────────────┼──────────┼──────────┼──────────┤\n│ Dividend Yield │ 1.34% │ 0.54% │ 1.41% │\n╘════════════════╧══════════╧══════════╧══════════╛\n```\n## screener <a name=\"screener\"></a>\n````python\nusage: screener [--config] [-p MIN_PRICE] [-P MAX_PRICE] [-a MIN_ASSETS] [-A MAX_ASSETS] [-n MIN_NAV] [-N MAX_NAV] [-e MIN_EXP] [-E MAX_EXP] [-r MIN_PE] [-R MAX_PE] [-d MIN_DIV]\n [-D MAX_DIV] [-b MIN_BETA] [-B MAX_BETA] [--num] [--export {csv,json,xlsx}] [-h]\n````\n\n* --config : Flag to load from etf_config.ini file. This supersedes any other arguments.\n* -p/--min_price : Minimum ETF price\n* -P/--max_price : Maximum ETF price\n* -a/--min_assets : Minimum ETF assets held\n* -A/--max_assets : Maximum ETF assets held\n* -n/--min_nav : Minimum ETF net asset valuation\n* -N/--max_nav : Maximum ETF net asset valuation\n* -e/--min_exp : Minimum ETF expense ratio\n* -E/--max_exp : Maximum ETF expense ratio\n* -r/--min_pe : Minimum ETF PE Ratio\n* -R/--max_pe : Maximum ETF PE Ratio\n* -d/--min_div : Minimum ETF dividend yield\n* -D/--max_div : Maximum ETF dividend yield\n* -b/--min_beta : Minimum ETF 5Y beta\n* -B/--max_beta : Maximum ETF 5Y beta\n* --num : Number of etfs to display in console. Defaults to 20\n* --export {csv,json,xlsx} : Export to the selected file\n\nScreen ETFs based on the overview data from stockanalysis.com. This data is scraped nightly at midnight EST, so it will not be up to date or change during the day. Repo can be found at https://github.com/jmaslek/etf_scraper.\nFor screeners with many results (exceeding the `--num` flag), the console will display a random subset of them.\n\nNote that to use the config file, locate the desired data column then change MIN and MAX. Make sure unused columns are set to None.\nExample which will screen for ETFs between $45 and $57:\n```python\n[Price]\nMIN = 45\nMAX = 57\n```\n\nSample usage:\n```python\n(✨) (etf)> screener -p 45 -P 88 -E 1 -d 3 -r 12 -a 10000\nETFs downloaded\n\n╒═════╤═════════╤══════════╤═══════╤═══════════╤═══════╤════════╤═══════╤════════════╕\n│ │ Price │ Assets │ NAV │ Expense │ PE │ Beta │ Div │ DivYield │\n╞═════╪═════════╪══════════╪═══════╪═══════════╪═══════╪════════╪═══════╪════════════╡\n│ VGK │ 66.97 │ 19750 │ 67.38 │ 0.08 │ 20.7 │ 0.94 │ 2.07 │ 3.07 │\n├─────┼─────────┼──────────┼───────┼───────────┼───────┼────────┼───────┼────────────┤\n│ XLE │ 51.75 │ 24950 │ 52.92 │ 0.12 │ 20 │ 1.75 │ 2.11 │ 3.99 │\n├─────┼─────────┼──────────┼───────┼───────────┼───────┼────────┼───────┼────────────┤\n│ XLU │ 64.52 │ 11640 │ 64.31 │ 0.12 │ 19.94 │ 0.35 │ 1.98 │ 3.07 │\n╘═════╧═════════╧══════════╧═══════╧═══════════╧═══════╧════════╧═══════╧════════════╛\n```\n# WSJ <a name=\"wsj\"></a>\nThe following functions atake the information from the [WSJ Market Data Page](https://www.wsj.com/market-data)\n\n## gainers <a name=\"gainers\"></a>\n\n```python\nusage: gainers [-n NUM] [--export {csv,json,xlsx}] [-h]\n```\nShows top gaining ETFs\n* -n/--num: Number to show. Defaults to 25, which is the max provided\n* --export: Export data to one of {csv, json, xlsx}.\n\n## decliners <a name=\"decliners\"></a>\n\n```python\nusage: decliners [-n NUM] [--export {csv,json,xlsx}] [-h]\n```\nShows highest declining ETFs\n* -n/--num: Number to show. Defaults to 25, which is the max provided\n* --export: Export data to one of {csv, json, xlsx}.\n\n## active <a name=\"active\"></a>\n\n```python\nusage: active [-n NUM] [--export {csv,json,xlsx}] [-h]\n```\nShows most active ETFs\n* -n/--num: Number to show. Defaults to 25, which is the max provided\n* --export: Export data to one of {csv, json, xlsx}.\n"},{"alpha_fraction":0.5504080057144165,"alphanum_fraction":0.55077064037323,"avg_line_length":31.827381134033203,"blob_id":"fb7846aa8ee4e0a1c577a5e1026bac8438329bbc","content_id":"7d97b9a3bc87db90143d4e2e977193e4e820de40","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11030,"license_type":"permissive","max_line_length":108,"num_lines":336,"path":"/gamestonk_terminal/options/options_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Options Controller Module.\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport os\nfrom typing import List\nimport matplotlib.pyplot as plt\n\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.options import (\n barchart_view,\n syncretism_view,\n calculator_model,\n op_helpers,\n yfinance_view,\n yfinance_model,\n tradier_view,\n tradier_model,\n fdscanner_view,\n)\n\nfrom gamestonk_terminal.config_terminal import TRADIER_TOKEN\nfrom gamestonk_terminal.menu import session\n\n\nclass OptionsController:\n \"\"\"Options Controller class.\"\"\"\n\n CHOICES = [\n \"cls\",\n \"?\",\n \"help\",\n \"q\",\n \"quit\",\n ]\n\n CHOICES_COMMANDS = [\n \"disp\",\n \"scr\",\n \"calc\",\n \"yf\",\n \"tr\",\n \"info\",\n \"load\",\n \"exp\",\n \"vol\",\n \"voi\",\n \"oi\",\n \"hist\",\n \"chains\",\n \"grhist\",\n \"unu\",\n ]\n\n CHOICES += CHOICES_COMMANDS\n\n def __init__(self):\n \"\"\"Construct data.\"\"\"\n self.ticker = \"\"\n self.expiry_dates = []\n self.selected_date = \"\"\n\n self.op_parser = argparse.ArgumentParser(add_help=False, prog=\"options\")\n self.op_parser.add_argument(\n \"cmd\",\n choices=self.CHOICES,\n )\n\n def print_help(self):\n \"\"\"Print help.\"\"\"\n print(\n \"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/options\"\n )\n print(\"\\nOptions:\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\")\n print(\" disp display all preset screeners filters\")\n print(\" scr output screener options\")\n print(\" unu show unusual options activity\")\n print(\"\")\n print(f\"Current Ticker: {self.ticker or None}\")\n print(\" load load new ticker\")\n print(\" info display option information (volatility, IV rank etc)\")\n print(\"\")\n print(\" calc basic call/put PnL calculator\")\n print(\"\")\n print(f\"Current Expiration: {self.selected_date or None}\")\n print(\" exp see and set expiration dates\")\n print(\"\")\n if self.selected_date and self.ticker:\n print(\" chains display option chains with greeks [Tradier]]\")\n print(\" oi plot open interest [Tradier/YF]\")\n print(\" vol plot volume [Tradier/YF]\")\n print(\" voi plot volume and open interest [Tradier/YF]\")\n print(\" hist plot option history [Tradier\")\n print(\" grhist plot option greek history [Syncretism]\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input.\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self.op_parser.parse_known_args(an_input.split())\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command.\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu.\"\"\"\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program.\"\"\"\n return True\n\n def call_calc(self, other_args: List[str]):\n \"\"\"Process calc command\"\"\"\n calculator_model.pnl_calculator(other_args)\n\n def call_unu(self, other_args: List[str]):\n \"\"\"Process act command\"\"\"\n\n parser = argparse.ArgumentParser(\n prog=\"unu\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"This command gets unusual options from fdscanner.com\",\n )\n parser.add_argument(\n \"-n\",\n \"--num\",\n dest=\"num\",\n type=int,\n default=20,\n help=\"Number of options to show. Each scraped page gives 20 results.\",\n )\n\n parser.add_argument(\n \"--sortby\",\n dest=\"sortby\",\n default=\"Vol/OI\",\n choices=[\"Option\", \"Vol/OI\", \"Vol\", \"OI\", \"Bid\", \"Ask\"],\n help=\"Column to sort by. Vol/OI is the default and typical variable to be considered unusual.\",\n )\n parser.add_argument(\n \"-a\",\n \"--ascending\",\n dest=\"ascend\",\n default=False,\n action=\"store_true\",\n help=\"Flag to sort in ascending order\",\n )\n\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n fdscanner_view.display_options(\n num=ns_parser.num,\n sort_column=ns_parser.sortby,\n export=ns_parser.export,\n ascending=ns_parser.ascend,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n\n def call_info(self, other_args: List[str]):\n \"\"\"Process info command\"\"\"\n barchart_view.print_options_data(self.ticker, other_args)\n\n def call_disp(self, other_args: List[str]):\n \"\"\"Process disp command\"\"\"\n syncretism_view.view_available_presets(other_args)\n\n def call_scr(self, other_args: List[str]):\n \"\"\"Process scr command\"\"\"\n syncretism_view.screener_output(other_args)\n\n def call_load(self, other_args: List[str]):\n \"\"\"Process load command\"\"\"\n self.ticker = op_helpers.load(other_args)\n if TRADIER_TOKEN == \"REPLACE_ME\":\n self.expiry_dates = yfinance_model.option_expirations(self.ticker)\n else:\n self.expiry_dates = tradier_model.option_expirations(self.ticker)\n\n def call_exp(self, other_args: List[str]):\n \"\"\"Process exp command\"\"\"\n if self.ticker:\n self.selected_date = op_helpers.select_option_date(\n self.expiry_dates, other_args\n )\n else:\n print(\"Please select a ticker using load {ticker}\", \"\\n\")\n\n def call_hist(self, other_args: List[str]):\n if TRADIER_TOKEN != \"REPLACE_ME\":\n tradier_view.display_historical(self.ticker, self.selected_date, other_args)\n else:\n print(\"TRADIER TOKEN not supplied. \\n\")\n\n def call_chains(self, other_args: List[str]):\n \"\"\"Process chains command\"\"\"\n if TRADIER_TOKEN != \"REPLACE_ME\":\n tradier_view.display_chains(self.ticker, self.selected_date, other_args)\n else:\n print(\"TRADIER TOKEN not supplied. \\n\")\n\n def call_vol(self, other_args: List[str]):\n \"\"\"Process vol command\"\"\"\n if not self.ticker and not self.selected_date:\n print(\"Ticker and expiration required.\\n\")\n return\n parsed = op_helpers.vol(other_args)\n if not parsed:\n return\n if parsed.source == \"tr\" and TRADIER_TOKEN != \"REPLACE_ME\":\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\n tradier_view.plot_vol(options, self.ticker, self.selected_date, parsed)\n else:\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\n yfinance_view.plot_vol(\n options.calls, options.puts, self.ticker, self.selected_date, parsed\n )\n\n def call_voi(self, other_args: List[str]):\n \"\"\"Process voi command\"\"\"\n if not self.ticker and not self.selected_date:\n print(\"Ticker and expiration required.\")\n return\n parsed = op_helpers.voi(other_args)\n if not parsed:\n return\n if parsed.source == \"tr\" and TRADIER_TOKEN != \"REPLACE_ME\":\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\n tradier_view.plot_volume_open_interest(\n self.ticker, self.selected_date, options, parsed\n )\n else:\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\n yfinance_view.plot_volume_open_interest(\n self.ticker, self.selected_date, options.calls, options.puts, parsed\n )\n\n def call_oi(self, other_args: List[str]):\n \"\"\"Process oi command\"\"\"\n if not self.ticker and not self.selected_date:\n print(\"Ticker and expiration required.\")\n return\n parsed = op_helpers.oi(other_args)\n if not parsed:\n return\n if parsed.source == \"tr\" and TRADIER_TOKEN != \"REPLACE_ME\":\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\n tradier_view.plot_oi(options, self.ticker, self.selected_date, parsed)\n else:\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\n yfinance_view.plot_oi(\n options.calls, options.puts, self.ticker, self.selected_date, parsed\n )\n\n def call_grhist(self, other_args: List[str]):\n \"\"\"Process grhist command\"\"\"\n syncretism_view.historical_greeks(self.ticker, self.selected_date, other_args)\n\n\ndef menu():\n \"\"\"Options Menu.\"\"\"\n op_controller = OptionsController()\n op_controller.call_help(None)\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in op_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (options)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (options)> \")\n\n try:\n plt.close(\"all\")\n\n process_input = op_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.6685304045677185,"alphanum_fraction":0.7232474684715271,"avg_line_length":45.52631759643555,"blob_id":"fc4ca822a285a9a53281a9499c5e3ede04e353e4","content_id":"af6d9e7d7eaee0fdb541fb7ad823d0344d66abbd","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":30941,"license_type":"permissive","max_line_length":527,"num_lines":665,"path":"/gamestonk_terminal/cryptocurrency/coingecko/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# CoinGecko\n\nThis menu aims to explore crypto world with [CoinGecko](#https://www.coingecko.com/en)\nThe usage of the following commands along with an example will be exploited below.\n\nNote that we have added the ability to look at technical analysis in the ta menu. Data loaded from CoinGecko has no candle data,\nso indicators that rely on anything other than close will fail with an error.\n\n[COIN](#COIN)\n* [load](#load)\n * load a given coin, you can use either coin symbol or coin id\n* [find](#find)\n * find similar coin by coin name,symbol or id\n* [clear](#clear)\n * clear loaded coin\n* [chart](#chart)\n * plot the loaded crypto chart\n* [ta](technical_analysis/README.md)\n * open technical analysis menu\n* [info](#info)\n * show coin base information\n* [market](#market)\n * show market data\n* [ath](#ath)\n * show all time high related metrics\n* [atl](#atl)\n * show all time low related metrics\n* [score](#score)\n * show different scores related to loaded coin\n* [web](#web)\n * show websites related to loaded coin\n* [social](#social)\n * show social media urls for loaded coin\n* [bc](#bc)\n * show block-chain explorers urls for loaded coins\n* [dev](#dev)\n * show developers data for loaded coins\n\n[OVERVIEW](#OVERVIEW)\n* [global](#global)\n * show global crypto market info\n* [coins](#coins)\n * show coins available on CoinGecko\n* [defi](#defi)\n * show global DeFi market info\n* [trending](#trending)\n * show trending coins on CoinGecko\n* [most_voted](#most_voted)\n * show most voted coins on CoinGecko\n* [top_volume](#top_volume)\n * show coins with highest volume on CoinGecko\n* [recently](#recently)\n * show recently added on CoinGecko\n* [sentiment](#sentiment)\n * show coins with most positive sentiment\n* [gainers](#gainers)\n * show top gainers - coins which price gained the most in given period\n* [losers](#losers)\n * show top losers - coins which price dropped the most in given period\n* [stables](#stables)\n * show Stable Coins\n* [yfarms](#yfarms)\n * show top Yield Farms\n* [top_defi](#top_defi)\n * show top DeFi Protocols\n* [top_dex](#top_dex)\n * show top Decentralized Exchanges\n* [top_nft](#top_nft)\n * show top Non Fungible Tokens\n* [nft_today](#nft_today)\n * show NFT Of The Day\n* [nft_market](#nft_market)\n * show NFT Market Status\n* [exchanges](#exchanges)\n * show Top Crypto Exchanges\n* [ex_rates](#ex_rates)\n * show Coin Exchange Rates\n* [platforms](#platforms)\n * show Crypto Financial Platforms\n* [products](#products)\n * show Crypto Financial Products\n* [indexes](#indexes)\n * show Crypto Indexes\n* [derivatives](#derivatives)\n * show Crypto Derivatives\n* [categories](#categories)\n * show Crypto Categories\n* [hold](#hold)\n * show eth, btc holdings overview statistics\n* [hold_comp](#hold_comp)\n * show eth, btc holdings by public companies\n\n\n\n# COIN <a name=\"COIN\"></a>\n## load <a name=\"load\"></a>\n\n````\nusage: load [-c --coin]\n````\n\nLoad a given coin vs a given currency. Currently only retrieves price, not volume or MarketCap. The current crypto data is [Powered by CoinGecko API](#https://www.coingecko.com/en), which is an awesome service that currently requires no API Key!\nBy loading a coin you will have access to a lot of statistics on that coin like price data, coin development stats, social media and many others. Loading coin also will open access to technical analysis menu\n\n\n* -c/--coin The coin you wish to load. This can either be the symbol or the name. `load -c btc` and `load -c bitcoin`\n will load. The -c flag is optional, the above is equivalent to `load btc`.\n\n\n## chart <a name=\"chart\"></a>\n\n````\nusage: chart [-d --days] [--vs]\n````\n\nDisplay chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for.\nBy default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `chart --vs eth --days 90`\n\n\n* -d/--days The number of days to look. Defaults to 30 days. As per the API: Minutely data will be used for duration within 1 day,\n Hourly data will be used for duration between 1 day and 90 days, Daily data will be used for duration above 90 days.\n* --vs The currency to look against. Defaults to \"usd\".\n\n\n\n\n## ta <a name=\"ta\"></a>\n\n````\nusage: ta [-d --days] [--vs]\n````\n\nOpen Technical Analysis menu for loaded coin\nLoads data for technical analysis. You can specify currency vs which you want to show chart and also number of days to get data for.\nE.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `ta --vs eth --days 90`\n\n\n* -d/--days The number of days to look. Defaults to 30 days. As per the API: Minutely data will be used for duration within 1 day,\n Hourly data will be used for duration between 1 day and 90 days, Daily data will be used for duration above 90 days.\n* --vs The currency to look against. Defaults to \"usd\".\n\n## info <a name=\"info\"></a>\n\n````\nusage: info\n````\n\nShows basic information about loaded coin like: id, name, symbol, asset_platform, description, contract_address, market_cap_rank, public_interest_score, total_supply, max_supply, price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d, current_price_btc, current_price_eth, current_price_usd\n\n\n\n## market <a name=\"market\"></a>\n\n````\nusage: market\n````\n\nMarket data for loaded coin. There you find metrics like: market_cap_rank, total_supply, max_supply, circulating_supply, price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d, price_change_percentage_60d, price_change_percentage_1y, market_cap_change_24h, market_cap_btc, market_cap_eth, market_cap_usd, total_volume_btc, total_volume_eth, total_volume_usd, high_24h_btc, high_24h_eth, high_24h_usd, low_24h_btc, low_24h_eth, low_24h_usd\n\n\n\n## ath <a name=\"ath\"></a>\n\n````\nusage: ath\n````\n\nAll time high data for loaded coin. You can find there most important metrics regarding ath of coin price like: current_price_btc, current_price_eth, current_price_usd, ath_btc, ath_eth, ath_usd, ath_date_btc, ath_date_eth, ath_date_usd, ath_change_percentage_btc, ath_change_percentage_btc, ath_change_percentage_eth, ath_change_percentage_usd\n\n\n\n## atl <a name=\"atl\"></a>\n\n````\nusage: atl\n````\n\nAll time low data for loaded coin. You can find there most important metrics regarding atl of coin price like: current_price_btc, current_price_eth, current_price_usd, atl_btc, atl_eth, atl_usd, atl_date_btc, atl_date_eth, atl_date_usd, atl_change_percentage_btc, atl_change_percentage_btc, atl_change_percentage_eth, atl_change_percentage_usd\n\n\n\n## score <a name=\"score\"></a>\n\n````\nusage: score\n````\n\nIn this view you can find different kind of scores for loaded coin. Those scores represents different rankings, sentiment metrics, some user stats and other, like: coingecko_rank, coingecko_score, developer_score, community_score, liquidity_score, sentiment_votes_up_percentage, sentiment_votes_down_percentage, public_interest_score, facebook_likes, twitter_followers, reddit_average_posts_48h, reddit_average_comments_48h, reddit_subscribers, reddit_accounts_active_48h, telegram_channel_user_count, alexa_rank, bing_matches\n\n\n\n## dev <a name=\"dev\"></a>\n\n````\nusage: dev\n````\n\nDevelopers data for loaded coin. If the development data is available you can see how the code development of given coin is going on.\nThere are some statistics that shows number of stars, forks, subscribers, pull requests, commits, merges, contributors on github.\n\n\n\n## web <a name=\"web\"></a>\n\n````\nusage: web\n````\n\nWebsites found for given Coin. You can find there urls to homepage, forum, announcement site and others.\n\n\n\n## social <a name=\"social\"></a>\n\n````\nusage: social\n````\n\nDisplay social media corresponding to loaded coin. You can find there name of telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.\n\n\n\n## bc <a name=\"bc\"></a>\n\n````\nusage: bc\n````\n\nBlockchain explorers URLs for loaded coin. Those are sites like etherescan.io or polkascan.io in which you can see all blockchain data e.g. all txs, all tokens, all contracts...\n\n\n\n## clear <a name=\"clear\"></a>\n\n````\nusage: clear\n````\n\nJust remove previously loaded coin. (set coin = None)\n\n## find <a name=\"find\"></a>\n\n````\nusage: find [-c --coin] [-t --top] [-k --key]\n````\n\nFind similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinGecko, you can use this command to display coins with similar name, symbol or id to your search query.\nExample of usage: coin name is something like \"polka\". So you can try: `find -c polka -k name -t 25`\nIt will search for coin that has similar name to polka and display top 25 matches.\n* -c, --coin stands for coin - you provide here your search query\n* -k, --key it's a searching key. You can search by symbol, id or name of coin\n* -t, --top it displays top N number of records.\n\n\n\n\n# OVERVIEW <a name=\"OVERVIEW\"></a>\n## global <a name=\"global\"></a>\n\n````\nusage: global\n````\n\nDisplay global statistics about Crypto Market like: active_cryptocurrencies, upcoming_icos, ongoing_icos, ended_icos, markets, market_cap_change_percentage_24h, eth_market_cap_in_pct, btc_market_cap_in_pct, altcoin_market_cap_in_pct\n\n\n\n## news <a name=\"news\"></a>\n\n````\nusage: news [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows latest news from https://www.coingecko.com/en/news. Display columns: index, title, author, posted columns. You can sort by each of column above, using `--sort` parameter and also do it descending with `--descend` flag. It's also possible to display urls to news with `--links` flag. If you want to display top 75 news, sorted by author use `news -t 75 -s author` if you want to see urls to source of the news use `news -t 75 -l`\n\n* -t/--top - number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\n* -s/--sortby - sort by given column. You can chose on from `index, title, author, posted`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls and display only `index, url` columns\n\n\n\n## coins <a name=\"coins\"></a>\n\n````\nusage: coins [-s --skip] [-t --top] [-l --letter] [-k --key]\n````\n\nDisplay all available coins in coingecko. You can search with pagination mechanism with `news --skip [num] --top [num]` or you can just search by letter like\n`news --letter d --key name --top 10` it will display top 10 matches for coins which name starts with letter `d` and search will be done in column `name`\n\n* -s/--skip - number of records to skip. Default is 0. There are thousands of coins, so there is mechanism to paginate through them.\ne.q if you want to see only records from 500-750 you should use `coins --skip 500 --top 250` or if you want to see 1200-1880 you should use `coins --skip 1200 --top 680`.\n* -t/--top - number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\n* -l/--letter - first letters of coin by which you want to search\n* -k/--key - search key. With this parameter you can specify in which column you would like to search. Choose on from `name, id, symbol`\n\n\n\n\n## defi <a name=\"defi\"></a>\n\n````\nusage: defi\n````\n\nShows global DeFi statistics. DeFi or Decentralized Finance refers to financial services that are built on top of distributed networks with no central intermediaries. Displays metrics like: defi_market_cap, eth_market_cap, defi_to_eth_ratio, trading_volume_24h, defi_dominance, top_coin_name, top_coin_defi_dominance.\n\n\n\n\n## trending <a name=\"trending\"></a>\n\n````\nusage: trending [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows most trending coins on CoinGecko.\n\n* -t/--top - number of coins to display. To display top 10 coins: `trending --top 10`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `trending --top 10 --links`\n\n\n\n## most_voted <a name=\"most_voted\"></a>\n\n````\nusage: most_voted [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows most voted coins on CoinGecko.\n\n* -t/--top - number of coins to display. To display top 10 coins: `most_voted --top 10`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `most_voted --top 10 --links`\n\n\n\n## most_visited <a name=\"most_visited\"></a>\n\n````\nusage: most_visited [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows most visited coins on CoinGecko.\n\n* -t/--top - number of coins to display. To display top 10 coins: `most_visited --top 10`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `most_visited --top 10 --links`\n\n\n\n\n## sentiment <a name=\"sentiment\"></a>\n\n````\nusage: sentiment [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows coins with the most positive sentiment on CoinGecko\n\n* -t/--top - number of coins to display. To display top 10 coins: `sentiment --top 10`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `sentiment --top 10 --links`\n\n\n\n\n## recently <a name=\"recently\"></a>\n\n````\nusage: recently [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows coins which were recently added on CoinGecko\n\n* -t/--top - number of coins to display. To display top 10 coins: `recently --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change_24h, change_1h, added`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `recently --top 10 --links`\n\n\n\n## top_volume <a name=\"top_volume\"></a>\n\n````\nusage: top_volume [-t --top] [-s --sortby] [--descend]\n````\n\nShows coins with the highest transactions volume on CoinGecko\n\n* -t/--top - number of coins to display. To display top 10 coins: `top_volume --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n\n## gainers <a name=\"gainers\"></a>\n\n````\nusage: gainers [-p --period] [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows largest gainers - coins which gain the most in given period.\n\n* -p/--period - time period in which coins gained the most in price. One from `1h, 24h, 7d, 14d, 30d, 60d, 1y`. If you want to see top gainers in last 24h `gainers -p 24h` or top gainers in last 60days `gainers -p 60d`\n* -t/--top - number of coins to display. To display top 10 coins: `gainers --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change, volume`.\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `gainers --period 24h --links`\n\n\n\n## losers <a name=\"losers\"></a>\n\n````\nusage: losers [-p --period] [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows largest losers - coins which lost the most in given period.\n\n* -p/--period - time period in which coins lost the most in price. One from `1h, 24h, 7d, 14d, 30d, 60d, 1y`. If you want to see top losers in last 24h `losers -p 24h` or top losers in last 60days `losers -p 60d`\n* -t/--top - number of coins to display. To display top 10 coins: `losers --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change, volume`.\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `losers --period 24h --links`\n\n)\n\n## stables <a name=\"stables\"></a>\n\n````\nusage: stables [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows stablecoins by market capitalization. Stablecoins are cryptocurrencies that attempt to peg their market value to some external reference\nlike the U.S. dollar or to a commodity's price such as gold.\n\n* -t/--top - number of coins to display. To display top 10 coins: `stables --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, exchanges, market_cap, change_30d`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `stables --links`. If you will use this flag `rank, name, symbol, url` columns will be displayed.\n\n\n\n\n## yfarms <a name=\"yfarms\"></a>\n\n````\nusage: yfarms [-t --top] [-s --sortby] [--descend]\n````\n\nShows Top Yield Farming Pools by Value Locked from https://www.coingecko.com/en/yield-farming\nYield farming, also referred to as liquidity mining, is a way to generate rewards with cryptocurrency holdings.\nIn simple terms, it means locking up cryptocurrencies and getting rewards.\n\n* -t/--top - number of yield farms to display. To display top 10 yield farms: `yfarms --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, value_locked, return_year`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n\n## top_defi <a name=\"top_defi\"></a>\n\n````\nusage: top_defi [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows Top DeFi Coins by Market Capitalization from https://www.coingecko.com/en/defi\nDeFi or Decentralized Finance refers to financial services that are built on top of distributed networks with no central intermediaries.\n\n* -t/--top - number of defi coins to display. To display top 10 defi coins: `top_defi --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, change_1h, change_7d`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `top_defi --links`.\n\n\n\n## top_dex <a name=\"top_dex\"></a>\n\n````\nusage: top_dex [-t --top] [-s --sortby] [--descend]\n````\n\nShows Top Decentralized Exchanges on CoinGecko by Trading Volume from https://www.coingecko.com/en/dex\nDecentralized exchanges or DEXs are autonomous decentralized applications (DApps) that allow cryptocurrency buyers or\nsellers to trade without having to give up control over their funds to any intermediary or custodian. Source: [coinmarketcap](#https://coinmarketcap.com/alexandria/article/what-are-decentralized-exchanges-dex)\n\n* -t/--top - number of Decentralized Exchanges to display. To display top 10 DEX: `top_dex --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol most_traded_pairs, market_share_by_volume`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n## top_nft <a name=\"top_nft\"></a>\n\n````\nusage: top_nft [-t --top] [-s --sortby] [--descend] [-l --links]\n````\n\nShows Top 100 NFT Coins by Market Capitalization from https://www.coingecko.com/en/nft\nNFT (Non-fungible Token) refers to digital assets with unique characteristics.\nExamples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n\n* -t/--top - number of NFT to display. To display top 10 NFT Coins: `top_nft --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, change_1h, change_7d, volume_24h, market_cap`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `top_nft --links`.\n\n\n\n\n## nft_today <a name=\"nft_today\"></a>\n\n````\nusage: nft_today\n````\nGet Non-fungible Token of the Day. Everyday on CoinGecko there is chosen new NFT. NFT (Non-fungible Token) refers to digital assets with unique characteristics. Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n`nft_today` command you will display: author, description, url, img url.\n\n\n\n## nft_market <a name=\"nft_market\"></a>\n\n````\nusage: nft_market\n````\nGet current state of NFTs market. NFT (Non-fungible Token) refers to digital assets with unique characteristics.\nExamples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n`nft_market` will display: NFT Market Cap, 24h Trading Volume, NFT Dominance vs Global market, Theta Network NFT Dominance.\n\n\n\n\n## exchanges <a name=\"exchanges\"></a>\n\n````\nusage: exchanges [-t --top] [-s --sortby] [--descend] [-l --links]\n````\nShows top crypto exchanges base on trust score.\n\n* -t/--top - number of exchanges to display. To display top 10 exchanges by trust_score: `exchanges --top 10 --sortby trust_score --descend`\n* -s/--sortby - sort by given column. You can sort data by `rank, trust_score, id, name, country, established, trade_volume_24h_btc`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `exchanges --links`. Using `links` parameter will display only `rank, name, url` columns\n\n\n\n## ex_rates <a name=\"ex_rates\"></a>\n\n````\nusage: ex_rates [-t --top] [-s --sortby] [--descend]\n````\n\nShows list of crypto, fiats, commodity exchange rates from CoinGecko\n* -t/--top - number of exchanges to display. To display top 10 exchanges by trust_score: `ex_rates --top 10 --sortby name --descend`\n* -s/--sortby - sort by given column. You can sort by `index,name,unit, value, type`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n## platforms <a name=\"platforms\"></a>\n\n````\nusage: platforms [-t --top] [-s --sortby] [--descend]\n````\nShows Top Crypto Financial Platforms in which you can borrow or lend your crypto e.g. Celsius, Nexo, Crypto.com, Aave and others.\n\n* -t/--top - number of crypto platforms to display. To display top 10 platforms: `platforms --top 10 --sortby name`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, category, centralized`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n\n\n## products <a name=\"products\"></a>\n\n````\nusage: products [-t --top] [-s --sortby] [--descend]\n````\nShows Top Crypto Financial Products with which you can earn yield, borrow or lend your crypto.\n\n* -t/--top - number of crypto products to display. To display top 10 products: `products --top 10 --sortby name`\n* -s/--sortby - sort by given column. You can sort data by `rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n## indexes <a name=\"indexes\"></a>\n\n````\nusage: indexes [-t --top] [-s --sortby] [--descend]\n````\n\nShows list of crypto indexes from CoinGecko.Each crypto index is made up of a selection of cryptocurrencies, grouped together and weighted by market cap.\n* -t/--top - number of crypto indexes to display. To display top 10 indexes: `indexes --top 10 --sortby name`\n* -s/--sortby - sort by given column. You can sort data by `rank, name, id, market, last, is_multi_asset_composite`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n## derivatives <a name=\"derivatives\"></a>\n\n````\nusage: derivatives [-t --top] [-s --sortby] [--descend]\n````\nShows list of crypto derivatives from CoinGecko. Crypto derivatives are secondary contracts or financial tools that derive their value from a primary\nunderlying asset. In this case, the primary asset would be a cryptocurrency such as Bitcoin. The most popular crypto derivatives are crypto futures, crypto options, and perpetual contracts.\n* -t/--top - number of crypto derivatives to display. To display top 10 derivatives: `derivatives --top 10 --sortby symbol`\n* -s/--sortby - sort by given column. You can sort by `rank, market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h`\n* --descend - flag to sort in descending order (lowest first)\n\n\n\n\n## categories <a name=\"categories\"></a>\n\n````\nusage: categories [-t --top] [-s --sortby] [--descend] [-l --links]\n````\nShows top cryptocurrency categories by market capitalization from https://www.coingecko.com/en/categories\nIt includes categories like: stablecoins, defi, solana ecosystem, polkadot ecosystem and many others.\n\n* -t/--top - number of crypto categories to display. To display top 10 crypto categories: `categories --top 10 --sortby rank`\n* -s/--sortby - sort by given column. You can sort by `rank, name, change_1h, change_24h, change_7d, market_cap, volume_24h, n_of_coins`\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `categories --links`. Using `links` parameter will display only `rank, name, url` columns\n\n\n\n\n## hold <a name=\"hold\"></a>\n\n````\nusage: hold [-c --coin]\n````\n\nShows overview of public companies that holds ethereum or bitcoin\nDisplays most important metrics like: Total Bitcoin/Ethereum Holdings, Total Value (USD), Public Companies Bitcoin/Ethereum Dominance, Companies.\n\n* -c/--coin - chose a coin. Only available for ethereum or bitcoin. If you want to see overview of public companies that holds ethereum use `hold --coin ethereum` for bitcoin `hold --coin bitcoin`\n\n\n\n\n## hold_comp <a name=\"hold_comp\"></a>\n\n````\nusage: hold_comp [-c --coin] [- --links]\n````\n\nShows Ethereum/Bitcoin Holdings by Public Companies. Track publicly traded companies around the world that are buying ethereum as part of corporate treasury\n\n* -c/--coin - chose a coin. Only available for ethereum or bitcoin. List of public companies that holds ethereum `hold_comp --coin ethereum` for bitcoin `hold_comp --coin bitcoin`\n* -l/--links - You can use additional flag `--links` to see urls to announcement about buying btc or eth by given company. In this case of usage `links` only columns `rank, company, url` will be displayed\n\n\n\n"},{"alpha_fraction":0.5437082648277283,"alphanum_fraction":0.5486685037612915,"avg_line_length":28.438465118408203,"blob_id":"fa5fc53d7de023341fd8048160f532507abcf5b0","content_id":"94f791fa195d86f30a4806c3a666c6ad72a3fbf3","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":74391,"license_type":"permissive","max_line_length":120,"num_lines":2527,"path":"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" pycoingecko_api \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport textwrap\nimport difflib\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\nfrom pycoingecko import CoinGeckoAPI\nfrom gamestonk_terminal.helper_funcs import (\n check_positive,\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nimport gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_overview_model as gecko\nimport gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_coin_model as gecko_coin\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import wrap_text_in_df\n\nregister_matplotlib_converters()\n\n# Generate a list of valid coins to be checked against later\ncg_api = CoinGeckoAPI()\ncoins = cg_api.get_coins()\n\n# pylint: disable=inconsistent-return-statements\n# pylint: disable=R0904, C0302\n\n\ndef load(other_args: List[str]):\n \"\"\"Load selected Cryptocurrency. You can pass either symbol of id of the coin\n\n Parameters\n ----------\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"load\",\n description=\"\"\"Load cryptocurrency, from CoinGecko.\n You will have access to a lot of statistics on that coin like price data,\n coin development stats, social media and many others. Loading coin\n also will open access to technical analysis menu.\"\"\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n required=True,\n type=str,\n dest=\"coin\",\n help=\"Coin to load data for (symbol or coin id). You can use either symbol of the coin or coinId\"\n \"You can find all coins using command `coins` or visit https://www.coingecko.com/en. \"\n \"To use load a coin use command load -c [symbol or coinId]\",\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-c\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n coin = gecko_coin.Coin(ns_parser.coin)\n print(\"\")\n return coin\n\n except KeyError:\n print(f\"Could not find coin with the id: {ns_parser.coin}\", \"\\n\")\n return None\n except SystemExit:\n print(\"\")\n return None\n except Exception as e:\n print(e, \"\\n\")\n return None\n\n\ndef chart(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Plots chart for loaded cryptocurrency\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"chart\",\n description=\"\"\"\n Display chart for loaded coin. You can specify currency vs which you want\n to show chart and also number of days to get data for.\n By default currency: usd and days: 30.\n E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum\n in last 90 days range use `chart --vs eth --days 90`\n \"\"\",\n )\n parser.add_argument(\n \"--vs\", default=\"usd\", dest=\"vs\", help=\"Currency to display vs coin\"\n )\n parser.add_argument(\n \"-d\", \"--days\", default=30, dest=\"days\", help=\"Number of days to get data for\"\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days)\n plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\n\n plt.plot(df.index, df.price, \"-ok\", ms=2)\n plt.xlabel(\"Time\")\n plt.xlim(df.index[0], df.index[-1])\n plt.ylabel(\"Price\")\n plt.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n plt.minorticks_on()\n plt.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n plt.title(f\"{coin.coin_symbol}/{df['currency'][0]}\")\n plt.show()\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef ta(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Load data for Technical Analysis\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ta\",\n description=\"\"\"\n Loads data for technical analysis. You can specify currency vs which you want\n to show chart and also number of days to get data for.\n By default currency: usd and days: 30.\n E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum\n in last 90 days range use `ta --vs eth --days 90`\n \"\"\",\n )\n parser.add_argument(\n \"--vs\", default=\"usd\", dest=\"vs\", help=\"Currency to display vs coin\"\n )\n parser.add_argument(\n \"-d\", \"--days\", default=30, dest=\"days\", help=\"Number of days to get data for\"\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return None, None\n\n df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days)\n return df, ns_parser.vs\n\n except SystemExit:\n print(\"\")\n return None, None\n except Exception as e:\n print(e, \"\\n\")\n return None, None\n\n\ndef info(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows basic information about loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"info\",\n description=\"\"\"\n Shows basic information about loaded coin like:\n id, name, symbol, asset_platform, description, contract_address,\n market_cap_rank, public_interest_score, total_supply, max_supply,\n price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d,\n current_price_btc, current_price_eth, current_price_usd\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = wrap_text_in_df(coin.base_info, w=80)\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef web(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows found websites corresponding to loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"web\",\n description=\"\"\"Websites found for given Coin. You can find there urls to\n homepage, forum, announcement site and others.\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.websites\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef social(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows social media corresponding to loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"social\",\n description=\"\"\"Shows social media corresponding to loaded coin. You can find there name of\n telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.social_media\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef dev(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows developers data for loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"dev\",\n description=\"\"\"Developers data for loaded coin. If the development data is available you can see\n how the code development of given coin is going on.\n There are some statistics that shows number of stars, forks, subscribers, pull requests,\n commits, merges, contributors on github.\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.developers_data\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef ath(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows all time high data for loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ath\",\n description=\"\"\"All time high data for loaded coin. You can find there most important metrics regarding\n ath of coin price like:\n current_price_btc, current_price_eth, current_price_usd, ath_btc, ath_eth, ath_usd,\n ath_date_btc, ath_date_eth, ath_date_usd, ath_change_percentage_btc, ath_change_percentage_btc,\n ath_change_percentage_eth, ath_change_percentage_usd\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.all_time_high\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef atl(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows all time low data for loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"atl\",\n description=\"\"\"\n All time low data for loaded coin. You can find there most important metrics regarding\n atl of coin price like:\n current_price_btc, ,current_price_eth, current_price_usd, atl_btc, atl_eth, atl_usd,\n atl_date_btc, atl_date_eth, atl_date_usd, atl_change_percentage_btc, atl_change_percentage_btc,\n atl_change_percentage_eth, atl_change_percentage_usd\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.all_time_low\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef score(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows different kind of scores for loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"score\",\n description=\"\"\"\n In this view you can find different kind of scores for loaded coin.\n Those scores represents different rankings, sentiment metrics, some user stats and others.\n coingecko_rank, coingecko_score, developer_score, community_score, liquidity_score,\n sentiment_votes_up_percentage, sentiment_votes_down_percentage, public_interest_score,\n facebook_likes, twitter_followers, reddit_average_posts_48h, reddit_average_comments_48h,\n reddit_subscribers, reddit_accounts_active_48h, telegram_channel_user_count, alexa_rank,\n bing_matches\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.scores\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef bc(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows urls to blockchain explorers\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"bc\",\n description=\"\"\"\n Blockchain explorers URLs for loaded coin. Those are sites like etherescan.io or polkascan.io\n in which you can see all blockchain data e.g. all txs, all tokens, all contracts...\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.blockchain_explorers\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef market(coin: gecko_coin.Coin, other_args: List[str]):\n \"\"\"Shows market data for loaded coin\n\n Parameters\n ----------\n coin : gecko_coin.Coin\n Cryptocurrency\n other_args : List[str]\n argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"market\",\n description=\"\"\"\n Market data for loaded coin. There you find metrics like:\n market_cap_rank, total_supply, max_supply, circulating_supply,\n price_change_percentage_24h, price_change_percentage_7d, 'price_change_percentage_30d',\n price_change_percentage_60d', 'price_change_percentage_1y', 'market_cap_change_24h',\n market_cap_btc', 'market_cap_eth', 'market_cap_usd', 'total_volume_btc', 'total_volume_eth',\n total_volume_usd', 'high_24h_btc', 'high_24h_eth', 'high_24h_usd', 'low_24h_btc', 'low_24h_eth',\n low_24h_usd'\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n df = coin.market_data\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef holdings_overview(other_args: List[str]):\n \"\"\"Shows overview of public companies that holds ethereum or bitcoin from www.coingecko.com\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"hold\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows overview of public companies that holds ethereum or bitcoin.\n You can find there most important metrics like:\n Total Bitcoin Holdings, Total Value (USD), Public Companies Bitcoin Dominance, Companies\n \"\"\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n dest=\"coin\",\n type=str,\n help=\"companies with ethereum or bitcoin\",\n default=\"bitcoin\",\n choices=[\"ethereum\", \"bitcoin\"],\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_holdings_overview(endpoint=ns_parser.coin)\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef holdings_companies_list(other_args: List[str]):\n \"\"\"Shows Ethereum/Bitcoin Holdings by Public Companies from www.coingecko.com\n\n Track publicly traded companies around the world that are buying ethereum as part of corporate treasury\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"hold_comp\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Track publicly traded companies around the world that\n are buying ethereum or bitcoin as part of corporate treasury:\n rank, company, ticker, country, total_btc, entry_value, today_value, pct_of_supply\n You can use additional flag --links to see urls to announcement about buying btc or eth by given company.\n In this case you will see only columns like rank, company, url\n \"\"\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n dest=\"coin\",\n type=str,\n help=\"companies with ethereum or bitcoin\",\n default=\"bitcoin\",\n choices=[\"ethereum\", \"bitcoin\"],\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will see only rank, company, url columns\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_companies_assets(endpoint=ns_parser.coin)\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"company\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef gainers(other_args: List[str]):\n \"\"\"Shows Largest Gainers - coins which gain the most in given period from www.coingecko.com\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"gainers\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows Largest Gainers - coins which gain the most in given period.\n You can use parameter --period to set which timeframe are you interested in. eg. 1h, 24h, 7d, 14d, 30d, 60d, 1y\n You can look on only top N number of records with --top,\n You can sort by rank, symbol, name, volume, price, change with --sort and also with --descend flag to set it\n to sort descending.\n There is --links flag, which will display one additional column you all urls for coins.\n \"\"\",\n )\n parser.add_argument(\n \"-p\",\n \"--period\",\n dest=\"period\",\n type=str,\n help=\"time period, one from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\",\n default=\"1h\",\n choices=[\"1h\", \"24h\", \"7d\", \"14d\", \"30d\", \"60d\", \"1y\"],\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=int,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"symbol\", \"name\", \"volume\", \"price\", \"change\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will additional column with urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if ns_parser.sortby == \"change\":\n sortby = f\"%change_{ns_parser.period}\"\n else:\n sortby = ns_parser.sortby\n\n df = gecko.get_gainers_or_losers(\n period=ns_parser.period, typ=\"gainers\"\n ).sort_values(by=sortby, ascending=ns_parser.descend)\n\n if not ns_parser.links:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef losers(other_args: List[str]):\n \"\"\"Shows Largest Losers - coins which lost the most in given period of time from www.coingecko.com\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"losers\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows Largest Losers - coins which price dropped the most in given period\n You can use parameter --period to set which timeframe are you interested in. eg. 1h, 24h, 7d, 14d, 30d, 60d, 1y\n You can look on only top N number of records with --top,\n You can sort by rank, symbol, name, volume, price, change with --sort and also with --descend flag\n to sort descending.\n Flag --links will display one additional column with all coingecko urls for listed coins.\n \"\"\",\n )\n parser.add_argument(\n \"-p\",\n \"--period\",\n dest=\"period\",\n type=str,\n help=\"time period, one from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\",\n default=\"1h\",\n choices=[\"1h\", \"24h\", \"7d\", \"14d\", \"30d\", \"60d\", \"1y\"],\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: change\",\n default=\"rank\",\n choices=[\"rank\", \"symbol\", \"name\", \"volume\", \"price\", \"change\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will additional column with urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if ns_parser.sortby == \"change\":\n sortby = f\"%change_{ns_parser.period}\"\n else:\n sortby = ns_parser.sortby\n\n df = gecko.get_gainers_or_losers(\n period=ns_parser.period, typ=\"losers\"\n ).sort_values(by=sortby, ascending=ns_parser.descend)\n\n if not ns_parser.links:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef discover(category: str, other_args: List[str]):\n \"\"\"Discover coins by different categories\n - Most voted coins\n - Most popular coins\n - Recently added coins\n - Most positive sentiment coins\n\n Parameters\n ----------\n category: str\n one from list: [trending, most_voted, positive_sentiment, most_visited]\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=f\"{category}\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=f\"\"\"Discover {category} coins.\n Use --top parameter to display only top N number of records,\n You can sort by rank, name, price_btc, price_usd, using --sort parameter and also with --descend flag\n to sort descending.\n Flag --links will display one additional column with all coingecko urls for listed coins.\n {category} will display: rank, name, price_usd, price_btc\n \"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"name\", \"price_usd\", \"price_btc\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will additional column with urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.discover_coins(category=category)\n df.index = df.index + 1\n df.reset_index(inplace=True)\n df.rename(columns={\"index\": \"rank\"}, inplace=True)\n\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n if not ns_parser.links:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".5f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef news(other_args: List[str]):\n \"\"\"Shows latest crypto news from www.coingecko.com\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"news\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Shows latest crypto news from CoinGecko. \"\n \"You will see index, title, author, posted columns. \"\n \"You can sort by each of column above, using --sort parameter and also do it descending with --descend flag\"\n \"To display urls to news use --links flag.\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=int,\n help=\"top N number of news >=10\",\n default=50,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: index\",\n default=\"index\",\n choices=[\"index\", \"title\", \"author\", \"posted\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will additional column with urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_news(n=ns_parser.top).sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n df[\"title\"] = df[\"title\"].apply(\n lambda x: \"\\n\".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x\n )\n\n if not ns_parser.links:\n df.drop(\"url\", axis=1, inplace=True)\n else:\n df = df[[\"index\", \"url\"]]\n\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef categories(other_args: List[str]):\n \"\"\"Shows top cryptocurrency categories by market capitalization from https://www.coingecko.com/en/categories\n\n The cryptocurrency category ranking is based on market capitalization.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"categories\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows top cryptocurrency categories by market capitalization. It includes categories like:\n stablecoins, defi, solana ecosystem, polkadot ecosystem and many others.\n \"You can sort by each of column above, using --sort parameter and also do it descending with --descend flag\"\n \"To display urls to news use --links flag.\",\n Displays: rank, name, change_1h, change_24h, change_7d, market_cap, volume_24h, n_of_coins\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number of news >=10\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"change_1h\",\n \"change_24h\",\n \"change_7d\",\n \"market_cap\",\n \"volume_24h\",\n \"n_of_coins\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls. If you will use that flag you will additional column with urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_top_crypto_categories().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if not ns_parser.links:\n df.drop(\"url\", axis=1, inplace=True)\n else:\n df = df[[\"rank\", \"name\", \"url\"]]\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef recently_added(other_args: List[str]):\n \"\"\"Shows recently added coins from \"https://www.coingecko.com/en/coins/recently_added\"\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"recently\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows recently added coins on CoinGecko. You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, change_24h, change_1h, added with --sort\n and also with --descend flag to sort descending.\n Flag --links will display urls\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"name\", \"symbol\", \"price\", \"change_24h\", \"change_1h\", \"added\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_recently_added_coins().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"symbol\", \"added\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef stablecoins(other_args: List[str]):\n \"\"\"Shows stablecoins data from \"https://www.coingecko.com/en/stablecoins\"\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"stables\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows stablecoins by market capitalization.\n Stablecoins are cryptocurrencies that attempt to peg their market value to some external reference\n like the U.S. dollar or to a commodity's price such as gold.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, change_24h, exchanges, market_cap, change_30d with --sort\n and also with --descend flag to sort descending.\n Flag --links will display stablecoins urls\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"change_24h\",\n \"exchanges\",\n \"market_cap\",\n \"change_30d\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_stable_coins().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"name\", \"symbol\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef yfarms(other_args: List[str]):\n \"\"\"Shows Top Yield Farming Pools by Value Locked from \"https://www.coingecko.com/en/yield-farming\"\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"yfarms\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top Yield Farming Pools by Value Locked\n Yield farming, also referred to as liquidity mining, is a way to generate rewards with cryptocurrency holdings.\n In simple terms, it means locking up cryptocurrencies and getting rewards.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, value_locked, return_year with --sort parameter\n and also with --descend flag to sort descending.\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"Top N of records. Default 20\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"name\", \"value_locked\", \"return_year\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_yield_farms()\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".0f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef top_volume_coins(other_args: List[str]):\n \"\"\"Shows Top 100 Coins by Trading Volume from \"https://www.coingecko.com/en/yield-farming\"\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"top_volume\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top Coins by Trading Volume.\n You can display only top N number of coins with --top parameter.\n You can sort data by on of columns rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h ,\n market_cap with --sort parameter and also with --descend flag to sort descending.\n Displays columns: rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h ,market_cap\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"Top N of records. Default 20\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"change_1h\",\n \"change_24h\",\n \"change_7d\",\n \"volume_24h\",\n \"market_cap\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_top_volume_coins().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef top_defi_coins(other_args: List[str]):\n \"\"\"Shows Top 100 DeFi Coins by Market Capitalization from \"https://www.coingecko.com/en/defi\"\n DeFi or Decentralized Finance refers to financial services that are built\n on top of distributed networks with no central intermediaries.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"top_defi\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top DeFi Coins by Market Capitalization\n DeFi or Decentralized Finance refers to financial services that are built\n on top of distributed networks with no central intermediaries.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, change_24h, change_1h, change_7d,\n volume_24h, market_cap with --sort and also with --descend flag to sort descending.\n Flag --links will display urls\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"change_1h\",\n \"change_24h\",\n \"change_7d\",\n \"volume_24h\",\n \"market_cap\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_top_defi_coins().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"name\", \"symbol\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef top_dex(other_args: List[str]):\n \"\"\"Shows Top Decentralized Exchanges on CoinGecko by Trading Volume from \"https://www.coingecko.com/en/dex\"\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"top_dex\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows Top Decentralized Exchanges on CoinGecko by Trading Volume\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\n most_traded_pairs, market_share_by_volume with --sort and also with --descend flag to sort descending.\n Display columns:\n rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"volume_24h\",\n \"n_coins\",\n \"visits\",\n \"most_traded\",\n \"market_share_by_vol\",\n \"n_pairs\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_top_dexes().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef top_nft(other_args: List[str]):\n \"\"\"Shows Top 100 NFT Coins by Market Capitalization from \"https://www.coingecko.com/en/nft\"\n Top 100 NFT Coins by Market Capitalization\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"top_nft\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top NFT Coins by Market Capitalization\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, symbol, price, change_24h, change_1h, change_7d,\n volume_24h, market_cap with --sort and also with --descend flag to sort descending.\n Flag --links will display urls\n Displays : rank, name, symbol, price, change_1h, change_24h, change_7d, market_cap, url\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"change_1h\",\n \"change_24h\",\n \"change_7d\",\n \"volume_24h\",\n \"market_cap\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_top_nfts().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"name\", \"symbol\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".4f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef nft_of_the_day(other_args: List[str]):\n \"\"\"Shows NFT of the day \"https://www.coingecko.com/en/nft\"\n\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"nft_today\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows NFT of the day\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n With nft_today command you will display:\n author, description, url, img url for NFT which was chosen on CoinGecko as a nft of the day.\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_nft_of_the_day()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef nft_market_status(other_args: List[str]):\n \"\"\"Shows overview data of nft markets \"https://www.coingecko.com/en/nft\"\n\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"nft_market\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows NFT market status\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\n Displays: NFT Market Cap, 24h Trading Volume, NFT Dominance vs Global market, Theta Network NFT Dominance\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_nft_market_status()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef exchanges(other_args: List[str]):\n \"\"\"Shows list of top exchanges from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"exchanges\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top Crypto Exchanges\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, trust_score, id, name, country, established, trade_volume_24h_btc with --sort\n and also with --descend flag to sort descending.\n Flag --links will display urls.\n Displays: rank, trust_score, id, name, country, established, trade_volume_24h_btc\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"trust_score\",\n \"id\",\n \"name\",\n \"country\",\n \"year_established\",\n \"trade_volume_24h_btc\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n parser.add_argument(\n \"-l\",\n \"--links\",\n dest=\"links\",\n action=\"store_true\",\n help=\"Flag to show urls\",\n default=False,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_exchanges().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n if ns_parser.links is True:\n df = df[[\"rank\", \"name\", \"url\"]]\n else:\n df.drop(\"url\", axis=1, inplace=True)\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef platforms(other_args: List[str]):\n \"\"\"Shows list of financial platforms from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"platforms\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top Crypto Financial Platforms in which you can borrow or lend your crypto.\n e.g Celsius, Nexo, Crypto.com, Aave and others.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, category, centralized with --sort\n and also with --descend flag to sort descending.\n Displays: rank, name, category, centralized, website_url\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"name\", \"category\", \"centralized\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_financial_platforms().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef products(other_args: List[str]):\n \"\"\"Shows list of financial products from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"products\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows Top Crypto Financial Products with which you can earn yield, borrow or lend your crypto.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage with --sort\n and also with --descend flag to sort descending.\n Displays: rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"platform\",\n \"identifier\",\n \"supply_rate_percentage\",\n \"borrow_rate_percentage\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_finance_products().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef indexes(other_args: List[str]):\n \"\"\"Shows list of crypto indexes from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"indexes\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows list of crypto indexes from CoinGecko.\n Each crypto index is made up of a selection of cryptocurrencies, grouped together and weighted by market cap.\n You can display only top N number of coins with --top parameter.\n You can sort data by rank, name, id, market, last, is_multi_asset_composite with --sort\n and also with --descend flag to sort descending.\n Displays: rank, name, id, market, last, is_multi_asset_composite\n \"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\"rank\", \"name\", \"id\", \"market\", \"last\", \"is_multi_asset_composite\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_indexes().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef derivatives(other_args: List[str]):\n \"\"\"Shows list of crypto derivatives from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"derivatives\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows list of crypto derivatives from CoinGecko\n Crypto derivatives are secondary contracts or financial tools that derive their value from a primary\n underlying asset. In this case, the primary asset would be a cryptocurrency such as Bitcoin.\n The most popular crypto derivatives are crypto futures, crypto options, and perpetual contracts.\n You can look on only top N number of records with --top,\n You can sort by rank, market, symbol, price, pct_change_24h, contract_type, basis, spread,\n funding_rate, volume_24h with --sort and also with --descend flag to set it to sort descending.\n Displays:\n rank, market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: rank\",\n default=\"rank\",\n choices=[\n \"rank\",\n \"market\",\n \"symbol\",\n \"price\",\n \"pct_change_24h\",\n \"contract_type\",\n \"basis\",\n \"spread\",\n \"funding_rate\",\n \"volume_24h\",\n ],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_derivatives().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef exchange_rates(other_args: List[str]):\n \"\"\"Shows list of crypto, fiats, commodity exchange rates from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"ex_rates\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Shows list of crypto, fiats, commodity exchange rates from CoinGecko\n You can look on only top N number of records with --top,\n You can sort by index,name,unit, value, type, and also use --descend flag to sort descending.\"\"\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n dest=\"top\",\n type=check_positive,\n help=\"top N number records\",\n default=20,\n )\n parser.add_argument(\n \"-s\",\n \"--sort\",\n dest=\"sortby\",\n type=str,\n help=\"Sort by given column. Default: index\",\n default=\"index\",\n choices=[\"index\", \"name\", \"unit\", \"value\", \"type\"],\n )\n parser.add_argument(\n \"--descend\",\n action=\"store_false\",\n help=\"Flag to sort in descending order (lowest first)\",\n dest=\"descend\",\n default=True,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_exchange_rates().sort_values(\n by=ns_parser.sortby, ascending=ns_parser.descend\n )\n print(\n tabulate(\n df.head(ns_parser.top),\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef global_market_info(other_args: List[str]):\n \"\"\"Shows global statistics about crypto from CoinGecko\n - market cap change\n - number of markets\n - icos\n - number of active crypto\n - market_cap_pct\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"global\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows global statistics about Crypto Market like:\n active_cryptocurrencies, upcoming_icos, ongoing_icos, ended_icos, markets, market_cap_change_percentage_24h,\n eth_market_cap_in_pct, btc_market_cap_in_pct, altcoin_market_cap_in_pct\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_global_info()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".2f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef global_defi_info(other_args: List[str]):\n \"\"\"Shows global statistics about Decentralized Finances from CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"defi\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Shows global DeFi statistics\n DeFi or Decentralized Finance refers to financial services that are built\n on top of distributed networks with no central intermediaries.\n Displays metrics like:\n defi_market_cap, eth_market_cap, defi_to_eth_ratio, trading_volume_24h, defi_dominance, top_coin_name,\n top_coin_defi_dominance\"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_global_defi_info()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef coin_list(other_args: List[str]):\n \"\"\"Shows list of coins available on CoinGecko\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"coins\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Shows list of coins available on CoinGecko\",\n )\n parser.add_argument(\n \"-s\",\n \"--skip\",\n default=0,\n dest=\"skip\",\n help=\"Skip n of records\",\n type=check_positive,\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=100,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n parser.add_argument(\"-l\", \"--letter\", dest=\"letter\", help=\"First letters\", type=str)\n parser.add_argument(\n \"-k\",\n \"--key\",\n dest=\"key\",\n help=\"Search in column symbol, name, id\",\n type=str,\n choices=[\"id\", \"symbol\", \"name\"],\n default=\"symbol\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n df = gecko.get_coin_list()\n\n letter = ns_parser.letter\n if letter and isinstance(letter, str):\n if letter.isalpha():\n letter = letter.lower()\n df = df[df[ns_parser.key].str.startswith(letter)]\n\n try:\n df = df[ns_parser.skip : ns_parser.skip + ns_parser.top]\n except Exception:\n df = gecko.get_coin_list()\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef find(other_args: List[str]):\n \"\"\"Find similar coin by coin name,symbol or id.\n\n If you don't remember exact name or id of the Coin at CoinGecko,\n you can use this command to display coins with similar name, symbol or id to your search query.\n Example of usage: coin name is something like \"polka\". So I can try: find -c polka -k name -t 25\n It will search for coin that has similar name to polka and display top 25 matches.\n -c, --coin stands for coin - you provide here your search query\n -k, --key it's a searching key. You can search by symbol, id or name of coin\n -t, --top it displays top N number of records.\n\n Parameters\n ----------\n other_args: List[str]\n Arguments to pass to argparse\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"find\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"\n Find similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinGecko,\n you can use this command to display coins with similar name, symbol or id to your search query.\n Example of usage: coin name is something like \"polka\". So I can try: find -c polka -k name -t 25\n It will search for coin that has similar name to polka and display top 25 matches.\n -c, --coin stands for coin - you provide here your search query\n -k, --key it's a searching key. You can search by symbol, id or name of coin\n -t, --top it displays top N number of records.\"\"\",\n )\n parser.add_argument(\n \"-c\",\n \"--coin\",\n help=\"Symbol Name or Id of Coin\",\n dest=\"coin\",\n required=True,\n type=str,\n )\n parser.add_argument(\n \"-k\",\n \"--key\",\n dest=\"key\",\n help=\"Specify by which column you would like to search: symbol, name, id\",\n type=str,\n choices=[\"id\", \"symbol\", \"name\"],\n default=\"symbol\",\n )\n parser.add_argument(\n \"-t\",\n \"--top\",\n default=10,\n dest=\"top\",\n help=\"Limit of records\",\n type=check_positive,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n coins_df = gecko.get_coin_list()\n coins_list = coins_df[ns_parser.key].to_list()\n sim = difflib.get_close_matches(ns_parser.coin, coins_list, ns_parser.top)\n df = pd.Series(sim).to_frame().reset_index()\n df.columns = [\"index\", ns_parser.key]\n coins_df.drop(\"index\", axis=1, inplace=True)\n df = df.merge(coins_df, on=ns_parser.key)\n print(\n tabulate(\n df,\n headers=df.columns,\n floatfmt=\".1f\",\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5443524718284607,"alphanum_fraction":0.5479006767272949,"avg_line_length":26.721311569213867,"blob_id":"9113afdc10388394b2a7f85e7e3938379529f898","content_id":"4c799719f4928689be9ea69d5000789f1e076a7f","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3382,"license_type":"permissive","max_line_length":86,"num_lines":122,"path":"/gamestonk_terminal/cryptocurrency/coinmarketcap/coinmarketcap_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Cryptocurrency Controller\"\"\"\n__docformat__ = \"numpy\"\n# pylint: disable=R0904, C0302, W0622\nimport argparse\nimport os\nimport pandas as pd\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.cryptocurrency.coinmarketcap import (\n coinmarketcap_view as cmc_view,\n)\n\n\nclass CMCController:\n\n CHOICES = [\n \"?\",\n \"cls\",\n \"help\",\n \"q\",\n \"quit\",\n \"top\",\n ]\n\n def __init__(self):\n \"\"\"CONSTRUCTOR\"\"\"\n\n self._cmc_parser = argparse.ArgumentParser(add_help=False, prog=\"cmc\")\n self._cmc_parser.add_argument(\"cmd\", choices=self.CHOICES)\n self.current_coin = None\n self.current_currency = None\n self.current_df = pd.DataFrame()\n self.source = \"\"\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\"\\nCoinmarketcap:\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\" top view top coins from coinmarketcap\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self._cmc_parser.parse_known_args(an_input.split())\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu.\"\"\"\n print(\"Moving back to (crypto) menu\")\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program.\"\"\"\n return True\n\n def call_top(self, other_args):\n \"\"\"Process top command\"\"\"\n cmc_view.get_cmc_top_n(other_args)\n\n\ndef menu():\n cmc_controller = CMCController()\n cmc_controller.print_help()\n\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in cmc_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (crypto)>(cmc)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (crypto)>(cmc)> \")\n\n try:\n process_input = cmc_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.6182795763015747,"alphanum_fraction":0.6182795763015747,"avg_line_length":7.08695650100708,"blob_id":"c874e7669251014a1a49d68bb08b8b4f3d0c92e1","content_id":"4429740a9885cea0385e096310e21ade00344e63","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":186,"license_type":"permissive","max_line_length":10,"num_lines":23,"path":"/gamestonk_terminal/etf/etf_config.ini","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"[Price]\nMIN = None\nMAX = None\n\n[Assets]\nMIN = None\nMAX = None\n\n[NAV]\nMIN = None\nMAX = None\n\n[Expense]\nMIN = None\nMAX = None\n\n[PE]\nMIN = None\nMAX = None\n\n[DivYield]\nMIN = None\nMAX = None\n"},{"alpha_fraction":0.5868619084358215,"alphanum_fraction":0.5876533389091492,"avg_line_length":26.467391967773438,"blob_id":"3fd8b1f43c7915cfaa88df009dc8590d9f0fb26a","content_id":"6dc74c9e27802bd7f9777a10a3118981ed45593b","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2527,"license_type":"permissive","max_line_length":106,"num_lines":92,"path":"/gamestonk_terminal/stocks/due_diligence/csimarket_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Stockgrid DD View\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom bs4 import BeautifulSoup\nimport requests\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n)\n\n\ndef suppliers(ticker: str, other_args: List[str]):\n \"\"\"Print suppliers from ticker provided\n\n Parameters\n ----------\n ticker: str\n Ticker to select suppliers from\n other_args : List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"supplier\",\n add_help=False,\n description=\"List of suppliers from ticker provided. [Source: CSIMarket]\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n url_supply_chain = f\"https://csimarket.com/stocks/competitionNO3.php?supply&code={ticker.upper()}\"\n text_supplier_chain = BeautifulSoup(requests.get(url_supply_chain).text, \"lxml\")\n\n l_supplier = list()\n for supplier in text_supplier_chain.findAll(\n \"td\", {\"class\": \"plavat svjetlirub dae al\"}\n ):\n l_supplier.append(supplier.text)\n\n if l_supplier:\n print(\"List of Suppliers: \" + \", \".join(l_supplier) + \"\\n\")\n else:\n print(\"No suppliers found.\\n\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef customers(ticker: str, other_args: List[str]):\n \"\"\"Print customers from ticker provided\n\n Parameters\n ----------\n ticker: str\n Ticker to select customers from\n other_args : List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"customer\",\n add_help=False,\n description=\"List of customers from ticker provided. [Source: CSIMarket]\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n url_customer_chain = (\n f\"https://csimarket.com/stocks/custexNO.php?markets&code={ticker.upper()}\"\n )\n text_customer_chain = BeautifulSoup(\n requests.get(url_customer_chain).text, \"lxml\"\n )\n\n l_customer = list()\n for customer in text_customer_chain.findAll(\n \"td\", {\"class\": \"plava svjetlirub\"}\n ):\n l_customer.append(customer.text)\n\n if l_customer:\n print(\"List of Customers: \" + \", \".join(l_customer) + \"\\n\")\n else:\n print(\"No customers found.\\n\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5344297885894775,"alphanum_fraction":0.5435711145401001,"avg_line_length":26.460905075073242,"blob_id":"c0702410468a5c2e6defbc76a5ad6a163b881c29","content_id":"76e3448ca87434fc655cedab7d7b012bcacb710d","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":26692,"license_type":"permissive","max_line_length":104,"num_lines":972,"path":"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_overview_model.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"import math\nimport requests\nimport pandas as pd\nimport numpy as np\nfrom bs4 import BeautifulSoup\nfrom pycoingecko import CoinGeckoAPI\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import (\n wrap_text_in_df,\n percent_to_float,\n create_df_index,\n)\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_helpers import (\n changes_parser,\n replace_qm,\n clean_row,\n collateral_auditors_parse,\n swap_columns,\n)\n\nPERIODS = {\n \"1h\": \"?time=h1\",\n \"24h\": \"?time=h24\",\n \"7d\": \"?time=d7\",\n \"14d\": \"?time=d14\",\n \"30d\": \"?time=d30\",\n \"60d\": \"?time=d60\",\n \"1y\": \"?time=y1\",\n}\n\nCATEGORIES = {\n \"trending\": 0,\n \"most_voted\": 1,\n \"positive_sentiment\": 2,\n \"recently_added\": 3,\n \"most_visited\": 4,\n}\n\nCOLUMNS = {\n \"id\": \"id\",\n \"rank\": \"rank\",\n \"name\": \"name\",\n \"symbol\": \"symbol\",\n \"price\": \"price\",\n \"change_1h\": \"change_1h\",\n \"change_24h\": \"change_24h\",\n \"change_7d\": \"change_7d\",\n \"volume_24h\": \"volume_24h\",\n \"market_cap\": \"market_cap\",\n \"country\": \"country\",\n \"total_market_cap\": \"total_market_cap\",\n \"total_volume\": \"total_volume\",\n \"market_cap_percentage\": \"market_cap_percentage\",\n \"company\": \"company\",\n \"ticker\": \"ticker\",\n \"last_added\": \"added\",\n \"title\": \"title\",\n \"author\": \"author\",\n \"posted\": \"posted\",\n \"article\": \"article\",\n \"url\": \"url\",\n \"price_btc\": \"price_btc\",\n \"price_usd\": \"price_usd\",\n \"n_of_coins\": \"n_of_coins\",\n \"exchanges\": \"exchanges\",\n \"change_30d\": \"change_30d\",\n}\n\nCHANNELS = {\n \"telegram_channel_identifier\": \"telegram\",\n \"twitter_screen_name\": \"twitter\",\n \"subreddit_url\": \"subreddit\",\n \"bitcointalk_thread_identifier\": \"bitcointalk\",\n \"facebook_username\": \"facebook\",\n \"discord\": \"discord\",\n}\n\nBASE_INFO = [\n \"id\",\n \"name\",\n \"symbol\",\n \"asset_platform_id\",\n \"description\",\n \"contract_address\",\n \"market_cap_rank\",\n \"public_interest_score\",\n]\n\nDENOMINATION = (\"usd\", \"btc\", \"eth\")\n\nclient = CoinGeckoAPI()\n\nGECKO_BASE_URL = \"https://www.coingecko.com\"\n\n\ndef scrape_gecko_data(url: str) -> BeautifulSoup:\n \"\"\"Helper method that scrape Coin Gecko site.\n\n Parameters\n ----------\n url : str\n coin gecko url to scrape e.g: \"https://www.coingecko.com/en/discover\"\n\n Returns\n -------\n BeautifulSoup object\n \"\"\"\n\n req = requests.get(url)\n return BeautifulSoup(req.text, features=\"lxml\")\n\n\ndef get_holdings_overview(endpoint: str = \"bitcoin\"):\n \"\"\"Scrapes overview of public companies that holds ethereum or bitcoin\n from \"https://www.coingecko.com/en/public-companies-{bitcoin/ethereum}\"\n\n Parameters\n ----------\n endpoint : str\n \"bitcoin\" or \"ethereum\"\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n url = f\"https://www.coingecko.com/en/public-companies-{endpoint}\"\n rows = scrape_gecko_data(url).find_all(\n \"span\", class_=\"overview-box d-inline-block p-3 mr-2\"\n )\n kpis = {}\n for row in rows:\n row_cleaned = clean_row(row)\n if row_cleaned:\n value, *kpi = row_cleaned\n name = \" \".join(kpi)\n kpis[name] = value\n\n df = pd.Series(kpis).to_frame().reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef get_companies_assets(endpoint=\"bitcoin\"):\n \"\"\"Scrapes list of companies that holds ethereum or bitcoin\n from \"https://www.coingecko.com/en/public-companies-{bitcoin/ethereum}\"\n\n Parameters\n ----------\n endpoint : str\n \"bitcoin\" or \"ethereum\"\n\n Returns\n -------\n pandas.DataFrame\n rank, company, ticker, country, total_btc, entry_value, today_value, pct_of_supply, url\n \"\"\"\n url = f\"https://www.coingecko.com/en/public-companies-{endpoint}\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n link = row.find(\"a\")[\"href\"]\n row_cleaned = clean_row(row)\n row_cleaned.append(link)\n results.append(row_cleaned)\n df = pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"rank\"],\n COLUMNS[\"company\"],\n COLUMNS[\"ticker\"],\n COLUMNS[\"country\"],\n \"total_btc\",\n \"entry_value\",\n \"today_value\",\n \"pct_of_supply\",\n COLUMNS[\"url\"],\n ],\n )\n return df\n\n\ndef get_gainers_or_losers(period=\"1h\", typ=\"gainers\"):\n \"\"\"Scrape data about top gainers - coins which gain the most in given period and\n top losers - coins that lost the most in given period of time.\n\n Parameters\n ----------\n period: str\n One from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\n typ: str\n Either \"gainers\" or \"losers\"\n Returns\n -------\n pandas.DataFrame\n symbol, name, volume, price, change_{period}, url\n \"\"\"\n category = {\n \"gainers\": 0,\n \"losers\": 1,\n }\n\n if period not in PERIODS:\n raise ValueError(\n f\"Wrong time period\\nPlease chose one from list: {PERIODS.keys()}\"\n )\n\n url = f\"https://www.coingecko.com/en/coins/trending{PERIODS.get(period)}\"\n rows = scrape_gecko_data(url).find_all(\"tbody\")[category.get(typ)].find_all(\"tr\")\n results = []\n for row in rows:\n url = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n symbol, name, *_, volume, price, change = clean_row(row)\n try:\n change = percent_to_float(change)\n except (ValueError, TypeError):\n ...\n results.append([symbol, name, volume, price, change, url])\n df = pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"symbol\"],\n COLUMNS[\"name\"],\n \"volume\",\n COLUMNS[\"price\"],\n f\"%change_{period}\",\n COLUMNS[\"url\"],\n ],\n )\n df.index = df.index + 1\n df.reset_index(inplace=True)\n df = df.rename(columns={\"index\": \"rank\"})\n return df\n\n\ndef get_btc_price():\n \"\"\"Get BTC/USD price from CoinGecko API\n\n Returns\n -------\n str\n latest bitcoin price in usd.\n \"\"\"\n req = requests.get(\n \"https://api.coingecko.com/api/v3/simple/\"\n \"price?ids=bitcoin&vs_currencies=usd&include_market_cap\"\n \"=false&include_24hr_vol\"\n \"=false&include_24hr_change=false&include_last_updated_at=false\"\n )\n return req.json()[\"bitcoin\"][\"usd\"]\n\n\ndef discover_coins(category: str = \"trending\") -> pd.DataFrame:\n \"\"\"Scrapes data from \"https://www.coingecko.com/en/discover\"\n - Most voted coins\n - Most popular coins\n - Recently added coins\n - Most positive sentiment coins\n Parameters\n ----------\n category: str\n - one from list: [trending, most_voted, positive_sentiment,recently_added, most_visited]\n Returns\n -------\n pandas.DataFrame:\n name, price_btc, price_usd, url\n \"\"\"\n if category not in CATEGORIES:\n raise ValueError(\n f\"Wrong category name\\nPlease chose one from list: {CATEGORIES.keys()}\"\n )\n url = \"https://www.coingecko.com/en/discover\"\n popular = scrape_gecko_data(url).find_all(\n \"div\", class_=\"col-12 col-sm-6 col-md-6 col-lg-4\"\n )[CATEGORIES[category]]\n rows = popular.find_all(\"a\")\n results = []\n btc_price = get_btc_price()\n\n for row in rows:\n name, *_, price = clean_row(row)\n url = GECKO_BASE_URL + row[\"href\"]\n if price.startswith(\"BTC\"):\n price = price.replace(\"BTC\", \"\").replace(\",\", \".\")\n\n price_usd = (int(btc_price) * float(price)) if btc_price else None\n results.append([name, price, price_usd, url])\n return pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"name\"],\n COLUMNS[\"price_btc\"],\n COLUMNS[\"price_usd\"],\n COLUMNS[\"url\"],\n ],\n )\n\n\ndef get_news(n: int = 100) -> pd.DataFrame:\n \"\"\"Scrapes news from \"https://www.coingecko.com/en/news?page={}\"\n\n Parameters\n ----------\n n: int\n Number of news, by default n=100, one page has 25 news, so 4 pages are scraped.\n Returns\n -------\n pandas.DataFrame:\n title, author, posted, article\n \"\"\"\n\n n_of_pages = (math.ceil(n / 25) + 1) if n else 2\n dfs = []\n for page in range(1, n_of_pages):\n url = f\"https://www.coingecko.com/en/news?page={page}\"\n rows = scrape_gecko_data(url).find_all(COLUMNS[\"article\"])\n results = []\n for row in rows:\n header = row.find(\"header\")\n link = header.find(\"a\")[\"href\"]\n text = [t for t in header.text.strip().split(\"\\n\") if t not in [\"\", \" \"]]\n article = row.find(\"div\", class_=\"post-body\").text.strip()\n title, *by_who = text\n author, posted = \" \".join(by_who).split(\"(\")\n posted = posted.strip().replace(\")\", \"\")\n results.append([title, author.strip(), posted, article, link])\n dfs.append(\n pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"title\"],\n COLUMNS[\"author\"],\n COLUMNS[\"posted\"],\n COLUMNS[\"article\"],\n COLUMNS[\"url\"],\n ],\n )\n )\n df = pd.concat(dfs, ignore_index=True).head(n)\n df.drop(\"article\", axis=1, inplace=True)\n df.index = df.index + 1\n df.reset_index(inplace=True)\n return df\n\n\ndef get_top_crypto_categories():\n \"\"\"Scrapes top crypto categories from \"https://www.coingecko.com/en/categories\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, change_1h, change_7d, market_cap, volume_24h, n_of_coins, url\n \"\"\"\n columns = [\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n COLUMNS[\"change_1h\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"change_7d\"],\n COLUMNS[\"market_cap\"],\n COLUMNS[\"volume_24h\"],\n COLUMNS[\"n_of_coins\"],\n COLUMNS[\"url\"],\n ]\n url = \"https://www.coingecko.com/en/categories\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n url = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n (\n rank,\n *names,\n change_1h,\n change_24h,\n change_7d,\n market_cap,\n volume,\n n_of_coins,\n ) = row.text.strip().split()\n results.append(\n [\n rank,\n \" \".join(names),\n change_1h,\n change_24h,\n change_7d,\n market_cap,\n volume,\n n_of_coins,\n url,\n ]\n )\n\n df = pd.DataFrame(results, columns=columns)\n df[\"rank\"] = df[\"rank\"].astype(int)\n return df\n\n\ndef get_recently_added_coins():\n \"\"\"Scrape recently added coins on CoinGecko from \"https://www.coingecko.com/en/coins/recently_added\"\n\n Returns\n -------\n pandas.DataFrame\n name, symbol, price, change_1h, change_24h, last_added\n \"\"\"\n columns = [\n COLUMNS[\"name\"],\n COLUMNS[\"symbol\"],\n COLUMNS[\"price\"],\n COLUMNS[\"change_1h\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"last_added\"],\n COLUMNS[\"url\"],\n ]\n\n url = \"https://www.coingecko.com/en/coins/recently_added\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n\n for row in rows:\n url = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n row_cleaned = clean_row(row)\n (\n name,\n symbol,\n _,\n price,\n *changes,\n _,\n _volume,\n last_added,\n ) = row_cleaned\n change_1h, change_24h, _ = changes_parser(changes)\n results.append([name, symbol, price, change_1h, change_24h, last_added, url])\n df = replace_qm(pd.DataFrame(results, columns=columns))\n df.index = df.index + 1\n df.reset_index(inplace=True)\n df.rename(columns={\"index\": \"rank\"}, inplace=True)\n return df\n\n\ndef get_stable_coins():\n \"\"\"Scrapes stable coins data from \"https://www.coingecko.com/en/stablecoins\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, change_24h, exchanges, market_cap, change_30, url\n \"\"\"\n columns = [\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n COLUMNS[\"symbol\"],\n COLUMNS[\"price\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"exchanges\"],\n COLUMNS[\"market_cap\"],\n COLUMNS[\"change_30d\"],\n COLUMNS[\"url\"],\n ]\n url = \"https://www.coingecko.com/en/stablecoins\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n link = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n row_cleaned = clean_row(row)\n if len(row_cleaned) == 8:\n row_cleaned.append(None)\n\n (\n rank,\n name,\n *symbols,\n price,\n volume_24h,\n exchanges,\n market_cap,\n change_30d,\n ) = row_cleaned\n symbol = symbols[0] if symbols else symbols\n results.append(\n [\n rank,\n name,\n symbol,\n price,\n volume_24h,\n exchanges,\n market_cap,\n change_30d,\n link,\n ]\n )\n df = replace_qm(pd.DataFrame(results, columns=columns))\n df.drop(\"rank\", axis=1, inplace=True)\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_yield_farms():\n \"\"\"Scrapes yield farms data from \"https://www.coingecko.com/en/yield-farming\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, pool, audits, collateral,value_locked, returns_year, returns_hour\n \"\"\"\n columns = [\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n \"pool\",\n \"audits\",\n \"collateral\",\n \"value_locked\",\n \"return_year\",\n ]\n url = \"https://www.coingecko.com/en/yield-farming\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n row_cleaned = clean_row(row)[:-2]\n if \" New\" in row_cleaned: # find better way to fix it in future\n row_cleaned.remove(\" New\")\n\n if len(row_cleaned) == 7:\n row_cleaned.insert(2, None)\n (\n rank,\n name,\n pool,\n *others,\n _,\n value_locked,\n apy1,\n _, # hourly removed for most cases it's 0.00 so it doesn't bring any value for user\n ) = row_cleaned\n auditors, collateral = collateral_auditors_parse(others)\n auditors = \", \".join(aud.strip() for aud in auditors)\n collateral = \", \".join(coll.strip() for coll in collateral)\n results.append(\n [\n rank,\n name,\n pool,\n auditors,\n collateral,\n value_locked,\n apy1,\n ]\n )\n df = pd.DataFrame(results, columns=columns).replace({\"\": None})\n for col in [\"return_year\"]:\n df[col] = df[col].apply(\n lambda x: x.replace(\" Yearly\", \"\") if isinstance(x, str) else x\n )\n df[\"rank\"] = df[\"rank\"].astype(int)\n df = wrap_text_in_df(df, w=30)\n return df\n\n\ndef get_top_volume_coins():\n \"\"\"Scrapes top coins by trading volume \"https://www.coingecko.com/en/coins/high_volume\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, change_1h, change_24h, change_7d, volume_24h, market_cap\n \"\"\"\n columns = [\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n COLUMNS[\"symbol\"],\n COLUMNS[\"price\"],\n COLUMNS[\"change_1h\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"change_7d\"],\n COLUMNS[\"volume_24h\"],\n COLUMNS[\"market_cap\"],\n ]\n url = \"https://www.coingecko.com/en/coins/high_volume\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n row_cleaned = clean_row(row)\n if len(row_cleaned) == 9:\n row_cleaned.insert(0, \"?\")\n row_cleaned.pop(3)\n results.append(row_cleaned)\n df = replace_qm(pd.DataFrame(results, columns=columns))\n df.drop(\"rank\", axis=1, inplace=True)\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_top_defi_coins():\n \"\"\"Scrapes top decentralized finance coins \"https://www.coingecko.com/en/defi\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, change_1h, change_24h, change_7d, volume_24h, market_cap, url\n \"\"\"\n url = \"https://www.coingecko.com/en/defi\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n\n row_cleaned = clean_row(row)\n row_cleaned.pop(2)\n url = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n row_cleaned.append(url)\n if len(row_cleaned) == 11:\n row_cleaned.insert(4, \"?\")\n results.append(row_cleaned)\n\n df = pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n COLUMNS[\"symbol\"],\n COLUMNS[\"price\"],\n COLUMNS[\"change_1h\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"change_7d\"],\n COLUMNS[\"volume_24h\"],\n COLUMNS[\"market_cap\"],\n \"fully_diluted_market_cap\",\n \"market_cap_to_tvl_ratio\",\n COLUMNS[\"url\"],\n ],\n )\n df.drop(\n [\"fully_diluted_market_cap\", \"market_cap_to_tvl_ratio\"],\n axis=1,\n inplace=True,\n )\n df[\"rank\"] = df[\"rank\"].astype(int)\n return df\n\n\ndef get_top_dexes():\n \"\"\"Scrapes top decentralized exchanges from \"https://www.coingecko.com/en/dex\"\n\n Returns\n -------\n pandas.DataFrame\n name, rank, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\n \"\"\"\n columns = [\n COLUMNS[\"name\"],\n COLUMNS[\"rank\"],\n COLUMNS[\"volume_24h\"],\n \"n_coins\",\n \"n_pairs\",\n \"visits\",\n \"most_traded\",\n \"market_share_by_vol\",\n ]\n url = \"https://www.coingecko.com/en/dex\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n row_cleaned = clean_row(row)\n if \" Trading Incentives\" in row_cleaned:\n row_cleaned.remove(\" Trading Incentives\")\n if len(row_cleaned) == 8:\n row_cleaned.insert(-3, \"N/A\")\n results.append(row_cleaned)\n df = pd.DataFrame(results)\n df[COLUMNS[\"name\"]] = df.iloc[:, 1] + \" \" + df.iloc[:, 2].replace(\"N/A\", \"\")\n df.drop(df.columns[1:3], axis=1, inplace=True)\n df = swap_columns(df)\n df.columns = columns\n df[\"most_traded\"] = (\n df[\"most_traded\"]\n .apply(lambda x: x.split(\"$\")[0])\n .str.replace(\",\", \"\", regex=True)\n .str.replace(\".\", \"\", regex=True)\n )\n df[\"most_traded\"] = df[\"most_traded\"].apply(lambda x: None if x.isdigit() else x)\n df[\"rank\"] = df[\"rank\"].astype(int)\n df.set_index(\"rank\", inplace=True)\n return df.reset_index()\n\n\ndef get_top_nfts():\n \"\"\"Scrapes top nfts from \"https://www.coingecko.com/en/nft\"\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, change_1d, change_24h, change_7d, market_cap, url\n \"\"\"\n url = \"https://www.coingecko.com/en/nft\"\n rows = scrape_gecko_data(url).find(\"tbody\").find_all(\"tr\")\n results = []\n for row in rows:\n link = GECKO_BASE_URL + row.find(\"a\")[\"href\"]\n row_cleaned = clean_row(row)\n if len(row_cleaned) == 9:\n row_cleaned.insert(5, \"N/A\")\n row_cleaned.append(link)\n row_cleaned.pop(3)\n results.append(row_cleaned)\n df = pd.DataFrame(\n results,\n columns=[\n COLUMNS[\"rank\"],\n COLUMNS[\"name\"],\n COLUMNS[\"symbol\"],\n COLUMNS[\"price\"],\n COLUMNS[\"change_1h\"],\n COLUMNS[\"change_24h\"],\n COLUMNS[\"change_7d\"],\n COLUMNS[\"volume_24h\"],\n COLUMNS[\"market_cap\"],\n COLUMNS[\"url\"],\n ],\n )\n df[\"rank\"] = df[\"rank\"].astype(int)\n return df\n\n\ndef get_nft_of_the_day():\n \"\"\"Scrapes data about nft of the day.\n\n Returns\n -------\n pandas.DataFrame\n metric, value\n \"\"\"\n url = \"https://www.coingecko.com/en/nft\"\n soup = scrape_gecko_data(url)\n row = soup.find(\"div\", class_=\"tw-px-4 tw-py-5 sm:tw-p-6\")\n try:\n *author, description, _ = clean_row(row)\n if len(author) > 3:\n author, description = author[:3], author[3]\n except (ValueError, IndexError):\n return {}\n df = (\n pd.Series(\n {\n COLUMNS[\"author\"]: \" \".join(author),\n \"desc\": description,\n COLUMNS[\"url\"]: GECKO_BASE_URL + row.find(\"a\")[\"href\"],\n \"img\": row.find(\"img\")[\"src\"],\n }\n )\n .to_frame()\n .reset_index()\n )\n df.columns = [\"Metric\", \"Value\"]\n df = wrap_text_in_df(df, w=100)\n return df\n\n\ndef get_nft_market_status():\n \"\"\"Scrapes overview data of nft markets from \"https://www.coingecko.com/en/nft\"\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n url = \"https://www.coingecko.com/en/nft\"\n rows = scrape_gecko_data(url).find_all(\n \"span\", class_=\"overview-box d-inline-block p-3 mr-2\"\n )\n kpis = {}\n for row in rows:\n value, *kpi = clean_row(row)\n name = \" \".join(kpi)\n kpis[name] = value\n df = pd.Series(kpis).to_frame().reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef get_exchanges():\n \"\"\"Get list of top exchanges from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n trust_score, id, name, country, year_established, trade_volume_24h_btc, url\n \"\"\"\n df = pd.DataFrame(client.get_exchanges_list(per_page=250))\n df.replace({float(np.NaN): None}, inplace=True)\n df = df[\n [\n \"trust_score\",\n COLUMNS[\"id\"],\n COLUMNS[\"name\"],\n COLUMNS[\"country\"],\n \"year_established\",\n \"trade_volume_24h_btc\",\n COLUMNS[\"url\"],\n ]\n ]\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_financial_platforms():\n \"\"\"Get list of financial platforms from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n rank, name, category, centralized, website_url\n \"\"\"\n df = pd.DataFrame(client.get_finance_platforms())\n df.drop(\"facts\", axis=1, inplace=True)\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_finance_products():\n \"\"\"Get list of financial products from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage\n \"\"\"\n df = pd.DataFrame(\n client.get_finance_products(per_page=250),\n columns=[\n \"platform\",\n \"identifier\",\n \"supply_rate_percentage\",\n \"borrow_rate_percentage\",\n ],\n )\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_indexes():\n \"\"\"Get list of crypto indexes from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n name, id, market, last, is_multi_asset_composite\n \"\"\"\n df = pd.DataFrame(client.get_indexes(per_page=250))\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_derivatives():\n \"\"\"Get list of crypto derivatives from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h\n \"\"\"\n df = pd.DataFrame(client.get_derivatives(include_tickers=\"unexpired\"))\n df.drop(\n [\"index\", \"last_traded_at\", \"expired_at\", \"index_id\", \"open_interest\"],\n axis=1,\n inplace=True,\n )\n\n df.rename(columns={\"price_percentage_change_24h\": \"pct_change_24h\"}, inplace=True)\n create_df_index(df, \"rank\")\n return df\n\n\ndef get_exchange_rates():\n \"\"\"Get list of crypto, fiats, commodity exchange rates from CoinGecko API\n\n Returns\n -------\n pandas.DataFrame\n index, name, unit, value, type\n \"\"\"\n df = pd.DataFrame(client.get_exchange_rates()[\"rates\"]).T.reset_index()\n df.drop(\"index\", axis=1, inplace=True)\n create_df_index(df, \"index\")\n return df\n\n\ndef get_global_info():\n \"\"\"Get global statistics about crypto from CoinGecko API like:\n - market cap change\n - number of markets\n - icos\n - number of active crypto\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n results = client.get_global()\n\n total_mcap = results.pop(\"market_cap_percentage\")\n eth, btc = total_mcap.get(\"btc\"), total_mcap.get(\"eth\")\n for key in [COLUMNS[\"total_market_cap\"], COLUMNS[\"total_volume\"], \"updated_at\"]:\n del results[key]\n results[\"eth_market_cap_in_pct\"] = eth\n results[\"btc_market_cap_in_pct\"] = btc\n results[\"altcoin_market_cap_in_pct\"] = 100 - (float(eth) + float(btc))\n df = pd.Series(results).reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef get_global_markets_info():\n \"\"\"Get global statistics about crypto markets from CoinGecko API like:\n - total_market_cap\n - total_volume\n - market_cap_percentage\n\n Returns\n -------\n pandas.DataFrame\n total_market_cap, total_volume, market_cap_percentage\n \"\"\"\n columns = [\n COLUMNS[\"total_market_cap\"],\n COLUMNS[\"total_volume\"],\n COLUMNS[\"market_cap_percentage\"],\n ]\n data = []\n results = client.get_global()\n for key in columns:\n data.append(results.get(key))\n df = pd.DataFrame(data).T\n df.columns = columns\n df.replace({float(\"nan\"): None}, inplace=True)\n return df.reset_index()\n\n\ndef get_global_defi_info():\n \"\"\"Get global statistics about Decentralized Finances from CoinGecko API like:\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n results = client.get_global_decentralized_finance_defi()\n for key, value in results.items():\n try:\n results[key] = round(float(value), 4)\n except (ValueError, TypeError):\n pass\n\n df = pd.Series(results).reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef get_coin_list():\n \"\"\"Get list of coins available on CoinGecko\n\n Returns\n -------\n pandas.DataFrame\n id, symbol, name\n \"\"\"\n\n return pd.DataFrame(\n client.get_coins_list(),\n columns=[COLUMNS[\"id\"], COLUMNS[\"symbol\"], COLUMNS[\"name\"]],\n ).reset_index()\n"},{"alpha_fraction":0.7459016442298889,"alphanum_fraction":0.7704917788505554,"avg_line_length":23.399999618530273,"blob_id":"2d51823dbc6a59d7823f49c8c099034e34b8a4d2","content_id":"b9c6074028a8b0246effa54f3045fc16012290a9","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":122,"license_type":"permissive","max_line_length":57,"num_lines":5,"path":"/docker/gst-deps.dockerfile","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"FROM gst/gst-python:1.0.0\n\nCOPY --chown=python:python requirements.txt /home/python/\n\nRUN pip install -r requirements.txt\n"},{"alpha_fraction":0.5154705047607422,"alphanum_fraction":0.5336523056030273,"avg_line_length":30.989795684814453,"blob_id":"cc39ce87547640763bc2d6ce0372eef94ea99c25","content_id":"ff44f326049022f7df05df459afd66e085089eb5","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9405,"license_type":"permissive","max_line_length":100,"num_lines":294,"path":"/gamestonk_terminal/common/technical_analysis/volume.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Volume Technical Analysis\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom datetime import timedelta\n\nimport matplotlib.pyplot as plt\nimport pandas_ta as ta\nimport pandas as pd\n\nfrom pandas.plotting import register_matplotlib_converters\nfrom gamestonk_terminal.helper_funcs import (\n check_positive,\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import feature_flags as gtff\n\nregister_matplotlib_converters()\n\n\ndef ad(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Accumulation Dictribution Line\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock data interval\n df_stock: pd.DataFrame\n Dataframe of stock prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ad\",\n description=\"\"\"\n The Accumulation/Distribution Line is similar to the On Balance\n Volume (OBV), which sums the volume times +1/-1 based on whether the close is\n higher than the previous close. The Accumulation/Distribution indicator, however\n multiplies the volume by the close location value (CLV). The CLV is based on the\n movement of the issue within a single bar and can be +1, -1 or zero. \\n \\n\n The Accumulation/Distribution Line is interpreted by looking for a divergence in\n the direction of the indicator relative to price. If the Accumulation/Distribution\n Line is trending upward it indicates that the price may follow. Also, if the\n Accumulation/Distribution Line becomes flat while the price is still rising (or falling)\n then it signals an impending flattening of the price.\n \"\"\",\n )\n\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n parser.add_argument(\n \"--open\",\n action=\"store_true\",\n default=False,\n dest=\"b_use_open\",\n help=\"uses open value of stock\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n bar_colors = [\n \"r\" if x[1].Open < x[1].Close else \"g\" for x in df_stock.iterrows()\n ]\n\n if s_interval == \"1440min\":\n bar_width = timedelta(days=1)\n else:\n bar_width = timedelta(minutes=int(s_interval.split(\"m\")[0]))\n\n if ns_parser.b_use_open:\n df_ta = ta.ad(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Adj Close\"],\n volume=df_stock[\"Volume\"],\n offset=ns_parser.n_offset,\n open_=df_stock[\"Open\"],\n ).dropna()\n # Do not use open stock values\n else:\n # Note this should always be Close not Adj Close\n df_ta = ta.ad(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Close\"],\n volume=df_stock[\"Volume\"],\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(\n 3,\n 1,\n gridspec_kw={\"height_ratios\": [2, 1, 1]},\n figsize=plot_autoscale(),\n dpi=PLOT_DPI,\n )\n ax = axes[0]\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n ax.set_title(f\"{s_ticker} AD\")\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n ax2 = axes[1]\n ax2.set_ylabel(\"Volume\")\n if s_interval == \"1440min\":\n ax2.bar(\n df_stock.index,\n df_stock[\"Volume\"].values,\n color=bar_colors,\n alpha=0.8,\n width=0.3,\n )\n else:\n ax2.bar(\n df_stock.index,\n df_stock[\"Volume\"].values,\n color=bar_colors,\n alpha=0.8,\n width=bar_width,\n )\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n\n ax3 = axes[2]\n ax3.plot(df_ta.index, df_ta.values, \"b\", lw=1)\n ax3.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax3.axhline(0, linewidth=2, color=\"k\", ls=\"--\")\n ax3.set_ylabel(\"A/D\")\n ax3.set_xlabel(\"Time\")\n ax3.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax3.minorticks_on()\n ax3.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef obv(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"On Balance Volume\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock data interval\n df_stock: pd.DataFrame\n Dataframe of stock prices\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"obv\",\n description=\"\"\"\n The On Balance Volume (OBV) is a cumulative total of the up and\n down volume. When the close is higher than the previous close, the volume is added\n to the running total, and when the close is lower than the previous close, the volume\n is subtracted from the running total. \\n \\n To interpret the OBV, look for the OBV\n to move with the price or precede price moves. If the price moves before the OBV,\n then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the\n OBV indicates a strong trend. If the OBV is flat, then the market is not trending.\n \"\"\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n bar_colors = [\n \"r\" if x[1].Open < x[1].Close else \"g\" for x in df_stock.iterrows()\n ]\n\n if s_interval == \"1440min\":\n bar_width = timedelta(days=1)\n else:\n bar_width = timedelta(minutes=int(s_interval.split(\"m\")[0]))\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.obv(\n close=df_stock[\"Adj Close\"],\n volume=df_stock[\"Volume\"],\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.obv(\n close=df_stock[\"Close\"],\n volume=df_stock[\"Volume\"],\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(\n 3,\n 1,\n gridspec_kw={\"height_ratios\": [2, 1, 1]},\n figsize=plot_autoscale(),\n dpi=PLOT_DPI,\n )\n ax = axes[0]\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n\n ax.set_title(f\"{s_ticker} OBV\")\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2 = axes[1]\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n\n if s_interval == \"1440min\":\n ax2.bar(\n df_stock.index,\n df_stock[\"Volume\"].values,\n color=bar_colors,\n alpha=0.8,\n width=bar_width,\n )\n else:\n ax2.bar(\n df_stock.index,\n df_stock[\"Volume\"].values,\n color=bar_colors,\n alpha=0.8,\n width=bar_width,\n )\n ax3 = axes[2]\n ax3.plot(df_ta.index, df_ta.values, \"b\", lw=1)\n ax3.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax3.set_xlabel(\"Time\")\n ax3.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax3.minorticks_on()\n ax3.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5352464318275452,"alphanum_fraction":0.5370139479637146,"avg_line_length":22.34465980529785,"blob_id":"71258097abcf1448c8e627da8acc5459faf57677","content_id":"f701fae7234155f95bb3a662b5f3cf38b6fbec3f","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9618,"license_type":"permissive","max_line_length":104,"num_lines":412,"path":"/gamestonk_terminal/options/op_helpers.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Option helper functions\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\n\nimport pandas as pd\nimport numpy as np\n\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n check_non_negative,\n)\n\n# pylint: disable=R1710\n\n\ndef load(other_args: List[str]) -> str:\n \"\"\"Load ticker into object\n\n Parameters\n ----------\n other_args: List[str]\n Agrparse arguments\n\n Returns\n -------\n str:\n Ticker\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"opload\",\n description=\"Load a ticker into option menu\",\n )\n\n parser.add_argument(\n \"-t\",\n \"--ticker\",\n action=\"store\",\n dest=\"ticker\",\n required=\"-h\" not in other_args,\n help=\"Stock ticker\",\n )\n\n try:\n if other_args:\n if \"-t\" not in other_args and \"-h\" not in other_args:\n other_args.insert(0, \"-t\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return \"\"\n print(\"\")\n return ns_parser.ticker\n except Exception as e:\n print(e, \"\\n\")\n return \"\"\n except SystemExit:\n print(\"\")\n return \"\"\n\n\n# pylint: disable=no-else-return\n\n\ndef select_option_date(avalaiable_dates: List[str], other_args: List[str]) -> str:\n \"\"\"Select an option date out of a supplied list\n\n Parameters\n ----------\n avalaiable_dates: List[str]\n Possible date options\n other_args: List[str]\n Arparse arguments\n Returns\n -------\n expiry_date: str\n Selected expiry date\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"exp\",\n description=\"See and set expiration date\",\n )\n parser.add_argument(\n \"-d\",\n \"--date\",\n dest=\"n_date\",\n action=\"store\",\n type=int,\n default=-1,\n choices=range(len(avalaiable_dates)),\n help=\"Select index for expiry date.\",\n )\n\n parser.add_argument(\n \"-D\",\n dest=\"date\",\n type=str,\n choices=avalaiable_dates + [\"\"],\n help=\"Select date (YYYY-MM-DD)\",\n default=\"\",\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-d\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return \"\"\n\n # Print possible expiry dates\n if ns_parser.n_date == -1 and not ns_parser.date:\n print(\"\\nAvailable expiry dates:\")\n for i, d in enumerate(avalaiable_dates):\n print(f\" {(2 - len(str(i))) * ' '}{i}. {d}\")\n print(\"\")\n return \"\"\n\n # It means an expiry date was correctly selected\n else:\n if ns_parser.date:\n if ns_parser.date in avalaiable_dates:\n print(f\"Expiraration set to {ns_parser.date} \\n\")\n return ns_parser.date\n else:\n print(\"Expiration not an option\")\n return \"\"\n else:\n expiry_date = avalaiable_dates[ns_parser.n_date]\n print(f\"Expiraration set to {expiry_date} \\n\")\n return expiry_date\n\n except Exception as e:\n print(e, \"\\n\")\n return \"\"\n\n\ndef get_loss_at_strike(strike: float, chain: pd.DataFrame) -> float:\n \"\"\"Function to get the loss at the given expiry\n\n Parameters\n ----------\n strike: Union[int,float]\n Value to calculate total loss at\n chain: Dataframe:\n Dataframe containing at least strike and openInterest\n\n Returns\n -------\n loss: Union[float,int]\n Total loss\n \"\"\"\n\n itm_calls = chain[chain.index < strike][[\"OI_call\"]]\n itm_calls[\"loss\"] = (strike - itm_calls.index) * itm_calls[\"OI_call\"]\n call_loss = itm_calls[\"loss\"].sum()\n\n itm_puts = chain[chain.index > strike][[\"OI_put\"]]\n itm_puts[\"loss\"] = (itm_puts.index - strike) * itm_puts[\"OI_put\"]\n put_loss = itm_puts.loss.sum()\n loss = call_loss + put_loss\n\n return loss\n\n\ndef calculate_max_pain(chain: pd.DataFrame) -> int:\n \"\"\"Returns the max pain for a given call/put dataframe\n\n Parameters\n ----------\n chain: DataFrame\n Dataframe to calculate value from\n\n Returns\n -------\n max_pain : int\n Max pain value\n \"\"\"\n\n strikes = np.array(chain.index)\n if (\"OI_call\" not in chain.columns) or (\"OI_put\" not in chain.columns):\n print(\"Incorrect columns. Unable to parse max pain\")\n return np.nan\n\n loss = []\n for price_at_exp in strikes:\n loss.append(get_loss_at_strike(price_at_exp, chain))\n\n chain[\"loss\"] = loss\n max_pain = chain[\"loss\"].idxmin()\n\n return max_pain\n\n\ndef vol(other_args: List[str]):\n \"\"\"Parse volume argparse\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n Returns\n -------\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"vol\",\n description=\"Plot volume. Volume refers to the number of contracts traded today.\",\n )\n\n parser.add_argument(\n \"-m\",\n \"--min\",\n default=-1,\n type=check_non_negative,\n help=\"Min strike to plot\",\n dest=\"min\",\n )\n parser.add_argument(\n \"-M\",\n \"--max\",\n default=-1,\n type=check_non_negative,\n help=\"Max strike to plot\",\n dest=\"max\",\n )\n\n parser.add_argument(\n \"--calls\",\n action=\"store_true\",\n default=False,\n dest=\"calls\",\n help=\"Flag to plot call options only\",\n )\n\n parser.add_argument(\n \"--puts\",\n action=\"store_true\",\n default=False,\n dest=\"puts\",\n help=\"Flag to plot put options only\",\n )\n\n parser.add_argument(\n \"--source\",\n type=str,\n default=\"tr\",\n choices=[\"tr\", \"yf\"],\n dest=\"source\",\n help=\"Source to get data from\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n return ns_parser\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef voi(other_args: List[str]):\n \"\"\"Parse Volume + open interest argparse\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n Returns\n -------\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"voi\",\n description=\"\"\"\n Plots Volume + Open Interest of calls vs puts.\n \"\"\",\n )\n parser.add_argument(\n \"-v\",\n \"--minv\",\n dest=\"min_vol\",\n type=check_non_negative,\n default=-1,\n help=\"minimum volume (considering open interest) threshold of the plot.\",\n )\n parser.add_argument(\n \"-m\",\n \"--min\",\n dest=\"min_sp\",\n type=check_non_negative,\n default=-1,\n help=\"minimum strike price to consider in the plot.\",\n )\n parser.add_argument(\n \"-M\",\n \"--max\",\n dest=\"max_sp\",\n type=check_non_negative,\n default=-1,\n help=\"maximum strike price to consider in the plot.\",\n )\n parser.add_argument(\n \"--source\",\n type=str,\n default=\"tr\",\n choices=[\"tr\", \"yf\"],\n dest=\"source\",\n help=\"Source to get data from\",\n )\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return None\n return ns_parser\n\n except Exception as e:\n print(e, \"\\n\")\n return None\n\n\ndef oi(other_args: List[str]):\n \"\"\"Parse Open Interest argparse\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n\n Returns\n -------\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"oi\",\n description=\"Plot open interest. Open interest represents the number of contracts that exist.\",\n )\n\n parser.add_argument(\n \"-m\",\n \"--min\",\n default=-1,\n type=check_non_negative,\n help=\"Min strike to plot\",\n dest=\"min\",\n )\n parser.add_argument(\n \"-M\",\n \"--max\",\n default=-1,\n type=check_non_negative,\n help=\"Max strike to plot\",\n dest=\"max\",\n )\n\n parser.add_argument(\n \"--calls\",\n action=\"store_true\",\n default=False,\n dest=\"calls\",\n help=\"Flag to plot call options only\",\n )\n\n parser.add_argument(\n \"--puts\",\n action=\"store_true\",\n default=False,\n dest=\"puts\",\n help=\"Flag to plot put options only\",\n )\n parser.add_argument(\n \"--source\",\n type=str,\n default=\"tr\",\n choices=[\"tr\", \"yf\"],\n dest=\"source\",\n help=\"Source to get data from\",\n )\n\n try:\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return None\n\n return ns_parser\n\n except Exception as e:\n print(e, \"\\n\")\n return None\n"},{"alpha_fraction":0.5389886498451233,"alphanum_fraction":0.5440404415130615,"avg_line_length":27.35886573791504,"blob_id":"cda83353de8dc95c1f3dc52ada708ea40a4ea410","content_id":"4043b2bd43f1b75bdcf0ff0f39e99e9991e627ab","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":19993,"license_type":"permissive","max_line_length":120,"num_lines":705,"path":"/gamestonk_terminal/options/tradier_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Tradier options view\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nfrom bisect import bisect_left\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport mplfinance as mpf\nfrom colorama import Fore, Style\nfrom tabulate import tabulate\n\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n check_non_negative,\n export_data,\n plot_autoscale,\n patch_pandas_text_adjustment,\n)\nfrom gamestonk_terminal.options import op_helpers, tradier_model\nfrom gamestonk_terminal import config_plot as cfp\nfrom gamestonk_terminal import feature_flags as gtff\n\ncolumn_map = {\"mid_iv\": \"iv\", \"open_interest\": \"oi\", \"volume\": \"vol\"}\n\n\ndef red_highlight(val):\n \"\"\"Red highlight\n\n Parameters\n ----------\n val\n dataframe values to color\n\n Returns\n ----------\n str\n colored dataframes values\n \"\"\"\n return f\"{Fore.RED}{val}{Style.RESET_ALL}\"\n\n\ndef green_highlight(val):\n \"\"\"Green highlight\n\n Parameters\n ----------\n values : List[str]\n dataframe values to color\n\n Returns\n ----------\n List[str]\n colored dataframes values\n \"\"\"\n return f\"{Fore.GREEN}{val}{Style.RESET_ALL}\"\n\n\ndef check_valid_option_chains_headers(headers: str) -> List[str]:\n \"\"\"Check valid option chains headers\n\n Parameters\n ----------\n headers : str\n Option chains headers\n\n Returns\n ----------\n List[str]\n List of columns string\n \"\"\"\n columns = [str(item) for item in headers.split(\",\")]\n\n for header in columns:\n if header not in tradier_model.df_columns:\n raise argparse.ArgumentTypeError(\"Invalid option chains header selected!\")\n\n return columns\n\n\ndef display_chains(ticker: str, expiry: str, other_args: List[str]):\n \"\"\"Display option chain\n\n Parameters\n ----------\n ticker: str\n Stock ticker\n expiry: str\n Expiration date of option\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n prog=\"chains\",\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"Display option chains\",\n )\n parser.add_argument(\n \"--calls\",\n action=\"store_true\",\n default=False,\n dest=\"calls_only\",\n help=\"Flag to show calls only\",\n )\n parser.add_argument(\n \"--puts\",\n action=\"store_true\",\n default=False,\n dest=\"puts_only\",\n help=\"Flag to show puts only\",\n )\n parser.add_argument(\n \"-m\",\n \"--min\",\n dest=\"min_sp\",\n type=check_non_negative,\n default=-1,\n help=\"minimum strike price to consider.\",\n )\n parser.add_argument(\n \"-M\",\n \"--max\",\n dest=\"max_sp\",\n type=check_non_negative,\n default=-1,\n help=\"maximum strike price to consider.\",\n )\n parser.add_argument(\n \"-d\",\n \"--display\",\n dest=\"to_display\",\n default=tradier_model.default_columns,\n type=check_valid_option_chains_headers,\n help=\"columns to look at. Columns can be: {bid, ask, strike, bidsize, asksize, volume, open_interest, delta, \"\n \"gamma, theta, vega, ask_iv, bid_iv, mid_iv} \",\n )\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n chains_df = tradier_model.get_option_chains(ticker, expiry)\n columns = ns_parser.to_display + [\"strike\", \"option_type\"]\n chains_df = chains_df[columns].rename(columns=column_map)\n\n if ns_parser.min_sp == -1:\n min_strike = np.percentile(chains_df[\"strike\"], 25)\n else:\n min_strike = ns_parser.min_sp\n\n if ns_parser.max_sp == -1:\n max_strike = np.percentile(chains_df[\"strike\"], 75)\n else:\n max_strike = ns_parser.max_sp\n\n print(f\"The strike prices are displayed between {min_strike} and {max_strike}\")\n\n chains_df = chains_df[chains_df[\"strike\"] >= min_strike]\n chains_df = chains_df[chains_df[\"strike\"] <= max_strike]\n\n if ns_parser.export:\n # Note the extra dirname needed due to the subfolder in options\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.dirname(os.path.abspath(__file__))),\n f\"chains_{ticker}_{expiry}\",\n chains_df,\n )\n\n calls_df = chains_df[chains_df.option_type == \"call\"].drop(\n columns=[\"option_type\"]\n )\n puts_df = chains_df[chains_df.option_type == \"put\"].drop(\n columns=[\"option_type\"]\n )\n\n if ns_parser.calls_only:\n print(\n tabulate(\n calls_df,\n headers=calls_df.columns,\n tablefmt=\"grid\",\n showindex=False,\n floatfmt=\".2f\",\n )\n )\n\n elif ns_parser.puts_only:\n print(\n tabulate(\n puts_df,\n headers=puts_df.columns,\n tablefmt=\"grid\",\n showindex=False,\n floatfmt=\".2f\",\n )\n )\n\n else:\n puts_df = puts_df[puts_df.columns[::-1]]\n chain_table = calls_df.merge(puts_df, on=\"strike\")\n\n if gtff.USE_COLOR:\n call_cols = [col for col in chain_table if col.endswith(\"_x\")]\n put_cols = [col for col in chain_table if col.endswith(\"_y\")]\n patch_pandas_text_adjustment()\n pd.set_option(\"display.max_colwidth\", 0)\n pd.set_option(\"display.max_rows\", None)\n for cc in call_cols:\n chain_table[cc] = chain_table[cc].astype(str).apply(green_highlight)\n for pc in put_cols:\n chain_table[pc] = chain_table[pc].astype(str).apply(red_highlight)\n headers = [\n col.strip(\"_x\")\n if col.endswith(\"_x\")\n else col.strip(\"_y\")\n if col.endswith(\"_y\")\n else col\n for col in chain_table.columns\n ]\n print(\n tabulate(\n chain_table,\n headers=headers,\n tablefmt=\"fancy_grid\",\n showindex=False,\n floatfmt=\".2f\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef plot_oi(\n options: pd.DataFrame,\n ticker: str,\n expiry: str,\n ns_parser: argparse.Namespace,\n):\n \"\"\"Plot open interest\n\n Parameters\n ----------\n options: pd.DataFrame\n Options dataframe with both calls and puts\n ticker: str\n Ticker\n expiry: str\n Expiry date for options\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n\n current_price = tradier_model.last_price(ticker)\n\n if ns_parser.min == -1:\n min_strike = 0.75 * current_price\n else:\n min_strike = ns_parser.min\n\n if ns_parser.max == -1:\n max_strike = 1.25 * current_price\n else:\n max_strike = ns_parser.max\n\n if ns_parser.calls and ns_parser.puts:\n print(\"Both flags selected, please select one\", \"\\n\")\n return\n\n calls = options[options.option_type == \"call\"][[\"strike\", \"open_interest\"]]\n puts = options[options.option_type == \"put\"][[\"strike\", \"open_interest\"]]\n call_oi = calls.set_index(\"strike\")[\"open_interest\"] / 1000\n put_oi = puts.set_index(\"strike\")[\"open_interest\"] / 1000\n\n df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)\n df_opt = df_opt.rename(\n columns={\"open_interest_x\": \"OI_call\", \"open_interest_y\": \"OI_put\"}\n )\n\n max_pain = op_helpers.calculate_max_pain(df_opt)\n plt.style.use(\"classic\")\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\n\n if not ns_parser.calls:\n put_oi.plot(\n x=\"strike\",\n y=\"open_interest\",\n label=\"Puts\",\n ax=ax,\n marker=\"o\",\n ls=\"-\",\n c=\"r\",\n )\n if not ns_parser.puts:\n call_oi.plot(\n x=\"strike\",\n y=\"open_interest\",\n label=\"Calls\",\n ax=ax,\n marker=\"o\",\n ls=\"-\",\n c=\"g\",\n )\n ax.axvline(\n current_price, lw=2, c=\"k\", ls=\"--\", label=\"Current Price\", alpha=0.7\n )\n ax.axvline(max_pain, lw=3, c=\"k\", label=f\"Max Pain: {max_pain}\", alpha=0.7)\n ax.grid(\"on\")\n ax.set_xlabel(\"Strike Price\")\n ax.set_ylabel(\"Open Interest (1k) \")\n ax.set_xlim(min_strike, max_strike)\n\n if gtff.USE_ION:\n plt.ion()\n\n ax.set_title(f\"Open Interest for {ticker.upper()} expiring {expiry}\")\n plt.legend(loc=0)\n fig.tight_layout(pad=1)\n\n plt.show()\n plt.style.use(\"default\")\n print(\"\")\n\n\ndef plot_vol(\n options: pd.DataFrame,\n ticker: str,\n expiry: str,\n ns_parser: argparse.Namespace,\n):\n \"\"\"Plot volume\n\n Parameters\n ----------\n options: pd.DataFrame\n Options dataframe with both calls and puts\n ticker: str\n Ticker\n expiry: str\n Expiry date for options\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n\n current_price = tradier_model.last_price(ticker)\n\n if ns_parser.min == -1:\n min_strike = 0.75 * current_price\n else:\n min_strike = ns_parser.min\n\n if ns_parser.max == -1:\n max_strike = 1.25 * current_price\n else:\n max_strike = ns_parser.max\n\n if ns_parser.calls and ns_parser.puts:\n print(\"Both flags selected, please select one\", \"\\n\")\n return\n\n calls = options[options.option_type == \"call\"][[\"strike\", \"volume\"]]\n puts = options[options.option_type == \"put\"][[\"strike\", \"volume\"]]\n call_v = calls.set_index(\"strike\")[\"volume\"] / 1000\n put_v = puts.set_index(\"strike\")[\"volume\"] / 1000\n plt.style.use(\"classic\")\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\n\n if not ns_parser.calls:\n put_v.plot(\n x=\"strike\",\n y=\"volume\",\n label=\"Puts\",\n ax=ax,\n marker=\"o\",\n ls=\"-\",\n c=\"r\",\n )\n if not ns_parser.puts:\n call_v.plot(\n x=\"strike\",\n y=\"volume\",\n label=\"Calls\",\n ax=ax,\n marker=\"o\",\n ls=\"-\",\n c=\"g\",\n )\n ax.axvline(current_price, lw=2, c=\"k\", ls=\"--\", label=\"Current Price\", alpha=0.7)\n ax.grid(\"on\")\n ax.set_xlabel(\"Strike Price\")\n ax.set_ylabel(\"Volume (1k) \")\n ax.set_xlim(min_strike, max_strike)\n\n if gtff.USE_ION:\n plt.ion()\n\n ax.set_title(f\"Volume for {ticker.upper()} expiring {expiry}\")\n plt.legend(loc=0)\n fig.tight_layout(pad=1)\n\n plt.show()\n plt.style.use(\"default\")\n print(\"\")\n\n\ndef plot_volume_open_interest(\n ticker: str, exp_date: str, options: pd.DataFrame, ns_parser: argparse.Namespace\n):\n \"\"\"Plot volume and open interest\n\n Parameters\n ----------\n ticker : str\n Main ticker to compare income\n exp_date : str\n Expiry date of the option\n Options: pd.DataFrame\n Options dataframe with both calls and puts\n ns_parser: argparse.Namespace\n Parsed namespace\n \"\"\"\n\n current_price = tradier_model.last_price(ticker)\n calls = options[options.option_type == \"call\"][\n [\"strike\", \"volume\", \"open_interest\"]\n ]\n puts = options[options.option_type == \"put\"][[\"strike\", \"volume\", \"open_interest\"]]\n\n # Process Calls Data\n df_calls = calls.pivot_table(\n index=\"strike\", values=[\"volume\", \"open_interest\"], aggfunc=\"sum\"\n ).reindex()\n df_calls[\"strike\"] = df_calls.index\n df_calls[\"type\"] = \"calls\"\n df_calls[\"open_interest\"] = df_calls[\"open_interest\"]\n df_calls[\"volume\"] = df_calls[\"volume\"]\n df_calls[\"oi+v\"] = df_calls[\"open_interest\"] + df_calls[\"volume\"]\n df_calls[\"spot\"] = round(current_price, 2)\n\n df_puts = puts.pivot_table(\n index=\"strike\", values=[\"volume\", \"open_interest\"], aggfunc=\"sum\"\n ).reindex()\n df_puts[\"strike\"] = df_puts.index\n df_puts[\"type\"] = \"puts\"\n df_puts[\"open_interest\"] = df_puts[\"open_interest\"]\n df_puts[\"volume\"] = -df_puts[\"volume\"]\n df_puts[\"open_interest\"] = -df_puts[\"open_interest\"]\n df_puts[\"oi+v\"] = df_puts[\"open_interest\"] + df_puts[\"volume\"]\n df_puts[\"spot\"] = round(current_price, 2)\n\n call_oi = calls.set_index(\"strike\")[\"open_interest\"] / 1000\n put_oi = puts.set_index(\"strike\")[\"open_interest\"] / 1000\n\n df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)\n df_opt = df_opt.rename(\n columns={\"open_interest_x\": \"OI_call\", \"open_interest_y\": \"OI_put\"}\n )\n\n max_pain = op_helpers.calculate_max_pain(df_opt)\n\n if ns_parser.min_vol == -1 and ns_parser.min_sp == -1 and ns_parser.max_sp == -1:\n # If no argument provided, we use the percentile 50 to get 50% of upper volume data\n volume_percentile_threshold = 50\n min_vol_calls = np.percentile(df_calls[\"oi+v\"], volume_percentile_threshold)\n min_vol_puts = np.percentile(df_puts[\"oi+v\"], volume_percentile_threshold)\n\n df_calls = df_calls[df_calls[\"oi+v\"] > min_vol_calls]\n df_puts = df_puts[df_puts[\"oi+v\"] < min_vol_puts]\n\n else:\n if ns_parser.min_vol > -1:\n df_calls = df_calls[df_calls[\"oi+v\"] > ns_parser.min_vol]\n df_puts = df_puts[df_puts[\"oi+v\"] < -ns_parser.min_vol]\n\n if ns_parser.min_sp > -1:\n df_calls = df_calls[df_calls[\"strike\"] > ns_parser.min_sp]\n df_puts = df_puts[df_puts[\"strike\"] > ns_parser.min_sp]\n\n if ns_parser.max_sp > -1:\n df_calls = df_calls[df_calls[\"strike\"] < ns_parser.max_sp]\n df_puts = df_puts[df_puts[\"strike\"] < ns_parser.max_sp]\n\n if df_calls.empty and df_puts.empty:\n print(\n \"The filtering applied is too strong, there is no data available for such conditions.\\n\"\n )\n return\n\n # Initialize the matplotlib figure\n _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\n\n # make x axis symmetric\n axis_origin = max(abs(max(df_puts[\"oi+v\"])), abs(max(df_calls[\"oi+v\"])))\n ax.set_xlim(-axis_origin, +axis_origin)\n\n sns.set_style(style=\"darkgrid\")\n\n g = sns.barplot(\n x=\"oi+v\",\n y=\"strike\",\n data=df_calls,\n label=\"Calls: Open Interest\",\n color=\"lightgreen\",\n orient=\"h\",\n )\n\n g = sns.barplot(\n x=\"volume\",\n y=\"strike\",\n data=df_calls,\n label=\"Calls: Volume\",\n color=\"green\",\n orient=\"h\",\n )\n\n g = sns.barplot(\n x=\"oi+v\",\n y=\"strike\",\n data=df_puts,\n label=\"Puts: Open Interest\",\n color=\"pink\",\n orient=\"h\",\n )\n\n g = sns.barplot(\n x=\"volume\",\n y=\"strike\",\n data=df_puts,\n label=\"Puts: Volume\",\n color=\"red\",\n orient=\"h\",\n )\n\n # draw spot line\n s = [float(strike.get_text()) for strike in ax.get_yticklabels()]\n spot_index = bisect_left(s, current_price) # find where the spot is on the graph\n spot_line = ax.axhline(spot_index, ls=\"--\", color=\"dodgerblue\", alpha=0.3)\n\n # draw max pain line\n max_pain_index = bisect_left(s, max_pain)\n max_pain_line = ax.axhline(max_pain_index, ls=\"-\", color=\"black\", alpha=0.3)\n max_pain_line.set_linewidth(3)\n\n # format ticklabels without - for puts\n g.set_xticks(g.get_xticks())\n xlabels = [f\"{x:,.0f}\".replace(\"-\", \"\") for x in g.get_xticks()]\n g.set_xticklabels(xlabels)\n\n plt.title(\n f\"{ticker} volumes for {exp_date} (open interest displayed only during market hours)\"\n )\n ax.invert_yaxis()\n\n _ = ax.legend()\n handles, _ = ax.get_legend_handles_labels()\n handles.append(spot_line)\n handles.append(max_pain_line)\n\n # create legend labels + add to graph\n labels = [\n \"Calls open interest\",\n \"Calls volume \",\n \"Puts open interest\",\n \"Puts volume\",\n \"Current stock price\",\n f\"Max pain = {max_pain}\",\n ]\n\n plt.legend(handles=handles[:], labels=labels)\n sns.despine(left=True, bottom=True)\n\n if gtff.USE_ION:\n plt.ion()\n plt.show()\n plt.style.use(\"default\")\n print(\"\")\n\n\ndef display_historical(ticker: str, expiry: str, other_args: List[str]):\n \"\"\"Plot historical option data\n\n Parameters\n ----------\n ticker: str\n Ticker\n expiry: str\n Expiration of option\n other_args: List[str]\n Argparse arguments\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"hist\",\n description=\"Gets historical quotes for given option chain\",\n )\n parser.add_argument(\n \"-s\",\n \"--strike\",\n dest=\"strike\",\n type=float,\n required=\"--chain\" not in other_args or \"-h\" not in other_args,\n help=\"Strike price to look at\",\n )\n parser.add_argument(\n \"--put\",\n dest=\"put\",\n action=\"store_true\",\n default=False,\n help=\"Flag for showing put option\",\n )\n parser.add_argument(\"--chain\", dest=\"chain_id\", type=str, help=\"OCC option symbol\")\n\n parser.add_argument(\n \"--raw\", dest=\"raw\", action=\"store_true\", default=False, help=\"Display raw data\"\n )\n\n parser.add_argument(\n \"--export\",\n choices=[\"csv\", \"json\", \"xlsx\"],\n default=\"\",\n dest=\"export\",\n help=\"Export dataframe data to csv,json,xlsx file\",\n )\n\n try:\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if not ns_parser.chain_id:\n strike = float(ns_parser.strike)\n op_type = [\"call\", \"put\"][ns_parser.put]\n chain = tradier_model.get_option_chains(ticker, expiry)\n try:\n symbol = chain[\n (chain.strike == strike) & (chain.option_type == op_type)\n ][\"symbol\"].values[0]\n except IndexError:\n print(f\"Strike: {strike}, Option type: {op_type} not not found \\n\")\n return\n else:\n symbol = ns_parser.chain_id\n\n df_hist = tradier_model.historical_prices(symbol)\n df_hist.index = pd.DatetimeIndex(df_hist.index)\n\n if ns_parser.raw:\n print(df_hist)\n\n mc = mpf.make_marketcolors(\n up=\"green\", down=\"red\", edge=\"black\", wick=\"black\", volume=\"in\", ohlc=\"i\"\n )\n\n s = mpf.make_mpf_style(marketcolors=mc, gridstyle=\":\", y_on_right=True)\n\n if gtff.USE_ION:\n plt.ion()\n\n mpf.plot(\n df_hist,\n type=\"candle\",\n volume=True,\n title=f\"\\n{ticker.upper()} {strike} {op_type} expiring {expiry} Historical\",\n style=s,\n figratio=(10, 7),\n figscale=1.10,\n figsize=(plot_autoscale()),\n update_width_config=dict(\n candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0\n ),\n )\n\n print(\"\")\n\n if ns_parser.export:\n export_data(\n ns_parser.export,\n os.path.dirname(os.path.abspath(__file__)),\n f\"historical_op_{ticker}_{expiry}_{str(strike).replace('.','p')}_{op_type}\",\n df_hist,\n )\n\n except Exception as e:\n print(e, \"\\n\")\n\n except SystemExit:\n print(\"\")\n"},{"alpha_fraction":0.588744580745697,"alphanum_fraction":0.6132755875587463,"avg_line_length":21.354839324951172,"blob_id":"0cf50c5e56d3edbc718086652e631f84a56a5e08","content_id":"d5cbce63305263fd5a63378373d45cb4dc03edf0","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"YAML","length_bytes":693,"license_type":"permissive","max_line_length":51,"num_lines":31,"path":"/docker/docker-compose.yaml","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"version: '3.8'\nservices:\n gst-python:\n build:\n context: ..\n dockerfile: docker/gst-python.dockerfile\n image: gst/gst-python:1.0.0\n gst-deps:\n build:\n context: ..\n dockerfile: docker/gst-deps.dockerfile\n image: gst/gst-deps:1.0.0\n gst-poetry-deps:\n build:\n context: ..\n dockerfile: docker/gst-poetry-deps.dockerfile\n image: gst/gst-poetry-deps:1.0.0\n gst:\n build:\n context: ..\n dockerfile: docker/gst.dockerfile\n image: gst/gst:1.1.0\n tty: true\n env_file: setenv\n gst-poetry:\n build:\n context: ..\n dockerfile: docker/gst-poetry.dockerfile\n image: gst/gst-poetry:1.1.0\n tty: true\n env_file: setenv\n"},{"alpha_fraction":0.5253863334655762,"alphanum_fraction":0.531776487827301,"avg_line_length":27.405941009521484,"blob_id":"fafa83f1591f2d470626f0ec9824b587bfb6ea44","content_id":"29c365fa513ddf5e88dcd05123cc9b158df1adcf","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8607,"license_type":"permissive","max_line_length":88,"num_lines":303,"path":"/gamestonk_terminal/etf/stockanalysis_model.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Stockanalysis.com/etf Model\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport webbrowser\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup as bs\nfrom tabulate import tabulate\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n)\n\n# Run this when called to get all available etfs and names\nr = requests.get(\"https://stockanalysis.com/etf/\")\nsoup = bs(r.text, \"html.parser\").findAll(\"table\", {\"class\": \"SymbolTable_table__3Q2qq\"})\nall_links = soup[0].findAll(\"tr\")\n\netf_symbols = []\netf_names = []\nfor link in all_links:\n data = link.findAll(\"td\")\n if data:\n etf_symbols.append(data[0].find(\"a\").get_text())\n etf_names.append(data[1].find(\"span\").get_text())\n\n\ndef limit_number_of_holdings(num: str) -> int:\n if int(num) > 200:\n raise argparse.ArgumentTypeError(\"Asking for too many holdings\")\n return int(num)\n\n\ndef open_web(other_args: List[str]):\n \"\"\"Opens webbrowser to the website page\n\n Parameters\n ----------\n other_args : List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"web\",\n description=\"Opens webbrowser to the website page\",\n )\n parser.add_argument(\n \"-n\", \"--name\", type=str, dest=\"name\", help=\"Symbol to look for\", required=False\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-n\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if not ns_parser.name:\n webbrowser.open(\"https://stockanalysis.com/etf/\")\n return\n\n if ns_parser.name.upper() in etf_symbols:\n webbrowser.open(f\"https://stockanalysis.com/etf/{ns_parser.name.lower()}\")\n else:\n print(\"ETF symbol not available\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef name_search(other_args: List[str]):\n \"\"\"Search all available etfs for matching input\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"search\",\n description=\"Search all available etfs for matching input\",\n )\n parser.add_argument(\n \"-n\", \"--name\", type=str, dest=\"name\", help=\"Name to search for\", required=True\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-n\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args[:2])\n if not ns_parser:\n return\n\n matching_etfs = [\n etf_symbols[idx] + \" - \" + etf\n for idx, etf in enumerate(etf_names)\n if \" \".join(other_args[1:]).lower() in etf.lower()\n ]\n print(*matching_etfs, sep=\"\\n\")\n if len(matching_etfs) == 0:\n print(\"No matches found\")\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef etf_overview(other_args: List[str]):\n \"\"\"\n Get overview data for selected etf\n Parameters\n ----------\n other_args : List[str]\n Argparse arguments\n\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"overview\",\n description=\"Get overview data for selected etf\",\n )\n parser.add_argument(\n \"-n\", \"--name\", type=str, dest=\"name\", help=\"Symbol to look for\", required=True\n )\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-n\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if ns_parser.name.upper() not in etf_symbols:\n print(\"ETF symbol not available\")\n return\n\n r1 = requests.get(f\"https://stockanalysis.com/etf/{ns_parser.name}\")\n soup1 = bs(r1.text, \"html.parser\").find(\"div\", {\"class\": \"info\"}).findAll(\"td\")\n column = []\n value = []\n column.append(\"Last Price\")\n value.append(\n bs(r1.text, \"html.parser\")\n .find(\"div\", {\"class\": \"quote\"})\n .find(\"td\", {\"id\": \"qLast\"})\n .text\n )\n for row in soup1[:-4:2]:\n column.append(row.text)\n for row in soup1[1:-4:2]:\n value.append(row.text)\n df = pd.DataFrame(value, index=column, columns=[ns_parser.name.upper()])\n print(tabulate(df, headers=df.columns, tablefmt=\"fancy_grid\"))\n print(\"\")\n return\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef etf_holdings(other_args: List[str]):\n \"\"\"Look at ETF holdings\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"holdings\",\n description=\"Look at ETF holdings\",\n )\n parser.add_argument(\n \"-n\",\n \"--name\",\n type=str,\n dest=\"name\",\n help=\"ETF to get holdings for\",\n required=True,\n )\n parser.add_argument(\n \"-l\",\n \"--limit\",\n type=limit_number_of_holdings,\n dest=\"limit\",\n help=\"Number of holdings to get\",\n default=20,\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-n\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n r1 = requests.get(f\"https://stockanalysis.com/etf/{ns_parser.name}/holdings\")\n s1 = (\n bs(r1.text, \"html.parser\")\n .find(\"table\", {\"class\": \"fullholdings\"})\n .find(\"tbody\")\n )\n tick, percent, shares = [], [], []\n for idx, entry in enumerate(s1.findAll(\"tr\"), 1):\n tick.append(entry.findAll(\"td\")[1].text)\n percent.append(entry.findAll(\"td\")[3].text)\n shares.append(entry.findAll(\"td\")[4].text)\n if idx >= ns_parser.limit:\n break\n\n df = pd.DataFrame(data=[tick, percent, shares]).T\n print(\n tabulate(\n df, headers=[\"Ticker\", \"% of ETF\", \"Shares\"], tablefmt=\"fancy_grid\"\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef compare_etfs(other_args: List[str]):\n \"\"\"Compare selected ETFs\n\n Parameters\n ----------\n other_args : List[str]\n Argparse arguments\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"compare\",\n description=\"Compare selected ETFs\",\n )\n parser.add_argument(\n \"-n\",\n \"--names\",\n type=str,\n dest=\"names\",\n help=\"Symbols to compare\",\n required=True,\n )\n\n try:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-n\")\n\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n to_compare = [name.upper() for name in ns_parser.names.split(\",\")]\n df = pd.DataFrame(columns=to_compare)\n for etf in to_compare:\n if etf in etf_symbols:\n r1 = requests.get(f\"https://stockanalysis.com/etf/{etf}\")\n soup1 = (\n bs(r1.text, \"html.parser\")\n .find(\"div\", {\"class\": \"info\"})\n .findAll(\"td\")\n )\n column = []\n value = []\n column.append(\"Last Price\")\n value.append(\n bs(r1.text, \"html.parser\")\n .find(\"div\", {\"class\": \"quote\"})\n .find(\"td\", {\"id\": \"qLast\"})\n .text\n )\n for row in soup1[:-4:2]:\n column.append(row.text)\n for row in soup1[1:-4:2]:\n value.append(row.text)\n df[etf] = value\n else:\n print(f\"{etf} not found\")\n df = df.drop(etf, axis=1)\n df.index = column\n print(tabulate(df, headers=df.columns, tablefmt=\"fancy_grid\"))\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5581395626068115,"alphanum_fraction":0.5581395626068115,"avg_line_length":9.75,"blob_id":"3584ecd7188e7e9e3f0573d4579353bbed6d6bd4","content_id":"d640f2996a1e0839f00fd3b27e6f088ae9624487","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":43,"license_type":"permissive","max_line_length":14,"num_lines":4,"path":"/.coveragerc","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"[coverage:run]\nomit =\n env/*\nsource = .\n"},{"alpha_fraction":0.5176047682762146,"alphanum_fraction":0.5307784676551819,"avg_line_length":26.287582397460938,"blob_id":"a4f35b7e54b2d45012b1bb1cf55ad2a4a3e985fc","content_id":"a849e20d618a38840c80159e97e4781da5a30581","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4175,"license_type":"permissive","max_line_length":87,"num_lines":153,"path":"/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Custom TA indicators\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom tabulate import tabulate\n\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n plot_autoscale,\n valid_date,\n)\n\nfrom gamestonk_terminal import config_plot as cfp\nfrom gamestonk_terminal import feature_flags as gtff\n\n\ndef fibinocci_retracement(other_args: List[str], data: pd.DataFrame, ticker: str):\n \"\"\"Calculate fibinocci retracement levels\n\n Parameters\n ----------\n other_args:List[str]\n Argparse arguments\n data: pd.DataFrame\n Stock data\n ticker:str\n Stock ticker\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"fib\",\n description=\"Calculates the fibinocci retracement levels\",\n )\n parser.add_argument(\n \"-p\",\n \"--period\",\n dest=\"period\",\n type=int,\n help=\"Days to lookback for retracement\",\n default=120,\n )\n\n parser.add_argument(\n \"--start\",\n dest=\"start\",\n type=valid_date,\n help=\"Starting date to select\",\n required=\"--end\" in other_args,\n )\n\n parser.add_argument(\n \"--end\",\n dest=\"end\",\n type=valid_date,\n help=\"Ending date to select\",\n required=\"--start\" in other_args,\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n if ns_parser.start and ns_parser.end:\n\n if ns_parser.start not in data.index:\n date0 = data.index[\n data.index.get_loc(ns_parser.start, method=\"nearest\")\n ]\n print(f\"Start date not in data. Using nearest: {date0}\")\n else:\n date0 = ns_parser.start\n if ns_parser.end not in data.index:\n date1 = data.index[data.index.get_loc(ns_parser.end, method=\"nearest\")]\n print(f\"End date not in data. Using nearest: {date1}\")\n else:\n date1 = ns_parser.end\n\n data0 = data.loc[date0, \"Adj Close\"]\n data1 = data.loc[date1, \"Adj Close\"]\n\n min_pr = min(data0, data1)\n max_pr = max(data0, data1)\n\n if min_pr == data0:\n min_date = date0\n max_date = date1\n else:\n min_date = date1\n max_date = date0\n\n else:\n data_to_use = data.iloc[-ns_parser.period :][\"Adj Close\"]\n\n min_pr = data_to_use.min()\n min_date = data_to_use.idxmin()\n max_pr = data_to_use.max()\n max_date = data_to_use.idxmax()\n\n fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 1]\n price_dif = max_pr - min_pr\n\n levels = [round(max_pr - price_dif * f_lev, 2) for f_lev in fib_levels]\n\n df = pd.DataFrame()\n df[\"Level\"] = fib_levels\n df[\"Level\"] = df[\"Level\"].apply(lambda x: str(x * 100) + \"%\")\n df[\"Price\"] = levels\n\n fig, ax = plt.subplots(figsize=(plot_autoscale()), dpi=cfp.PLOT_DPI)\n\n ax.plot(data[\"Adj Close\"], \"b\")\n ax.plot([min_date, max_date], [min_pr, max_pr], c=\"k\")\n\n for i in levels:\n ax.axhline(y=i, c=\"g\", alpha=0.5)\n\n for i in range(5):\n ax.fill_between(data.index, levels[i], levels[i + 1], alpha=0.6)\n\n ax.set_ylabel(\"Price\")\n ax.set_title(f\"Fibonacci Support for {ticker.upper()}\")\n ax.set_xlim(data.index[0], data.index[-1])\n\n ax1 = ax.twinx()\n ax1.set_ylim(ax.get_ylim())\n ax1.set_yticks(levels)\n ax1.set_yticklabels(fib_levels)\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n if gtff.USE_ION:\n plt.ion()\n plt.show()\n\n print(\n tabulate(\n df,\n headers=[\"Fib Level\", \"Price\"],\n showindex=False,\n tablefmt=\"fancy_grid\",\n )\n )\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5439841747283936,"alphanum_fraction":0.5671164393424988,"avg_line_length":29.646974563598633,"blob_id":"0bb84306047c71abbb83e5457122e134999d7900","content_id":"9ca8b000447abd97a12c7c57f8a0d1e943abe264","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":21269,"license_type":"permissive","max_line_length":118,"num_lines":694,"path":"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_model.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"from datetime import datetime, timedelta\nimport textwrap\nimport requests\nimport pandas as pd\nfrom dateutil import parser\nfrom requests.adapters import HTTPAdapter\n\nENDPOINTS = {\n \"global\": \"/global\",\n \"coin\": \"/coins/{}\",\n \"coins\": \"/coins\",\n \"coin_tweeter\": \"/coins/{}/twitter\",\n \"coin_events\": \"/coins/{}/events\",\n \"coin_exchanges\": \"/coins/{}/exchanges\",\n \"coin_markets\": \"/coins/{}/markets\",\n \"ohlcv\": \"/coins/{}/ohlcv/latest\",\n \"ohlcv_hist\": \"/coins/{}/ohlcv/historical\",\n \"people\": \"/people/{}\",\n \"tickers\": \"/tickers\",\n \"ticker_info\": \"/tickers/{}\",\n \"exchanges\": \"/exchanges\",\n \"exchange_info\": \"/exchanges/{}\",\n \"exchange_markets\": \"/exchanges/{}/markets\",\n \"contract_platforms\": \"/contracts\",\n \"contract_platform_addresses\": \"/contracts/{}\",\n \"search\": \"/search\",\n}\n\nPAPRIKA_BASE_URL = \"https://api.coinpaprika.com/v1\"\n\n# Mount a session with adapter. It's solution for time to time timeouts\nsession = requests.Session()\nsession.mount(PAPRIKA_BASE_URL, HTTPAdapter(max_retries=5))\n\n\ndef make_request(endpoint, payload=None, **kwargs):\n \"\"\"Helper method that handles request for coinpaprika api.\n It prepares URL for given endpoint and payload if it's part of requests\n\n Parameters\n ----------\n endpoint: str,\n it's an endpoint that we want to query. e.g. to get twitter data for given coin we need to use:\n https://api.coinpaprika.com/v1/coins/{}/twitter\n payload: dict\n the body of your request. Contains the data send to the CoinPaprika API when making an API request\n kwargs:\n additional parameters that will be added to payload\n Returns\n -------\n dict with response data\n\n \"\"\"\n url = f\"{PAPRIKA_BASE_URL}{endpoint}\"\n if payload is None:\n payload = {}\n if kwargs:\n payload.update(kwargs)\n return session.get(url, params=payload).json()\n\n\ndef get_global_market():\n \"\"\"Return data frame with most important global crypto statistics like:\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\n market_cap_change_24h, volume_24h_change_24h, last_updated,\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n global_markets = make_request(ENDPOINTS[\"global\"])\n global_markets[\"last_updated\"] = datetime.fromtimestamp(\n global_markets[\"last_updated\"]\n )\n\n for key, date in global_markets.items():\n if \"date\" in key:\n try:\n global_markets[key] = parser.parse(date).strftime(\"%Y-%m-%d %H:%M:%S\")\n except (KeyError, ValueError, TypeError):\n ...\n df = pd.Series(global_markets).to_frame().reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef get_list_of_coins():\n \"\"\"Get list of all available coins on CoinPaprika\n\n Returns\n -------\n pandas.DataFrame\n rank, id, name, symbol, type\n \"\"\"\n coins = make_request(ENDPOINTS[\"coins\"])\n df = pd.DataFrame(coins)\n df = df[df[\"is_active\"]]\n return df[[\"rank\", \"id\", \"name\", \"symbol\", \"type\"]]\n\n\ndef get_coin(coin_id=\"eth-ethereum\"):\n \"\"\"Get coin by id\n Parameters\n ----------\n coin_id: str\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\n Returns\n -------\n dict with response\n \"\"\"\n coin = make_request(ENDPOINTS[\"coin\"].format(coin_id))\n return coin\n\n\ndef get_coin_twitter_timeline(coin_id=\"eth-ethereum\"):\n \"\"\"Get twitter timeline for given coin id. Not more than last 50 tweets\n\n Parameters\n ----------\n coin_id: str\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\n Returns\n -------\n pandas.DataFrame\n date, user_name, status, retweet_count, like_count\n\n \"\"\"\n res = make_request(ENDPOINTS[\"coin_tweeter\"].format(coin_id))\n if \"error\" in res:\n print(res)\n return pd.DataFrame()\n if isinstance(res, list) and len(res) == 0:\n return pd.DataFrame()\n df = pd.DataFrame(res)[\n [\"date\", \"user_name\", \"status\", \"retweet_count\", \"like_count\"]\n ]\n\n df = df.applymap(\n lambda x: \"\\n\".join(textwrap.wrap(x, width=80)) if isinstance(x, str) else x\n )\n df[\"status\"] = df[\"status\"].apply(lambda x: x.replace(\" \", \"\"))\n df[\"date\"] = df[\"date\"].apply(lambda x: x.replace(\"T\", \"\\n\"))\n df[\"date\"] = df[\"date\"].apply(lambda x: x.replace(\"Z\", \"\"))\n return df\n\n\ndef get_coin_events_by_id(coin_id=\"eth-ethereum\"):\n \"\"\"\n Example of response from API:\n {\n \"id\": \"17398-cme-april-first-trade\",\n \"date\": \"2018-04-02T00:00:00Z\",\n \"date_to\": \"string\",\n \"name\": \"CME: April First Trade\",\n \"description\": \"First trade of Bitcoin futures contract for April 2018.\",\n \"is_conference\": false,\n \"link\": \"http://www.cmegroup.com/trading/equity-index/us-index/bitcoin_product_calendar_futures.html\",\n \"proof_image_link\": \"https://static.coinpaprika.com/storage/cdn/event_images/16635.jpg\"\n }\n\n Parameters\n ----------\n coin_id: str\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\n Returns\n -------\n pandas.DataFrame\n id, date , date_to, name, description, is_conference, link, proof_image_link\n\n \"\"\"\n res = make_request(ENDPOINTS[\"coin_events\"].format(coin_id))\n if not res:\n return pd.DataFrame()\n data = pd.DataFrame(res)\n data[\"description\"] = data[\"description\"].apply(\n lambda x: \"\\n\".join(textwrap.wrap(x, width=40)) if isinstance(x, str) else x\n )\n data.drop([\"id\", \"proof_image_link\"], axis=1, inplace=True)\n for col in [\"date\", \"date_to\"]:\n data[col] = data[col].apply(lambda x: x.replace(\"T\", \"\\n\"))\n data[col] = data[col].apply(lambda x: x.replace(\"Z\", \"\"))\n return data\n\n\ndef get_coin_exchanges_by_id(coin_id=\"eth-ethereum\"):\n \"\"\"Get all exchanges for given coin id.\n\n Parameters\n ----------\n coin_id: Identifier of Coin from CoinPaprika\n\n Returns\n -------\n pandas.DataFrame\n id, name, adjusted_volume_24h_share, fiats\n \"\"\"\n res = make_request(ENDPOINTS[\"coin_exchanges\"].format(coin_id))\n df = pd.DataFrame(res)\n df[\"fiats\"] = df[\"fiats\"].copy().apply(lambda x: len([i[\"symbol\"] for i in x if x]))\n return df\n\n\ndef get_coin_markets_by_id(coin_id=\"eth-ethereum\", quotes=\"USD\"):\n \"\"\"\n\n Parameters\n ----------\n coin_id: Coin Parpika identifier of coin e.g. eth-ethereum\n quotes: Comma separated list of quotes to return.\n Example: quotes=USD,BTC\n Allowed values:\n BTC, ETH, USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN, BRL,\n THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB, COP, PEN, ARS, ISK\n\n Returns\n -------\n pandas.DataFrame\n \"\"\"\n\n markets = make_request(ENDPOINTS[\"coin_markets\"].format(coin_id), quotes=quotes)\n if \"error\" in markets:\n print(markets)\n return pd.DataFrame()\n\n data = []\n for r in markets:\n dct = {\n \"exchange\": r.get(\"exchange_name\"),\n \"pair\": r.get(\"pair\"),\n \"trust_score\": r.get(\"trust_score\"),\n \"pct_volume_share\": r.get(\"adjusted_volume_24h_share\"),\n }\n quotes = r.get(\"quotes\")\n for k, v in quotes.items():\n dct[f\"{k.lower()}_price\"] = v.get(\"price\")\n dct[f\"{k.lower()}_volume\"] = v.get(\"volume_24h\")\n dct[\"market_url\"] = r.get(\"market_url\")\n data.append(dct)\n\n return pd.DataFrame(data)\n\n\ndef get_ohlc_historical(coin_id=\"eth-ethereum\", quotes=\"USD\", days=90):\n \"\"\"\n Open/High/Low/Close values with volume and market_cap.\n Request example: https://api.coinpaprika.com/v1/coins/btc-bitcoin/ohlcv/historical?start=2019-01-01&end=2019-01-20\n if the last day is current day it can an change with every request until actual close of the day at 23:59:59\n\n\n Parameters\n ----------\n coin_id: str\n Paprika coin identifier e.g. eth-ethereum\n quotes: str\n returned data quote (available values: usd btc)\n days: int\n time range for chart in days. Maximum 365\n\n Returns\n -------\n pandas.DataFrame\n\n \"\"\"\n if quotes.lower() not in [\"usd\", \"btc\"]:\n quotes = \"USD\"\n\n if abs(int(days)) > 365:\n days = 365\n\n end = datetime.now().strftime(\"%Y-%m-%d\")\n start = (datetime.now() - timedelta(days=days)).strftime(\"%Y-%m-%d\")\n\n data = make_request(\n ENDPOINTS[\"ohlcv_hist\"].format(coin_id), quotes=quotes, start=start, end=end\n )\n if \"error\" in data:\n print(data)\n return pd.DataFrame()\n return pd.DataFrame(data)\n\n\ndef _get_coins_info_helper(quotes=\"USD\"):\n \"\"\"Helper method that call /tickers endpoint which returns for all coins quoted in provided currency/crypto\n\n {\n \"id\": \"btc-bitcoin\",\n \"name\": \"Bitcoin\",\n \"symbol\": \"BTC\",\n \"rank\": 1,\n \"circulating_supply\": 17007062,\n \"total_supply\": 17007062,\n \"max_supply\": 21000000,\n \"beta_value\": 0.735327,\n \"first_data_at\": \"2010-11-14T07:20:41Z\",\n \"last_updated\": \"2018-11-14T07:20:41Z\",\n \"quotes\" : {\n \"USD\": {\n \"price\": 5162.15941296,\n \"volume_24h\": 7304207651.1585,\n \"volume_24h_change_24h\": -2.5,\n \"market_cap\": 91094433242,\n \"market_cap_change_24h\": 1.6,\n \"percent_change_15m\": 0,\n \"percent_change_30m\": 0,\n \"percent_change_1h\": 0,\n \"percent_change_6h\": 0,\n \"percent_change_12h\": -0.09,\n \"percent_change_24h\": 1.59,\n \"percent_change_7d\": 0.28,\n \"percent_change_30d\": 27.39,\n \"percent_change_1y\": -37.99,\n \"ath_price\": 20089,\n \"ath_date\": \"2017-12-17T12:19:00Z\",\n \"percent_from_price_ath\": -74.3\n }\n }\n }\n\n Parameters\n ----------\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\n\n Returns\n -------\n pandas.DataFrame\n id, name, symbol, rank, circulating_supply, total_supply, max_supply, beta_value, first_data_at,\n last_updated, price, volume_24h, volume_24h_change_24h, market_cap, market_cap_change_24h,\n percent_change_15m, percent_change_30m, percent_change_1h, percent_change_6h, percent_change_12h,\n percent_change_24h, percent_change_7d, percent_change_30d, percent_change_1y,\n ath_price, ath_date, percent_from_price_ath\n \"\"\"\n tickers = make_request(ENDPOINTS[\"tickers\"], quotes=quotes)\n data = pd.json_normalize(tickers)\n try:\n # data.columns = [col.replace(f\"quotes.{quotes}.\", f\"{quotes.lower()}_\") for col in data.columns.tolist()]\n data.columns = [\n col.replace(f\"quotes.{quotes}.\", \"\") for col in data.columns.tolist()\n ]\n data.columns = [col.replace(\"percent\", \"pct\") for col in data.columns.tolist()]\n except KeyError as e:\n print(e)\n data.rename(\n columns={\n \"market_cap_change_24h\": \"mcap_change_24h\",\n \"pct_from_price_ath\": \"pct_from_ath\",\n },\n inplace=True,\n )\n return data\n\n\ndef get_coins_info(quotes=\"USD\"): # > format big numbers fix\n \"\"\"Returns basic coin information for all coins from CoinPaprika API\n Parameters\n ----------\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, volume_24h, circulating_supply, total_supply,\n max_supply, market_cap, beta_value, ath_price,\n \"\"\"\n cols = [\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"volume_24h\",\n \"circulating_supply\",\n \"total_supply\",\n \"max_supply\",\n \"market_cap\",\n \"beta_value\",\n \"ath_price\",\n ]\n return _get_coins_info_helper(quotes)[cols].sort_values(by=\"rank\")\n\n\ndef get_coins_market_info(quotes=\"USD\"):\n \"\"\"Returns basic coin information for all coins from CoinPaprika API\n Parameters\n ----------\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\n\n Returns\n -------\n pandas.DataFrame\n rank, name, symbol, price, volume_24h, mcap_change_24h,\n pct_change_1h, pct_change_24h, ath_price, pct_from_ath,\n \"\"\"\n cols = [\n \"rank\",\n \"name\",\n \"symbol\",\n \"price\",\n \"volume_24h\",\n \"mcap_change_24h\",\n \"pct_change_1h\",\n \"pct_change_24h\",\n # \"pct_change_7d\",\n # \"pct_change_30d\",\n \"ath_price\",\n \"pct_from_ath\",\n ]\n return _get_coins_info_helper(quotes=quotes)[cols].sort_values(by=\"rank\")\n\n\ndef get_list_of_exchanges(quotes=\"USD\"):\n \"\"\"\n List exchanges from CoinPaprika API\n Parameters\n ----------\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\n\n Returns\n -------\n pandas.DataFrame\n rank, name, currencies, markets, fiats, confidence_score, reported_volume_24h,\n reported_volume_7d ,reported_volume_30d, sessions_per_month,\n \"\"\"\n exchanges = make_request(ENDPOINTS[\"exchanges\"], quotes=quotes)\n df = pd.json_normalize(exchanges)\n try:\n df.columns = [\n col.replace(f\"quotes.{quotes}.\", \"\") for col in df.columns.tolist()\n ]\n except KeyError as e:\n print(e)\n df = df[df[\"active\"]]\n cols = [\n \"adjusted_rank\",\n \"id\",\n \"name\",\n \"currencies\",\n \"markets\",\n \"fiats\",\n \"confidence_score\",\n \"reported_volume_24h\",\n \"reported_volume_7d\",\n \"reported_volume_30d\",\n \"sessions_per_month\",\n ]\n df.loc[:, \"fiats\"] = df[\"fiats\"].apply(lambda x: len([i[\"symbol\"] for i in x if x]))\n df = df[cols]\n df = df.applymap(\n lambda x: \"\\n\".join(textwrap.wrap(x, width=28)) if isinstance(x, str) else x\n )\n df.rename(\n columns={\"adjusted_rank\": \"rank\", \"confidence_score\": \"confidence\"},\n inplace=True,\n )\n df.columns = [x.replace(\"reported_\", \"\") for x in df.columns]\n return df.sort_values(by=\"rank\")\n\n\ndef get_exchanges_market(exchange_id=\"binance\", quotes=\"USD\"):\n \"\"\"List markets by exchange ID\n Parameters\n ----------\n exchange_id: identifier of exchange e.g for Binance Exchange -> binance\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\n\n Returns\n -------\n pandas.DataFrame\n pair, base_currency_name, quote_currency_name, market_url,\n category, reported_volume_24h_share, trust_score,\n \"\"\"\n data = make_request(\n ENDPOINTS[\"exchange_markets\"].format(exchange_id), quotes=quotes\n )\n if \"error\" in data:\n print(data)\n return pd.DataFrame()\n cols = [\n \"exchange_id\",\n \"pair\",\n \"base_currency_name\",\n \"quote_currency_name\",\n \"category\",\n \"reported_volume_24h_share\",\n \"trust_score\",\n \"market_url\",\n ]\n df = pd.DataFrame(data)\n df[\"exchange_id\"] = exchange_id\n return df[cols]\n\n\ndef get_tickers_info_for_coin(coin_id=\"btc-bitcoin\", quotes=\"USD\"):\n \"\"\"Get all most important ticker related information for given coin id\n {\n \"id\": \"btc-bitcoin\",\n \"name\": \"Bitcoin\",\n \"symbol\": \"BTC\",\n \"rank\": 1,\n \"circulating_supply\": 17007062,\n \"total_supply\": 17007062,\n \"max_supply\": 21000000,\n \"beta_value\": 0.735327,\n \"first_data_at\": \"2010-11-14T07:20:41Z\",\n \"last_updated\": \"2018-11-14T07:20:41Z\",\n \"quotes\": {\n \"USD\": {\n \"price\": 5162.15941296,\n \"volume_24h\": 7304207651.1585,\n \"volume_24h_change_24h\": -2.5,\n \"market_cap\": 91094433242,\n \"market_cap_change_24h\": 1.6,\n \"percent_change_15m\": 0,\n \"percent_change_30m\": 0,\n \"percent_change_1h\": 0,\n \"percent_change_6h\": 0,\n \"percent_change_12h\": -0.09,\n \"percent_change_24h\": 1.59,\n \"percent_change_7d\": 0.28,\n \"percent_change_30d\": 27.39,\n \"percent_change_1y\": -37.99,\n \"ath_price\": 20089,\n \"ath_date\": \"2017-12-17T12:19:00Z\",\n \"percent_from_price_ath\": -74.3\n }\n }\n }\n\n Parameters\n ----------\n coin_id: Id of coin from CoinPaprika\n quotes: Coma separated quotes to return e.g quotes = USD, BTC\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n tickers = make_request(ENDPOINTS[\"ticker_info\"].format(coin_id), quotes=quotes)\n\n for key, date in tickers.items():\n if \"date\" in key or \"data\" in key:\n try:\n tickers[key] = parser.parse(date).strftime(\"%Y-%m-%d %H:%M:%S\")\n except (KeyError, ValueError, TypeError):\n ...\n if key == \"quotes\":\n try:\n tickers[key][quotes][\"ath_date\"] = parser.parse(\n tickers[key][quotes][\"ath_date\"]\n ).strftime(\"%Y-%m-%d %H:%M:%S\")\n except (KeyError, ValueError, TypeError) as e:\n print(e)\n\n df = pd.json_normalize(tickers)\n try:\n df.columns = [col.replace(\"quotes.\", \"\") for col in list(df.columns)]\n df.columns = [col.replace(\".\", \"_\").lower() for col in list(df.columns)]\n except KeyError as e:\n print(e)\n df = df.T.reset_index()\n df.columns = [\"Metric\", \"Value\"]\n return df\n\n\ndef search(q, c=None, modifier=None):\n \"\"\"Search CoinPaprika\n Parameters\n ----------\n q: phrase for search\n c: one or more categories (comma separated) to search.\n Available options: currencies|exchanges|icos|people|tags\n Default: currencies,exchanges,icos,people,tags\n modifier: set modifier for search results. Available options: symbol_search -\n search only by symbol (works for currencies only)\n\n Returns\n -------\n pandas.DataFrame\n Metric, Value\n \"\"\"\n if c is None:\n c = \"currencies,exchanges,icos,people,tags\"\n data = make_request(ENDPOINTS[\"search\"], q=q, c=c, modifier=modifier, limit=100)\n results = []\n for item in data:\n category = data[item]\n for r in category:\n results.append(\n {\n \"id\": r.get(\"id\"),\n \"name\": r.get(\"name\"),\n \"category\": item,\n }\n )\n return pd.DataFrame(results)\n\n\ndef get_all_contract_platforms():\n \"\"\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama ...\n\n Returns\n -------\n pandas.DataFrame\n index, platform_id\n \"\"\"\n\n contract_platforms = make_request(ENDPOINTS[\"contract_platforms\"])\n df = pd.DataFrame(contract_platforms).reset_index()\n df.columns = [\"index\", \"platform_id\"]\n df[\"index\"] = df[\"index\"] + 1\n return df\n\n\ndef get_contract_platform(platform_id=\"eth-ethereum\"):\n \"\"\"Gets all contract addresses for given platform\n Parameters\n ----------\n platform_id: Blockchain platform like eth-ethereum\n\n Returns\n -------\n pandas.DataFrame\n id, type, active, address\n \"\"\"\n contract_platforms = make_request(\n ENDPOINTS[\"contract_platform_addresses\"].format(platform_id)\n )\n\n return pd.DataFrame(contract_platforms)[[\"id\", \"type\", \"active\", \"address\"]]\n\n\ndef validate_coin(coin: str, coins_dct: dict):\n \"\"\"Helper method that validates if proper coin id or symbol was provided\n\n Parameters\n ----------\n coin: id or symbol of coin for CoinPaprika\n coins_dct: dictionary of coins\n\n Returns\n -------\n coin id, coin symbol\n\n \"\"\"\n coin_found, symbol = None, None\n if coin in coins_dct:\n coin_found = coin\n symbol = coins_dct.get(coin_found)\n else:\n for key, value in coins_dct.items():\n if coin.upper() == value:\n coin_found = key\n symbol = value\n\n if not coin_found:\n raise ValueError(f\"Could not find coin with given id: {coin}\\n\")\n print(f\"Coin found : {coin_found} with symbol {symbol}\")\n return coin_found, symbol\n\n\ndef basic_coin_info(coin_id: str):\n coin = get_coin(coin_id)\n tags = coin.get(\"tags\") or []\n keys = [\n \"id\",\n \"name\",\n \"symbol\",\n \"rank\",\n \"type\",\n \"description\",\n \"platform\",\n \"proof_type\",\n \"contract\",\n ]\n results = {}\n for key in keys:\n results[key] = coin.get(key)\n\n try:\n tags = \", \".join(t.get(\"name\") for t in tags)\n parent = coin.get(\"parent\") or {}\n except (KeyError, IndexError):\n tags, parent = [], {}\n\n results[\"tags\"] = tags\n results[\"parent\"] = parent.get(\"id\")\n df = pd.Series(results).reset_index()\n df.columns = [\"Metric\", \"Value\"]\n df[\"Value\"] = df[\"Value\"].apply(\n lambda x: \"\\n\".join(textwrap.wrap(x, width=80)) if isinstance(x, str) else x\n )\n df.dropna(subset=[\"Value\"], inplace=True)\n return df\n"},{"alpha_fraction":0.5414535403251648,"alphanum_fraction":0.5488064885139465,"avg_line_length":28.696203231811523,"blob_id":"467e16c500509c2729232d4d33f2dfb62fd38bbe","content_id":"dd1e8eb28f50242c557418542e013119bcc14d5a","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9384,"license_type":"permissive","max_line_length":96,"num_lines":316,"path":"/gamestonk_terminal/common/technical_analysis/overlap.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Overlap Technical Analysis\"\"\"\n___docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport pandas_ta as ta\nfrom pandas.plotting import register_matplotlib_converters\nimport mplfinance as mpf\n\nfrom gamestonk_terminal.helper_funcs import (\n check_positive,\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import feature_flags as gtff\n\nregister_matplotlib_converters()\n\n\ndef ema(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Plots exponential moving average (EMA) over stock\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Data interval\n df_stock: pd.DataFrame\n Dataframe of dates and prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"ema\",\n description=\"\"\"\n The Exponential Moving Average is a staple of technical\n analysis and is used in countless technical indicators. In a Simple Moving\n Average, each value in the time period carries equal weight, and values outside\n of the time period are not included in the average. However, the Exponential\n Moving Average is a cumulative calculation, including all data. Past values have\n a diminishing contribution to the average, while more recent values have a greater\n contribution. This method allows the moving average to be more responsive to changes\n in the data.\n \"\"\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--length\",\n action=\"store\",\n dest=\"n_length\",\n type=check_positive,\n default=20,\n help=\"length\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.ema(\n df_stock[\"Adj Close\"],\n length=ns_parser.n_length,\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.ema(\n df_stock[\"Close\"],\n length=ns_parser.n_length,\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, _ = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\n plt.title(f\"{ns_parser.n_length} Day EMA on {s_ticker}\")\n\n if s_interval == \"1440min\":\n plt.plot(\n df_stock[\"Adj Close\"].index,\n df_stock[\"Adj Close\"].values,\n \"k\",\n lw=3,\n )\n plt.xlim(\n df_stock[\"Adj Close\"].index[0],\n df_stock[\"Adj Close\"].index[-1],\n )\n else:\n plt.plot(df_stock[\"Close\"].index, df_stock[\"Close\"].values, \"k\", lw=3)\n plt.xlim(df_stock[\"Close\"].index[0], df_stock[\"Close\"].index[-1])\n\n plt.xlabel(\"Time\")\n plt.ylabel(f\"{s_ticker} Price($)\")\n plt.plot(df_ta.index, df_ta.values, c=\"tab:blue\")\n l_legend = list()\n l_legend.append(s_ticker)\n # Pandas series\n if len(df_ta.shape) == 1:\n l_legend.append(f\"EMA {ns_parser.n_length}\")\n # Pandas dataframe\n else:\n l_legend.append(df_ta.columns.tolist())\n\n plt.legend(l_legend)\n plt.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n plt.minorticks_on()\n plt.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef sma(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Plots simple moving average (SMA) over stock\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Data interval\n df_stock: pd.DataFrame\n Dataframe of dates and prices\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"sma\",\n description=\"\"\"\n Moving Averages are used to smooth the data in an array to\n help eliminate noise and identify trends. The Simple Moving Average is literally\n the simplest form of a moving average. Each output value is the average of the\n previous n values. In a Simple Moving Average, each value in the time period carries\n equal weight, and values outside of the time period are not included in the average.\n This makes it less responsive to recent changes in the data, which can be useful for\n filtering out those changes.\n \"\"\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--length\",\n dest=\"l_length\",\n type=lambda s: [int(item) for item in s.split(\",\")],\n default=[20, 50],\n help=\"length of MA window\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n fig, _ = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\n if s_interval == \"1440min\":\n plt.plot(df_stock.index, df_stock[\"Adj Close\"].values, color=\"k\")\n else:\n plt.plot(df_stock.index, df_stock[\"Close\"].values, color=\"k\")\n l_legend = list()\n l_legend.append(s_ticker)\n for length in ns_parser.l_length:\n if s_interval == \"1440min\":\n df_ta = ta.sma(\n df_stock[\"Adj Close\"],\n length=length,\n offset=ns_parser.n_offset,\n ).dropna()\n else:\n df_ta = ta.sma(\n df_stock[\"Close\"], length=length, offset=ns_parser.n_offset\n ).dropna()\n plt.plot(df_ta.index, df_ta.values)\n l_legend.append(f\"SMA{length} \")\n\n plt.title(f\"SMA on {s_ticker}\")\n plt.xlim(df_stock.index[0], df_stock.index[-1])\n plt.xlabel(\"Time\")\n plt.ylabel(f\"{s_ticker} Price ($)\")\n plt.legend(l_legend)\n plt.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n plt.minorticks_on()\n plt.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef vwap(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Plots volume weighted average price (VWAP)\n\n Parameters\n ----------\n other_args: List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Data interval\n df_stock: pd.DataFrame\n Dataframe of dates and prices\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"vwap\",\n description=\"\"\"\n The Volume Weighted Average Price that measures the average typical price\n by volume. It is typically used with intraday charts to identify general direction.\n \"\"\",\n )\n\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n print(\"VWAP should be used with intraday data. \\n\")\n return\n\n df_stock.index = df_stock.index.tz_localize(None)\n df_stock[\"Day\"] = [idx.date() for idx in df_stock.index]\n\n day_df = df_stock[df_stock.Day == df_stock.Day[-1]]\n\n df_vwap = ta.vwap(\n high=day_df[\"High\"],\n low=day_df[\"Low\"],\n close=day_df[\"Close\"],\n volume=day_df[\"Volume\"],\n )\n\n mc = mpf.make_marketcolors(\n up=\"green\", down=\"red\", edge=\"black\", wick=\"black\", volume=\"in\", ohlc=\"i\"\n )\n\n s = mpf.make_mpf_style(marketcolors=mc, gridstyle=\":\", y_on_right=True)\n apdict = mpf.make_addplot(df_vwap, color=\"k\")\n mpf.plot(\n day_df,\n style=s,\n type=\"candle\",\n addplot=apdict,\n volume=True,\n title=f\"\\n{s_ticker} {s_interval} VWAP\",\n )\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.6302816867828369,"alphanum_fraction":0.6332159638404846,"avg_line_length":24.432836532592773,"blob_id":"137a9dd92c4529274b09c26d4379ba452f8dd9d6","content_id":"99dd5646dfe4f9928a1c204b4bec9debcde63149","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1704,"license_type":"permissive","max_line_length":76,"num_lines":67,"path":"/gamestonk_terminal/stocks/technical_analysis/finviz_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" Finviz View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport io\nfrom typing import List\nimport requests\nimport matplotlib.pyplot as plt\nfrom finvizfinance.quote import finvizfinance\nfrom finvizfinance.util import headers\nfrom PIL import Image\n\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import feature_flags as gtff\n\n\ndef view(other_args: List[str], ticker: str):\n \"\"\"View historical price with trendlines. [Source: Finviz]\n\n Parameters\n ----------\n other_args : List[str]\n argparse other args\n ticker: str\n stock ticker\n \"\"\"\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"view\",\n description=\"\"\"\n View historical price with trendlines. [Source: Finviz]\n \"\"\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n stock = finvizfinance(ticker)\n image_url = stock.TickerCharts(urlonly=True)\n\n r = requests.get(image_url, stream=True, headers=headers, timeout=5)\n r.raise_for_status()\n r.raw.decode_content = True\n\n dataBytesIO = io.BytesIO(r.content)\n im = Image.open(dataBytesIO)\n\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax.set_axis_off()\n fig.subplots_adjust(left=0, right=1, top=1, bottom=0)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.imshow(im)\n plt.show()\n print(\"\")\n\n except SystemExit:\n print(\"\")\n"},{"alpha_fraction":0.6856231093406677,"alphanum_fraction":0.7020165324211121,"avg_line_length":41.94392395019531,"blob_id":"7efd8fc0a2b4f9cb26a66e0023a049a4f6454fd3","content_id":"9f5f9787a53fda7c3c2ae481182c88602d55ab44","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":13786,"license_type":"permissive","max_line_length":244,"num_lines":321,"path":"/gamestonk_terminal/cryptocurrency/coinpaprika/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# Coinpaprika\n\nThis menu aims to explore crypto world with [Coinpaprika](#https://coinpaprika.com/)\nThe usage of the following commands along with an example will be exploited below.\n\nCoinpaprika research platform was founded in April 2018. Coinpaprika mission is to provide comprehensive, reliable, transparent\nand objective access to information about crypto projects from all around the world. [Source: See https://coinpaprika.com/about-us/]\n\n\n[COIN](#COIN)\n* [find](#find)\n * find similar coin by coin name,symbol or id\n* [load](#load)\n * load a given coin, you can use either coin symbol or coin id\n* [clear](#clear)\n * clear loaded coin\n* [chart](#chart)\n * plot the loaded crypto chart\n* [ta](technical_analysis/README.md)\n * open technical analysis menu\n* [basic](#basic)\n * show coin basic information\n* [ps](#ps)\n * show coin price, supply, market cap related metrics.\n* [mkt](#mkt)\n * show all markets for loaded coin\n* [ex](#ex)\n * show all exchanges where loaded coin is listed\n* [twitter](#twitter)\n * show up to 50 last tweets for loaded coin\n* [events](#events )\n * show events related to loaded coin\n\n\n[OVERVIEW](#OVERVIEW)\n* [global](#global)\n * show global crypto market info\n* [coins](#coins)\n * show coins available on CoinGecko\n* [info](#info)\n * show basic info about all coins available on CoinPaprika\n* [markets](#markets)\n * show market related info about all coins available on CoinPaprika\n* [search](#search)\n * search for coins, exchanges, people on CoinPaprika\n* [exchanges](#exchanges)\n * list all exchanges available on CoinPaprika\n* [ex_markets](#ex_markets)\n * show all available markets on given exchange\n* [platforms](#platforms)\n * list blockchain platforms/ecosystems eg. ethereum, solana, kusama, terra\n* [contracts](#contracts)\n * show all smart contracts for given platform\n\n\n\n\n# COIN <a name=\"COIN\"></a>\n## load <a name=\"load\"></a>\n\n````\nusage: load [-c --coin]\n````\n\nLoad a given coin. The current crypto data is [Powered by Coinpaprika API](#https://coinpaprika.com/en/api/), which is an awesome service that currently requires no API Key!\nBy loading a coin you will have access to a lot of statistics on that coin like price data, available markets, exchanges where coin is listed and also open access to technical analysis menu\n\n\n* -c/--coin: The coin you wish to load. This can either be the symbol or the name. `load -c BTC` and `load -c btc-bitcoin`\n will load. The -c flag is optional, the above is equivalent to `load btc`. If you not sure about coin symbol or coin id. You can use either `search`, `find` or `coins` commands and look for id of coin that you are interested in.\n\n\n## chart <a name=\"chart\"></a>\n\n````\nusage: chart [-d --days] [--vs]\n````\n\nDisplay chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for.\nMaximum range for chart is 1 year (365 days). If you use bigger range it will be automatically converted to 365 limit.\nBy default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs USD in last 90 days range use `chart --vs USD --days 90`\n\n\n* -d/--days: The number of days to look. Maximum 365 days. Default: 30\n* --vs: The currency to look against. Available options are: `USD, BTC`. Default: `USD`\n\n\n## ta <a name=\"ta\"></a>\n\n````\nusage: ta [-d --days] [--vs]\n````\n\nOpen Technical Analysis menu for loaded coin\nLoads data for technical analysis. You can specify currency vs which you want to show chart and also number of days to get data for.\nE.g. if you loaded in previous step Bitcoin and you want to see it's price vs USDin last 90 days range use `ta --vs USD --days 90`\n\n\n* -d/--days: The number of days to look. Maximum 365 days. Default: 30\n* --vs: The currency to look against. Available options are: `USD, BTC`. Default: `USD`\n\n## basic <a name=\"basic\"></a>\n\n````\nusage: basic\n````\n\nShows basic information about loaded coin like: name, symbol, rank, type, description, platform, proof_type, contract, tags, parent\n\n\n## ps <a name=\"ps\"></a>\n\n````\nusage: ps [--vs]\n````\n\nPrice, supply related data for loaded coin. There you find metrics like:\nname, symbol, rank, supply, volume, ath, market cap, price change related metrics\n\n\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\n\n## mkt <a name=\"mkt\"></a>\n\n````\nusage: mkt [--vs] [-t --top] [-s --sort] [--descend] [-l --links]\n````\n\nGet all markets found for given coin.\n\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\n* -t/--top - number of markets to display. To display top 10 markets: `mkt --top 10 --sort trust_score`\n* -s/--sort - sort by given column. You can chose on from `pct_volume_share, exchange, pair, trust_score, volume,price`.\n* --descend - flag to sort in descending order (lowest first)\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `mkt --links`\n\n## ex <a name=\"ex\"></a>\n\n````\nusage: ex [-t --top] [-s --sort] [--descend]\n````\n\nGet all exchanges found for given coin.\n\n* -t/--top: Number of exchanges to display. To display top 10 exchanges: `ex --top 10 --sort fiats`\n* -s/--sort: Sort by given column. You can chose on from `id, name, adjusted_volume_24h_share, fiats`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## twitter <a name=\"twitter\"></a>\n\n````\nusage: twitter [-t --top] [-s --sort] [--descend]\n````\n\nShow last tweets for given coin.\n\n* -t/--top: Number of tweets to display. To display top 10 tweets: `twitter --top 10 --sort date`\n* -s/--sort: Sort by given column. You can chose on from `date, user_name, status, retweet_count, like_count`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## events <a name=\"events\"></a>\n\n````\nusage: events [-t --top] [-s --sort] [--descend] [-l --links]\n````\n\nShow information about most important coins events.\n\n* -t/--top: Number of events to display. To display top 10 events: `events --top 10 --sort date`\n* -s/--sort: Sort by given column. You can chose on from `date, date_to, name, description, is_conference`.\n* --descend: Flag to sort in descending order (lowest first)\n* -l/--links: Flag to show urls. Using this flag will add additional column with urls e.g. `events --links`. If you will use this flag url column will be displayed.\n\n## clear <a name=\"clear\"></a>\n\n````\nusage: clear\n````\n\nJust remove previously loaded coin. (set coin = None)\n\n\n## find <a name=\"find\"></a>\n\n````\nusage: find [-c --coin] [-t --top] [-k --key]\n````\n\nFind similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at Coinpaprika, you can use this command to display coins with similar name, symbol or id to your search query.\nExample of usage: coin name is something like `polka`. So you can try: `find -c polka -k name -t 25`\nIt will search for coin that has similar name to polka and display top 25 matches.\n\n* -c, --coin: Stands for coin - you provide here your search query\n* -k, --key: It's a searching key. You can search by symbol, id or name of coin\n* -t, --top: It displays top N number of records.\n\n\n\n\n# OVERVIEW <a name=\"OVERVIEW\"></a>\n\n## global <a name=\"global\"></a>\n\n````\nusage: global\n````\n\nShow most important global crypto statistics like: market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\nmarket_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date, market_cap_change_24h, volume_24h_change_24h, last_updated\n\n## coins <a name=\"coins\"></a>\n\n````\nusage: coins [-s --skip] [-t --top] [-l --letter] [-k --key]\n````\n\nDisplay all available coins in coinpaprika. You can search with pagination mechanism with `coins --skip [num] --top [num]` or you can just search by letter like\n`coins --letter d --key name --top 10` it will display top 10 matches for coins which name starts with letter `d` and search will be done in column `name`\n\n* -s/--skip: Number of records to skip. Default is 0. There are thousands of coins, so there is mechanism to paginate through them.\ne.q if you want to see only records from 500-750 you should use `coins --skip 500 --top 250` or if you want to see 1200-1880 you should use `coins --skip 1200 --top 680`.\n* -t/--top: Number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\n* -l/--letter: First letters of coin by which you want to search\n* -k/--key: Search key. With this parameter you can specify in which column you would like to search. Choose on from `name, id, symbol`\n\n## info <a name=\"info\"></a>\n\n````\nusage: info [-t --top] [-s --sort] [--descend]\n````\nShow basic coin information for all coins from CoinPaprika\n\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\n* -t/--top: Number of coins to display. To display top 10 coins: `info --top 10 --sort rank`\n* -s/--sort: Sort by given column. You can chose on from `rank, name, symbol, price, volume_24h, circulating_supply, total_supply, max_supply, ath_price,market_cap, beta_value`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## markets <a name=\"markets\"></a>\n\n````\nusage: markets [--vs] [-t --top] [-s --sort] [--descend]\n````\n\nShow market related (price, supply, volume) coin information for all coins on CoinPaprika\n\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\n* -t/--top: Number of coins to display. To display top 10 coins: `markets --top 10 --sort rank`\n* -s/--sort: Sort by given column. You can chose on from `rank, name, symbol, price, volume_24h, mcap_change_24h, pct_change_1h, pct_change_24h, ath_price, pct_from_ath`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## exchanges <a name=\"exchanges\"></a>\n\n````\nusage: exchanges [--vs] [-t --top] [-s --sort] [--descend]\n````\n\nShow all exchanges from CoinPaprika\n\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\n* -t/--top: Number of coins to display. To display top 10 coins: `exchanges --top 10 --sort rank`\n* -s/--sort: Sort by given column. You can chose on from `rank, name, currencies, markets, fiats, confidence, volume_24h, volume_7d, volume_30d, sessions_per_month`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## ex_markets <a name=\"ex_markets\"></a>\n\n````\nusage: ex_markets [-e --exchange] [-t --top] [-s --sort] [--descend] [-l --links]\n````\n\nGet all exchange markets found for given exchange\n\n* -e/--exchange: Identifier of exchange e.g for Binance Exchange: `binance`\n* -t/--top: Number of markets to display. To display top 10 markets: `ex_markets --top 10 --sort reported_volume_24h_share`\n* -s/--sort: Sort by given column. You can chose on from `pair , base_currency_name, quote_currency_name, category, reported_volume_24h_share, trust_score, market_url`.\n* --descend: Flag to sort in descending order (lowest first)\n* -l/--links: Flag to show urls. Using this flag will add additional column with urls e.g. `ex_markets --links`. If you will use this flag url column will be displayed.\n\n## platforms <a name=\"platforms\"></a>\n\n````\nusage: platforms\n````\n\nList all smart contract platforms like ethereum, solana, cosmos, eos\n\n\n## contracts <a name=\"contracts\"></a>\n\n````\nusage: contracts [-p --platform] [-t --top] [-s --sort] [--descend]\n````\n\nGets all contract addresses for given blockchain platform\n\n* -p/--platform: smart contract platform id. Available coins:\n```\nbtc-bitcoin, eos-eos, eth-ethereum, xrp-xrp, bch-bitcoin-cash, xem-nem, neo-neo, xlm-stellar, etc-ethereum-classic,\nqtum-qtum, zec-zcash, bts-bitshares, waves-waves, nxt-nxt, act-achain, ubq-ubiq, xcp-counterparty, etp-metaverse-etp,\nburst-burst, omni-omni, trx-tron, bnb-binance-coin, ardr-ardor, ht-huobi-token, blvr-believer, cake-pa\nncakeswap, fsxu-flashx-ultra, chik-chickenkebab-finance, jgn-juggernaut7492, crx-cryptex, whirl-whirl-finance,\n eubi-eubi-token, swam-swapmatic-token, shells-shells\n```\n* -t/--top: Number of smart contracts to display. To display 10 smart contracts: `contracts -p eth-ethereum --top 10 --sort index`\n* -s/--sort: Sort by given column. You can chose on from `index, id, type, active, address`.\n* --descend: Flag to sort in descending order (lowest first)\n\n## search <a name=\"search\"></a>\n\n````\nusage: search [-q --query] [-c --cat] [-s --sort] [-t --top] [--descend]\n````\n\nSearch over CoinPaprika API\n\n* -q/--query: Phrase for search\n* -c/--cat: Categories to search: currencies|exchanges|icos|people|tags|all. Default: all\n* -t/--top: Number of found records to display.\n* -s/--sort: Sort by given column. You can chose on from `category, id, name`.\n* --descend: Flag to sort in descending order (lowest first)\n\n\n\n"},{"alpha_fraction":0.6315789222717285,"alphanum_fraction":0.6631578803062439,"avg_line_length":18,"blob_id":"4090b8d46cb98f24a729a2b6bdcfa8f55a61ae31","content_id":"e7c66172a3deed61c1fc71fff5c2611805754bd3","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Dockerfile","length_bytes":95,"license_type":"permissive","max_line_length":37,"num_lines":5,"path":"/docker/gst.dockerfile","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"FROM gst/gst-deps:1.0.0\n\nCOPY --chown=python:python . .\n\nCMD [\"bash\", \"docker/entry-point.sh\"]\n"},{"alpha_fraction":0.5145893096923828,"alphanum_fraction":0.5369328260421753,"avg_line_length":30.200716018676758,"blob_id":"1814d862a23e0172c10cb0c9937c60c86882af67","content_id":"65e003ff91503bfaa7308ea2bf4582a826392523","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":17410,"license_type":"permissive","max_line_length":97,"num_lines":558,"path":"/gamestonk_terminal/common/technical_analysis/momentum.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Momentum Technical Analysis\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\n\nimport matplotlib.pyplot as plt\nimport pandas_ta as ta\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\n\nfrom gamestonk_terminal.helper_funcs import (\n check_positive,\n parse_known_args_and_warn,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import feature_flags as gtff\n\nregister_matplotlib_converters()\n\n\ndef cci(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Commodity channel index\n\n Parameters\n ----------\n other_args:List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock time interval\n df_stock: pd.DataFrame\n Dataframe of prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"cci\",\n description=\"\"\"\n The CCI is designed to detect beginning and ending market trends.\n The range of 100 to -100 is the normal trading range. CCI values outside of this\n range indicate overbought or oversold conditions. You can also look for price\n divergence in the CCI. If the price is making new highs, and the CCI is not,\n then a price correction is likely.\n \"\"\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--length\",\n action=\"store\",\n dest=\"n_length\",\n type=check_positive,\n default=14,\n help=\"length\",\n )\n parser.add_argument(\n \"-s\",\n \"--scalar\",\n action=\"store\",\n dest=\"n_scalar\",\n type=check_positive,\n default=0.015,\n help=\"scalar\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.cci(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Adj Close\"],\n length=ns_parser.n_length,\n scalar=ns_parser.n_scalar,\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.cci(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Close\"],\n length=ns_parser.n_length,\n scalar=ns_parser.n_scalar,\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax = axes[0]\n ax.set_title(f\"{s_ticker} CCI\")\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2 = axes[1]\n ax2.plot(df_ta.index, df_ta.values, \"b\", lw=2)\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax2.axhspan(100, plt.gca().get_ylim()[1], facecolor=\"r\", alpha=0.2)\n ax2.axhspan(plt.gca().get_ylim()[0], -100, facecolor=\"g\", alpha=0.2)\n ax2.axhline(100, linewidth=3, color=\"r\", ls=\"--\")\n ax2.axhline(-100, linewidth=3, color=\"g\", ls=\"--\")\n ax2.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax2.minorticks_on()\n ax2.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n ax3 = ax2.twinx()\n ax3.set_ylim(ax2.get_ylim())\n ax3.set_yticks([-100, 100])\n ax3.set_yticklabels([\"OVERSOLD\", \"OVERBOUGHT\"])\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef macd(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Moving average convergence divergence\n\n Parameters\n ----------\n other_args:List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock time interval\n df_stock: pd.DataFrame\n Dataframe of prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"macd\",\n description=\"\"\"\n The Moving Average Convergence Divergence (MACD) is the difference\n between two Exponential Moving Averages. The Signal line is an Exponential Moving\n Average of the MACD. \\n \\n The MACD signals trend changes and indicates the start\n of new trend direction. High values indicate overbought conditions, low values\n indicate oversold conditions. Divergence with the price indicates an end to the\n current trend, especially if the MACD is at extreme high or low values. When the MACD\n line crosses above the signal line a buy signal is generated. When the MACD crosses\n below the signal line a sell signal is generated. To confirm the signal, the MACD\n should be above zero for a buy, and below zero for a sell.\n \"\"\",\n )\n\n parser.add_argument(\n \"-f\",\n \"--fast\",\n action=\"store\",\n dest=\"n_fast\",\n type=check_positive,\n default=12,\n help=\"The short period.\",\n )\n parser.add_argument(\n \"-s\",\n \"--slow\",\n action=\"store\",\n dest=\"n_slow\",\n type=check_positive,\n default=26,\n help=\"The long period.\",\n )\n parser.add_argument(\n \"--signal\",\n action=\"store\",\n dest=\"n_signal\",\n type=check_positive,\n default=9,\n help=\"The signal period.\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"How many periods to offset the result.\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.macd(\n df_stock[\"Adj Close\"],\n fast=ns_parser.n_fast,\n slow=ns_parser.n_slow,\n signal=ns_parser.n_signal,\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.macd(\n df_stock[\"Close\"],\n fast=ns_parser.n_fast,\n slow=ns_parser.n_slow,\n signal=ns_parser.n_signal,\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax = axes[0]\n ax.set_title(f\"{s_ticker} MACD\")\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2 = axes[1]\n ax2.plot(df_ta.index, df_ta.iloc[:, 0].values, \"b\", lw=2)\n ax2.plot(df_ta.index, df_ta.iloc[:, 2].values, \"r\", lw=2)\n ax2.bar(df_ta.index, df_ta.iloc[:, 1].values, color=\"g\")\n ax2.legend(\n [\n f\"MACD Line {df_ta.columns[0]}\",\n f\"Signal Line {df_ta.columns[2]}\",\n f\"Histogram {df_ta.columns[1]}\",\n ],\n loc=\"upper left\",\n )\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax2.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax2.minorticks_on()\n ax2.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef rsi(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\n \"\"\"Relative strength index\n\n Parameters\n ----------\n other_args:List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock time interval\n df_stock: pd.DataFrame\n Dataframe of prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"rsi\",\n description=\"\"\"\n The Relative Strength Index (RSI) calculates a ratio of the\n recent upward price movements to the absolute price movement. The RSI ranges\n from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when\n the value is over 70/below 30. You can also look for divergence with price. If\n the price is making new highs/lows, and the RSI is not, it indicates a reversal.\n \"\"\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--length\",\n action=\"store\",\n dest=\"n_length\",\n type=check_positive,\n default=14,\n help=\"length\",\n )\n parser.add_argument(\n \"-s\",\n \"--scalar\",\n action=\"store\",\n dest=\"n_scalar\",\n type=check_positive,\n default=100,\n help=\"scalar\",\n )\n parser.add_argument(\n \"-d\",\n \"--drift\",\n action=\"store\",\n dest=\"n_drift\",\n type=check_positive,\n default=1,\n help=\"drift\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.rsi(\n df_stock[\"Adj Close\"],\n length=ns_parser.n_length,\n scalar=ns_parser.n_scalar,\n drift=ns_parser.n_drift,\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.rsi(\n df_stock[\"Close\"],\n length=ns_parser.n_length,\n scalar=ns_parser.n_scalar,\n drift=ns_parser.n_drift,\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax = axes[0]\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n ax.set_title(f\" {s_ticker} RSI{ns_parser.n_length} \")\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2 = axes[1]\n ax2.plot(df_ta.index, df_ta.values, \"b\", lw=2)\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax2.axhspan(70, 100, facecolor=\"r\", alpha=0.2)\n ax2.axhspan(0, 30, facecolor=\"g\", alpha=0.2)\n ax2.axhline(70, linewidth=3, color=\"r\", ls=\"--\")\n ax2.axhline(30, linewidth=3, color=\"g\", ls=\"--\")\n ax2.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax2.minorticks_on()\n ax2.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2.set_ylim([0, 100])\n ax3 = ax2.twinx()\n ax3.set_ylim(ax2.get_ylim())\n ax3.set_yticks([30, 70])\n ax3.set_yticklabels([\"OVERSOLD\", \"OVERBOUGHT\"])\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n\n plt.show()\n\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n\n\ndef stoch(\n other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame\n):\n \"\"\"Stochastic oscillator\n\n Parameters\n ----------\n other_args:List[str]\n Argparse arguments\n s_ticker: str\n Ticker\n s_interval: str\n Stock time interval\n df_stock: pd.DataFrame\n Dataframe of prices\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n prog=\"stoch\",\n description=\"\"\"\n The Stochastic Oscillator measures where the close is in relation\n to the recent trading range. The values range from zero to 100. %D values over 75\n indicate an overbought condition; values under 25 indicate an oversold condition.\n When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses\n below, it is a sell signal. The Raw %K is generally considered too erratic to use\n for crossover signals.\n \"\"\",\n )\n\n parser.add_argument(\n \"-k\",\n \"--fastkperiod\",\n action=\"store\",\n dest=\"n_fastkperiod\",\n type=check_positive,\n default=14,\n help=\"The time period of the fastk moving average\",\n )\n parser.add_argument(\n \"-d\",\n \"--slowdperiod\",\n action=\"store\",\n dest=\"n_slowdperiod\",\n type=check_positive,\n default=3,\n help=\"The time period of the slowd moving average\",\n )\n parser.add_argument(\n \"--slowkperiod\",\n action=\"store\",\n dest=\"n_slowkperiod\",\n type=check_positive,\n default=3,\n help=\"The time period of the slowk moving average\",\n )\n parser.add_argument(\n \"-o\",\n \"--offset\",\n action=\"store\",\n dest=\"n_offset\",\n type=check_positive,\n default=0,\n help=\"offset\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n # Daily\n if s_interval == \"1440min\":\n df_ta = ta.stoch(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Adj Close\"],\n k=ns_parser.n_fastkperiod,\n d=ns_parser.n_slowdperiod,\n smooth_k=ns_parser.n_slowkperiod,\n offset=ns_parser.n_offset,\n ).dropna()\n\n # Intraday\n else:\n df_ta = ta.stoch(\n high=df_stock[\"High\"],\n low=df_stock[\"Low\"],\n close=df_stock[\"Close\"],\n k=ns_parser.n_fastkperiod,\n d=ns_parser.n_slowdperiod,\n smooth_k=ns_parser.n_slowkperiod,\n offset=ns_parser.n_offset,\n ).dropna()\n\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax = axes[0]\n if s_interval == \"1440min\":\n ax.plot(df_stock.index, df_stock[\"Adj Close\"].values, \"k\", lw=2)\n else:\n ax.plot(df_stock.index, df_stock[\"Close\"].values, \"k\", lw=2)\n ax.set_title(f\"Stochastic Relative Strength Index (STOCH RSI) on {s_ticker}\")\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\n ax.set_xticklabels([])\n ax.set_ylabel(\"Share Price ($)\")\n ax.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax.minorticks_on()\n ax.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n\n ax2 = axes[1]\n ax2.plot(df_ta.index, df_ta.iloc[:, 0].values, \"k\", lw=2)\n ax2.plot(df_ta.index, df_ta.iloc[:, 1].values, \"b\", lw=2, ls=\"--\")\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\n\n ax2.axhspan(80, 100, facecolor=\"r\", alpha=0.2)\n ax2.axhspan(0, 20, facecolor=\"g\", alpha=0.2)\n ax2.axhline(80, linewidth=3, color=\"r\", ls=\"--\")\n ax2.axhline(20, linewidth=3, color=\"g\", ls=\"--\")\n\n ax2.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n ax2.minorticks_on()\n ax2.grid(b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2)\n ax2.set_ylim([0, 100])\n\n ax3 = ax2.twinx()\n ax3.set_ylim(ax2.get_ylim())\n ax3.set_yticks([20, 80])\n ax3.set_yticklabels([\"OVERSOLD\", \"OVERBOUGHT\"])\n ax2.legend(\n [f\"%K {df_ta.columns[0]}\", f\"%D {df_ta.columns[1]}\"], loc=\"lower left\"\n )\n\n if gtff.USE_ION:\n plt.ion()\n\n plt.gcf().autofmt_xdate()\n fig.tight_layout(pad=1)\n plt.show()\n\n print(\"\")\n\n except Exception as e:\n print(e, \"\\n\")\n"},{"alpha_fraction":0.5603761672973633,"alphanum_fraction":0.561191201210022,"avg_line_length":33.97806930541992,"blob_id":"41795a878ffeaf375e615587050ef608cffd6d72","content_id":"798814600c55cb795f5d948f62ae7c22180fb170","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":15950,"license_type":"permissive","max_line_length":118,"num_lines":456,"path":"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Cryptocurrency Controller\"\"\"\n__docformat__ = \"numpy\"\n# pylint: disable=R0904, C0302, W0622\nimport argparse\nimport os\nimport pandas as pd\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.cryptocurrency.coingecko import pycoingecko_view\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\n\n\nclass GeckoController:\n\n CHOICES = [\n \"?\",\n \"cls\",\n \"help\",\n \"q\",\n \"quit\",\n \"global\",\n \"find\",\n \"coins\",\n \"defi\",\n \"news\",\n \"trending\",\n \"most_voted\",\n \"most_visited\",\n \"top_volume\",\n \"recently\",\n \"sentiment\",\n \"gainers\",\n \"losers\",\n \"stables\",\n \"yfarms\",\n \"top_defi\",\n \"top_dex\",\n \"top_nft\",\n \"nft_today\",\n \"nft_market\",\n \"exchanges\",\n \"ex_rates\",\n \"platforms\",\n \"products\",\n \"indexes\",\n \"derivatives\",\n \"categories\",\n \"hold\",\n \"hold_comp\",\n \"load\",\n \"clear\",\n \"chart\",\n \"ta\",\n \"info\",\n \"market\",\n \"ath\",\n \"atl\",\n \"score\",\n \"web\",\n \"social\",\n \"bc\",\n \"dev\",\n ]\n\n def __init__(self):\n \"\"\"CONSTRUCTOR\"\"\"\n\n self._gecko_parser = argparse.ArgumentParser(add_help=False, prog=\"cg\")\n self._gecko_parser.add_argument(\"cmd\", choices=self.CHOICES)\n self.current_coin = None\n self.current_currency = None\n self.current_df = pd.DataFrame()\n self.source = \"\"\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\"\\nCoinGecko:\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\")\n print(\"Coin\")\n print(\" find find coin by name, symbol or id\")\n print(\" load load cryptocurrency data\")\n print(\" clear remove loaded coin\")\n print(\"\")\n print(f\"Loaded coin: {self.current_coin}\")\n print(\"\")\n print(\" chart price chart for loaded coin\")\n print(\"> ta technical analysis menu for loaded coin\")\n print(\" info nasic information about loaded coin\")\n print(\" market market stats about loaded coin\")\n print(\" ath all time high related stats for loaded coin\")\n print(\" atl all time low related stats for loaded coin\")\n print(\" web found websites for loaded coin e.g forum, homepage\")\n print(\n \" social social portals urls for loaded coin, e.g reddit, twitter\"\n )\n print(\n \" score different kind of scores for loaded coin, e.g developer score, sentiment score\"\n )\n print(\" dev github, bitbucket coin development statistics\")\n print(\" bc links to blockchain explorers for loaded coin\")\n print(\"\")\n print(\"Overview:\")\n print(\" global global crypto market info\")\n print(\" coins coins available on CoinGecko\")\n print(\" defi global DeFi market info\")\n print(\" trending trending coins on CoinGecko\")\n print(\" most_voted most voted coins on CoinGecko\")\n print(\" most_visited most visited coins on CoinGecko\")\n print(\" top_volume coins with highest volume on CoinGecko\")\n print(\" recently recently added on CoinGecko\")\n print(\" sentiment coins with most positive sentiment\")\n print(\n \" gainers top gainers - coins which price gained the most in given period \"\n )\n print(\n \" losers top losers - coins which price dropped the most in given period \"\n )\n print(\" stables stablecoins\")\n print(\" yfarms top yield farms\")\n print(\" top_defi top defi protocols\")\n print(\" top_dex top decentralized exchanges\")\n print(\" top_nft top non fungible tokens\")\n print(\" nft_today non fungible token of the day\")\n print(\" nft_market non fungible token market status\")\n print(\" exchanges top crypto exchanges\")\n print(\" ex_rates coin exchange rates\")\n print(\" platforms crypto financial platforms\")\n print(\" products crypto financial products\")\n print(\" indexes crypto indexes\")\n print(\" derivatives crypto derivatives\")\n print(\" categories crypto categories\")\n print(\" hold ethereum, bitcoin holdings overview statistics\")\n print(\" hold_comp ethereum, bitcoin holdings by public companies\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self._gecko_parser.parse_known_args(an_input.split())\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu.\"\"\"\n print(\"Moving back to (crypto) menu\")\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program.\"\"\"\n return True\n\n def call_find(self, other_args):\n \"\"\"Process find command\"\"\"\n pycoingecko_view.find(other_args=other_args)\n\n def call_load(self, other_args):\n \"\"\"Process load command\"\"\"\n self.current_coin = pycoingecko_view.load(other_args)\n\n def call_chart(self, other_args):\n \"\"\"Process chart command\"\"\"\n if self.current_coin:\n pycoingecko_view.chart(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_info(self, other_args):\n \"\"\"Process info command\"\"\"\n if self.current_coin:\n pycoingecko_view.info(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_market(self, other_args):\n \"\"\"Process market command\"\"\"\n if self.current_coin:\n pycoingecko_view.market(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_web(self, other_args):\n \"\"\"Process web command\"\"\"\n if self.current_coin:\n pycoingecko_view.web(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_social(self, other_args):\n \"\"\"Process social command\"\"\"\n if self.current_coin:\n pycoingecko_view.social(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_dev(self, other_args):\n \"\"\"Process dev command\"\"\"\n if self.current_coin:\n pycoingecko_view.dev(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_ath(self, other_args):\n \"\"\"Process ath command\"\"\"\n if self.current_coin:\n pycoingecko_view.ath(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_atl(self, other_args):\n \"\"\"Process atl command\"\"\"\n if self.current_coin:\n pycoingecko_view.atl(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_score(self, other_args):\n \"\"\"Process score command\"\"\"\n if self.current_coin:\n pycoingecko_view.score(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_bc(self, other_args):\n \"\"\"Process bc command\"\"\"\n if self.current_coin:\n pycoingecko_view.bc(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_clear(self, _):\n \"\"\"Process clear command\"\"\"\n if self.current_coin:\n print(\n f\"Current coin {self.current_coin.coin_symbol} was removed. You can load new coin with load -c <coin>\"\n )\n self.current_coin = None\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n # pylint: disable=inconsistent-return-statements\n def call_ta(self, other_args):\n \"\"\"Process ta command\"\"\"\n if self.current_coin:\n self.current_df, self.current_currency = pycoingecko_view.ta(\n self.current_coin, other_args\n )\n if self.current_df is not None:\n try:\n self.current_df = self.current_df[[\"price\"]].rename(\n columns={\"price\": \"Close\"}\n )\n self.current_df.index.name = \"date\"\n quit = ta_controller.menu(\n stock=self.current_df,\n ticker=self.current_coin.coin_symbol,\n start=self.current_df.index[0],\n interval=\"\",\n )\n print(\"\")\n if quit is not None:\n if quit is True:\n return quit\n\n except (ValueError, KeyError) as e:\n print(e)\n else:\n return\n else:\n print(\"Please load a coin through either load - coin\", \"\\n\")\n return\n\n def call_hold(self, other_args):\n \"\"\"Process hold command\"\"\"\n pycoingecko_view.holdings_overview(other_args)\n\n def call_hold_comp(self, other_args):\n \"\"\"Process hold_comp command\"\"\"\n pycoingecko_view.holdings_companies_list(other_args)\n\n def call_gainers(self, other_args):\n \"\"\"Process hold_comp command\"\"\"\n pycoingecko_view.gainers(other_args)\n\n def call_losers(self, other_args):\n \"\"\"Process hold_comp command\"\"\"\n pycoingecko_view.losers(other_args)\n\n def call_trending(self, other_args):\n \"\"\"Process trending command\"\"\"\n pycoingecko_view.discover(category=\"trending\", other_args=other_args)\n\n def call_most_voted(self, other_args):\n \"\"\"Process most_voted command\"\"\"\n pycoingecko_view.discover(category=\"most_voted\", other_args=other_args)\n\n def call_recently(self, other_args):\n \"\"\"Process recently command\"\"\"\n pycoingecko_view.recently_added(other_args=other_args)\n\n def call_most_visited(self, other_args):\n \"\"\"Process most_voted command\"\"\"\n pycoingecko_view.discover(category=\"most_visited\", other_args=other_args)\n\n def call_sentiment(self, other_args):\n \"\"\"Process sentiment command\"\"\"\n pycoingecko_view.discover(category=\"positive_sentiment\", other_args=other_args)\n\n def call_news(self, other_args):\n \"\"\"Process news command\"\"\"\n pycoingecko_view.news(other_args=other_args)\n\n def call_categories(self, other_args):\n \"\"\"Process top_categories command\"\"\"\n pycoingecko_view.categories(other_args=other_args)\n\n def call_stables(self, other_args):\n \"\"\"Process stables command\"\"\"\n pycoingecko_view.stablecoins(other_args=other_args)\n\n def call_yfarms(self, other_args):\n \"\"\"Process yield_farms command\"\"\"\n pycoingecko_view.yfarms(other_args=other_args)\n\n def call_top_volume(self, other_args):\n \"\"\"Process top_volume command\"\"\"\n pycoingecko_view.top_volume_coins(other_args=other_args)\n\n def call_top_defi(self, other_args):\n \"\"\"Process top_volume command\"\"\"\n pycoingecko_view.top_defi_coins(other_args=other_args)\n\n def call_top_dex(self, other_args):\n \"\"\"Process top_volume command\"\"\"\n pycoingecko_view.top_dex(other_args=other_args)\n\n def call_top_nft(self, other_args):\n \"\"\"Process top_volume command\"\"\"\n pycoingecko_view.top_dex(other_args=other_args)\n\n def call_nft_market(self, other_args):\n \"\"\"Process top_volume command\"\"\"\n pycoingecko_view.nft_market_status(other_args=other_args)\n\n def call_nft_today(self, other_args):\n \"\"\"Process nft_today command\"\"\"\n pycoingecko_view.nft_of_the_day(other_args=other_args)\n\n def call_products(self, other_args):\n \"\"\"Process products command\"\"\"\n pycoingecko_view.products(other_args=other_args)\n\n def call_platforms(self, other_args):\n \"\"\"Process platforms command\"\"\"\n pycoingecko_view.platforms(other_args=other_args)\n\n def call_exchanges(self, other_args):\n \"\"\"Process exchanges command\"\"\"\n pycoingecko_view.exchanges(other_args=other_args)\n\n def call_ex_rates(self, other_args):\n \"\"\"Process exchange_rates command\"\"\"\n pycoingecko_view.exchange_rates(other_args=other_args)\n\n def call_indexes(self, other_args):\n \"\"\"Process indexes command\"\"\"\n pycoingecko_view.indexes(other_args=other_args)\n\n def call_derivatives(self, other_args):\n \"\"\"Process derivatives command\"\"\"\n pycoingecko_view.derivatives(other_args=other_args)\n\n def call_global(self, other_args):\n \"\"\"Process global command\"\"\"\n pycoingecko_view.global_market_info(other_args=other_args)\n\n def call_defi(self, other_args):\n \"\"\"Process defi command\"\"\"\n pycoingecko_view.global_defi_info(other_args=other_args)\n\n def call_coins(self, other_args):\n \"\"\"Process coins command\"\"\"\n pycoingecko_view.coin_list(other_args=other_args)\n\n\ndef menu():\n gecko_controller = GeckoController()\n gecko_controller.print_help()\n\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in gecko_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (crypto)>(cg)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (crypto)>(cg)> \")\n\n try:\n process_input = gecko_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.6789960265159607,"alphanum_fraction":0.7384412288665771,"avg_line_length":43.52941131591797,"blob_id":"7fa811c406c65045d13f4400f271f1750e211817","content_id":"0a0ad2b3c0e5dadfbc37a984d54c551b95f58e66","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":757,"license_type":"permissive","max_line_length":138,"num_lines":17,"path":"/gamestonk_terminal/cryptocurrency/coinmarketcap/README.md","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"# COINMARKETCAP\n\nThis menu aims to explore CoinMarketCap API and provide to user meaningful crypto currency data and different kind of metrics.\n\n## top <a name=\"top\"></a>\n\n````\ntop [-n] [-s --sort SORTBY] [--descend]\n````\n\nThis command displays the top n cryptocurrencies from coinmarketcap.com.\n\n* -n: Number of coins to look at (after sort). Defaults to 10.\n* -s/--sort : Column to sort by. One of {Symbol,CMC_Rank,LastPrice,DayPctChange,MarketCap}. Defaults to sorting by the CMC rank.\n* --descend : Flag to change the sorting order. Sorts in ascending order (since default order is the Rank which starts at 1).\n\n<img width=\"990\" alt=\"crypto\" src=\"https://user-images.githubusercontent.com/25267873/115787544-4746d100-a3ba-11eb-9433-b7cb9142404a.png\">\n"},{"alpha_fraction":0.5500816106796265,"alphanum_fraction":0.5501776337623596,"avg_line_length":32.48231506347656,"blob_id":"63ca7c208235a8dcb80b91d39ec0becb93de7c0b","content_id":"9ac4c58429e263a4df8531a761b674ff1482d00d","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10413,"license_type":"permissive","max_line_length":108,"num_lines":311,"path":"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"CoinPaprika Controller\"\"\"\n__docformat__ = \"numpy\"\n\n# pylint: disable=inconsistent-return-statements, too-many-public-methods, too-many-lines, redefined-builtin\n\nimport argparse\nimport os\nimport pandas as pd\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_view\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\n\n\nclass PaprikaController:\n\n CHOICES = [\n \"?\",\n \"cls\",\n \"help\",\n \"q\",\n \"quit\",\n \"clear\",\n \"global\",\n \"search\",\n \"coins\",\n \"info\",\n \"markets\",\n \"exchanges\",\n \"ex_markets\",\n \"platforms\",\n \"contracts\",\n \"events\",\n \"twitter\",\n \"ex\",\n \"mkt\",\n \"chart\",\n \"ta\",\n \"ps\",\n \"basic\",\n \"load\",\n \"find\",\n ]\n\n def __init__(self):\n \"\"\"CONSTRUCTOR\"\"\"\n\n self._paprika_parser = argparse.ArgumentParser(add_help=False, prog=\"cp\")\n self._paprika_parser.add_argument(\"cmd\", choices=self.CHOICES)\n self.current_coin = None\n self.current_currency = None\n self.current_df = pd.DataFrame()\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\"\\nCoinPaprika:\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\")\n print(\"Coin\")\n print(\" find alternate way to search for coins\")\n print(\" load load cryptocurrency data\")\n print(\" clear remove loaded coin\")\n print(\"\")\n print(f\"Loaded coin: {self.current_coin}\")\n print(\"\")\n print(\" chart price chart for loaded coin\")\n print(\"> ta technical analysis menu for loaded coin\")\n print(\" basic basic information about loaded coin\")\n print(\" ps price and supply related metrics for loaded coin\")\n print(\" mkt all markets for loaded coin\")\n print(\" ex all exchanges where loaded coin is listed\")\n print(\" twitter tweets for loaded coin\")\n print(\" events events related to loaded coin\")\n print(\"\")\n print(\"Overview:\")\n print(\" global global crypto market info\")\n print(\" coins coins available on CoinPaprika\")\n print(\" info basic info about all coins available on CoinPaprika\")\n print(\n \" markets market related info about all coins available on CoinPaprika\"\n )\n print(\" search search for coins, exchanges, people on CoinPaprika\")\n print(\" exchanges list all exchanges\")\n print(\" ex_markets all available markets on given exchange\")\n print(\n \" platforms list blockchain platforms eg. ethereum, solana, kusama, terra\"\n )\n print(\" contracts all smart contracts for given platform\")\n\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self._paprika_parser.parse_known_args(\n an_input.split()\n )\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu.\"\"\"\n print(\"Moving back to (crypto) menu\")\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program.\"\"\"\n return True\n\n def call_search(self, other_args):\n \"\"\"Process search command\"\"\"\n coinpaprika_view.search(other_args=other_args)\n\n def call_load(self, other_args):\n \"\"\"Process load command\"\"\"\n self.current_coin = coinpaprika_view.load(other_args)\n\n def call_chart(self, other_args):\n \"\"\"Process chart command\"\"\"\n if self.current_coin:\n coinpaprika_view.chart(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_ps(self, other_args):\n \"\"\"Process ps command\"\"\"\n if self.current_coin:\n coinpaprika_view.price_supply(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_basic(self, other_args):\n \"\"\"Process ps command\"\"\"\n if self.current_coin:\n coinpaprika_view.basic(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_mkt(self, other_args):\n \"\"\"Process market command\"\"\"\n if self.current_coin:\n coinpaprika_view.markets(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_ex(self, other_args):\n \"\"\"Process ex command\"\"\"\n if self.current_coin:\n coinpaprika_view.exchanges(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_events(self, other_args):\n \"\"\"Process events command\"\"\"\n if self.current_coin:\n coinpaprika_view.events(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_twitter(self, other_args):\n \"\"\"Process twitter command\"\"\"\n if self.current_coin:\n coinpaprika_view.twitter(self.current_coin, other_args)\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n def call_clear(self, _):\n \"\"\"Process clear command\"\"\"\n if self.current_coin:\n print(\n f\"Current coin {self.current_coin} was removed. You can load new coin with load -c <coin>\"\n )\n print(\"\")\n self.current_coin = None\n self.current_df = pd.DataFrame()\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n # pylint: disable=inconsistent-return-statements\n def call_ta(self, other_args):\n \"\"\"Process ta command\"\"\"\n if self.current_coin:\n self.current_df, self.current_currency = coinpaprika_view.ta(\n self.current_coin, other_args\n )\n if self.current_df is not None:\n try:\n quit = ta_controller.menu(\n stock=self.current_df,\n ticker=self.current_coin,\n start=self.current_df.index[0],\n interval=\"\",\n )\n print(\"\")\n if quit is not None:\n if quit is True:\n return quit\n\n except (ValueError, KeyError) as e:\n print(e)\n else:\n return\n else:\n print(\"Please load a coin through either load - coin\", \"\\n\")\n return\n\n def call_global(self, other_args):\n \"\"\"Process global command\"\"\"\n coinpaprika_view.global_market(other_args=other_args)\n\n def call_coins(self, other_args):\n \"\"\"Process coins command\"\"\"\n coinpaprika_view.coins(other_args=other_args)\n\n def call_markets(self, other_args):\n \"\"\"Process markets command\"\"\"\n coinpaprika_view.all_coins_market_info(other_args=other_args)\n\n def call_ex_markets(self, other_args):\n \"\"\"Process ex_markets command\"\"\"\n coinpaprika_view.exchange_markets(other_args=other_args)\n\n def call_info(self, other_args):\n \"\"\"Process info command\"\"\"\n coinpaprika_view.all_coins_info(other_args=other_args)\n\n def call_exchanges(self, other_args):\n \"\"\"Process coins_market command\"\"\"\n coinpaprika_view.all_exchanges(other_args=other_args)\n\n def call_platforms(self, other_args):\n \"\"\"Process platforms command\"\"\"\n coinpaprika_view.all_platforms(other_args=other_args)\n\n def call_contracts(self, other_args):\n \"\"\"Process contracts command\"\"\"\n coinpaprika_view.contracts(other_args=other_args)\n\n def call_find(self, other_args):\n \"\"\"Process find command\"\"\"\n coinpaprika_view.find(other_args=other_args)\n\n\ndef menu():\n paprika_controller = PaprikaController()\n paprika_controller.print_help()\n\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in paprika_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (crypto)>(cp)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (crypto)>(cp)> \")\n\n try:\n process_input = paprika_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.5619261860847473,"alphanum_fraction":0.5619261860847473,"avg_line_length":27.848648071289062,"blob_id":"2bcae65395369791e1cf1727bb1679b09f8a7920","content_id":"7de9aa21654accb99af6d6be9f769cc3ccd9057e","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5337,"license_type":"permissive","max_line_length":101,"num_lines":185,"path":"/gamestonk_terminal/etf/etf_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"ETF Controller\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport os\nfrom typing import List\nimport matplotlib.pyplot as plt\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.etf.stockanalysis_model import (\n name_search,\n open_web,\n etf_overview,\n compare_etfs,\n etf_holdings,\n)\nfrom gamestonk_terminal.etf.screener_model import etf_screener\nfrom gamestonk_terminal.etf import wsj_view\n\n\nclass ETFController:\n \"\"\"ETF Controller class\"\"\"\n\n CHOICES = [\n \"cls\",\n \"?\",\n \"help\",\n \"q\",\n \"quit\",\n ]\n\n CHOICES_COMMANDS = [\n \"web\",\n \"search\",\n \"overview\",\n \"compare\",\n \"holdings\",\n \"screener\",\n \"gainers\",\n \"decliners\",\n \"active\",\n ]\n\n CHOICES += CHOICES_COMMANDS\n\n def __init__(self):\n \"\"\"Constructor\"\"\"\n self.etf_parser = argparse.ArgumentParser(add_help=False, prog=\"etf\")\n self.etf_parser.add_argument(\"cmd\", choices=self.CHOICES)\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\n \"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/etf\"\n )\n print(\"\")\n print(\">> ETF <<\")\n print(\"\")\n print(\"What do you want to do?\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\\nStockAnalysis.com\")\n print(\" web open StockAnalysis.com/etf\")\n print(\" search search ETFs matching name (i.e. BlackRock or Invesco)\")\n print(\" overview get overview of ETF symbol\")\n print(\" holdings get top holdings for ETF\")\n print(\" compare compare overview of multiple ETF\")\n print(\" screener screen etfs based on overview data\")\n print(\"\\nWall St. Journal\")\n print(\" gainers show top gainers\")\n print(\" decliners show top decliners\")\n print(\" active show most active\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self.etf_parser.parse_known_args(an_input.split())\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu\"\"\"\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program\"\"\"\n return True\n\n def call_web(self, other_args: List[str]):\n \"\"\"Process web command\"\"\"\n open_web(other_args)\n\n def call_search(self, other_args: List[str]):\n \"\"\"Process search command\"\"\"\n name_search(other_args)\n\n def call_overview(self, other_args: List[str]):\n \"\"\"Process overview command\"\"\"\n etf_overview(other_args)\n\n def call_holdings(self, other_args: List[str]):\n \"\"\"Process holdings command\"\"\"\n etf_holdings(other_args)\n\n def call_compare(self, other_args):\n \"\"\"Process compare command\"\"\"\n compare_etfs(other_args)\n\n def call_screener(self, other_args):\n \"\"\"Process screener command\"\"\"\n etf_screener(other_args)\n\n def call_gainers(self, other_args):\n \"\"\"Process gainers command\"\"\"\n wsj_view.show_top_mover(\"gainers\", other_args)\n\n def call_decliners(self, other_args):\n \"\"\"Process decliners command\"\"\"\n wsj_view.show_top_mover(\"decliners\", other_args)\n\n def call_active(self, other_args):\n \"\"\"Process gainers command\"\"\"\n wsj_view.show_top_mover(\"active\", other_args)\n\n\ndef menu():\n etf_controller = ETFController()\n etf_controller.print_help()\n plt.close(\"all\")\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in etf_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (etf)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (etf)> \")\n\n try:\n process_input = etf_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.49760839343070984,"alphanum_fraction":0.5079463124275208,"avg_line_length":32.4072151184082,"blob_id":"5f3a8300689f4cb62a78df001713837e897aada4","content_id":"e5dbdd142efde5e1147066b72de966284f3eb189","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6481,"license_type":"permissive","max_line_length":113,"num_lines":194,"path":"/gamestonk_terminal/economy/fred_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\" Fred View \"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nfrom typing import List\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\nimport matplotlib.pyplot as plt\nfrom fredapi import Fred\nfrom gamestonk_terminal.helper_funcs import (\n parse_known_args_and_warn,\n valid_date,\n plot_autoscale,\n)\nfrom gamestonk_terminal.config_plot import PLOT_DPI\nfrom gamestonk_terminal import config_terminal as cfg\nfrom gamestonk_terminal import feature_flags as gtff\n\nregister_matplotlib_converters()\n\n\nfred_series_description = {\n \"VIXCLS\": \"Volatility Index\",\n \"GDP\": \"Gross Domestic Product\",\n \"UNRATE\": \"Unemployment Rate\",\n \"DGS1\": \"1-Year Treasury Constant Maturity Rate\",\n \"DGS5\": \"5-Year Treasury Constant Maturity Rate\",\n \"DGS10\": \"10-Year Treasury Constant Maturity Rate\",\n \"DGS30\": \"30-Year Treasury Constant Maturity Rate\",\n \"MORTGAGE30US\": \"30-Year Mortgage Rate\",\n \"FEDFUNDS\": \"Effective Federal Funds Rate\",\n \"AAA\": \"Moody's Seasoned AAA Corporate Bond Yield\",\n \"DEXCAUS\": \"Canada / U.S. Foreign Exchange Rate\",\n}\n\n\ndef display_fred(other_args: List[str], choice: str):\n \"\"\"Display customized Federal Reserve Economic Data (FRED) from https://fred.stlouisfed.org.\n It is possible to display multiple series.\n\n Parameters\n ----------\n other_args : List[str]\n argparse other args\n choice : str\n Fred data series: \"VIXCLS\", \"GDP\", \"DGS1\", \"DGS5\", \"DGS10\", \"DGS30\", \"MORTGAGE30US\", \"FEDFUNDS\",\n \"AAA\", \"DEXCAUS\", \"UNRATE\",\n \"\"\"\n\n parser = argparse.ArgumentParser(\n add_help=False,\n prog=choice if choice else \"fred\",\n description=\"\"\"\n Display customized Federal Reserve Economic Data (FRED) from https://fred.stlouisfed.org.\n It is possible to display multiple series.\n \"\"\",\n )\n\n parser.add_argument(\n \"-i\",\n \"--id\",\n dest=\"series_id\",\n required=not bool(choice) and \"-h\" not in other_args,\n type=str,\n default=choice.upper(),\n help=\"FRED Series ID from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3\",\n )\n\n parser.add_argument(\n \"-s\",\n dest=\"start_date\",\n type=valid_date,\n default=\"2019-01-01\",\n help=\"Starting date (YYYY-MM-DD) of data\",\n )\n\n parser.add_argument(\n \"-t\",\n \"--text\",\n action=\"store_true\",\n dest=\"text\",\n help=\"Only output text data\",\n )\n\n try:\n if not choice:\n if other_args:\n if \"-\" not in other_args[0]:\n other_args.insert(0, \"-i\")\n ns_parser = parse_known_args_and_warn(parser, other_args)\n\n if not ns_parser:\n return\n\n fred = Fred(api_key=cfg.API_FRED_KEY)\n\n if \",\" in ns_parser.series_id:\n if not ns_parser.text:\n l_colors = [\n \"tab:blue\",\n \"tab:orange\",\n \"tab:green\",\n \"tab:red\",\n \"tab:purple\",\n \"tab:brown\",\n \"tab:pink\",\n \"tab:gray\",\n \"tab:olive\",\n \"tab:cyan\",\n ]\n l_ts_start = list()\n l_ts_end = list()\n p = {}\n _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\n ax.axes.get_yaxis().set_visible(False)\n plt.subplots_adjust(right=0.9 - ns_parser.series_id.count(\",\") * 0.1)\n\n series = ns_parser.series_id.split(\",\")\n\n success = -1\n success_series = list()\n for series_id in series:\n try:\n d_data = fred.get_series(series_id, ns_parser.start_date)\n success += 1\n success_series.append(series_id.upper())\n except Exception:\n print(f\"Series '{series_id}' doesn't exist!\")\n continue\n\n df_fred = pd.DataFrame(d_data, columns=[f\"{series_id}\"])\n df_fred.index.name = \"Date\"\n\n if ns_parser.text:\n print(df_fred.dropna().to_string(), \"\\n\")\n\n else:\n axes = ax.twinx()\n axes.spines[\"right\"].set_position((\"axes\", 1 + success * 0.15))\n axes.spines[\"right\"].set_color(l_colors[success])\n (p[success],) = axes.plot(\n df_fred.index,\n df_fred.iloc[:, 0],\n c=l_colors[success],\n label=series_id.upper(),\n )\n\n axes.yaxis.label.set_color(l_colors[success])\n\n l_ts_start.append(df_fred.index[0])\n l_ts_end.append(df_fred.index[-1])\n\n if not ns_parser.text and success:\n plt.title(\"FRED: \" + \", \".join(success_series))\n plt.xlim(min(l_ts_start), max(l_ts_end))\n plt.gcf().autofmt_xdate()\n plt.xlabel(\"Time\")\n plt.legend([val for _, val in p.items()], success_series)\n plt.gca().spines[\"left\"].set_visible(False)\n plt.show()\n print(\"\")\n\n else:\n d_data = fred.get_series(ns_parser.series_id, ns_parser.start_date)\n\n df_fred = pd.DataFrame(d_data, columns=[f\"{ns_parser.series_id}\"])\n df_fred.index.name = \"Date\"\n\n if ns_parser.text:\n print(df_fred.to_string())\n\n else:\n plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\n plt.plot(df_fred.index, df_fred.iloc[:, 0])\n plt.xlabel(\"Time\")\n plt.xlim(df_fred.index[0], df_fred.index[-1])\n plt.ylabel(f\"{ns_parser.series_id.upper()}\")\n plt.grid(b=True, which=\"major\", color=\"#666666\", linestyle=\"-\")\n plt.minorticks_on()\n plt.grid(\n b=True, which=\"minor\", color=\"#999999\", linestyle=\"-\", alpha=0.2\n )\n if choice:\n plt.title(fred_series_description[ns_parser.series_id])\n if gtff.USE_ION:\n plt.ion()\n plt.show()\n print(\"\")\n\n except SystemExit:\n print(\"\")\n except Exception as e:\n print(e, \"\\n\")\n return\n"},{"alpha_fraction":0.6118078231811523,"alphanum_fraction":0.6121937036514282,"avg_line_length":32.655845642089844,"blob_id":"f826646e67c14571e6ba7a40eedef074571cd01e","content_id":"652309dc107b700c2b4efa04ed67b2c4413f87ca","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5183,"license_type":"permissive","max_line_length":74,"num_lines":154,"path":"/tests/test_cryptocurrency/test_coinpaprika_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"from unittest import TestCase, mock\nimport sys\nimport io\n\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_view\n\n\nclass TestCoinPaprikaView(TestCase):\n def test_global_markets(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.global_market([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"market_cap_usd\", capt)\n\n def test_coins(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.coins([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"rank\", capt)\n\n def test_all_coins_market_info(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.all_coins_market_info([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Displaying data vs USD\", capt)\n\n def test_all_coins_info(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.all_coins_info([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Displaying data vs USD\", capt)\n\n def test_all_exchanges(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.all_exchanges([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Displaying data vs USD\", capt)\n\n def test_search(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.search([\"-q\", \"bt\"])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"category\", capt)\n\n def test_all_platforms(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.all_platforms([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"platform_id\", capt)\n\n def test_contracts(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.contracts([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"active\", capt)\n\n def test_find(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.find([\"-c\", \"BTC\"])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"index\", capt)\n\n def test_twitter(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.twitter(\"eth-ethereum\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Couldn't find\", capt)\n\n def test_events(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.events(\"eth-ethereum\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"description\", capt)\n\n def test_exchanges(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.exchanges(\"btc-bitcoin\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"name\", capt)\n\n def test_markets(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.markets(\"eth-ethereum\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"exchange\", capt)\n\n @mock.patch(\"matplotlib.pyplot.show\")\n def test_chart(self, mock_matplot):\n # pylint: disable=unused-argument\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.chart(\"btc-bitcoin\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"\\n\", capt)\n\n def test_exchange_markets(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.exchange_markets([])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"base_currency_name\", capt)\n\n def price_supply(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.price_supply(\"btc\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"asset_platform_id\", capt)\n\n def test_load(self):\n value = coinpaprika_view.load([\"-c\", \"BTC\"])\n self.assertEqual(value, \"btc-bitcoin\")\n\n def test_ta(self):\n value = coinpaprika_view.ta(\"eth-ethereum\", [])\n print(value[0])\n self.assertIn(\"Open\", value[0])\n\n def test_basic(self):\n capturedOutput = io.StringIO()\n sys.stdout = capturedOutput\n coinpaprika_view.basic(\"BTC\", [])\n sys.stdout = sys.__stdout__\n capt = capturedOutput.getvalue()\n self.assertIn(\"Metric\", capt)\n"},{"alpha_fraction":0.5311464667320251,"alphanum_fraction":0.5333002209663391,"avg_line_length":29.18000030517578,"blob_id":"380d2d21531b2d4340e47974bbab6996a4ca91db","content_id":"3b29dc81f7089c2ebce54bccbe359d51b674148d","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6036,"license_type":"permissive","max_line_length":106,"num_lines":200,"path":"/gamestonk_terminal/cryptocurrency/binance/binance_controller.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"\"\"\"Cryptocurrency Controller\"\"\"\n__docformat__ = \"numpy\"\n# pylint: disable=R0904, C0302, W0622\nimport argparse\nimport os\nimport pandas as pd\nfrom prompt_toolkit.completion import NestedCompleter\nfrom gamestonk_terminal import feature_flags as gtff\nfrom gamestonk_terminal.helper_funcs import get_flair\nfrom gamestonk_terminal.menu import session\nfrom gamestonk_terminal.cryptocurrency.binance import binance_model\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\n\n\nclass BinanceController:\n\n CHOICES = [\n \"?\",\n \"cls\",\n \"help\",\n \"q\",\n \"quit\",\n \"book\",\n \"candle\",\n \"balance\",\n \"load\",\n \"ta\",\n \"clear\",\n ]\n\n def __init__(self):\n \"\"\"CONSTRUCTOR\"\"\"\n\n self._binance_parser = argparse.ArgumentParser(add_help=False, prog=\"bin\")\n self._binance_parser.add_argument(\"cmd\", choices=self.CHOICES)\n self.current_coin = None\n self.current_currency = None\n self.current_df = pd.DataFrame()\n self.source = \"\"\n\n def print_help(self):\n \"\"\"Print help\"\"\"\n print(\"\\nBinance:\")\n print(\" cls clear screen\")\n print(\" ?/help show this menu again\")\n print(\" q quit this menu, and shows back to main menu\")\n print(\" quit quit to abandon program\")\n print(\"\")\n print(f\"Loaded coin: {self.current_coin}\")\n print(\"\")\n print(\" load load coin/currency to use and load candle data\")\n print(\" clear clear loaded coin\")\n print(\" book show order book\")\n print(\" candle show candles\")\n print(\" balance show coin balance\")\n print(\"> ta open technical analysis menu\")\n print(\"\")\n\n def switch(self, an_input: str):\n \"\"\"Process and dispatch input\n\n Returns\n -------\n True, False or None\n False - quit the menu\n True - quit the program\n None - continue in the menu\n \"\"\"\n\n # Empty command\n if not an_input:\n print(\"\")\n return None\n\n (known_args, other_args) = self._binance_parser.parse_known_args(\n an_input.split()\n )\n\n # Help menu again\n if known_args.cmd == \"?\":\n self.print_help()\n return None\n\n # Clear screen\n if known_args.cmd == \"cls\":\n os.system(\"cls||clear\")\n return None\n\n return getattr(\n self, \"call_\" + known_args.cmd, lambda: \"Command not recognized!\"\n )(other_args)\n\n def call_help(self, _):\n \"\"\"Process Help command\"\"\"\n self.print_help()\n\n def call_q(self, _):\n \"\"\"Process Q command - quit the menu.\"\"\"\n print(\"Moving back to (crypto) menu\")\n return False\n\n def call_quit(self, _):\n \"\"\"Process Quit command - quit the program.\"\"\"\n return True\n\n def call_book(self, other_args):\n \"\"\"Process book command\"\"\"\n binance_model.order_book(other_args, self.current_coin, self.current_currency)\n\n def call_candle(self, _):\n \"\"\"Process candle command\"\"\"\n if self.current_coin is None and self.current_df.empty:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n else:\n binance_model.show_candles(\n self.current_df, self.current_coin, self.current_currency\n )\n\n def call_balance(self, _):\n \"\"\"Process balance command\"\"\"\n binance_model.balance(self.current_coin)\n\n def call_load(self, other_args):\n \"\"\"Process select command\"\"\"\n (\n self.current_coin,\n self.current_currency,\n self.current_df,\n ) = binance_model.select_binance_coin(other_args)\n self.source = \"BIN\"\n print(\"\")\n\n def call_clear(self, _):\n \"\"\"Process clear command\"\"\"\n if self.current_coin:\n print(\n f\"Current coin {self.current_coin} was removed. You can load new coin with load -c <coin>\"\n )\n print(\"\")\n self.current_coin = None\n self.current_currency = None\n self.current_df = pd.DataFrame()\n else:\n print(\"No coin selected. Use 'load' to load the coin you want to look at.\")\n print(\"\")\n\n # pylint: disable=inconsistent-return-statements\n def call_ta(self, _):\n \"\"\"Process ta command\"\"\"\n if not self.current_coin:\n print(\"Please load a coin through either load -c \", \"\\n\")\n\n elif self.current_df.empty:\n print(\"Price dataframe is empty\")\n\n else:\n\n try:\n quit = ta_controller.menu(\n stock=self.current_df,\n ticker=self.current_coin,\n start=self.current_df.index[0],\n interval=\"\",\n )\n print(\"\")\n if quit is not None:\n if quit is True:\n return quit\n\n except (ValueError, KeyError) as e:\n print(e)\n\n\ndef menu():\n binance_controller = BinanceController()\n binance_controller.print_help()\n\n while True:\n # Get input command from user\n if session and gtff.USE_PROMPT_TOOLKIT:\n completer = NestedCompleter.from_nested_dict(\n {c: None for c in binance_controller.CHOICES}\n )\n an_input = session.prompt(\n f\"{get_flair()} (crypto)>(bin)> \",\n completer=completer,\n )\n else:\n an_input = input(f\"{get_flair()} (crypto)>(bin)> \")\n\n try:\n process_input = binance_controller.switch(an_input)\n\n if process_input is not None:\n return process_input\n\n except SystemExit:\n print(\"The command selected doesn't exist\\n\")\n continue\n"},{"alpha_fraction":0.6536450386047363,"alphanum_fraction":0.6576437950134277,"avg_line_length":34.72527313232422,"blob_id":"389908adb15d957951617b74616673e0223e1621","content_id":"a13dd7b14591d54930150bf01aa8d98b7dfa5eef","detected_licenses":["LicenseRef-scancode-free-unknown","MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3251,"license_type":"permissive","max_line_length":121,"num_lines":91,"path":"/gamestonk_terminal/stocks/discovery/sentimentinvestor_view.py","repo_name":"Meg1211/GamestonkTerminal","src_encoding":"UTF-8","text":"import argparse\nimport logging\nimport textwrap\nfrom typing import Any, List\n\nfrom colorama import Style\nfrom sentipy.sentipy import Sentipy\nfrom tabulate import tabulate\n\nfrom gamestonk_terminal import config_terminal as cfg\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\n\nsentipy: Sentipy = Sentipy(\n token=cfg.API_SENTIMENTINVESTOR_TOKEN, key=cfg.API_SENTIMENTINVESTOR_KEY\n)\n\"\"\"Initialise SentiPy with the user's API token and key\"\"\"\n\n\n__command_descriptions = {\n \"popular\": f\"\"\"\n The {Style.BRIGHT}popular{Style.RESET_ALL} command prints the stocks with highest Average Hype Index right now.\n\n {Style.BRIGHT}AHI (Absolute Hype Index){Style.RESET_ALL}\n ---\n AHI is a measure of how much people are talking about a stock on social media.\n It is calculated by dividing the total number of mentions for the chosen stock\n on a social network by the mean number of mentions any stock receives on that\n social medium.\n\n ===\n\n {Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to\n generate hourly metrics on over 2,000 stocks. Sentiment provides volume and\n sentiment metrics powered by proprietary NLP models.\n \"\"\",\n \"emerging\": f\"\"\"\n The {Style.BRIGHT}emerging{Style.RESET_ALL} command prints the stocks with highest Relative Hype Index right now.\n\n {Style.BRIGHT}RHI (Relative Hype Index){Style.RESET_ALL}\n ---\n RHI is a measure of whether people are talking about a stock more or less than\n usual, calculated by dividing the mean AHI for the past day by the mean AHI for\n for the past week for that stock.\n\n ===\n\n {Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to\n generate hourly metrics on over 2,000 stocks. Sentiment provides volume and\n sentiment metrics powered by proprietary NLP models.\n \"\"\",\n}\n\n\ndef sort(metric: str, other_args: List[str], command_name: str) -> None:\n parser = argparse.ArgumentParser(\n add_help=False,\n prog=command_name,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=textwrap.dedent(__command_descriptions[command_name]),\n )\n\n parser.add_argument(\n \"-l\",\n \"--limit\",\n action=\"store\",\n dest=\"limit\",\n type=int,\n default=10,\n help=\"the maximum number of stocks to retrieve\",\n )\n\n try:\n ns_parser = parse_known_args_and_warn(parser, other_args)\n if not ns_parser:\n return\n\n data = sentipy.sort(metric, ns_parser.limit)\n\n table: List[List[Any]] = []\n for index, stock in enumerate(data):\n if not hasattr(stock, \"symbol\") or not hasattr(stock, metric):\n logging.warning(\"data for stock %s is incomplete, ignoring\", index + 1)\n table.append([])\n else:\n table.append([index + 1, stock.symbol, stock.__getattribute__(metric)])\n\n print(tabulate(table, headers=[\"Rank\", \"Ticker\", metric], floatfmt=\".3f\"))\n print()\n except Exception as e:\n logging.error(e)\n print(e, \"\\n\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5313138961791992,\n \"alphanum_fraction\": 0.5361996293067932,\n \"avg_line_length\": 26.95806884765625,\n \"blob_id\": \"aa2a34288681e0c74c25c8cb30b483aa77fc7dfe\",\n \"content_id\": \"e1989dcd56ec2d16a70a82c74f619bf09ed43fac\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 44006,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 1574,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"CoinPaprika View\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport difflib\\nfrom tabulate import tabulate\\nfrom pandas.plotting import register_matplotlib_converters\\nimport matplotlib.pyplot as plt\\nimport mplfinance as mpf\\nimport pandas as pd\\n\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, check_positive\\nfrom gamestonk_terminal.helper_funcs import plot_autoscale\\nfrom gamestonk_terminal.feature_flags import USE_ION as ion\\nimport gamestonk_terminal.cryptocurrency.coinpaprika.coinpaprika_model as paprika\\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import (\\n long_number_format_with_type_check,\\n)\\n\\n\\nregister_matplotlib_converters()\\n\\n# pylint: disable=inconsistent-return-statements\\n# pylint: disable=C0302, too-many-lines\\n\\nCURRENCIES = [\\n \\\"BTC\\\",\\n \\\"ETH\\\",\\n \\\"USD\\\",\\n \\\"EUR\\\",\\n \\\"PLN\\\",\\n \\\"KRW\\\",\\n \\\"GBP\\\",\\n \\\"CAD\\\",\\n \\\"JPY\\\",\\n \\\"RUB\\\",\\n \\\"TRY\\\",\\n \\\"NZD\\\",\\n \\\"AUD\\\",\\n \\\"CHF\\\",\\n \\\"UAH\\\",\\n \\\"HKD\\\",\\n \\\"SGD\\\",\\n \\\"NGN\\\",\\n \\\"PHP\\\",\\n \\\"MXN\\\",\\n \\\"BRL\\\",\\n \\\"THB\\\",\\n \\\"CLP\\\",\\n \\\"CNY\\\",\\n \\\"CZK\\\",\\n \\\"DKK\\\",\\n \\\"HUF\\\",\\n \\\"IDR\\\",\\n \\\"ILS\\\",\\n \\\"INR\\\",\\n \\\"MYR\\\",\\n \\\"NOK\\\",\\n \\\"PKR\\\",\\n \\\"SEK\\\",\\n \\\"TWD\\\",\\n \\\"ZAR\\\",\\n \\\"VND\\\",\\n \\\"BOB\\\",\\n \\\"COP\\\",\\n \\\"PEN\\\",\\n \\\"ARS\\\",\\n \\\"ISK\\\",\\n]\\n\\n# see https://github.com/GamestonkTerminal/GamestonkTerminal/pull/562#issuecomment-887842888\\n# EXCHANGES = paprika.get_list_of_exchanges()\\n\\n\\ndef global_market(other_args: List[str]):\\n \\\"\\\"\\\"Return data frame with most important global crypto statistics like:\\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\\n market_cap_change_24h, volume_24h_change_24h, last_updated,\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"global\\\",\\n description=\\\"\\\"\\\"Show most important global crypto statistics like:\\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\\n market_cap_change_24h, volume_24h_change_24h, last_updated.\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_global_market()\\n df[\\\"Value\\\"] = df[\\\"Value\\\"].apply(lambda x: long_number_format_with_type_check(x))\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef coins(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of all available coins on CoinPaprika\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"coins\\\",\\n description=\\\"\\\"\\\"Shows list of all available coins on CoinPaprika.\\n You can display top N number of coins with --top N flag,\\n You can search by starting letters with -l/--letter flag like `coins -l M`\\n And you can also specify by which column you are searching for coin with --key\\n Displays columns like:\\n rank, id, name, type\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--skip\\\",\\n default=0,\\n dest=\\\"skip\\\",\\n help=\\\"Skip n of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=30,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\\"-l\\\", \\\"--letter\\\", dest=\\\"letter\\\", help=\\\"First letters\\\", type=str)\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--key\\\",\\n dest=\\\"key\\\",\\n help=\\\"Search in column symbol, name, id\\\",\\n type=str,\\n choices=[\\\"id\\\", \\\"symbol\\\", \\\"name\\\"],\\n default=\\\"symbol\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_list_of_coins()\\n\\n letter = ns_parser.letter\\n if letter and isinstance(letter, str):\\n df = df[\\n df[ns_parser.key].str.match(f\\\"^({letter.lower()}|{letter.upper()})\\\")\\n ]\\n\\n try:\\n df = df[ns_parser.skip : ns_parser.skip + ns_parser.top]\\n except Exception:\\n df = paprika.get_list_of_coins()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef all_coins_market_info(other_args: List[str]):\\n \\\"\\\"\\\"Displays basic market information for all coins from CoinPaprika API\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"markets\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Show market related (price, supply, volume) coin information for all coins on CoinPaprika.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, volume_24h, mcap_change_24h, pct_change_1h, pct_change_24h,\\n ath_price, pct_from_ath, --sort parameter and also with --descend flag to sort descending.\\n Displays:\\n rank, name, symbol, price, volume_24h, mcap_change_24h,\\n pct_change_1h, pct_change_24h, ath_price, pct_from_ath,\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n help=\\\"Quoted currency. Default USD\\\",\\n dest=\\\"vs\\\",\\n default=\\\"USD\\\",\\n type=str,\\n choices=CURRENCIES,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"volume_24h\\\",\\n \\\"mcap_change_24h\\\",\\n \\\"pct_change_1h\\\",\\n \\\"pct_change_24h\\\",\\n \\\"ath_price\\\",\\n \\\"pct_from_ath\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_coins_market_info(quotes=ns_parser.vs).sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return\\n\\n cols = [col for col in df.columns if col != \\\"rank\\\"]\\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\\n print(\\\"\\\")\\n print(f\\\"Displaying data vs {ns_parser.vs}\\\")\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".3f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef all_coins_info(other_args: List[str]):\\n \\\"\\\"\\\"Displays basic coin information for all coins from CoinPaprika API\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"info\\\",\\n description=\\\"\\\"\\\"Show basic coin information for all coins from CoinPaprika API\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, volume_24h, circulating_supply, total_supply, max_supply,\\n market_cap, beta_value, ath_price --sort parameter and also with --descend flag to sort descending.\\n Displays:\\n rank, name, symbol, price, volume_24h, circulating_supply,\\n total_supply, max_supply, market_cap, beta_value, ath_price\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n help=\\\"Quoted currency. Default USD\\\",\\n dest=\\\"vs\\\",\\n default=\\\"USD\\\",\\n type=str,\\n choices=CURRENCIES,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"volume_24h\\\",\\n \\\"circulating_supply\\\",\\n \\\"total_supply\\\",\\n \\\"max_supply\\\",\\n \\\"ath_price\\\",\\n \\\"market_cap\\\",\\n \\\"beta_value\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_coins_info(quotes=ns_parser.vs).sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if df.empty:\\n print(\\\"Not data found\\\", \\\"\\\\n\\\")\\n return\\n\\n cols = [col for col in df.columns if col != \\\"rank\\\"]\\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\\n\\n print(\\\"\\\")\\n print(f\\\"Displaying data vs {ns_parser.vs}\\\")\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".3f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef all_exchanges(other_args: List[str]):\\n \\\"\\\"\\\"List exchanges from CoinPaprika API\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"exchanges\\\",\\n description=\\\"\\\"\\\"Show all exchanges from CoinPaprika\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, currencies, markets, fiats, confidence,\\n volume_24h,volume_7d ,volume_30d, sessions_per_month --sort parameter\\n and also with --descend flag to sort descending.\\n Displays:\\n rank, name, currencies, markets, fiats, confidence, volume_24h,\\n volume_7d ,volume_30d, sessions_per_month\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n help=\\\"Quoted currency. Default USD\\\",\\n dest=\\\"vs\\\",\\n default=\\\"USD\\\",\\n type=str,\\n choices=CURRENCIES,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"currencies\\\",\\n \\\"markets\\\",\\n \\\"fiats\\\",\\n \\\"confidence\\\",\\n \\\"volume_24h\\\",\\n \\\"volume_7d\\\",\\n \\\"volume_30d\\\",\\n \\\"sessions_per_month\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_list_of_exchanges(quotes=ns_parser.vs).sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return\\n\\n cols = [col for col in df.columns if col != \\\"rank\\\"]\\n df[cols] = df[cols].applymap(lambda x: long_number_format_with_type_check(x))\\n print(\\\"\\\")\\n print(f\\\"Displaying data vs {ns_parser.vs}\\\")\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef search(other_args: List[str]):\\n \\\"\\\"\\\"Search in CoinPaprika\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"search\\\",\\n description=\\\"\\\"\\\"Search over CoinPaprika API\\n You can display only top N number of results with --top parameter.\\n You can sort data by id, name , category --sort parameter and also with --descend flag to sort descending.\\n To choose category in which you are searching for use --cat/-c parameter. Available categories:\\n currencies|exchanges|icos|people|tags|all\\n Displays:\\n id, name, category\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-q\\\",\\n \\\"--query\\\",\\n help=\\\"phrase for search\\\",\\n dest=\\\"query\\\",\\n type=str,\\n required=\\\"-h\\\" not in other_args,\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--cat\\\",\\n help=\\\"Categories to search: currencies|exchanges|icos|people|tags|all. Default: all\\\",\\n dest=\\\"category\\\",\\n default=\\\"all\\\",\\n type=str,\\n choices=[\\n \\\"currencies\\\",\\n \\\"exchanges\\\",\\n \\\"icos\\\",\\n \\\"people\\\",\\n \\\"tags\\\",\\n \\\"all\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: id\\\",\\n default=\\\"id\\\",\\n choices=[\\\"category\\\", \\\"id\\\", \\\"name\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n\\n if other_args:\\n if not other_args[0][0] == \\\"-\\\":\\n other_args.insert(0, \\\"-q\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n category = ns_parser.category\\n if category.lower() == \\\"all\\\":\\n category = \\\"currencies,exchanges,icos,people,tags\\\"\\n\\n df = paprika.search(q=ns_parser.query, c=category)\\n\\n if df.empty:\\n print(\\n f\\\"No results for search query '{ns_parser.query}' in category '{ns_parser.category}'\\\"\\n )\\n print(\\\"\\\")\\n return\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef exchange_markets(other_args: List[str]):\\n \\\"\\\"\\\"Get all markets for given exchange\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"ex_markets\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Get all exchange markets found for given exchange\\n You can display only top N number of records with --top parameter.\\n You can sort data by pair, base_currency_name, quote_currency_name, market_url, category,\\n reported_volume_24h_share, trust_score --sort parameter and also with --descend flag to sort descending.\\n You can use additional flag --links to see urls for each market\\n Displays:\\n exchange_id, pair, base_currency_name, quote_currency_name, market_url,\\n category, reported_volume_24h_share, trust_score,\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-e\\\",\\n \\\"--exchange\\\",\\n help=\\\"Identifier of exchange e.g for Binance Exchange -> binance\\\",\\n dest=\\\"exchange\\\",\\n default=\\\"binance\\\",\\n type=str,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: reported_volume_24h_share\\\",\\n default=\\\"reported_volume_24h_share\\\",\\n choices=[\\n \\\"pair\\\",\\n \\\"base_currency_name\\\",\\n \\\"quote_currency_name\\\",\\n \\\"category\\\",\\n \\\"reported_volume_24h_share\\\",\\n \\\"trust_score\\\",\\n \\\"market_url\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=False,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Flag to show urls. If you will use that flag you will see only:\\n exchange, pair, trust_score, market_url columns\\\"\\\"\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_exchanges_market(exchange_id=ns_parser.exchange)\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n if ns_parser.links is True:\\n df = df[[\\\"exchange_id\\\", \\\"pair\\\", \\\"trust_score\\\", \\\"market_url\\\"]]\\n else:\\n df.drop(\\\"market_url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef all_platforms(other_args: List[str]):\\n \\\"\\\"\\\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"platforms\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_all_contract_platforms()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef contracts(other_args: List[str]):\\n \\\"\\\"\\\"Gets all contract addresses for given platform\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"contracts\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Gets all contract addresses for given platform.\\n Provide platform id with -p/--platform parameter\\n You can display only top N number of smart contracts with --top parameter.\\n You can sort data by id, type, active, address --sort parameter\\n and also with --descend flag to sort descending.\\n\\n Displays:\\n id, type, active, address\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--platform\\\",\\n help=\\\"Blockchain platform like eth-ethereum\\\",\\n dest=\\\"platform\\\",\\n default=\\\"eth-ethereum\\\",\\n type=str,\\n choices=paprika.get_all_contract_platforms()[\\\"platform_id\\\"].tolist(),\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column\\\",\\n default=\\\"id\\\",\\n choices=[\\\"id\\\", \\\"type\\\", \\\"active\\\", \\\"address\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_contract_platform(ns_parser.platform)\\n\\n if df.empty:\\n print(f\\\"Nothing found for platform: {ns_parser.platform}\\\", \\\"\\\\n\\\")\\n return\\n\\n df = df.sort_values(ns_parser.sortby, ascending=ns_parser.descend)\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\n# Coin Related\\n\\n\\ndef find(other_args: List[str]):\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"find\\\",\\n description=\\\"\\\"\\\"\\n Find similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinPaprika,\\n you can use this command to display coins with similar name, symbol or id to your search query.\\n Example of usage: coin name is something like \\\"kusama\\\". So : find -c kusama -k name -t 25\\n It will search for coin that has similar name to kusama and display top 25 matches.\\n -c, --coin stands for coin - you provide here your search query\\n -k, --key it's a searching key. You can search by symbol, id or name of coin\\n -t, --top it displays top N number of records.\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n help=\\\"Coin name or id, or symbol\\\",\\n dest=\\\"coin\\\",\\n required=\\\"-h\\\" not in other_args,\\n type=str,\\n )\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--key\\\",\\n dest=\\\"key\\\",\\n help=\\\"Specify by which column you would like to search: symbol, name, id\\\",\\n type=str,\\n choices=[\\\"id\\\", \\\"symbol\\\", \\\"name\\\"],\\n default=\\\"name\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n\\n try:\\n\\n if other_args:\\n if not other_args[0][0] == \\\"-\\\":\\n other_args.insert(0, \\\"-c\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if not ns_parser.coin or ns_parser.coin is None:\\n print(\\n \\\"You didn't provide coin. Please use param -c/--coin <coin name>\\\", \\\"\\\\n\\\"\\n )\\n return\\n\\n coins_df = paprika.get_list_of_coins()\\n coins_list = coins_df[ns_parser.key].to_list()\\n\\n keys = {\\\"name\\\": \\\"title\\\", \\\"symbol\\\": \\\"upper\\\", \\\"id\\\": \\\"lower\\\"}\\n\\n key = keys.get(ns_parser.key)\\n coin = getattr(ns_parser.coin, str(key))()\\n\\n sim = difflib.get_close_matches(coin, coins_list, ns_parser.top)\\n df = pd.Series(sim).to_frame().reset_index()\\n df.columns = [\\\"index\\\", ns_parser.key]\\n df = df.merge(coins_df, on=ns_parser.key)\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef twitter(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get twitter timeline for given coin id. Not more than last 50 tweets\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"twitter\\\",\\n description=\\\"\\\"\\\"Show last 10 tweets for given coin.\\n You can display only top N number of tweets with --top parameter.\\n You can sort data by date, user_name, status, retweet_count, like_count --sort parameter\\n and also with --descend flag to sort descending.\\n Displays:\\n date, user_name, status, retweet_count, like_count\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: date\\\",\\n default=\\\"date\\\",\\n choices=[\\\"date\\\", \\\"user_name\\\", \\\"status\\\", \\\"retweet_count\\\", \\\"like_count\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_coin_twitter_timeline(coin_id)\\n\\n if df.empty:\\n print(f\\\"Couldn't find any tweets for coin {coin_id}\\\", \\\"\\\\n\\\")\\n return\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n # Remove unicode chars (it breaks pretty tables)\\n df[\\\"status\\\"] = df[\\\"status\\\"].apply(\\n lambda text: \\\"\\\".join(i if ord(i) < 128 else \\\"\\\" for i in text)\\n )\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef events(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get all events for given coin id.\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"events\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Show information about most important coins events. Most of coins doesn't have any events.\\n You can display only top N number of events with --top parameter.\\n You can sort data by id, date , date_to, name, description, is_conference --sort parameter\\n and also with --descend flag to sort descending.\\n You can use additional flag --links to see urls for each event\\n Displays:\\n date , date_to, name, description, is_conference, link, proof_image_link\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: date\\\",\\n default=\\\"date\\\",\\n choices=[\\\"date\\\", \\\"date_to\\\", \\\"name\\\", \\\"description\\\", \\\"is_conference\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=False,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will see only date, name, link columns\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_coin_events_by_id(coin_id)\\n\\n if df.empty:\\n print(f\\\"Couldn't find any events for coin {coin_id}\\\")\\n print(\\\"\\\")\\n return\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n if ns_parser.links is True:\\n df = df[[\\\"date\\\", \\\"name\\\", \\\"link\\\"]]\\n else:\\n df.drop(\\\"link\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef exchanges(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get all exchanges for given coin id.\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ex\\\",\\n description=\\\"\\\"\\\"Get all exchanges found for given coin.\\n You can display only top N number of exchanges with --top parameter.\\n You can sort data by id, name, adjusted_volume_24h_share, fiats --sort parameter\\n and also with --descend flag to sort descending.\\n Displays:\\n id, name, adjusted_volume_24h_share, fiats\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: date\\\",\\n default=\\\"adjusted_volume_24h_share\\\",\\n choices=[\\\"id\\\", \\\"name\\\", \\\"adjusted_volume_24h_share\\\", \\\"fiats\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_coin_exchanges_by_id(coin_id)\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef markets(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get all markets for given coin id.\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"mkt\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Get all markets found for given coin.\\n You can display only top N number of markets with --top parameter.\\n You can sort data by pct_volume_share, exchange, pair, trust_score, volume, price --sort parameter\\n and also with --descend flag to sort descending.\\n You can use additional flag --links to see urls for each market\\n Displays:\\n exchange, pair, trust_score, volume, price, pct_volume_share,\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n help=\\\"Quoted currency. Default USD\\\",\\n dest=\\\"vs\\\",\\n default=\\\"USD\\\",\\n type=str,\\n choices=CURRENCIES,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=20,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: pct_volume_share\\\",\\n default=\\\"pct_volume_share\\\",\\n choices=[\\n \\\"pct_volume_share\\\",\\n \\\"exchange\\\",\\n \\\"pair\\\",\\n \\\"trust_score\\\",\\n \\\"volume\\\",\\n \\\"price\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=False,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Flag to show urls. If you will use that flag you will see only:\\n exchange, pair, trust_score, market_url columns\\\"\\\"\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n vs = ns_parser.vs\\n sort = ns_parser.sortby\\n if sort in [\\\"volume\\\", \\\"price\\\"]:\\n sort = f\\\"{str(vs).lower()}_{sort}\\\"\\n\\n df = paprika.get_coin_markets_by_id(coin_id, vs)\\n\\n if df.empty:\\n print(\\\"There is no data\\\")\\n return\\n\\n df = df.sort_values(by=sort, ascending=ns_parser.descend)\\n\\n if ns_parser.links is True:\\n df = df[[\\\"exchange\\\", \\\"pair\\\", \\\"trust_score\\\", \\\"market_url\\\"]]\\n else:\\n df.drop(\\\"market_url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef chart(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Plots chart for loaded cryptocurrency\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"chart\\\",\\n description=\\\"\\\"\\\"Display chart for loaded coin. You can specify currency vs which you want\\n to show chart and also number of days to get data for.\\n By default currency: usd and days: 90.\\n E.g. if you loaded in previous step Ethereum and you want to see it's price vs bitcoin\\n in last 90 days range use `chart --vs btc --days 90`\\n Available quoted currencies are only btc and usd\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n default=\\\"usd\\\",\\n dest=\\\"vs\\\",\\n help=\\\"Currency to display vs coin\\\",\\n choices=[\\\"usd\\\", \\\"btc\\\", \\\"BTC\\\", \\\"USD\\\"],\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--days\\\",\\n default=30,\\n dest=\\\"days\\\",\\n type=check_positive,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = paprika.get_ohlc_historical(coin_id, ns_parser.vs.upper(), ns_parser.days)\\n\\n if df.empty:\\n print(\\\"There is not data to plot chart\\\\n\\\")\\n return\\n\\n df.drop([\\\"time_close\\\", \\\"market_cap\\\"], axis=1, inplace=True)\\n df.columns = [\\n \\\"Time0\\\",\\n \\\"Open\\\",\\n \\\"High\\\",\\n \\\"Low\\\",\\n \\\"Close\\\",\\n \\\"Volume\\\",\\n ]\\n df = df.set_index(pd.to_datetime(df[\\\"Time0\\\"])).drop(\\\"Time0\\\", axis=1)\\n title = (\\n f\\\"\\\\n{coin_id}/{ns_parser.vs} from {df.index[0].strftime('%Y/%m/%d')} to {df.index[-1].strftime('%Y/%m/%d')}\\\",\\n )\\n df[\\\"Volume\\\"] = df[\\\"Volume\\\"] / 1_000_000\\n mpf.plot(\\n df,\\n type=\\\"candle\\\",\\n volume=True,\\n ylabel_lower=\\\"Volume [1M]\\\",\\n title=str(title[0]) if isinstance(title, tuple) else title,\\n xrotation=20,\\n style=\\\"binance\\\",\\n figratio=(10, 7),\\n figscale=1.10,\\n figsize=(plot_autoscale()),\\n update_width_config=dict(\\n candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0\\n ),\\n )\\n\\n if ion:\\n plt.ion()\\n plt.show()\\n print(\\\"\\\")\\n except SystemExit:\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef price_supply(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get ticker information for single coin\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"ps\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Get price and supply related metrics for given coin.\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n help=\\\"Quoted currency. Default USD\\\",\\n dest=\\\"vs\\\",\\n default=\\\"USD\\\",\\n type=str,\\n choices=CURRENCIES,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.get_tickers_info_for_coin(coin_id, ns_parser.vs)\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return\\n\\n df = df.applymap(lambda x: long_number_format_with_type_check(x))\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef load(other_args: List[str]):\\n \\\"\\\"\\\"Select coin from CoinPaprika\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n\\n Returns\\n -------\\n coin: str\\n Coin that is defined on binance\\n df_coin : pd.DataFrame\\n Dataframe of prices for selected coin\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"load\\\",\\n description=\\\"Define the coin to be used from CoinPaprika and get data\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n help=\\\"Coin to get\\\",\\n dest=\\\"coin\\\",\\n type=str,\\n required=\\\"-h\\\" not in other_args,\\n )\\n\\n try:\\n\\n if other_args:\\n if not other_args[0][0] == \\\"-\\\":\\n other_args.insert(0, \\\"-c\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n COINS = paprika.get_list_of_coins()\\n COINS_DCT = dict(zip(COINS.id, COINS.symbol))\\n coin_id, _ = paprika.validate_coin(ns_parser.coin, COINS_DCT)\\n\\n if coin_id:\\n print(\\\"\\\")\\n return coin_id\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef ta(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Load data for Technical Analysis\\n\\n Parameters\\n ----------\\n coin_id: Identifier of coin for CoinPaprika\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ta\\\",\\n description=\\\"\\\"\\\"Loads data for technical analysis. You can specify currency vs which you want\\n to show chart and also number of days to get data for.\\n By default currency: usd and days: 30.\\n E.g. if you loaded in previous step Ethereum and you want to see it's price vs btc\\n in last 90 days range use `ta --vs btc --days 90`\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\",\\n default=\\\"usd\\\",\\n dest=\\\"vs\\\",\\n help=\\\"Currency to display vs coin\\\",\\n choices=[\\\"usd\\\", \\\"btc\\\", \\\"BTC\\\", \\\"USD\\\"],\\n type=str,\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--days\\\",\\n default=30,\\n dest=\\\"days\\\",\\n help=\\\"Number of days to get data for\\\",\\n type=check_positive,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return None, None\\n\\n df = paprika.get_ohlc_historical(coin_id, ns_parser.vs.upper(), ns_parser.days)\\n\\n if df.empty:\\n print(\\\"No data found\\\", \\\"\\\\n\\\")\\n return None, None\\n\\n df.drop([\\\"time_close\\\", \\\"market_cap\\\"], axis=1, inplace=True)\\n df.columns = [\\n \\\"Time0\\\",\\n \\\"Open\\\",\\n \\\"High\\\",\\n \\\"Low\\\",\\n \\\"Close\\\",\\n \\\"Volume\\\",\\n ]\\n df = df.set_index(pd.to_datetime(df[\\\"Time0\\\"])).drop(\\\"Time0\\\", axis=1)\\n return df, ns_parser.vs\\n\\n except SystemExit:\\n print(\\\"\\\")\\n return None, None\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return None, None\\n\\n\\ndef basic(coin_id: str, other_args: List[str]):\\n \\\"\\\"\\\"Get basic information for coin. Like:\\n name, symbol, rank, type, description, platform, proof_type,\\n contract, tags, parent\\n\\n Parameters\\n ----------\\n coin_id: str\\n Identifier of coin for CoinPaprika API\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"basic\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Get basic information for coin. Like:\\n name, symbol, rank, type, description, platform, proof_type,\\n contract, tags, parent\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = paprika.basic_coin_info(coin_id)\\n\\n if df.empty:\\n print(\\\"No data available\\\\n\\\")\\n return\\n\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5048558115959167,\n \"alphanum_fraction\": 0.5055915117263794,\n \"avg_line_length\": 32.810943603515625,\n \"blob_id\": \"8365c269a6fa1b9a6fb8cd3757430fb46cf60a91\",\n \"content_id\": \"99c2a1da539c97f9f34b5eca6926ce3a8cb87dbe\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6796,\n \"license_type\": \"permissive\",\n \"max_line_length\": 116,\n \"num_lines\": 201,\n \"path\": \"/gamestonk_terminal/etf/screener_model.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Screener model\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport os\\nimport configparser\\nimport pandas as pd\\nfrom tabulate import tabulate\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, export_data\\n\\n\\ndef etf_screener(other_args: List[str]):\\n \\\"\\\"\\\"\\n Screens the etfs pulled from my repo, which is updated daily at midnight EST\\n Parameters\\n ----------\\n other_args:\\n List or argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"etfscr\\\",\\n add_help=False,\\n description=\\\"Screens ETFS from a personal scraping github repository. Data scraped from stockanalysis.com\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-p\\\", \\\"--min_price\\\", help=\\\"min price\\\", dest=\\\"min_price\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-P\\\", \\\"--max_price\\\", help=\\\"max price\\\", dest=\\\"max_price\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-a\\\", \\\"--min_assets\\\", help=\\\"min assets ($M)\\\", dest=\\\"min_assets\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-A\\\", \\\"--max_assets\\\", help=\\\"max assets ($M)\\\", dest=\\\"max_assets\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--min_nav\\\",\\n help=\\\"min nav (net asset value)\\\",\\n dest=\\\"min_nav\\\",\\n default=False,\\n )\\n\\n parser.add_argument(\\n \\\"-N\\\",\\n \\\"--max_nav\\\",\\n help=\\\"max nav (net asset value)\\\",\\n dest=\\\"max_nav\\\",\\n default=False,\\n )\\n parser.add_argument(\\n \\\"-e\\\", \\\"--min_exp\\\", help=\\\"min expense ratio (%%)\\\", dest=\\\"min_exp\\\", default=False\\n )\\n\\n parser.add_argument(\\n \\\"-E\\\", \\\"--max_exp\\\", help=\\\"max expense ratio (%%)\\\", dest=\\\"max_exp\\\", default=False\\n )\\n\\n parser.add_argument(\\n \\\"-r\\\", \\\"--min_pe\\\", help=\\\"min pe ratio\\\", dest=\\\"min_pe\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-R\\\", \\\"--max_pe\\\", help=\\\"max pe ratio\\\", dest=\\\"max_pe\\\", default=False\\n )\\n\\n parser.add_argument(\\n \\\"-d\\\", \\\"--min_div\\\", help=\\\"min dividend yield (%%)\\\", dest=\\\"min_div\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-D\\\", \\\"--max_div\\\", help=\\\"max dividend yield (%%)\\\", dest=\\\"max_div\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-b\\\", \\\"--min_beta\\\", help=\\\"min 5Y beta\\\", dest=\\\"min_beta\\\", default=False\\n )\\n parser.add_argument(\\n \\\"-B\\\", \\\"--max_beta\\\", help=\\\"max beta\\\", dest=\\\"max_beta\\\", default=False\\n )\\n parser.add_argument(\\n \\\"--num\\\", type=int, help=\\\"Number of etfs to show\\\", dest=\\\"num\\\", default=20\\n )\\n\\n parser.add_argument(\\n \\\"--config\\\",\\n help=\\\"Load options from config file\\\",\\n dest=\\\"config\\\",\\n action=\\\"store_true\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n # pylint: disable=no-member\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = pd.read_csv(\\n \\\"https://raw.githubusercontent.com/jmaslek/etf_scraper/main/etf_overviews.csv\\\",\\n index_col=0,\\n )\\n print(\\\"ETFs downloaded\\\\n\\\")\\n param_string = \\\"etf_screener\\\"\\n if ns_parser.config:\\n cf = configparser.ConfigParser()\\n cf.read(\\\"gamestonk_terminal/etf/etf_config.ini\\\")\\n cols = cf.sections()\\n\\n for col in cols:\\n if cf[col][\\\"Min\\\"] != \\\"None\\\":\\n query = f\\\"{col} > {cf[col]['Min']} \\\"\\n df = df.query(query)\\n if cf[col][\\\"Max\\\"] != \\\"None\\\":\\n query = f\\\"{col} < {cf[col]['Max']} \\\"\\n df = df.query(query)\\n param_string += \\\"_from_config\\\"\\n else:\\n\\n if ns_parser.min_price:\\n df = df.query(f\\\"Price > {ns_parser.min_price}\\\")\\n param_string += f\\\"_p_{ns_parser.min_price}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.max_price:\\n df = df.query(f\\\"Price < {ns_parser.max_price}\\\")\\n param_string += f\\\"_P_{ns_parser.max_price}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_assets:\\n df = df.query(f\\\"Assets > {ns_parser.min_assets}\\\")\\n param_string += f\\\"_a_{ns_parser.min_assets}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.max_assets:\\n df = df.query(f\\\"Assets < {ns_parser.max_assets}\\\")\\n param_string += f\\\"_A_{ns_parser.max_assets}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_nav:\\n df = df.query(f\\\"NAV > {ns_parser.min_nav}\\\")\\n param_string += f\\\"_n_{ns_parser.min_nav}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.max_nav:\\n df = df.query(f\\\"NAV < {ns_parser.max_nav}\\\")\\n param_string += f\\\"_N_{ns_parser.max_nav}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_exp:\\n df = df.query(f\\\"Expense > {ns_parser.min_exp}\\\")\\n param_string += f\\\"_e_{ns_parser.min_exp}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.max_exp:\\n df = df.query(f\\\"Expense < {ns_parser.max_exp}\\\")\\n param_string += f\\\"_E_{ns_parser.max_exp}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_pe:\\n df = df.query(f\\\"PE > {ns_parser.min_pe}\\\")\\n param_string += f\\\"_r_{ns_parser.min_pe}\\\".replace(\\\".\\\", \\\"p\\\")\\n if ns_parser.max_pe:\\n df = df.query(f\\\"PE < {ns_parser.max_pe}\\\")\\n param_string += f\\\"_R_{ns_parser.max_pe}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_div:\\n df = df.query(f\\\"DivYield > {ns_parser.min_div}\\\")\\n param_string += f\\\"_d_{ns_parser.min_div}\\\".replace(\\\".\\\", \\\"p\\\")\\n if ns_parser.max_div:\\n df = df.query(f\\\"DivYield < {ns_parser.max_div}\\\")\\n param_string += f\\\"_D_{ns_parser.max_div}\\\".replace(\\\".\\\", \\\"p\\\")\\n\\n if ns_parser.min_beta:\\n df = df.query(f\\\"Beta > {ns_parser.min_beta}\\\")\\n param_string += f\\\"_b_{ns_parser.min_beta}\\\".replace(\\\".\\\", \\\"p\\\").replace(\\n \\\"-\\\", \\\"neg\\\"\\n )\\n\\n if ns_parser.max_beta:\\n df = df.query(f\\\"Beta < {ns_parser.max_beta}\\\")\\n param_string += f\\\"_B_{ns_parser.max_beta}\\\".replace(\\\".\\\", \\\"p\\\").replace(\\n \\\"-\\\", \\\"neg\\\"\\n )\\n\\n export_data(\\n ns_parser.export,\\n os.path.join(os.path.dirname(os.path.abspath(__file__)), \\\"screeners\\\"),\\n param_string,\\n df,\\n )\\n\\n if df.shape[0] > int(ns_parser.num):\\n df = df.sample(ns_parser.num)\\n print(\\n tabulate(\\n df.fillna(\\\"\\\"), tablefmt=\\\"fancy_grid\\\", headers=df.columns, showindex=True\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5245620608329773,\n \"alphanum_fraction\": 0.5257045030593872,\n \"avg_line_length\": 22.446428298950195,\n \"blob_id\": \"76b558d52c4a1383f88c2b914da6a22caac154b6\",\n \"content_id\": \"196487d0feed94a562f9ad79784b690da9cdb461\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5252,\n \"license_type\": \"permissive\",\n \"max_line_length\": 69,\n \"num_lines\": 224,\n \"path\": \"/gamestonk_terminal/economy/wsj_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"WSJ view \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom tabulate import tabulate\\n\\nfrom gamestonk_terminal.economy.wsj_model import (\\n market_overview,\\n us_bonds,\\n us_indices,\\n global_bonds,\\n global_currencies,\\n top_commodities,\\n)\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\\n\\n\\ndef display_overview(other_args: List[str]):\\n \\\"\\\"\\\"Display market overview\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"overview\\\",\\n description=\\\"WSJ market overview\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n data = market_overview()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef display_indices(other_args: List[str]):\\n \\\"\\\"\\\"Display us indices\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"indices\\\",\\n description=\\\"WSJ US Indices\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n data = us_indices()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef display_futures(other_args: List[str]):\\n \\\"\\\"\\\"Display futures/commodities\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"futures\\\",\\n description=\\\"WSJ futures/commodities\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n data = top_commodities()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef display_usbonds(other_args: List[str]):\\n \\\"\\\"\\\"Display us bonds overview\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"usbonds\\\",\\n description=\\\"WSJ US Bonds overview\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n data = us_bonds()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef display_glbonds(other_args: List[str]):\\n \\\"\\\"\\\"Display global bond overview\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"glbond\\\",\\n description=\\\"WSJ global bond overview\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n data = global_bonds()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef display_currencies(other_args: List[str]):\\n \\\"\\\"\\\"Display global currencies\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"currencies\\\",\\n description=\\\"WSJ currency overview\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n data = global_currencies()\\n print(\\n tabulate(\\n data,\\n showindex=False,\\n headers=data.columns,\\n floatfmt=\\\".2f\\\",\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.4917961359024048,\n \"alphanum_fraction\": 0.5026862025260925,\n \"avg_line_length\": 27.695833206176758,\n \"blob_id\": \"40b6cb89f5c09c5b022ac1bc5da6b4466fa7f28b\",\n \"content_id\": \"409fe69dcdc46334de886f8058551cf1617ef2fc\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6887,\n \"license_type\": \"permissive\",\n \"max_line_length\": 98,\n \"num_lines\": 240,\n \"path\": \"/gamestonk_terminal/stocks/discovery/stockgrid_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Stockgrid View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport os\\nfrom typing import List\\nimport requests\\nimport pandas as pd\\nfrom tabulate import tabulate\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n check_positive,\\n export_data,\\n)\\n\\n\\ndef darkshort(other_args: List[str]):\\n \\\"\\\"\\\"Get dark pool short positions.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"darkshort\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Get dark pool short positions. [Source: Stockgrid]\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--number\\\",\\n help=\\\"Number of top tickers to show\\\",\\n type=check_positive,\\n default=10,\\n dest=\\\"num\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n help=\\\"Field for which to sort by, where 'sv': Short Vol. (1M), \\\"\\n \\\"'sv_pct': Short Vol. %%, 'nsv': Net Short Vol. (1M), \\\"\\n \\\"'nsv_dollar': Net Short Vol. ($100M), 'dpp': DP Position (1M), \\\"\\n \\\"'dpp_dollar': DP Position ($1B)\\\",\\n choices=[\\\"sv\\\", \\\"sv_pct\\\", \\\"nsv\\\", \\\"nsv_dollar\\\", \\\"dpp\\\", \\\"dpp_dollar\\\"],\\n default=\\\"dpp_dollar\\\",\\n dest=\\\"sort_field\\\",\\n )\\n parser.add_argument(\\n \\\"-a\\\",\\n \\\"--ascending\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"ascending\\\",\\n help=\\\"Data in ascending order\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n d_fields_endpoints = {\\n \\\"sv\\\": \\\"Short+Volume\\\",\\n \\\"sv_pct\\\": \\\"Short+Volume+%25\\\",\\n \\\"nsv\\\": \\\"Net+Short+Volume\\\",\\n \\\"nsv_dollar\\\": \\\"Net+Short+Volume+$\\\",\\n \\\"dpp\\\": \\\"Dark+Pools+Position\\\",\\n \\\"dpp_dollar\\\": \\\"Dark+Pools+Position+$\\\",\\n }\\n\\n field = d_fields_endpoints[ns_parser.sort_field]\\n\\n if ns_parser.ascending:\\n order = \\\"asc\\\"\\n else:\\n order = \\\"desc\\\"\\n\\n link = f\\\"https://stockgridapp.herokuapp.com/get_dark_pool_data?top={field}&minmax={order}\\\"\\n\\n response = requests.get(link)\\n df = pd.DataFrame(response.json()[\\\"data\\\"])\\n\\n df = df[\\n [\\n \\\"Ticker\\\",\\n \\\"Date\\\",\\n \\\"Short Volume\\\",\\n \\\"Short Volume %\\\",\\n \\\"Net Short Volume\\\",\\n \\\"Net Short Volume $\\\",\\n \\\"Dark Pools Position\\\",\\n \\\"Dark Pools Position $\\\",\\n ]\\n ]\\n dp_date = df[\\\"Date\\\"].values[0]\\n df = df.drop(columns=[\\\"Date\\\"])\\n df[\\\"Net Short Volume $\\\"] = df[\\\"Net Short Volume $\\\"] / 100_000_000\\n df[\\\"Short Volume\\\"] = df[\\\"Short Volume\\\"] / 1_000_000\\n df[\\\"Net Short Volume\\\"] = df[\\\"Net Short Volume\\\"] / 1_000_000\\n df[\\\"Short Volume %\\\"] = df[\\\"Short Volume %\\\"] * 100\\n df[\\\"Dark Pools Position $\\\"] = df[\\\"Dark Pools Position $\\\"] / (1_000_000_000)\\n df[\\\"Dark Pools Position\\\"] = df[\\\"Dark Pools Position\\\"] / 1_000_000\\n df.columns = [\\n \\\"Ticker\\\",\\n \\\"Short Vol. (1M)\\\",\\n \\\"Short Vol. %\\\",\\n \\\"Net Short Vol. (1M)\\\",\\n \\\"Net Short Vol. ($100M)\\\",\\n \\\"DP Position (1M)\\\",\\n \\\"DP Position ($1B)\\\",\\n ]\\n\\n # Assuming that the datetime is the same, which from my experiments seems to be the case\\n print(f\\\"The following data corresponds to the date: {dp_date}\\\")\\n print(\\n tabulate(\\n df.iloc[: ns_parser.num],\\n tablefmt=\\\"fancy_grid\\\",\\n floatfmt=\\\".2f\\\",\\n headers=list(df.columns),\\n showindex=False,\\n )\\n )\\n print(\\\"\\\")\\n\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"darkshort\\\",\\n df,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef shortvol(other_args: List[str]):\\n \\\"\\\"\\\"Print short interest and days to cover\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"shortvol\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Print short interest and days to cover. [Source: Stockgrid]\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--number\\\",\\n help=\\\"Number of top tickers to show\\\",\\n type=check_positive,\\n default=10,\\n dest=\\\"num\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n help=\\\"Field for which to sort by, where 'float': Float Short %%, \\\"\\n \\\"'dtc': Days to Cover, 'si': Short Interest\\\",\\n choices=[\\\"float\\\", \\\"dtc\\\", \\\"si\\\"],\\n default=\\\"float\\\",\\n dest=\\\"sort_field\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n link = \\\"https://stockgridapp.herokuapp.com/get_short_interest?top=days\\\"\\n r = requests.get(link)\\n df = pd.DataFrame(r.json()[\\\"data\\\"])\\n df.head()\\n\\n d_fields = {\\n \\\"float\\\": \\\"%Float Short\\\",\\n \\\"dtc\\\": \\\"Days To Cover\\\",\\n \\\"si\\\": \\\"Short Interest\\\",\\n }\\n\\n df = df[\\n [\\\"Ticker\\\", \\\"Date\\\", \\\"%Float Short\\\", \\\"Days To Cover\\\", \\\"Short Interest\\\"]\\n ].sort_values(\\n by=d_fields[ns_parser.sort_field],\\n ascending=bool(ns_parser.sort_field == \\\"dtc\\\"),\\n )\\n dp_date = df[\\\"Date\\\"].values[0]\\n df = df.drop(columns=[\\\"Date\\\"])\\n df[\\\"Short Interest\\\"] = df[\\\"Short Interest\\\"] / 1_000_000\\n df.head()\\n df.columns = [\\n \\\"Ticker\\\",\\n \\\"Float Short %\\\",\\n \\\"Days to Cover\\\",\\n \\\"Short Interest (1M)\\\",\\n ]\\n\\n # Assuming that the datetime is the same, which from my experiments seems to be the case\\n print(f\\\"The following data corresponds to the date: {dp_date}\\\")\\n print(\\n tabulate(\\n df.iloc[: ns_parser.num],\\n tablefmt=\\\"fancy_grid\\\",\\n floatfmt=\\\".2f\\\",\\n headers=list(df.columns),\\n showindex=False,\\n )\\n )\\n print(\\\"\\\")\\n\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"shortvol\\\",\\n df,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.4743128716945648,\n \"alphanum_fraction\": 0.48047778010368347,\n \"avg_line_length\": 32.13191604614258,\n \"blob_id\": \"ed0482c00a481556fa6e614f9138e6e99882ebeb\",\n \"content_id\": \"45eebe5946503837d6cfdd240e5765bed10830c5\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7786,\n \"license_type\": \"permissive\",\n \"max_line_length\": 86,\n \"num_lines\": 235,\n \"path\": \"/gamestonk_terminal/stocks/due_diligence/sec_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" SEC View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport os\\nfrom typing import List\\nfrom datetime import datetime, timedelta\\nfrom tabulate import tabulate\\nimport requests\\nimport pandas as pd\\nfrom bs4 import BeautifulSoup\\nfrom matplotlib import pyplot as plt\\nimport matplotlib.dates as mdates\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import (\\n get_user_agent,\\n check_positive,\\n parse_known_args_and_warn,\\n valid_date,\\n export_data,\\n)\\n\\n\\ndef fails_to_deliver(other_args: List[str], ticker: str, stock: pd.DataFrame):\\n \\\"\\\"\\\"Display fails-to-deliver data for a given ticker\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse other args - [\\\"-n\\\", \\\"10\\\"]\\n ticker : str\\n Stock ticker\\n stock : pd.DataFrame\\n Stock data\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n prog=\\\"ftd\\\",\\n description=\\\"\\\"\\\"Prints latest fails-to-deliver data. [Source: SEC]\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--start\\\",\\n action=\\\"store\\\",\\n dest=\\\"start\\\",\\n type=valid_date,\\n default=(datetime.now() - timedelta(days=60)).strftime(\\\"%Y-%m-%d\\\"),\\n help=\\\"start of datetime to see FTD\\\",\\n )\\n parser.add_argument(\\n \\\"-e\\\",\\n \\\"--end\\\",\\n action=\\\"store\\\",\\n dest=\\\"end\\\",\\n type=valid_date,\\n default=datetime.now().strftime(\\\"%Y-%m-%d\\\"),\\n help=\\\"end of datetime to see FTD\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--num\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_num\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"number of latest fails-to-deliver being printed\\\",\\n )\\n parser.add_argument(\\n \\\"--raw\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"raw\\\",\\n help=\\\"Print raw data.\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n ftds_data = pd.DataFrame()\\n\\n # Filter by number of last FTD\\n if ns_parser.n_num > 0:\\n url_ftds = \\\"https://www.sec.gov/data/foiadocsfailsdatahtm\\\"\\n text_soup_ftds = BeautifulSoup(\\n requests.get(url_ftds, headers={\\\"User-Agent\\\": get_user_agent()}).text,\\n \\\"lxml\\\",\\n )\\n\\n table = text_soup_ftds.find(\\\"table\\\", {\\\"class\\\": \\\"list\\\"})\\n links = table.findAll(\\\"a\\\")\\n link_idx = 0\\n\\n while len(ftds_data) < ns_parser.n_num:\\n if link_idx > len(links):\\n break\\n link = links[link_idx]\\n url = \\\"https://www.sec.gov\\\" + link[\\\"href\\\"]\\n all_ftds = pd.read_csv(\\n url,\\n compression=\\\"zip\\\",\\n sep=\\\"|\\\",\\n engine=\\\"python\\\",\\n skipfooter=2,\\n usecols=[0, 2, 3, 5],\\n dtype={\\\"QUANTITY (FAILS)\\\": \\\"int\\\"},\\n )\\n tmp_ftds = all_ftds[all_ftds[\\\"SYMBOL\\\"] == ticker]\\n del tmp_ftds[\\\"PRICE\\\"]\\n del tmp_ftds[\\\"SYMBOL\\\"]\\n # merge the data from this archive\\n ftds_data = pd.concat([ftds_data, tmp_ftds], ignore_index=True)\\n link_idx += 1\\n\\n # clip away extra rows\\n ftds_data = ftds_data.sort_values(\\\"SETTLEMENT DATE\\\")[-ns_parser.n_num :]\\n\\n ftds_data[\\\"SETTLEMENT DATE\\\"] = ftds_data[\\\"SETTLEMENT DATE\\\"].apply(\\n lambda x: datetime.strptime(str(x), \\\"%Y%m%d\\\")\\n )\\n\\n # Filter by start and end dates for FTD\\n else:\\n\\n base_url = \\\"https://www.sec.gov/files/data/fails-deliver-data/cnsfails\\\"\\n ftd_dates = list()\\n\\n for y in range(ns_parser.start.year, ns_parser.end.year + 1):\\n if y < ns_parser.end.year:\\n for m in range(ns_parser.start.month, 13):\\n month = \\\"%02d\\\" % m\\n if m == ns_parser.start.month and y == ns_parser.start.year:\\n if ns_parser.start.day < 16:\\n ftd_dates.append(str(y) + month + \\\"a\\\")\\n ftd_dates.append(str(y) + month + \\\"b\\\")\\n else:\\n ftd_dates.append(str(y) + month + \\\"a\\\")\\n ftd_dates.append(str(y) + month + \\\"b\\\")\\n\\n else:\\n for m in range(1, ns_parser.end.month):\\n month = \\\"%02d\\\" % m\\n if m == ns_parser.end.month - 1:\\n ftd_dates.append(str(y) + month + \\\"a\\\")\\n if ns_parser.end.day > 15:\\n ftd_dates.append(str(y) + month + \\\"b\\\")\\n else:\\n ftd_dates.append(str(y) + month + \\\"a\\\")\\n ftd_dates.append(str(y) + month + \\\"b\\\")\\n\\n ftd_urls = [base_url + ftd_date + \\\".zip\\\" for ftd_date in ftd_dates]\\n\\n for ftd_link in ftd_urls:\\n all_ftds = pd.read_csv(\\n ftd_link,\\n compression=\\\"zip\\\",\\n sep=\\\"|\\\",\\n engine=\\\"python\\\",\\n skipfooter=2,\\n usecols=[0, 2, 3, 5],\\n dtype={\\\"QUANTITY (FAILS)\\\": \\\"int\\\"},\\n )\\n tmp_ftds = all_ftds[all_ftds[\\\"SYMBOL\\\"] == ticker]\\n del tmp_ftds[\\\"PRICE\\\"]\\n del tmp_ftds[\\\"SYMBOL\\\"]\\n # merge the data from this archive\\n ftds_data = pd.concat([ftds_data, tmp_ftds], ignore_index=True)\\n\\n ftds_data[\\\"SETTLEMENT DATE\\\"] = ftds_data[\\\"SETTLEMENT DATE\\\"].apply(\\n lambda x: datetime.strptime(str(x), \\\"%Y%m%d\\\")\\n )\\n\\n ftds_data = ftds_data[ftds_data[\\\"SETTLEMENT DATE\\\"] > ns_parser.start]\\n ftds_data = ftds_data[ftds_data[\\\"SETTLEMENT DATE\\\"] < ns_parser.end]\\n\\n plt.bar(\\n ftds_data[\\\"SETTLEMENT DATE\\\"],\\n ftds_data[\\\"QUANTITY (FAILS)\\\"] / 1000,\\n )\\n plt.ylabel(\\\"Shares [K]\\\")\\n plt.title(f\\\"Fails-to-deliver Data for {ticker}\\\")\\n plt.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\", alpha=0.2)\\n plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(\\\"%Y/%m/%d\\\"))\\n plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=7))\\n plt.gcf().autofmt_xdate()\\n plt.xlabel(\\\"Days\\\")\\n\\n _ = plt.gca().twinx()\\n\\n if ns_parser.n_num > 0:\\n stock_ftd = stock[\\n stock.index > (datetime.now() - timedelta(days=ns_parser.n_num + 31))\\n ]\\n else:\\n stock_ftd = stock[stock.index > ns_parser.start]\\n stock_ftd = stock_ftd[stock_ftd.index < ns_parser.end]\\n plt.plot(stock_ftd.index, stock_ftd[\\\"Adj Close\\\"], color=\\\"tab:orange\\\")\\n plt.ylabel(\\\"Share Price [$]\\\")\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n if ns_parser.raw:\\n print(\\n tabulate(\\n ftds_data,\\n headers=ftds_data.columns,\\n tablefmt=\\\"fancy_grid\\\",\\n stralign=\\\"right\\\",\\n showindex=False,\\n )\\n )\\n print(\\\"\\\")\\n\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"ftd\\\",\\n ftds_data.reset_index(),\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5628488659858704,\n \"alphanum_fraction\": 0.5655437707901001,\n \"avg_line_length\": 28.02234649658203,\n \"blob_id\": \"4277dd5f0d5975912177e42fe2e33159701c516d\",\n \"content_id\": \"cd4b5ed4473fe8ab616e46c3d45a40166bf9b5af\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5195,\n \"license_type\": \"permissive\",\n \"max_line_length\": 112,\n \"num_lines\": 179,\n \"path\": \"/gamestonk_terminal/cryptocurrency/crypto_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Cryptocurrency Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n# pylint: disable=R0904, C0302, R1710\\nimport argparse\\nimport os\\nimport matplotlib.pyplot as plt\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.cryptocurrency.coinmarketcap import coinmarketcap_controller\\nfrom gamestonk_terminal.cryptocurrency.binance import binance_controller\\nfrom gamestonk_terminal.cryptocurrency.coingecko import pycoingecko_controller\\nfrom gamestonk_terminal.cryptocurrency import finbrain_crypto_view\\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_controller\\nfrom gamestonk_terminal.cryptocurrency.report import report_controller\\n\\n\\nclass CryptoController:\\n\\n CHOICES = [\\n \\\"?\\\",\\n \\\"cls\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n ]\\n\\n CHOICES_COMMAND = [\\n \\\"finbrain\\\",\\n ]\\n\\n CHOICES_MENUS = [\\n \\\"cg\\\",\\n \\\"bin\\\",\\n \\\"cmc\\\",\\n \\\"cp\\\",\\n \\\"report\\\",\\n ]\\n\\n CHOICES += CHOICES_COMMAND\\n CHOICES += CHOICES_MENUS\\n\\n def __init__(self):\\n \\\"\\\"\\\"CONSTRUCTOR\\\"\\\"\\\"\\n\\n self.crypto_parser = argparse.ArgumentParser(add_help=False, prog=\\\"crypto\\\")\\n self.crypto_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\n \\\"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/cryptocurrency\\\"\\n )\\n print(\\\"\\\")\\n print(\\\">> CRYPTO <<\\\")\\n print(\\\"\\\")\\n print(\\\"What do you want to do?\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\")\\n print(\\\" finbrain Crypto sentiment from 15+ major news headlines\\\")\\n print(\\\"\\\")\\n print(\\\"> cg CoinGecko overview (market statistics) and coin menu\\\")\\n print(\\\"> cmc Coinmarketcap menu\\\")\\n print(\\\"> bin Binance menu with order book, candles, ta.. \\\")\\n print(\\\"> cp CoinPaprika menu\\\")\\n print(\\\"> report generate automatic report\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self.crypto_parser.parse_known_args(an_input.split())\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu\\\"\\\"\\\"\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program\\\"\\\"\\\"\\n return True\\n\\n def call_cg(self, _):\\n if pycoingecko_controller.menu():\\n return True\\n print(\\\"\\\")\\n\\n def call_bin(self, _):\\n \\\"\\\"\\\"Process bin command\\\"\\\"\\\"\\n if binance_controller.menu():\\n return True\\n print(\\\"\\\")\\n\\n def call_cmc(self, _):\\n \\\"\\\"\\\"Process top command\\\"\\\"\\\"\\n if coinmarketcap_controller.menu():\\n return True\\n print(\\\"\\\")\\n\\n def call_finbrain(self, other_args):\\n \\\"\\\"\\\"Process sentiment command\\\"\\\"\\\"\\n finbrain_crypto_view.crypto_sentiment_analysis(other_args=other_args)\\n\\n def call_cp(self, _):\\n \\\"\\\"\\\"Process cp command\\\"\\\"\\\"\\n if coinpaprika_controller.menu():\\n return True\\n print(\\\"\\\")\\n\\n def call_report(self, _):\\n \\\"\\\"\\\"Process report command\\\"\\\"\\\"\\n ret = report_controller.menu()\\n\\n if ret is False:\\n self.print_help()\\n else:\\n return True\\n\\n\\ndef menu():\\n crypto_controller = CryptoController()\\n crypto_controller.print_help()\\n plt.close(\\\"all\\\")\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in crypto_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (crypto)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (crypto)> \\\")\\n\\n try:\\n process_input = crypto_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.6077635884284973,\n \"alphanum_fraction\": 0.6106604933738708,\n \"avg_line_length\": 27.766666412353516,\n \"blob_id\": \"969c4018c4aa2dc9756dc3ef0b73d7de74a34e96\",\n \"content_id\": \"dc003098c6d000ccd26812c259be94c9e693c6a1\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1726,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 60,\n \"path\": \"/gamestonk_terminal/options/barchart_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Helper functions for scraping options data\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\n\\nimport requests\\nfrom bs4 import BeautifulSoup\\nimport pandas as pd\\nfrom tabulate import tabulate\\n\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\\n\\n\\ndef print_options_data(stock: str, other_args: List[str]):\\n \\\"\\\"\\\"Scrapes Barchart.com for the options information\\n\\n Parameters\\n ----------\\n stock: str\\n Ticker to get options info for\\n other_args: List[str]\\n Other arguments. Currently just accepts a browser flag for selenium\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"info\\\",\\n description=\\\"Display option data [Source: Barchart.com]\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n page = f\\\"https://www.barchart.com/stocks/quotes/{stock}/overview\\\"\\n\\n r = requests.get(page, headers={\\\"User-Agent\\\": \\\"Mozilla/5.0\\\"})\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n tags = soup.find(\\n \\\"div\\\",\\n attrs={\\n \\\"class\\\": \\\"barchart-content-block symbol-fundamentals bc-cot-table-wrapper\\\"\\n },\\n )\\n data = tags.find_all(\\\"li\\\")\\n labels = []\\n values = []\\n for row in data:\\n labels.append(row.find_all(\\\"span\\\")[0].getText())\\n values.append(row.find_all(\\\"span\\\")[1].getText())\\n\\n df = pd.DataFrame(data=[labels, values]).T\\n print(tabulate(df, tablefmt=\\\"fancy_grid\\\", showindex=False))\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.7263843417167664,\n \"alphanum_fraction\": 0.7361563444137573,\n \"avg_line_length\": 37.375,\n \"blob_id\": \"28446a10ffe5ffc5d34047567004aa0fb746eb81\",\n \"content_id\": \"3aa06d181430b0c0d5748154b594c07a8d79f4ec\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 307,\n \"license_type\": \"permissive\",\n \"max_line_length\": 102,\n \"num_lines\": 8,\n \"path\": \"/docker/gst-poetry-deps.dockerfile\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM gst/gst-python:1.0.0\\n\\nCOPY --chown=python:python pyproject.toml poetry.lock /home/python/\\n\\nRUN mkdir /home/python/gamestonk_terminal && \\\\\\n touch gamestonk_terminal/__init__.py && \\\\\\n chown python:python /home/python/gamestonk_terminal /home/python/gamestonk_terminal/__init__.py && \\\\\\n poetry install\\n\"\n },\n {\n \"alpha_fraction\": 0.5646258592605591,\n \"alphanum_fraction\": 0.5659863948822021,\n \"avg_line_length\": 27.269229888916016,\n \"blob_id\": \"5f6cd1b98c4a99736a7ff5c1983b32f1fe702ec0\",\n \"content_id\": \"38b3a4d503f3df91d121978e7d5d282cf57b4217\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2205,\n \"license_type\": \"permissive\",\n \"max_line_length\": 93,\n \"num_lines\": 78,\n \"path\": \"/gamestonk_terminal/stocks/due_diligence/financial_modeling_prep_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Financial Modeling Prep View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom tabulate import tabulate\\nimport FundamentalAnalysis as fa\\nfrom gamestonk_terminal import config_terminal as cfg\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, check_positive\\n\\n\\ndef rating(other_args: List[str], ticker: str):\\n \\\"\\\"\\\"Display ratings for a given ticker\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse other args\\n ticker : str\\n Stock ticker\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n prog=\\\"rating\\\",\\n description=\\\"\\\"\\\"\\n Based on specific ratios, prints information whether the company\\n is a (strong) buy, neutral or a (strong) sell. The following fields are expected:\\n P/B, ROA, DCF, P/E, ROE, and D/E. [Source: Financial Modeling Prep]\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--num\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_num\\\",\\n type=check_positive,\\n default=10,\\n help=\\\"number of regions to plot that show highest interest.\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df_fa = fa.rating(ticker, cfg.API_KEY_FINANCIALMODELINGPREP)\\n\\n # TODO: This could be displayed in a nice rating plot over time\\n\\n l_recoms = [col for col in df_fa.columns if \\\"Recommendation\\\" in col]\\n l_recoms_show = [\\n recom.replace(\\\"rating\\\", \\\"\\\")\\n .replace(\\\"Details\\\", \\\"\\\")\\n .replace(\\\"Recommendation\\\", \\\" Recommendation\\\")\\n for recom in l_recoms\\n ]\\n print(\\n tabulate(\\n df_fa[l_recoms].head(ns_parser.n_num),\\n headers=l_recoms_show,\\n floatfmt=\\\".2f\\\",\\n showindex=True,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except KeyError:\\n print(\\n f\\\"Financialmodelingprep.com is returning empty response the ticker {ticker}.\\\"\\n )\\n print(\\\"\\\")\\n return\\n\\n except Exception as e:\\n print(e)\\n print(\\\"\\\")\\n return\\n\"\n },\n {\n \"alpha_fraction\": 0.5406919121742249,\n \"alphanum_fraction\": 0.5413509011268616,\n \"avg_line_length\": 27.904762268066406,\n \"blob_id\": \"243163cf4fb8c8243312dc197f5d65940c4bbb6f\",\n \"content_id\": \"c0f122f2a9bb1f38d92d94100bf8432af13bd871\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3035,\n \"license_type\": \"permissive\",\n \"max_line_length\": 93,\n \"num_lines\": 105,\n \"path\": \"/gamestonk_terminal/economy/cnn_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Fred View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom fear_greed_index.CNNFearAndGreedIndex import CNNFearAndGreedIndex\\nfrom pandas.plotting import register_matplotlib_converters\\nimport matplotlib.pyplot as plt\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\nregister_matplotlib_converters()\\n\\n\\ndef fear_and_greed_index(other_args: List[str]):\\n \\\"\\\"\\\"Display CNN Fear And Greed Index.\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse other args\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n prog=\\\"feargreed\\\",\\n description=\\\"\\\"\\\"\\n Display CNN Fear And Greed Index from https://money.cnn.com/data/fear-and-greed/.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-i\\\",\\n \\\"--indicator\\\",\\n dest=\\\"indicator\\\",\\n required=False,\\n type=str,\\n choices=[\\\"jbd\\\", \\\"mv\\\", \\\"pco\\\", \\\"mm\\\", \\\"sps\\\", \\\"spb\\\", \\\"shd\\\", \\\"index\\\"],\\n help=\\\"\\\"\\\"\\n CNN Fear And Greed indicator or index. From Junk Bond Demand, Market Volatility,\\n Put and Call Options, Market Momentum Stock Price Strength, Stock Price Breadth,\\n Safe Heaven Demand, and Index.\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-i\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n cnn_fg = CNNFearAndGreedIndex()\\n\\n fig = plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n\\n if ns_parser.indicator:\\n if ns_parser.indicator == \\\"index\\\":\\n print(cnn_fg.index_summary)\\n\\n im = cnn_fg.index_chart\\n plt.imshow(im)\\n\\n else:\\n d_indicator_match = {\\n \\\"jbd\\\": \\\"Junk Bond Demand\\\",\\n \\\"mv\\\": \\\"Market Volatility\\\",\\n \\\"pco\\\": \\\"Put and Call Options\\\",\\n \\\"mm\\\": \\\"Market Momentum\\\",\\n \\\"sps\\\": \\\"Stock Price Strength\\\",\\n \\\"spb\\\": \\\"Stock Price Breadth\\\",\\n \\\"shd\\\": \\\"Safe Heaven Demand\\\",\\n }\\n indicator_name = d_indicator_match[ns_parser.indicator]\\n\\n for indicator in cnn_fg.all_indicators:\\n if indicator_name == indicator.type_indicator:\\n print(indicator.get_report())\\n im = indicator.chart\\n plt.imshow(im)\\n\\n else:\\n # print Fear and Greed complete report\\n print(cnn_fg.get_complete_report())\\n\\n # plot Fear and Greed charts\\n cnn_fg.plot_all_charts(fig)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return\\n\"\n },\n {\n \"alpha_fraction\": 0.6605504751205444,\n \"alphanum_fraction\": 0.6880733966827393,\n \"avg_line_length\": 20.799999237060547,\n \"blob_id\": \"40000596d52544c4a2a7ef8bd39fb0ee508c9b2b\",\n \"content_id\": \"212ac7ff6a8dcb47e204fcb4788528d89843e173\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 109,\n \"license_type\": \"permissive\",\n \"max_line_length\": 44,\n \"num_lines\": 5,\n \"path\": \"/docker/gst-poetry.dockerfile\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM gst/gst-poetry-deps:1.0.0\\n\\nCOPY --chown=python:python . .\\n\\nCMD [\\\"bash\\\", \\\"docker/entry-point-poetry.sh\\\"]\\n\"\n },\n {\n \"alpha_fraction\": 0.6982758641242981,\n \"alphanum_fraction\": 0.6982758641242981,\n \"avg_line_length\": 26.066667556762695,\n \"blob_id\": \"27c80ceea9ee6fab48aecf26810940d7b8662ae3\",\n \"content_id\": \"fa0cf64964debce734a29fd8652b4e44b8ef9499\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 812,\n \"license_type\": \"permissive\",\n \"max_line_length\": 84,\n \"num_lines\": 30,\n \"path\": \"/tests/test_fa/test_fa_fmp_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" fundamental_analysis/financial_modeling_prep/fmp_view.py tests \\\"\\\"\\\"\\n# Not testing these tests further. I do not have a fmp key\\nfrom contextlib import contextmanager\\nimport unittest\\nimport io\\nimport sys\\n\\n# pylint: disable=unused-import\\nfrom gamestonk_terminal.stocks.fundamental_analysis.financial_modeling_prep import (\\n fmp_view,\\n)\\n\\n\\n@contextmanager\\ndef replace_stdin(target):\\n orig = sys.stdin\\n sys.stdin = target\\n yield\\n sys.stdin = orig\\n\\n\\nclass TestFMPView(unittest.TestCase):\\n def test_fmp_valinvest_score(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n fmp_view.valinvest_score([], \\\"GME\\\")\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n print(capt)\\n self.assertIn(\\\"Ticker should be a NASDAQ\\\", capt)\\n\"\n },\n {\n \"alpha_fraction\": 0.45219168066978455,\n \"alphanum_fraction\": 0.4670678973197937,\n \"avg_line_length\": 27.590909957885742,\n \"blob_id\": \"cbd805d459d3a41800c65bdd160c4d8c56d9f2be\",\n \"content_id\": \"2b417c9c870a17d595fdf010752a6b1c11298e0a\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8806,\n \"license_type\": \"permissive\",\n \"max_line_length\": 98,\n \"num_lines\": 308,\n \"path\": \"/gamestonk_terminal/stocks/due_diligence/stockgrid_dd_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Stockgrid DD View\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport os\\nfrom typing import List\\nfrom datetime import timedelta\\nimport requests\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom tabulate import tabulate\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal.feature_flags import USE_ION\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n plot_autoscale,\\n check_positive,\\n export_data,\\n)\\n\\n\\ndef shortview(ticker: str, other_args: List[str]):\\n \\\"\\\"\\\"Plot price vs short interest volume\\n\\n Parameters\\n ----------\\n ticker: str\\n Stock to plot for\\n other_args : List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"shortvol\\\",\\n add_help=False,\\n description=\\\"Shows price vs short interest volume. [Source: Stockgrid]\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--number\\\",\\n help=\\\"Number of last open market days to show\\\",\\n type=check_positive,\\n default=10 if \\\"-r\\\" in other_args else 120,\\n dest=\\\"num\\\",\\n )\\n parser.add_argument(\\n \\\"-r\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"Flag to print raw data instead\\\",\\n dest=\\\"raw\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n link = f\\\"https://stockgridapp.herokuapp.com/get_dark_pool_individual_data?ticker={ticker}\\\"\\n response = requests.get(link)\\n\\n df = pd.DataFrame(response.json()[\\\"individual_short_volume_table\\\"][\\\"data\\\"])\\n df[\\\"date\\\"] = pd.to_datetime(df[\\\"date\\\"])\\n\\n if ns_parser.raw:\\n df = df.sort_values(by=\\\"date\\\", ascending=False)\\n\\n df[\\\"Short Vol. (1M)\\\"] = df[\\\"short_volume\\\"] / 1_000_000\\n df[\\\"Short Vol. %\\\"] = df[\\\"short_volume%\\\"] * 100\\n df[\\\"Short Exempt Vol. (1K)\\\"] = df[\\\"short_exempt_volume\\\"] / 1_000\\n df[\\\"Total Vol. (1M)\\\"] = df[\\\"total_volume\\\"] / 1_000_000\\n\\n df = df[\\n [\\n \\\"date\\\",\\n \\\"Short Vol. (1M)\\\",\\n \\\"Short Vol. %\\\",\\n \\\"Short Exempt Vol. (1K)\\\",\\n \\\"Total Vol. (1M)\\\",\\n ]\\n ]\\n\\n df.date = df.date.dt.date\\n\\n print(\\n tabulate(\\n df.iloc[: ns_parser.num],\\n tablefmt=\\\"fancy_grid\\\",\\n floatfmt=\\\".2f\\\",\\n headers=list(df.columns),\\n showindex=False,\\n )\\n )\\n else:\\n prices = response.json()[\\\"prices\\\"][\\\"prices\\\"]\\n\\n _, axes = plt.subplots(\\n 2,\\n 1,\\n figsize=(plot_autoscale()),\\n dpi=PLOT_DPI,\\n gridspec_kw={\\\"height_ratios\\\": [2, 1]},\\n )\\n\\n axes[0].bar(\\n df[\\\"date\\\"],\\n df[\\\"total_volume\\\"] / 1_000_000,\\n width=timedelta(days=1),\\n color=\\\"b\\\",\\n alpha=0.4,\\n label=\\\"Total Volume\\\",\\n )\\n axes[0].bar(\\n df[\\\"date\\\"],\\n df[\\\"short_volume\\\"] / 1_000_000,\\n width=timedelta(days=1),\\n color=\\\"r\\\",\\n alpha=0.4,\\n label=\\\"Short Volume\\\",\\n )\\n\\n axes[0].set_ylabel(\\\"Volume (1M)\\\")\\n ax2 = axes[0].twinx()\\n ax2.plot(\\n df[\\\"date\\\"].values, prices[len(prices) - len(df) :], c=\\\"k\\\", label=\\\"Price\\\"\\n )\\n ax2.set_ylabel(\\\"Price ($)\\\")\\n\\n lines, labels = axes[0].get_legend_handles_labels()\\n lines2, labels2 = ax2.get_legend_handles_labels()\\n ax2.legend(lines + lines2, labels + labels2, loc=\\\"upper left\\\")\\n\\n axes[0].set_xlim(\\n df[\\\"date\\\"].values[max(0, len(df) - ns_parser.num)],\\n df[\\\"date\\\"].values[len(df) - 1],\\n )\\n\\n axes[0].grid()\\n axes[0].ticklabel_format(style=\\\"plain\\\", axis=\\\"y\\\")\\n plt.title(f\\\"Price vs Short Volume Interest for {ticker}\\\")\\n plt.gcf().autofmt_xdate()\\n\\n axes[1].plot(\\n df[\\\"date\\\"].values,\\n 100 * df[\\\"short_volume%\\\"],\\n c=\\\"green\\\",\\n label=\\\"Short Vol. %\\\",\\n )\\n\\n axes[1].set_xlim(\\n df[\\\"date\\\"].values[max(0, len(df) - ns_parser.num)],\\n df[\\\"date\\\"].values[len(df) - 1],\\n )\\n axes[1].set_ylabel(\\\"Short Vol. %\\\")\\n\\n axes[1].grid(axis=\\\"y\\\")\\n lines, labels = axes[1].get_legend_handles_labels()\\n axes[1].legend(lines, labels, loc=\\\"upper left\\\")\\n axes[1].set_ylim([0, 100])\\n\\n if USE_ION:\\n plt.ion()\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"shortview\\\",\\n df,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef darkpos(ticker: str, other_args: List[str]):\\n \\\"\\\"\\\"Plot dark pool position\\n\\n Parameters\\n ----------\\n ticker: str\\n Stock to plot for\\n other_args : List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"darkpos\\\",\\n add_help=False,\\n description=\\\"Shows Net Short Vol. vs Position. [Source: Stockgrid]\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--number\\\",\\n help=\\\"Number of last open market days to show\\\",\\n type=check_positive,\\n default=10 if \\\"-r\\\" in other_args else 120,\\n dest=\\\"num\\\",\\n )\\n parser.add_argument(\\n \\\"-r\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"Flag to print raw data instead\\\",\\n dest=\\\"raw\\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n link = f\\\"https://stockgridapp.herokuapp.com/get_dark_pool_individual_data?ticker={ticker}\\\"\\n response = requests.get(link)\\n\\n df = pd.DataFrame(response.json()[\\\"individual_dark_pool_position_data\\\"])\\n df[\\\"dates\\\"] = pd.to_datetime(df[\\\"dates\\\"])\\n\\n if ns_parser.raw:\\n df = df.sort_values(by=\\\"dates\\\", ascending=False)\\n\\n df[\\\"Net Short Vol. (1k $)\\\"] = df[\\\"dollar_net_volume\\\"] / 1_000\\n df[\\\"Position (1M $)\\\"] = df[\\\"dollar_dp_position\\\"]\\n\\n df = df[\\n [\\n \\\"dates\\\",\\n \\\"Net Short Vol. (1k $)\\\",\\n \\\"Position (1M $)\\\",\\n ]\\n ]\\n\\n df[\\\"dates\\\"] = df[\\\"dates\\\"].dt.date\\n\\n print(\\n tabulate(\\n df.iloc[: ns_parser.num],\\n tablefmt=\\\"fancy_grid\\\",\\n floatfmt=\\\".2f\\\",\\n headers=list(df.columns),\\n showindex=False,\\n )\\n )\\n\\n else:\\n fig = plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n\\n ax = fig.add_subplot(111)\\n ax.bar(\\n df[\\\"dates\\\"],\\n df[\\\"dollar_net_volume\\\"] / 1_000,\\n color=\\\"r\\\",\\n alpha=0.4,\\n label=\\\"Net Short Vol. (1k $)\\\",\\n )\\n ax.set_ylabel(\\\"Net Short Vol. (1k $)\\\")\\n\\n ax2 = ax.twinx()\\n ax2.plot(\\n df[\\\"dates\\\"].values,\\n df[\\\"dollar_dp_position\\\"],\\n c=\\\"tab:blue\\\",\\n label=\\\"Position (1M $)\\\",\\n )\\n ax2.set_ylabel(\\\"Position (1M $)\\\")\\n\\n lines, labels = ax.get_legend_handles_labels()\\n lines2, labels2 = ax2.get_legend_handles_labels()\\n ax2.legend(lines + lines2, labels + labels2, loc=\\\"upper left\\\")\\n\\n ax.set_xlim(\\n df[\\\"dates\\\"].values[max(0, len(df) - ns_parser.num)],\\n df[\\\"dates\\\"].values[len(df) - 1],\\n )\\n\\n ax.grid()\\n plt.title(f\\\"Net Short Vol. vs Position for {ticker}\\\")\\n plt.gcf().autofmt_xdate()\\n\\n if USE_ION:\\n plt.ion()\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"darkpos\\\",\\n df,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5976826548576355,\n \"alphanum_fraction\": 0.5983592867851257,\n \"avg_line_length\": 29.24040985107422,\n \"blob_id\": \"800322ff8ce8f2867770b304f081e632bdf2b472\",\n \"content_id\": \"729a130b0a7ac1f8a3162dc72c4fe98e91ab1fcb\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11824,\n \"license_type\": \"permissive\",\n \"max_line_length\": 111,\n \"num_lines\": 391,\n \"path\": \"/tests/test_cryptocurrency/test_coingecko_crypto.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from unittest import mock, TestCase\\nimport json\\nimport sys\\nimport io\\n\\nfrom pycoingecko import CoinGeckoAPI\\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view import (\\n load,\\n ta,\\n chart,\\n info,\\n web,\\n social,\\n dev,\\n ath,\\n atl,\\n score,\\n bc,\\n market,\\n holdings_overview,\\n holdings_companies_list,\\n gainers,\\n losers,\\n discover,\\n news,\\n categories,\\n recently_added,\\n stablecoins,\\n yfarms,\\n top_volume_coins,\\n top_defi_coins,\\n top_dex,\\n top_nft,\\n nft_of_the_day,\\n nft_market_status,\\n exchanges,\\n platforms,\\n products,\\n indexes,\\n derivatives,\\n exchange_rates,\\n global_market_info,\\n global_defi_info,\\n coin_list,\\n find,\\n)\\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_coin_model import Coin\\n\\n# pylint: disable=unused-import\\n\\n\\n@mock.patch(\\n \\\"gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view.CoinGeckoAPI.get_coin_market_chart_by_id\\\"\\n)\\ndef get_bitcoin(mock_load):\\n # pylint: disable=unused-argument\\n with open(\\\"tests/data/btc_usd_test_data.json\\\") as f:\\n sample_return = json.load(f)\\n mock_load.return_value = sample_return\\n return load([\\\"-c\\\", \\\"bitcoin\\\"])\\n\\n\\n# pylint: disable=R0904\\nclass TestCoinGeckoAPI(TestCase):\\n # pylint: disable = no-value-for-parameter\\n coin = get_bitcoin()\\n\\n def test_coin_api_load(self):\\n \\\"\\\"\\\"\\n Mock load function through get_coin_market_chart_by_id.\\n Mock returns a dict saved as .json\\n\\n \\\"\\\"\\\"\\n self.assertEqual(self.coin.coin_symbol, \\\"bitcoin\\\")\\n self.assertIsInstance(self.coin, Coin)\\n\\n @mock.patch(\\n \\\"gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_view.CoinGeckoAPI.get_coin_market_chart_by_id\\\"\\n )\\n def test_coin_api_load_df_for_ta(self, mock_load):\\n \\\"\\\"\\\"\\n Mock load function through get_coin_market_chart_by_id.\\n Mock returns a dict saved as .json\\n \\\"\\\"\\\"\\n\\n with open(\\\"tests/data/btc_usd_test_data.json\\\") as f:\\n sample_return = json.load(f)\\n\\n mock_load.return_value = sample_return\\n coin = load([\\\"-c\\\", \\\"bitcoin\\\"])\\n mock_return, vs = ta(coin, [\\\"--vs\\\", \\\"usd\\\"])\\n self.assertTrue(mock_return.shape == (722, 2))\\n self.assertTrue(vs == \\\"usd\\\")\\n\\n def test_get_coins(self):\\n \\\"\\\"\\\"Test that pycoingecko retrieves the major coins\\\"\\\"\\\"\\n coins = CoinGeckoAPI().get_coins()\\n bitcoin_list = [coin[\\\"id\\\"] for coin in coins]\\n test_coins = [\\\"bitcoin\\\", \\\"ethereum\\\", \\\"dogecoin\\\"]\\n for test in test_coins:\\n self.assertIn(test, bitcoin_list)\\n\\n @mock.patch(\\\"matplotlib.pyplot.show\\\")\\n def test_coin_chart(self, mock_matplot):\\n # pylint: disable=unused-argument\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n chart(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertEqual(\\\"\\\\n\\\", capt)\\n\\n def test_coin_info(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n info(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"asset_platform_id\\\", capt)\\n\\n def test_coin_web(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n web(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"homepage\\\", capt)\\n\\n def test_coin_social(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n social(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"telegram\\\", capt)\\n\\n def test_coin_dev(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n dev(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"forks\\\", capt)\\n\\n def test_coin_ath(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n ath(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"ath_date_btc\\\", capt)\\n\\n def test_coin_atl(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n atl(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"atl_date_btc\\\", capt)\\n\\n def test_coin_score(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n score(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"twitter_followers\\\", capt)\\n\\n def test_coin_bc(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n bc(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Metric\\\", capt)\\n\\n def test_coin_market(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n market(self.coin, [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"max_supply\\\", capt)\\n\\n def test_coin_holdings_overview(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n holdings_overview([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Total Bitcoin Holdings\\\", capt)\\n\\n def test_coin_holdings_companies_list(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n holdings_companies_list([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"country\\\", capt)\\n\\n def test_coin_gainers(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n gainers([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"rank\\\", capt)\\n\\n def test_coin_losers(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n losers([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"rank\\\", capt)\\n\\n def test_coin_discover(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n discover(\\\"trending\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"CryptoBlades\\\", capt)\\n\\n def test_coin_news(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n news([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"author\\\", capt)\\n\\n def test_coin_categories(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n categories([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Decentralized Finance\\\", capt)\\n\\n def test_coin_recently_added(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n recently_added([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"rank\\\", capt)\\n\\n def test_coin_stablecoins(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n stablecoins([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_yfarms(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n yfarms([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_top_volume_coins(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n top_volume_coins([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_top_defi_coins(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n top_defi_coins([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_top_dex(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n top_dex([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_top_nft(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n top_nft([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_nft_of_the_day(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n nft_of_the_day([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Metric\\\", capt)\\n\\n def test_coin_nft_market_status(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n nft_market_status([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Metric\\\", capt)\\n\\n def test_coin_exchanges(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n exchanges([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_platforms(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n platforms([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_products(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n products([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"platform\\\", capt)\\n\\n def test_coin_indexes(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n indexes([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_derivatives(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n derivatives([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"price\\\", capt)\\n\\n def test_coin_exchange_rates(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n exchange_rates([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_global_market_info(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n global_market_info([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Metric\\\", capt)\\n\\n def test_coin_global_defi_info(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n global_defi_info([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_coin_list(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coin_list([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_coin_find(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n find([\\\"-c\\\", \\\"bitcoin\\\"])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\"\n },\n {\n \"alpha_fraction\": 0.6554973721504211,\n \"alphanum_fraction\": 0.7157068252563477,\n \"avg_line_length\": 35.730770111083984,\n \"blob_id\": \"2ed1831c2acf5abfda8e1e7d1937aef5985bc9e0\",\n \"content_id\": \"347b9c39c817924ce1fc89af0a0e0d5468707e17\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1910,\n \"license_type\": \"permissive\",\n \"max_line_length\": 333,\n \"num_lines\": 52,\n \"path\": \"/gamestonk_terminal/cryptocurrency/binance/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# BINANCE\\n\\nBinance is the largest cryptocurrency exchange in the world in terms of trading volume.\\nThis menu aims to explore Binance API and display data like order book, candles chart, account balance.\\n\\n## load <a name=\\\"load\\\"></a>\\n````\\nusage: load [-c --coin] [-q --quote] [-i --interval] [-l --limit]\\n````\\n\\nSelect a coin/currency to the current object and load in the price dataframe. Note that in binance, the exchange \\\"ticker\\\" is usually COINCURR, such as BTCEUR for BTC to EURO. There is no USD, but it uses a coin tethered to the USD (USDT), which is the default. Some symbols are a combination of coins, as `ETHBTC` is a valid symbol\\n* -c/--coin Coin to load. If not specified, BTC will be loaded to prevent errors later.\\n* -q/--quote Quote currency. Defaults to `USDT` (which is 1-to-1 with USD)\\n* -i/--interval. Interval for candles. One of [1day,3day,1hour,2hour,4hour,6hour,8hour,12hour,1week,1min,3min,5min,15min,30min,1month]. Defaults to 1day.\\n* -l/--limit. Number of candles to get. Defaults to 100.\\n\\n## book <a name=\\\"book\\\"></a>\\n````\\nusage: book -l/--limit\\n````\\n\\nGets and shows the order book for the given coin/currency. Shows the cumulative amount of orders.\\n\\n* -l/--limit Number of orders to get on each side. One of [5,10,20,50,100,500,1000,5000]. Defaults to 100.\\n\\n\\n\\n\\n## candle <a name=\\\"candle\\\"></a>\\n````\\nusage: candle\\n````\\n\\nShow candle chart for loaded coin/currency.\\n\\n\\n\\n\\n## balance <a name=\\\"balance\\\"></a>\\n````\\nusage: balance\\n````\\n\\nShows the current holding balance in your account.\\n\\n## ta <a name=\\\"ta\\\"></a>\\n\\n````\\nusage: ta\\n````\\n\\nOpen Technical Analysis menu for loaded coin. [technical analysis](/gamestonk_terminal/cryptocurrency/coinmarketcap/)\\n\"\n },\n {\n \"alpha_fraction\": 0.5166915655136108,\n \"alphanum_fraction\": 0.5313901305198669,\n \"avg_line_length\": 27.069929122924805,\n \"blob_id\": \"467edfe574162b83d39addae0bb785eaa5cd0269\",\n \"content_id\": \"6015cfe38ff15b0917046a0587dd7ccd08e3a1d3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4014,\n \"license_type\": \"permissive\",\n \"max_line_length\": 104,\n \"num_lines\": 143,\n \"path\": \"/gamestonk_terminal/options/calculator_model.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Calculator Model\\\"\\\"\\\"\\n___docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\n\\nimport numpy as np\\nfrom matplotlib import pyplot as plt\\n\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn, plot_autoscale\\nfrom gamestonk_terminal import config_plot as cfp\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\n\\ndef pnl_calculator(other_args: List[str]):\\n \\\"\\\"\\\"Plot profit/loss for different option variables\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"calc\\\",\\n description=\\\"Calculate profit or loss for given option settings.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--put\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"put\\\",\\n help=\\\"Flag to calculate put option\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--sell\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"sell\\\",\\n help=\\\"Flag to get profit chart of selling contract\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--strike\\\",\\n type=float,\\n dest=\\\"strike\\\",\\n help=\\\"Option strike price\\\",\\n default=10,\\n )\\n\\n parser.add_argument(\\n \\\"-p\\\", \\\"--premium\\\", type=float, dest=\\\"premium\\\", help=\\\"Premium price\\\", default=1\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n strike = ns_parser.strike\\n premium = ns_parser.premium\\n\\n price_at_expiry = np.linspace(ns_parser.strike / 2, 1.5 * ns_parser.strike, 301)\\n\\n sell = [1, -1][ns_parser.sell]\\n if ns_parser.put:\\n break_even = strike - sell * premium\\n pnl = strike - premium - price_at_expiry\\n pnl = sell * 100 * np.where(price_at_expiry < strike, pnl, -premium)\\n\\n else:\\n break_even = strike + sell * premium\\n pnl = price_at_expiry - strike - premium\\n pnl = sell * 100 * np.where(price_at_expiry > strike, pnl, -premium)\\n\\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\\n ax.plot(price_at_expiry, pnl, alpha=0.1, c=\\\"k\\\")\\n ax.fill_between(\\n price_at_expiry, 0, pnl, where=(pnl > 0), facecolor=\\\"green\\\", alpha=0.5\\n )\\n ax.fill_between(\\n price_at_expiry, 0, pnl, where=(pnl < 0), facecolor=\\\"red\\\", alpha=0.5\\n )\\n ax.axvline(\\n x=break_even, c=\\\"black\\\", lw=3, alpha=0.6, label=f\\\"Breakeven: ${break_even}\\\"\\n )\\n ax.axvline(\\n x=strike, c=\\\"dodgerblue\\\", lw=3, alpha=0.6, label=f\\\"Strike: ${strike}\\\"\\n )\\n if ns_parser.sell:\\n ax.axhline(\\n y=100 * premium,\\n c=\\\"seagreen\\\",\\n lw=3,\\n alpha=0.6,\\n label=f\\\"Max Profit: ${100*premium}\\\",\\n )\\n else:\\n ax.axhline(\\n y=-100 * premium,\\n c=\\\"firebrick\\\",\\n lw=3,\\n alpha=0.6,\\n label=f\\\"Max Loss: ${-100 * premium}\\\",\\n )\\n\\n ax.set_xlabel(\\\"Price at Expiry\\\")\\n\\n ax.set_ylabel(\\\"Profit\\\")\\n ax.set_title(\\n f\\\"Profit for {['Buying','Selling'][ns_parser.sell]} {['Call', 'Put'][ns_parser.put]} option\\\"\\n )\\n ax.grid(True)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.legend(loc=0)\\n fig.tight_layout(pad=1)\\n plt.show()\\n print(f\\\"Strike: ${strike}\\\")\\n print(f\\\"Premium: ${premium}\\\")\\n print(f\\\"Breakeven price: ${break_even}\\\")\\n if ns_parser.sell:\\n print(f\\\"Max profit: ${100 * premium}\\\")\\n print(\\\"Max loss: Unlimited\\\")\\n else:\\n print(\\\"Max profit: Unlimited\\\")\\n print(f\\\"Max loss: ${-100*premium}\\\")\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return\\n\\n except SystemExit:\\n print(\\\"\\\")\\n return\\n\"\n },\n {\n \"alpha_fraction\": 0.655235767364502,\n \"alphanum_fraction\": 0.6619718074798584,\n \"avg_line_length\": 31.65999984741211,\n \"blob_id\": \"ef7857fa24d2a5a4b6024df8cbbbc286f0ffbc07\",\n \"content_id\": \"9b82fc82c84e054c03e5b6924c2be06a71e3abb5\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1633,\n \"license_type\": \"permissive\",\n \"max_line_length\": 100,\n \"num_lines\": 50,\n \"path\": \"/tests/test_fa/test_fa_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" fundamental_analysis/fa_controller.py tests \\\"\\\"\\\"\\nfrom datetime import datetime, timedelta\\nfrom contextlib import contextmanager\\nimport unittest\\nimport io\\nimport sys\\n\\n# pylint: disable=unused-import\\nfrom gamestonk_terminal.stocks.fundamental_analysis import fa_controller\\n\\n\\n@contextmanager\\ndef replace_stdin(target):\\n orig = sys.stdin\\n sys.stdin = target\\n yield\\n sys.stdin = orig\\n\\n\\nclass TestFaController(unittest.TestCase):\\n start = datetime.now() - timedelta(days=200)\\n menuClass = fa_controller.FundamentalAnalysisController(\\\"GME\\\", start, \\\"1440min\\\") # type: ignore\\n choices = fa_controller.FundamentalAnalysisController.CHOICES\\n\\n def test_fa_controller_help(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n self.menuClass.print_help()\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n for item in self.choices:\\n self.assertIn(item, capt)\\n\\n def key_metrics_explained(self):\\n # Is this base function ever even used?\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n fa_controller.key_metrics_explained([\\\"\\\"])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"RETURN ON EQUITY\\\", capt)\\n\\n def test_menu(self):\\n with replace_stdin(io.StringIO(\\\"q\\\")):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n fa_controller.menu(\\\"GME\\\", self.start, \\\"1440min\\\")\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"fraud\\\", capt)\\n\"\n },\n {\n \"alpha_fraction\": 0.6150252819061279,\n \"alphanum_fraction\": 0.6150252819061279,\n \"avg_line_length\": 34.19178009033203,\n \"blob_id\": \"6000013d7f37418a3e3bf727ab28c95a06774b8c\",\n \"content_id\": \"f5f087c81e0a4a39b02ea074c4fae06fdddd4abb\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2569,\n \"license_type\": \"permissive\",\n \"max_line_length\": 83,\n \"num_lines\": 73,\n \"path\": \"/tests/test_portfolio_optimization/test_optimizer_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from unittest import TestCase, mock\\nimport sys\\nimport io\\n\\nfrom gamestonk_terminal.portfolio.portfolio_optimization import optimizer_view\\n\\n\\nclass TestOptimizerView(TestCase):\\n def test_equal_weights(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.equal_weight([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n def test_property_weighting(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.property_weighting([\\\"TSLA\\\", \\\"GME\\\"], [\\\"-p\\\", \\\"previousClose\\\"])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n def test_max_sharpe(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.max_sharpe([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n def test_min_volatility(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.min_volatility([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n def test_max_quadratic_utility(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.max_quadratic_utility([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n def test_efficient_risk(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.efficient_risk([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"The minimum volatility\\\", capt)\\n\\n def test_efficient_return(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.efficient_return([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"TSLA\\\", capt)\\n\\n @mock.patch(\\\"matplotlib.pyplot.show\\\")\\n def test_show_ef(self, mock_mlp):\\n # pylint: disable=unused-argument\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n optimizer_view.show_ef([\\\"TSLA\\\", \\\"GME\\\"], [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"\\\\n\\\", capt)\\n\"\n },\n {\n \"alpha_fraction\": 0.7211174368858337,\n \"alphanum_fraction\": 0.7452651262283325,\n \"avg_line_length\": 53.153846740722656,\n \"blob_id\": \"5f42bb0cca8a08e620480f0a8eb8974b2a0bbf3a\",\n \"content_id\": \"2d8a9237705623dce768302f15b3c7ace5b0104a\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2112,\n \"license_type\": \"permissive\",\n \"max_line_length\": 381,\n \"num_lines\": 39,\n \"path\": \"/gamestonk_terminal/cryptocurrency/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CRYPTOCURRENCY\\n\\nThis menu aims to explore crypto world, and the usage of the following commands along with an example will be exploited below.\\n\\nNote that we have added the ability to look at technical analysis in the ta menu. Data loaded from CoinGecko has no candle data,\\nso indicators that rely on anything other than close will fail with an error.\\n\\n\\n* [finbrain](#finbrain)\\n * crypto sentiment from 15+ major news headlines for more then 100 coins\\n\\n[COINGECKO standalone menu](/gamestonk_terminal/cryptocurrency/coingecko/)\\n\\n[COINMARKETCAP standalone menu](/gamestonk_terminal/cryptocurrency/coinmarketcap/)\\n\\n[BINANCE standalone menu](/gamestonk_terminal/cryptocurrency/binance/)\\n\\n[COINPAPRIKA standalone menu](/gamestonk_terminal/cryptocurrency/coinpaprika/)\\n\\n## finbrain <a name=\\\"finbrain\\\"></a>\\n\\n```bash\\nusage: finbrain [-c --coin]\\n```\\n\\nFinBrain collects the news headlines from 15+ major financial news sources on a daily basis and analyzes them to generate sentiment scores for more than 4500 US stocks. FinBrain Technologies develops deep learning algorithms for financial analysis and prediction, which currently serves traders from more than 150 countries all around the world. [Source: See https://finbrain.tech]\\n\\nCurrently all sentiment is gathered for `COIN-USD` pairs. Please use upper case symbols of coins.\\n\\n* -c/--coin - Symbol of the Coin for which you want to analyse sentiment. Currently available coins are:\\n```\\nAAVE, ADA, ADX, AE, ANT, ARDR, ARK, ATOM, BAT, BCCOIN, BCH, BCN, BLOCK, BNB, BNT, BTC, BTCD, BTG, BTM, BTS, CVC, DASH,\\nDCN, DCR, DGB, DGD, DNT, DOGE, DOT, EDG, EOS, ETH, ETP, FAIR, FCT, FUN, GAME, GAS, GBYTE, GNO, GNT, HSR, ICX, IOC, KIN,\\nKMD, KNC, LINK, LKK, LRC, LSK, LTC, MAID, MCAP, MCO, MGO, MKR, MLN, MONA, MTL, NAV, NEBL, NEO, NLC2, NXS, NXT, OMG, PAY,\\nPIVX, PPT, QASH, QRL, QTUM, REP, RLC, SALT, SC, SMART, SNGLS, STEEM, STORJ, SUB, SYS, TAAS, TRX, UBQ, UNI, USDT, VEN,\\nVERI, VTC, WAVES, WINGS, WTC, XCP, XLM, XMR, XRP, XTZ, XVG, YFI, ZEC, ZEN, ZRX\\n```\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.47911280393600464,\n \"alphanum_fraction\": 0.5174853801727295,\n \"avg_line_length\": 35.929649353027344,\n \"blob_id\": \"a6b6dcd197ca8a39f85699b1736b149625eacd62\",\n \"content_id\": \"84023883c9d35da6d72d291ea1ffc9e54be08f82\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 10083,\n \"license_type\": \"permissive\",\n \"max_line_length\": 226,\n \"num_lines\": 199,\n \"path\": \"/gamestonk_terminal/etf/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ETF\\nMenu for Exchange Traded Funds.\\n\\nData is currently scraped from StockAnalysis.com/etf\\n\\n* [web](#web)\\n * open webbroswer to stockanalysis.com\\n* [search](#search)\\n * search for ETFs matching an input\\n* [overview](#overview)\\n * get ETF overview\\n* [holdings](#holdings)\\n * show ETF holdings\\n* [compare](#compare)\\n * compare multiple ETFs overview\\n* [screener](#screener)\\n * screen ETFs\\n\\n[WSJ](#WSJ)\\n* [gainers](#gainers)\\n * show top gainers\\n* [decliners](#decliners)\\n * show top decliners\\n* [active](#active)\\n * show most active\\n\\n## web <a name=\\\"web\\\"></a>\\n```python\\nusage: web\\n```\\nOpens StockAnalysis.com/etf. This site shows all avalaiable ETFs (currently > 2800).\\n\\n## search <a name=\\\"search\\\"></a>\\n```python\\nusage: search [-n --name]\\n```\\nSearch for ETFs matching\\n* -n/--name : Name to search for. Can be a company or other search term\\n\\nExample Usage: Search for all ETFs that deal with AI\\n```python\\n(✨) (etf)> search artificial intelligence\\nBOTZ - Global X Robotics & Artificial Intelligence ETF\\nIRBO - iShares Robotics and Artificial Intelligence ETF\\nROBT - First Trust Nasdaq Artificial Intelligence and Robotics ETF\\nTHNQ - ROBO Global Artificial Intelligence ETF\\nUBOT - Daily Robotics, Artificial Intelligence & Automation Index Bull 3X Shares\\n```\\nNote that this searches for exact strings in the ETF name. Searching for SPY will return nothing.\\n\\n## overview <a name=\\\"overview\\\"></a>\\n```python\\nusage: overview [-n --name]\\n```\\n* -n/--name : ETF to get data for\\n\\nReturns the overview of the ETF. Shows Last Price, Assets, Net Asset Value (NAV), Expense Ratio, PE Ratio, 5Year Beta, Total Dividend and Dividend Yield.\\n\\nExample will be a single column from the [compare](#compare) example.\\n\\n## holdings <a name=\\\"holdings\\\"></a>\\n```python\\nusage: overview [-n --name] [-l --limit]\\n```\\n* -n/--name : Name of ETF to get data for.\\n* -l/--limit : Number of assets to show. Max 200. Defaults to 20.\\n\\nReturns the top holdings in the ETF. Shows percentage and total shares held.\\n\\nExample:\\n```\\n(✨) (etf)> holdings spy -l 5\\n╒════╤══════════╤════════════╤═════════════╕\\n│ │ Ticker │ % of ETF │ Shares │\\n╞════╪══════════╪════════════╪═════════════╡\\n│ 0 │ AAPL │ 5.66% │ 162,078,640 │\\n├────┼──────────┼────────────┼─────────────┤\\n│ 1 │ MSFT │ 5.26% │ 77,463,250 │\\n├────┼──────────┼────────────┼─────────────┤\\n│ 2 │ AMZN │ 3.89% │ 4,396,091 │\\n├────┼──────────┼────────────┼─────────────┤\\n│ 3 │ FB │ 2.11% │ 24,705,450 │\\n├────┼──────────┼────────────┼─────────────┤\\n│ 4 │ GOOGL │ 1.93% │ 3,088,769 │\\n╘════╧══════════╧════════════╧═════════════╛\\n```\\n\\n## compare <a name=\\\"compare\\\"></a>\\n```python\\nusage: compare [-n --names]\\n```\\n* -n/--names : ETFs to compare. Input as comma separated (ETF1,ETF2,..)\\n\\nCampares the overview of different ETFs.\\n\\nExample:\\n\\n```\\n(✨) (etf)> compare spy,qqq,voo,doge\\nDOGE not found\\n╒════════════════╤══════════╤══════════╤══════════╕\\n│ │ SPY │ QQQ │ VOO │\\n╞════════════════╪══════════╪══════════╪══════════╡\\n│ Last Price │ $414.94 │ $327.01 │ $381.47 │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ Assets │ $353.25B │ $157.51B │ $222.38B │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ NAV │ $415.21 │ $328.91 │ $381.70 │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ Expense Ratio │ 0.09% │ 0.20% │ 0.03% │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ PE Ratio │ 25.16 │ 35.32 │ 27.60 │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ Beta (5Y) │ 0.99 │ 1.04 │ 0.99 │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ Dividend (ttm) │ $5.56 │ $1.77 │ $5.39 │\\n├────────────────┼──────────┼──────────┼──────────┤\\n│ Dividend Yield │ 1.34% │ 0.54% │ 1.41% │\\n╘════════════════╧══════════╧══════════╧══════════╛\\n```\\n## screener <a name=\\\"screener\\\"></a>\\n````python\\nusage: screener [--config] [-p MIN_PRICE] [-P MAX_PRICE] [-a MIN_ASSETS] [-A MAX_ASSETS] [-n MIN_NAV] [-N MAX_NAV] [-e MIN_EXP] [-E MAX_EXP] [-r MIN_PE] [-R MAX_PE] [-d MIN_DIV]\\n [-D MAX_DIV] [-b MIN_BETA] [-B MAX_BETA] [--num] [--export {csv,json,xlsx}] [-h]\\n````\\n\\n* --config : Flag to load from etf_config.ini file. This supersedes any other arguments.\\n* -p/--min_price : Minimum ETF price\\n* -P/--max_price : Maximum ETF price\\n* -a/--min_assets : Minimum ETF assets held\\n* -A/--max_assets : Maximum ETF assets held\\n* -n/--min_nav : Minimum ETF net asset valuation\\n* -N/--max_nav : Maximum ETF net asset valuation\\n* -e/--min_exp : Minimum ETF expense ratio\\n* -E/--max_exp : Maximum ETF expense ratio\\n* -r/--min_pe : Minimum ETF PE Ratio\\n* -R/--max_pe : Maximum ETF PE Ratio\\n* -d/--min_div : Minimum ETF dividend yield\\n* -D/--max_div : Maximum ETF dividend yield\\n* -b/--min_beta : Minimum ETF 5Y beta\\n* -B/--max_beta : Maximum ETF 5Y beta\\n* --num : Number of etfs to display in console. Defaults to 20\\n* --export {csv,json,xlsx} : Export to the selected file\\n\\nScreen ETFs based on the overview data from stockanalysis.com. This data is scraped nightly at midnight EST, so it will not be up to date or change during the day. Repo can be found at https://github.com/jmaslek/etf_scraper.\\nFor screeners with many results (exceeding the `--num` flag), the console will display a random subset of them.\\n\\nNote that to use the config file, locate the desired data column then change MIN and MAX. Make sure unused columns are set to None.\\nExample which will screen for ETFs between $45 and $57:\\n```python\\n[Price]\\nMIN = 45\\nMAX = 57\\n```\\n\\nSample usage:\\n```python\\n(✨) (etf)> screener -p 45 -P 88 -E 1 -d 3 -r 12 -a 10000\\nETFs downloaded\\n\\n╒═════╤═════════╤══════════╤═══════╤═══════════╤═══════╤════════╤═══════╤════════════╕\\n│ │ Price │ Assets │ NAV │ Expense │ PE │ Beta │ Div │ DivYield │\\n╞═════╪═════════╪══════════╪═══════╪═══════════╪═══════╪════════╪═══════╪════════════╡\\n│ VGK │ 66.97 │ 19750 │ 67.38 │ 0.08 │ 20.7 │ 0.94 │ 2.07 │ 3.07 │\\n├─────┼─────────┼──────────┼───────┼───────────┼───────┼────────┼───────┼────────────┤\\n│ XLE │ 51.75 │ 24950 │ 52.92 │ 0.12 │ 20 │ 1.75 │ 2.11 │ 3.99 │\\n├─────┼─────────┼──────────┼───────┼───────────┼───────┼────────┼───────┼────────────┤\\n│ XLU │ 64.52 │ 11640 │ 64.31 │ 0.12 │ 19.94 │ 0.35 │ 1.98 │ 3.07 │\\n╘═════╧═════════╧══════════╧═══════╧═══════════╧═══════╧════════╧═══════╧════════════╛\\n```\\n# WSJ <a name=\\\"wsj\\\"></a>\\nThe following functions atake the information from the [WSJ Market Data Page](https://www.wsj.com/market-data)\\n\\n## gainers <a name=\\\"gainers\\\"></a>\\n\\n```python\\nusage: gainers [-n NUM] [--export {csv,json,xlsx}] [-h]\\n```\\nShows top gaining ETFs\\n* -n/--num: Number to show. Defaults to 25, which is the max provided\\n* --export: Export data to one of {csv, json, xlsx}.\\n\\n## decliners <a name=\\\"decliners\\\"></a>\\n\\n```python\\nusage: decliners [-n NUM] [--export {csv,json,xlsx}] [-h]\\n```\\nShows highest declining ETFs\\n* -n/--num: Number to show. Defaults to 25, which is the max provided\\n* --export: Export data to one of {csv, json, xlsx}.\\n\\n## active <a name=\\\"active\\\"></a>\\n\\n```python\\nusage: active [-n NUM] [--export {csv,json,xlsx}] [-h]\\n```\\nShows most active ETFs\\n* -n/--num: Number to show. Defaults to 25, which is the max provided\\n* --export: Export data to one of {csv, json, xlsx}.\\n\"\n },\n {\n \"alpha_fraction\": 0.5504080057144165,\n \"alphanum_fraction\": 0.55077064037323,\n \"avg_line_length\": 31.827381134033203,\n \"blob_id\": \"fb7846aa8ee4e0a1c577a5e1026bac8438329bbc\",\n \"content_id\": \"7d97b9a3bc87db90143d4e2e977193e4e820de40\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11030,\n \"license_type\": \"permissive\",\n \"max_line_length\": 108,\n \"num_lines\": 336,\n \"path\": \"/gamestonk_terminal/options/options_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Options Controller Module.\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport os\\nfrom typing import List\\nimport matplotlib.pyplot as plt\\n\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.options import (\\n barchart_view,\\n syncretism_view,\\n calculator_model,\\n op_helpers,\\n yfinance_view,\\n yfinance_model,\\n tradier_view,\\n tradier_model,\\n fdscanner_view,\\n)\\n\\nfrom gamestonk_terminal.config_terminal import TRADIER_TOKEN\\nfrom gamestonk_terminal.menu import session\\n\\n\\nclass OptionsController:\\n \\\"\\\"\\\"Options Controller class.\\\"\\\"\\\"\\n\\n CHOICES = [\\n \\\"cls\\\",\\n \\\"?\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n ]\\n\\n CHOICES_COMMANDS = [\\n \\\"disp\\\",\\n \\\"scr\\\",\\n \\\"calc\\\",\\n \\\"yf\\\",\\n \\\"tr\\\",\\n \\\"info\\\",\\n \\\"load\\\",\\n \\\"exp\\\",\\n \\\"vol\\\",\\n \\\"voi\\\",\\n \\\"oi\\\",\\n \\\"hist\\\",\\n \\\"chains\\\",\\n \\\"grhist\\\",\\n \\\"unu\\\",\\n ]\\n\\n CHOICES += CHOICES_COMMANDS\\n\\n def __init__(self):\\n \\\"\\\"\\\"Construct data.\\\"\\\"\\\"\\n self.ticker = \\\"\\\"\\n self.expiry_dates = []\\n self.selected_date = \\\"\\\"\\n\\n self.op_parser = argparse.ArgumentParser(add_help=False, prog=\\\"options\\\")\\n self.op_parser.add_argument(\\n \\\"cmd\\\",\\n choices=self.CHOICES,\\n )\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help.\\\"\\\"\\\"\\n print(\\n \\\"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/options\\\"\\n )\\n print(\\\"\\\\nOptions:\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\")\\n print(\\\" disp display all preset screeners filters\\\")\\n print(\\\" scr output screener options\\\")\\n print(\\\" unu show unusual options activity\\\")\\n print(\\\"\\\")\\n print(f\\\"Current Ticker: {self.ticker or None}\\\")\\n print(\\\" load load new ticker\\\")\\n print(\\\" info display option information (volatility, IV rank etc)\\\")\\n print(\\\"\\\")\\n print(\\\" calc basic call/put PnL calculator\\\")\\n print(\\\"\\\")\\n print(f\\\"Current Expiration: {self.selected_date or None}\\\")\\n print(\\\" exp see and set expiration dates\\\")\\n print(\\\"\\\")\\n if self.selected_date and self.ticker:\\n print(\\\" chains display option chains with greeks [Tradier]]\\\")\\n print(\\\" oi plot open interest [Tradier/YF]\\\")\\n print(\\\" vol plot volume [Tradier/YF]\\\")\\n print(\\\" voi plot volume and open interest [Tradier/YF]\\\")\\n print(\\\" hist plot option history [Tradier\\\")\\n print(\\\" grhist plot option greek history [Syncretism]\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input.\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self.op_parser.parse_known_args(an_input.split())\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command.\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu.\\\"\\\"\\\"\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program.\\\"\\\"\\\"\\n return True\\n\\n def call_calc(self, other_args: List[str]):\\n \\\"\\\"\\\"Process calc command\\\"\\\"\\\"\\n calculator_model.pnl_calculator(other_args)\\n\\n def call_unu(self, other_args: List[str]):\\n \\\"\\\"\\\"Process act command\\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n prog=\\\"unu\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"This command gets unusual options from fdscanner.com\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--num\\\",\\n dest=\\\"num\\\",\\n type=int,\\n default=20,\\n help=\\\"Number of options to show. Each scraped page gives 20 results.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--sortby\\\",\\n dest=\\\"sortby\\\",\\n default=\\\"Vol/OI\\\",\\n choices=[\\\"Option\\\", \\\"Vol/OI\\\", \\\"Vol\\\", \\\"OI\\\", \\\"Bid\\\", \\\"Ask\\\"],\\n help=\\\"Column to sort by. Vol/OI is the default and typical variable to be considered unusual.\\\",\\n )\\n parser.add_argument(\\n \\\"-a\\\",\\n \\\"--ascending\\\",\\n dest=\\\"ascend\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"Flag to sort in ascending order\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n fdscanner_view.display_options(\\n num=ns_parser.num,\\n sort_column=ns_parser.sortby,\\n export=ns_parser.export,\\n ascending=ns_parser.ascend,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n def call_info(self, other_args: List[str]):\\n \\\"\\\"\\\"Process info command\\\"\\\"\\\"\\n barchart_view.print_options_data(self.ticker, other_args)\\n\\n def call_disp(self, other_args: List[str]):\\n \\\"\\\"\\\"Process disp command\\\"\\\"\\\"\\n syncretism_view.view_available_presets(other_args)\\n\\n def call_scr(self, other_args: List[str]):\\n \\\"\\\"\\\"Process scr command\\\"\\\"\\\"\\n syncretism_view.screener_output(other_args)\\n\\n def call_load(self, other_args: List[str]):\\n \\\"\\\"\\\"Process load command\\\"\\\"\\\"\\n self.ticker = op_helpers.load(other_args)\\n if TRADIER_TOKEN == \\\"REPLACE_ME\\\":\\n self.expiry_dates = yfinance_model.option_expirations(self.ticker)\\n else:\\n self.expiry_dates = tradier_model.option_expirations(self.ticker)\\n\\n def call_exp(self, other_args: List[str]):\\n \\\"\\\"\\\"Process exp command\\\"\\\"\\\"\\n if self.ticker:\\n self.selected_date = op_helpers.select_option_date(\\n self.expiry_dates, other_args\\n )\\n else:\\n print(\\\"Please select a ticker using load {ticker}\\\", \\\"\\\\n\\\")\\n\\n def call_hist(self, other_args: List[str]):\\n if TRADIER_TOKEN != \\\"REPLACE_ME\\\":\\n tradier_view.display_historical(self.ticker, self.selected_date, other_args)\\n else:\\n print(\\\"TRADIER TOKEN not supplied. \\\\n\\\")\\n\\n def call_chains(self, other_args: List[str]):\\n \\\"\\\"\\\"Process chains command\\\"\\\"\\\"\\n if TRADIER_TOKEN != \\\"REPLACE_ME\\\":\\n tradier_view.display_chains(self.ticker, self.selected_date, other_args)\\n else:\\n print(\\\"TRADIER TOKEN not supplied. \\\\n\\\")\\n\\n def call_vol(self, other_args: List[str]):\\n \\\"\\\"\\\"Process vol command\\\"\\\"\\\"\\n if not self.ticker and not self.selected_date:\\n print(\\\"Ticker and expiration required.\\\\n\\\")\\n return\\n parsed = op_helpers.vol(other_args)\\n if not parsed:\\n return\\n if parsed.source == \\\"tr\\\" and TRADIER_TOKEN != \\\"REPLACE_ME\\\":\\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\\n tradier_view.plot_vol(options, self.ticker, self.selected_date, parsed)\\n else:\\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\\n yfinance_view.plot_vol(\\n options.calls, options.puts, self.ticker, self.selected_date, parsed\\n )\\n\\n def call_voi(self, other_args: List[str]):\\n \\\"\\\"\\\"Process voi command\\\"\\\"\\\"\\n if not self.ticker and not self.selected_date:\\n print(\\\"Ticker and expiration required.\\\")\\n return\\n parsed = op_helpers.voi(other_args)\\n if not parsed:\\n return\\n if parsed.source == \\\"tr\\\" and TRADIER_TOKEN != \\\"REPLACE_ME\\\":\\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\\n tradier_view.plot_volume_open_interest(\\n self.ticker, self.selected_date, options, parsed\\n )\\n else:\\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\\n yfinance_view.plot_volume_open_interest(\\n self.ticker, self.selected_date, options.calls, options.puts, parsed\\n )\\n\\n def call_oi(self, other_args: List[str]):\\n \\\"\\\"\\\"Process oi command\\\"\\\"\\\"\\n if not self.ticker and not self.selected_date:\\n print(\\\"Ticker and expiration required.\\\")\\n return\\n parsed = op_helpers.oi(other_args)\\n if not parsed:\\n return\\n if parsed.source == \\\"tr\\\" and TRADIER_TOKEN != \\\"REPLACE_ME\\\":\\n options = tradier_model.get_option_chains(self.ticker, self.selected_date)\\n tradier_view.plot_oi(options, self.ticker, self.selected_date, parsed)\\n else:\\n options = yfinance_model.get_option_chain(self.ticker, self.selected_date)\\n yfinance_view.plot_oi(\\n options.calls, options.puts, self.ticker, self.selected_date, parsed\\n )\\n\\n def call_grhist(self, other_args: List[str]):\\n \\\"\\\"\\\"Process grhist command\\\"\\\"\\\"\\n syncretism_view.historical_greeks(self.ticker, self.selected_date, other_args)\\n\\n\\ndef menu():\\n \\\"\\\"\\\"Options Menu.\\\"\\\"\\\"\\n op_controller = OptionsController()\\n op_controller.call_help(None)\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in op_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (options)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (options)> \\\")\\n\\n try:\\n plt.close(\\\"all\\\")\\n\\n process_input = op_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.6685304045677185,\n \"alphanum_fraction\": 0.7232474684715271,\n \"avg_line_length\": 45.52631759643555,\n \"blob_id\": \"fc4ca822a285a9a53281a9499c5e3ede04e353e4\",\n \"content_id\": \"af6d9e7d7eaee0fdb541fb7ad823d0344d66abbd\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 30941,\n \"license_type\": \"permissive\",\n \"max_line_length\": 527,\n \"num_lines\": 665,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coingecko/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# CoinGecko\\n\\nThis menu aims to explore crypto world with [CoinGecko](#https://www.coingecko.com/en)\\nThe usage of the following commands along with an example will be exploited below.\\n\\nNote that we have added the ability to look at technical analysis in the ta menu. Data loaded from CoinGecko has no candle data,\\nso indicators that rely on anything other than close will fail with an error.\\n\\n[COIN](#COIN)\\n* [load](#load)\\n * load a given coin, you can use either coin symbol or coin id\\n* [find](#find)\\n * find similar coin by coin name,symbol or id\\n* [clear](#clear)\\n * clear loaded coin\\n* [chart](#chart)\\n * plot the loaded crypto chart\\n* [ta](technical_analysis/README.md)\\n * open technical analysis menu\\n* [info](#info)\\n * show coin base information\\n* [market](#market)\\n * show market data\\n* [ath](#ath)\\n * show all time high related metrics\\n* [atl](#atl)\\n * show all time low related metrics\\n* [score](#score)\\n * show different scores related to loaded coin\\n* [web](#web)\\n * show websites related to loaded coin\\n* [social](#social)\\n * show social media urls for loaded coin\\n* [bc](#bc)\\n * show block-chain explorers urls for loaded coins\\n* [dev](#dev)\\n * show developers data for loaded coins\\n\\n[OVERVIEW](#OVERVIEW)\\n* [global](#global)\\n * show global crypto market info\\n* [coins](#coins)\\n * show coins available on CoinGecko\\n* [defi](#defi)\\n * show global DeFi market info\\n* [trending](#trending)\\n * show trending coins on CoinGecko\\n* [most_voted](#most_voted)\\n * show most voted coins on CoinGecko\\n* [top_volume](#top_volume)\\n * show coins with highest volume on CoinGecko\\n* [recently](#recently)\\n * show recently added on CoinGecko\\n* [sentiment](#sentiment)\\n * show coins with most positive sentiment\\n* [gainers](#gainers)\\n * show top gainers - coins which price gained the most in given period\\n* [losers](#losers)\\n * show top losers - coins which price dropped the most in given period\\n* [stables](#stables)\\n * show Stable Coins\\n* [yfarms](#yfarms)\\n * show top Yield Farms\\n* [top_defi](#top_defi)\\n * show top DeFi Protocols\\n* [top_dex](#top_dex)\\n * show top Decentralized Exchanges\\n* [top_nft](#top_nft)\\n * show top Non Fungible Tokens\\n* [nft_today](#nft_today)\\n * show NFT Of The Day\\n* [nft_market](#nft_market)\\n * show NFT Market Status\\n* [exchanges](#exchanges)\\n * show Top Crypto Exchanges\\n* [ex_rates](#ex_rates)\\n * show Coin Exchange Rates\\n* [platforms](#platforms)\\n * show Crypto Financial Platforms\\n* [products](#products)\\n * show Crypto Financial Products\\n* [indexes](#indexes)\\n * show Crypto Indexes\\n* [derivatives](#derivatives)\\n * show Crypto Derivatives\\n* [categories](#categories)\\n * show Crypto Categories\\n* [hold](#hold)\\n * show eth, btc holdings overview statistics\\n* [hold_comp](#hold_comp)\\n * show eth, btc holdings by public companies\\n\\n\\n\\n# COIN <a name=\\\"COIN\\\"></a>\\n## load <a name=\\\"load\\\"></a>\\n\\n````\\nusage: load [-c --coin]\\n````\\n\\nLoad a given coin vs a given currency. Currently only retrieves price, not volume or MarketCap. The current crypto data is [Powered by CoinGecko API](#https://www.coingecko.com/en), which is an awesome service that currently requires no API Key!\\nBy loading a coin you will have access to a lot of statistics on that coin like price data, coin development stats, social media and many others. Loading coin also will open access to technical analysis menu\\n\\n\\n* -c/--coin The coin you wish to load. This can either be the symbol or the name. `load -c btc` and `load -c bitcoin`\\n will load. The -c flag is optional, the above is equivalent to `load btc`.\\n\\n\\n## chart <a name=\\\"chart\\\"></a>\\n\\n````\\nusage: chart [-d --days] [--vs]\\n````\\n\\nDisplay chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for.\\nBy default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `chart --vs eth --days 90`\\n\\n\\n* -d/--days The number of days to look. Defaults to 30 days. As per the API: Minutely data will be used for duration within 1 day,\\n Hourly data will be used for duration between 1 day and 90 days, Daily data will be used for duration above 90 days.\\n* --vs The currency to look against. Defaults to \\\"usd\\\".\\n\\n\\n\\n\\n## ta <a name=\\\"ta\\\"></a>\\n\\n````\\nusage: ta [-d --days] [--vs]\\n````\\n\\nOpen Technical Analysis menu for loaded coin\\nLoads data for technical analysis. You can specify currency vs which you want to show chart and also number of days to get data for.\\nE.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum in last 90 days range use `ta --vs eth --days 90`\\n\\n\\n* -d/--days The number of days to look. Defaults to 30 days. As per the API: Minutely data will be used for duration within 1 day,\\n Hourly data will be used for duration between 1 day and 90 days, Daily data will be used for duration above 90 days.\\n* --vs The currency to look against. Defaults to \\\"usd\\\".\\n\\n## info <a name=\\\"info\\\"></a>\\n\\n````\\nusage: info\\n````\\n\\nShows basic information about loaded coin like: id, name, symbol, asset_platform, description, contract_address, market_cap_rank, public_interest_score, total_supply, max_supply, price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d, current_price_btc, current_price_eth, current_price_usd\\n\\n\\n\\n## market <a name=\\\"market\\\"></a>\\n\\n````\\nusage: market\\n````\\n\\nMarket data for loaded coin. There you find metrics like: market_cap_rank, total_supply, max_supply, circulating_supply, price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d, price_change_percentage_60d, price_change_percentage_1y, market_cap_change_24h, market_cap_btc, market_cap_eth, market_cap_usd, total_volume_btc, total_volume_eth, total_volume_usd, high_24h_btc, high_24h_eth, high_24h_usd, low_24h_btc, low_24h_eth, low_24h_usd\\n\\n\\n\\n## ath <a name=\\\"ath\\\"></a>\\n\\n````\\nusage: ath\\n````\\n\\nAll time high data for loaded coin. You can find there most important metrics regarding ath of coin price like: current_price_btc, current_price_eth, current_price_usd, ath_btc, ath_eth, ath_usd, ath_date_btc, ath_date_eth, ath_date_usd, ath_change_percentage_btc, ath_change_percentage_btc, ath_change_percentage_eth, ath_change_percentage_usd\\n\\n\\n\\n## atl <a name=\\\"atl\\\"></a>\\n\\n````\\nusage: atl\\n````\\n\\nAll time low data for loaded coin. You can find there most important metrics regarding atl of coin price like: current_price_btc, current_price_eth, current_price_usd, atl_btc, atl_eth, atl_usd, atl_date_btc, atl_date_eth, atl_date_usd, atl_change_percentage_btc, atl_change_percentage_btc, atl_change_percentage_eth, atl_change_percentage_usd\\n\\n\\n\\n## score <a name=\\\"score\\\"></a>\\n\\n````\\nusage: score\\n````\\n\\nIn this view you can find different kind of scores for loaded coin. Those scores represents different rankings, sentiment metrics, some user stats and other, like: coingecko_rank, coingecko_score, developer_score, community_score, liquidity_score, sentiment_votes_up_percentage, sentiment_votes_down_percentage, public_interest_score, facebook_likes, twitter_followers, reddit_average_posts_48h, reddit_average_comments_48h, reddit_subscribers, reddit_accounts_active_48h, telegram_channel_user_count, alexa_rank, bing_matches\\n\\n\\n\\n## dev <a name=\\\"dev\\\"></a>\\n\\n````\\nusage: dev\\n````\\n\\nDevelopers data for loaded coin. If the development data is available you can see how the code development of given coin is going on.\\nThere are some statistics that shows number of stars, forks, subscribers, pull requests, commits, merges, contributors on github.\\n\\n\\n\\n## web <a name=\\\"web\\\"></a>\\n\\n````\\nusage: web\\n````\\n\\nWebsites found for given Coin. You can find there urls to homepage, forum, announcement site and others.\\n\\n\\n\\n## social <a name=\\\"social\\\"></a>\\n\\n````\\nusage: social\\n````\\n\\nDisplay social media corresponding to loaded coin. You can find there name of telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.\\n\\n\\n\\n## bc <a name=\\\"bc\\\"></a>\\n\\n````\\nusage: bc\\n````\\n\\nBlockchain explorers URLs for loaded coin. Those are sites like etherescan.io or polkascan.io in which you can see all blockchain data e.g. all txs, all tokens, all contracts...\\n\\n\\n\\n## clear <a name=\\\"clear\\\"></a>\\n\\n````\\nusage: clear\\n````\\n\\nJust remove previously loaded coin. (set coin = None)\\n\\n## find <a name=\\\"find\\\"></a>\\n\\n````\\nusage: find [-c --coin] [-t --top] [-k --key]\\n````\\n\\nFind similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinGecko, you can use this command to display coins with similar name, symbol or id to your search query.\\nExample of usage: coin name is something like \\\"polka\\\". So you can try: `find -c polka -k name -t 25`\\nIt will search for coin that has similar name to polka and display top 25 matches.\\n* -c, --coin stands for coin - you provide here your search query\\n* -k, --key it's a searching key. You can search by symbol, id or name of coin\\n* -t, --top it displays top N number of records.\\n\\n\\n\\n\\n# OVERVIEW <a name=\\\"OVERVIEW\\\"></a>\\n## global <a name=\\\"global\\\"></a>\\n\\n````\\nusage: global\\n````\\n\\nDisplay global statistics about Crypto Market like: active_cryptocurrencies, upcoming_icos, ongoing_icos, ended_icos, markets, market_cap_change_percentage_24h, eth_market_cap_in_pct, btc_market_cap_in_pct, altcoin_market_cap_in_pct\\n\\n\\n\\n## news <a name=\\\"news\\\"></a>\\n\\n````\\nusage: news [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows latest news from https://www.coingecko.com/en/news. Display columns: index, title, author, posted columns. You can sort by each of column above, using `--sort` parameter and also do it descending with `--descend` flag. It's also possible to display urls to news with `--links` flag. If you want to display top 75 news, sorted by author use `news -t 75 -s author` if you want to see urls to source of the news use `news -t 75 -l`\\n\\n* -t/--top - number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\\n* -s/--sortby - sort by given column. You can chose on from `index, title, author, posted`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls and display only `index, url` columns\\n\\n\\n\\n## coins <a name=\\\"coins\\\"></a>\\n\\n````\\nusage: coins [-s --skip] [-t --top] [-l --letter] [-k --key]\\n````\\n\\nDisplay all available coins in coingecko. You can search with pagination mechanism with `news --skip [num] --top [num]` or you can just search by letter like\\n`news --letter d --key name --top 10` it will display top 10 matches for coins which name starts with letter `d` and search will be done in column `name`\\n\\n* -s/--skip - number of records to skip. Default is 0. There are thousands of coins, so there is mechanism to paginate through them.\\ne.q if you want to see only records from 500-750 you should use `coins --skip 500 --top 250` or if you want to see 1200-1880 you should use `coins --skip 1200 --top 680`.\\n* -t/--top - number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\\n* -l/--letter - first letters of coin by which you want to search\\n* -k/--key - search key. With this parameter you can specify in which column you would like to search. Choose on from `name, id, symbol`\\n\\n\\n\\n\\n## defi <a name=\\\"defi\\\"></a>\\n\\n````\\nusage: defi\\n````\\n\\nShows global DeFi statistics. DeFi or Decentralized Finance refers to financial services that are built on top of distributed networks with no central intermediaries. Displays metrics like: defi_market_cap, eth_market_cap, defi_to_eth_ratio, trading_volume_24h, defi_dominance, top_coin_name, top_coin_defi_dominance.\\n\\n\\n\\n\\n## trending <a name=\\\"trending\\\"></a>\\n\\n````\\nusage: trending [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows most trending coins on CoinGecko.\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `trending --top 10`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `trending --top 10 --links`\\n\\n\\n\\n## most_voted <a name=\\\"most_voted\\\"></a>\\n\\n````\\nusage: most_voted [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows most voted coins on CoinGecko.\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `most_voted --top 10`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `most_voted --top 10 --links`\\n\\n\\n\\n## most_visited <a name=\\\"most_visited\\\"></a>\\n\\n````\\nusage: most_visited [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows most visited coins on CoinGecko.\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `most_visited --top 10`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `most_visited --top 10 --links`\\n\\n\\n\\n\\n## sentiment <a name=\\\"sentiment\\\"></a>\\n\\n````\\nusage: sentiment [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows coins with the most positive sentiment on CoinGecko\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `sentiment --top 10`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, price_usd, price_btc`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `sentiment --top 10 --links`\\n\\n\\n\\n\\n## recently <a name=\\\"recently\\\"></a>\\n\\n````\\nusage: recently [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows coins which were recently added on CoinGecko\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `recently --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change_24h, change_1h, added`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `recently --top 10 --links`\\n\\n\\n\\n## top_volume <a name=\\\"top_volume\\\"></a>\\n\\n````\\nusage: top_volume [-t --top] [-s --sortby] [--descend]\\n````\\n\\nShows coins with the highest transactions volume on CoinGecko\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `top_volume --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n\\n## gainers <a name=\\\"gainers\\\"></a>\\n\\n````\\nusage: gainers [-p --period] [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows largest gainers - coins which gain the most in given period.\\n\\n* -p/--period - time period in which coins gained the most in price. One from `1h, 24h, 7d, 14d, 30d, 60d, 1y`. If you want to see top gainers in last 24h `gainers -p 24h` or top gainers in last 60days `gainers -p 60d`\\n* -t/--top - number of coins to display. To display top 10 coins: `gainers --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change, volume`.\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `gainers --period 24h --links`\\n\\n\\n\\n## losers <a name=\\\"losers\\\"></a>\\n\\n````\\nusage: losers [-p --period] [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows largest losers - coins which lost the most in given period.\\n\\n* -p/--period - time period in which coins lost the most in price. One from `1h, 24h, 7d, 14d, 30d, 60d, 1y`. If you want to see top losers in last 24h `losers -p 24h` or top losers in last 60days `losers -p 60d`\\n* -t/--top - number of coins to display. To display top 10 coins: `losers --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can chose on from `rank, name, symbol, price, change, volume`.\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `losers --period 24h --links`\\n\\n)\\n\\n## stables <a name=\\\"stables\\\"></a>\\n\\n````\\nusage: stables [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows stablecoins by market capitalization. Stablecoins are cryptocurrencies that attempt to peg their market value to some external reference\\nlike the U.S. dollar or to a commodity's price such as gold.\\n\\n* -t/--top - number of coins to display. To display top 10 coins: `stables --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, exchanges, market_cap, change_30d`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `stables --links`. If you will use this flag `rank, name, symbol, url` columns will be displayed.\\n\\n\\n\\n\\n## yfarms <a name=\\\"yfarms\\\"></a>\\n\\n````\\nusage: yfarms [-t --top] [-s --sortby] [--descend]\\n````\\n\\nShows Top Yield Farming Pools by Value Locked from https://www.coingecko.com/en/yield-farming\\nYield farming, also referred to as liquidity mining, is a way to generate rewards with cryptocurrency holdings.\\nIn simple terms, it means locking up cryptocurrencies and getting rewards.\\n\\n* -t/--top - number of yield farms to display. To display top 10 yield farms: `yfarms --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, value_locked, return_year`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n\\n## top_defi <a name=\\\"top_defi\\\"></a>\\n\\n````\\nusage: top_defi [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows Top DeFi Coins by Market Capitalization from https://www.coingecko.com/en/defi\\nDeFi or Decentralized Finance refers to financial services that are built on top of distributed networks with no central intermediaries.\\n\\n* -t/--top - number of defi coins to display. To display top 10 defi coins: `top_defi --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, change_1h, change_7d`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `top_defi --links`.\\n\\n\\n\\n## top_dex <a name=\\\"top_dex\\\"></a>\\n\\n````\\nusage: top_dex [-t --top] [-s --sortby] [--descend]\\n````\\n\\nShows Top Decentralized Exchanges on CoinGecko by Trading Volume from https://www.coingecko.com/en/dex\\nDecentralized exchanges or DEXs are autonomous decentralized applications (DApps) that allow cryptocurrency buyers or\\nsellers to trade without having to give up control over their funds to any intermediary or custodian. Source: [coinmarketcap](#https://coinmarketcap.com/alexandria/article/what-are-decentralized-exchanges-dex)\\n\\n* -t/--top - number of Decentralized Exchanges to display. To display top 10 DEX: `top_dex --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol most_traded_pairs, market_share_by_volume`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n## top_nft <a name=\\\"top_nft\\\"></a>\\n\\n````\\nusage: top_nft [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\n\\nShows Top 100 NFT Coins by Market Capitalization from https://www.coingecko.com/en/nft\\nNFT (Non-fungible Token) refers to digital assets with unique characteristics.\\nExamples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n\\n* -t/--top - number of NFT to display. To display top 10 NFT Coins: `top_nft --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, symbol, price, change_24h, change_1h, change_7d, volume_24h, market_cap`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `top_nft --links`.\\n\\n\\n\\n\\n## nft_today <a name=\\\"nft_today\\\"></a>\\n\\n````\\nusage: nft_today\\n````\\nGet Non-fungible Token of the Day. Everyday on CoinGecko there is chosen new NFT. NFT (Non-fungible Token) refers to digital assets with unique characteristics. Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n`nft_today` command you will display: author, description, url, img url.\\n\\n\\n\\n## nft_market <a name=\\\"nft_market\\\"></a>\\n\\n````\\nusage: nft_market\\n````\\nGet current state of NFTs market. NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\nExamples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n`nft_market` will display: NFT Market Cap, 24h Trading Volume, NFT Dominance vs Global market, Theta Network NFT Dominance.\\n\\n\\n\\n\\n## exchanges <a name=\\\"exchanges\\\"></a>\\n\\n````\\nusage: exchanges [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\nShows top crypto exchanges base on trust score.\\n\\n* -t/--top - number of exchanges to display. To display top 10 exchanges by trust_score: `exchanges --top 10 --sortby trust_score --descend`\\n* -s/--sortby - sort by given column. You can sort data by `rank, trust_score, id, name, country, established, trade_volume_24h_btc`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `exchanges --links`. Using `links` parameter will display only `rank, name, url` columns\\n\\n\\n\\n## ex_rates <a name=\\\"ex_rates\\\"></a>\\n\\n````\\nusage: ex_rates [-t --top] [-s --sortby] [--descend]\\n````\\n\\nShows list of crypto, fiats, commodity exchange rates from CoinGecko\\n* -t/--top - number of exchanges to display. To display top 10 exchanges by trust_score: `ex_rates --top 10 --sortby name --descend`\\n* -s/--sortby - sort by given column. You can sort by `index,name,unit, value, type`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n## platforms <a name=\\\"platforms\\\"></a>\\n\\n````\\nusage: platforms [-t --top] [-s --sortby] [--descend]\\n````\\nShows Top Crypto Financial Platforms in which you can borrow or lend your crypto e.g. Celsius, Nexo, Crypto.com, Aave and others.\\n\\n* -t/--top - number of crypto platforms to display. To display top 10 platforms: `platforms --top 10 --sortby name`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, category, centralized`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n\\n\\n## products <a name=\\\"products\\\"></a>\\n\\n````\\nusage: products [-t --top] [-s --sortby] [--descend]\\n````\\nShows Top Crypto Financial Products with which you can earn yield, borrow or lend your crypto.\\n\\n* -t/--top - number of crypto products to display. To display top 10 products: `products --top 10 --sortby name`\\n* -s/--sortby - sort by given column. You can sort data by `rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n## indexes <a name=\\\"indexes\\\"></a>\\n\\n````\\nusage: indexes [-t --top] [-s --sortby] [--descend]\\n````\\n\\nShows list of crypto indexes from CoinGecko.Each crypto index is made up of a selection of cryptocurrencies, grouped together and weighted by market cap.\\n* -t/--top - number of crypto indexes to display. To display top 10 indexes: `indexes --top 10 --sortby name`\\n* -s/--sortby - sort by given column. You can sort data by `rank, name, id, market, last, is_multi_asset_composite`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n## derivatives <a name=\\\"derivatives\\\"></a>\\n\\n````\\nusage: derivatives [-t --top] [-s --sortby] [--descend]\\n````\\nShows list of crypto derivatives from CoinGecko. Crypto derivatives are secondary contracts or financial tools that derive their value from a primary\\nunderlying asset. In this case, the primary asset would be a cryptocurrency such as Bitcoin. The most popular crypto derivatives are crypto futures, crypto options, and perpetual contracts.\\n* -t/--top - number of crypto derivatives to display. To display top 10 derivatives: `derivatives --top 10 --sortby symbol`\\n* -s/--sortby - sort by given column. You can sort by `rank, market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h`\\n* --descend - flag to sort in descending order (lowest first)\\n\\n\\n\\n\\n## categories <a name=\\\"categories\\\"></a>\\n\\n````\\nusage: categories [-t --top] [-s --sortby] [--descend] [-l --links]\\n````\\nShows top cryptocurrency categories by market capitalization from https://www.coingecko.com/en/categories\\nIt includes categories like: stablecoins, defi, solana ecosystem, polkadot ecosystem and many others.\\n\\n* -t/--top - number of crypto categories to display. To display top 10 crypto categories: `categories --top 10 --sortby rank`\\n* -s/--sortby - sort by given column. You can sort by `rank, name, change_1h, change_24h, change_7d, market_cap, volume_24h, n_of_coins`\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `categories --links`. Using `links` parameter will display only `rank, name, url` columns\\n\\n\\n\\n\\n## hold <a name=\\\"hold\\\"></a>\\n\\n````\\nusage: hold [-c --coin]\\n````\\n\\nShows overview of public companies that holds ethereum or bitcoin\\nDisplays most important metrics like: Total Bitcoin/Ethereum Holdings, Total Value (USD), Public Companies Bitcoin/Ethereum Dominance, Companies.\\n\\n* -c/--coin - chose a coin. Only available for ethereum or bitcoin. If you want to see overview of public companies that holds ethereum use `hold --coin ethereum` for bitcoin `hold --coin bitcoin`\\n\\n\\n\\n\\n## hold_comp <a name=\\\"hold_comp\\\"></a>\\n\\n````\\nusage: hold_comp [-c --coin] [- --links]\\n````\\n\\nShows Ethereum/Bitcoin Holdings by Public Companies. Track publicly traded companies around the world that are buying ethereum as part of corporate treasury\\n\\n* -c/--coin - chose a coin. Only available for ethereum or bitcoin. List of public companies that holds ethereum `hold_comp --coin ethereum` for bitcoin `hold_comp --coin bitcoin`\\n* -l/--links - You can use additional flag `--links` to see urls to announcement about buying btc or eth by given company. In this case of usage `links` only columns `rank, company, url` will be displayed\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5437082648277283,\n \"alphanum_fraction\": 0.5486685037612915,\n \"avg_line_length\": 28.438465118408203,\n \"blob_id\": \"fa5fc53d7de023341fd8048160f532507abcf5b0\",\n \"content_id\": \"94f791fa195d86f30a4806c3a666c6ad72a3fbf3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 74391,\n \"license_type\": \"permissive\",\n \"max_line_length\": 120,\n \"num_lines\": 2527,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" pycoingecko_api \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport textwrap\\nimport difflib\\nimport pandas as pd\\nfrom pandas.plotting import register_matplotlib_converters\\nimport matplotlib.pyplot as plt\\nfrom tabulate import tabulate\\nfrom pycoingecko import CoinGeckoAPI\\nfrom gamestonk_terminal.helper_funcs import (\\n check_positive,\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nimport gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_overview_model as gecko\\nimport gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_coin_model as gecko_coin\\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import wrap_text_in_df\\n\\nregister_matplotlib_converters()\\n\\n# Generate a list of valid coins to be checked against later\\ncg_api = CoinGeckoAPI()\\ncoins = cg_api.get_coins()\\n\\n# pylint: disable=inconsistent-return-statements\\n# pylint: disable=R0904, C0302\\n\\n\\ndef load(other_args: List[str]):\\n \\\"\\\"\\\"Load selected Cryptocurrency. You can pass either symbol of id of the coin\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"load\\\",\\n description=\\\"\\\"\\\"Load cryptocurrency, from CoinGecko.\\n You will have access to a lot of statistics on that coin like price data,\\n coin development stats, social media and many others. Loading coin\\n also will open access to technical analysis menu.\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n required=True,\\n type=str,\\n dest=\\\"coin\\\",\\n help=\\\"Coin to load data for (symbol or coin id). You can use either symbol of the coin or coinId\\\"\\n \\\"You can find all coins using command `coins` or visit https://www.coingecko.com/en. \\\"\\n \\\"To use load a coin use command load -c [symbol or coinId]\\\",\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-c\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n coin = gecko_coin.Coin(ns_parser.coin)\\n print(\\\"\\\")\\n return coin\\n\\n except KeyError:\\n print(f\\\"Could not find coin with the id: {ns_parser.coin}\\\", \\\"\\\\n\\\")\\n return None\\n except SystemExit:\\n print(\\\"\\\")\\n return None\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return None\\n\\n\\ndef chart(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Plots chart for loaded cryptocurrency\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"chart\\\",\\n description=\\\"\\\"\\\"\\n Display chart for loaded coin. You can specify currency vs which you want\\n to show chart and also number of days to get data for.\\n By default currency: usd and days: 30.\\n E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum\\n in last 90 days range use `chart --vs eth --days 90`\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\", default=\\\"usd\\\", dest=\\\"vs\\\", help=\\\"Currency to display vs coin\\\"\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--days\\\", default=30, dest=\\\"days\\\", help=\\\"Number of days to get data for\\\"\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days)\\n plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n\\n plt.plot(df.index, df.price, \\\"-ok\\\", ms=2)\\n plt.xlabel(\\\"Time\\\")\\n plt.xlim(df.index[0], df.index[-1])\\n plt.ylabel(\\\"Price\\\")\\n plt.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n plt.minorticks_on()\\n plt.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n plt.title(f\\\"{coin.coin_symbol}/{df['currency'][0]}\\\")\\n plt.show()\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef ta(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Load data for Technical Analysis\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ta\\\",\\n description=\\\"\\\"\\\"\\n Loads data for technical analysis. You can specify currency vs which you want\\n to show chart and also number of days to get data for.\\n By default currency: usd and days: 30.\\n E.g. if you loaded in previous step Bitcoin and you want to see it's price vs ethereum\\n in last 90 days range use `ta --vs eth --days 90`\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"--vs\\\", default=\\\"usd\\\", dest=\\\"vs\\\", help=\\\"Currency to display vs coin\\\"\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--days\\\", default=30, dest=\\\"days\\\", help=\\\"Number of days to get data for\\\"\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return None, None\\n\\n df = coin.get_coin_market_chart(ns_parser.vs, ns_parser.days)\\n return df, ns_parser.vs\\n\\n except SystemExit:\\n print(\\\"\\\")\\n return None, None\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return None, None\\n\\n\\ndef info(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows basic information about loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"info\\\",\\n description=\\\"\\\"\\\"\\n Shows basic information about loaded coin like:\\n id, name, symbol, asset_platform, description, contract_address,\\n market_cap_rank, public_interest_score, total_supply, max_supply,\\n price_change_percentage_24h, price_change_percentage_7d, price_change_percentage_30d,\\n current_price_btc, current_price_eth, current_price_usd\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = wrap_text_in_df(coin.base_info, w=80)\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef web(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows found websites corresponding to loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"web\\\",\\n description=\\\"\\\"\\\"Websites found for given Coin. You can find there urls to\\n homepage, forum, announcement site and others.\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.websites\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef social(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows social media corresponding to loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"social\\\",\\n description=\\\"\\\"\\\"Shows social media corresponding to loaded coin. You can find there name of\\n telegram channel, urls to twitter, reddit, bitcointalk, facebook and discord.\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.social_media\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef dev(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows developers data for loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"dev\\\",\\n description=\\\"\\\"\\\"Developers data for loaded coin. If the development data is available you can see\\n how the code development of given coin is going on.\\n There are some statistics that shows number of stars, forks, subscribers, pull requests,\\n commits, merges, contributors on github.\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.developers_data\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef ath(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows all time high data for loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ath\\\",\\n description=\\\"\\\"\\\"All time high data for loaded coin. You can find there most important metrics regarding\\n ath of coin price like:\\n current_price_btc, current_price_eth, current_price_usd, ath_btc, ath_eth, ath_usd,\\n ath_date_btc, ath_date_eth, ath_date_usd, ath_change_percentage_btc, ath_change_percentage_btc,\\n ath_change_percentage_eth, ath_change_percentage_usd\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.all_time_high\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef atl(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows all time low data for loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"atl\\\",\\n description=\\\"\\\"\\\"\\n All time low data for loaded coin. You can find there most important metrics regarding\\n atl of coin price like:\\n current_price_btc, ,current_price_eth, current_price_usd, atl_btc, atl_eth, atl_usd,\\n atl_date_btc, atl_date_eth, atl_date_usd, atl_change_percentage_btc, atl_change_percentage_btc,\\n atl_change_percentage_eth, atl_change_percentage_usd\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.all_time_low\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef score(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows different kind of scores for loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"score\\\",\\n description=\\\"\\\"\\\"\\n In this view you can find different kind of scores for loaded coin.\\n Those scores represents different rankings, sentiment metrics, some user stats and others.\\n coingecko_rank, coingecko_score, developer_score, community_score, liquidity_score,\\n sentiment_votes_up_percentage, sentiment_votes_down_percentage, public_interest_score,\\n facebook_likes, twitter_followers, reddit_average_posts_48h, reddit_average_comments_48h,\\n reddit_subscribers, reddit_accounts_active_48h, telegram_channel_user_count, alexa_rank,\\n bing_matches\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.scores\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef bc(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows urls to blockchain explorers\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"bc\\\",\\n description=\\\"\\\"\\\"\\n Blockchain explorers URLs for loaded coin. Those are sites like etherescan.io or polkascan.io\\n in which you can see all blockchain data e.g. all txs, all tokens, all contracts...\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.blockchain_explorers\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef market(coin: gecko_coin.Coin, other_args: List[str]):\\n \\\"\\\"\\\"Shows market data for loaded coin\\n\\n Parameters\\n ----------\\n coin : gecko_coin.Coin\\n Cryptocurrency\\n other_args : List[str]\\n argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"market\\\",\\n description=\\\"\\\"\\\"\\n Market data for loaded coin. There you find metrics like:\\n market_cap_rank, total_supply, max_supply, circulating_supply,\\n price_change_percentage_24h, price_change_percentage_7d, 'price_change_percentage_30d',\\n price_change_percentage_60d', 'price_change_percentage_1y', 'market_cap_change_24h',\\n market_cap_btc', 'market_cap_eth', 'market_cap_usd', 'total_volume_btc', 'total_volume_eth',\\n total_volume_usd', 'high_24h_btc', 'high_24h_eth', 'high_24h_usd', 'low_24h_btc', 'low_24h_eth',\\n low_24h_usd'\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n df = coin.market_data\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef holdings_overview(other_args: List[str]):\\n \\\"\\\"\\\"Shows overview of public companies that holds ethereum or bitcoin from www.coingecko.com\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"hold\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows overview of public companies that holds ethereum or bitcoin.\\n You can find there most important metrics like:\\n Total Bitcoin Holdings, Total Value (USD), Public Companies Bitcoin Dominance, Companies\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n dest=\\\"coin\\\",\\n type=str,\\n help=\\\"companies with ethereum or bitcoin\\\",\\n default=\\\"bitcoin\\\",\\n choices=[\\\"ethereum\\\", \\\"bitcoin\\\"],\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_holdings_overview(endpoint=ns_parser.coin)\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef holdings_companies_list(other_args: List[str]):\\n \\\"\\\"\\\"Shows Ethereum/Bitcoin Holdings by Public Companies from www.coingecko.com\\n\\n Track publicly traded companies around the world that are buying ethereum as part of corporate treasury\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"hold_comp\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Track publicly traded companies around the world that\\n are buying ethereum or bitcoin as part of corporate treasury:\\n rank, company, ticker, country, total_btc, entry_value, today_value, pct_of_supply\\n You can use additional flag --links to see urls to announcement about buying btc or eth by given company.\\n In this case you will see only columns like rank, company, url\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n dest=\\\"coin\\\",\\n type=str,\\n help=\\\"companies with ethereum or bitcoin\\\",\\n default=\\\"bitcoin\\\",\\n choices=[\\\"ethereum\\\", \\\"bitcoin\\\"],\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will see only rank, company, url columns\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_companies_assets(endpoint=ns_parser.coin)\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"company\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef gainers(other_args: List[str]):\\n \\\"\\\"\\\"Shows Largest Gainers - coins which gain the most in given period from www.coingecko.com\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"gainers\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows Largest Gainers - coins which gain the most in given period.\\n You can use parameter --period to set which timeframe are you interested in. eg. 1h, 24h, 7d, 14d, 30d, 60d, 1y\\n You can look on only top N number of records with --top,\\n You can sort by rank, symbol, name, volume, price, change with --sort and also with --descend flag to set it\\n to sort descending.\\n There is --links flag, which will display one additional column you all urls for coins.\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--period\\\",\\n dest=\\\"period\\\",\\n type=str,\\n help=\\\"time period, one from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\\\",\\n default=\\\"1h\\\",\\n choices=[\\\"1h\\\", \\\"24h\\\", \\\"7d\\\", \\\"14d\\\", \\\"30d\\\", \\\"60d\\\", \\\"1y\\\"],\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=int,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"symbol\\\", \\\"name\\\", \\\"volume\\\", \\\"price\\\", \\\"change\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will additional column with urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if ns_parser.sortby == \\\"change\\\":\\n sortby = f\\\"%change_{ns_parser.period}\\\"\\n else:\\n sortby = ns_parser.sortby\\n\\n df = gecko.get_gainers_or_losers(\\n period=ns_parser.period, typ=\\\"gainers\\\"\\n ).sort_values(by=sortby, ascending=ns_parser.descend)\\n\\n if not ns_parser.links:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef losers(other_args: List[str]):\\n \\\"\\\"\\\"Shows Largest Losers - coins which lost the most in given period of time from www.coingecko.com\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"losers\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows Largest Losers - coins which price dropped the most in given period\\n You can use parameter --period to set which timeframe are you interested in. eg. 1h, 24h, 7d, 14d, 30d, 60d, 1y\\n You can look on only top N number of records with --top,\\n You can sort by rank, symbol, name, volume, price, change with --sort and also with --descend flag\\n to sort descending.\\n Flag --links will display one additional column with all coingecko urls for listed coins.\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--period\\\",\\n dest=\\\"period\\\",\\n type=str,\\n help=\\\"time period, one from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\\\",\\n default=\\\"1h\\\",\\n choices=[\\\"1h\\\", \\\"24h\\\", \\\"7d\\\", \\\"14d\\\", \\\"30d\\\", \\\"60d\\\", \\\"1y\\\"],\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: change\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"symbol\\\", \\\"name\\\", \\\"volume\\\", \\\"price\\\", \\\"change\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will additional column with urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if ns_parser.sortby == \\\"change\\\":\\n sortby = f\\\"%change_{ns_parser.period}\\\"\\n else:\\n sortby = ns_parser.sortby\\n\\n df = gecko.get_gainers_or_losers(\\n period=ns_parser.period, typ=\\\"losers\\\"\\n ).sort_values(by=sortby, ascending=ns_parser.descend)\\n\\n if not ns_parser.links:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef discover(category: str, other_args: List[str]):\\n \\\"\\\"\\\"Discover coins by different categories\\n - Most voted coins\\n - Most popular coins\\n - Recently added coins\\n - Most positive sentiment coins\\n\\n Parameters\\n ----------\\n category: str\\n one from list: [trending, most_voted, positive_sentiment, most_visited]\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=f\\\"{category}\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=f\\\"\\\"\\\"Discover {category} coins.\\n Use --top parameter to display only top N number of records,\\n You can sort by rank, name, price_btc, price_usd, using --sort parameter and also with --descend flag\\n to sort descending.\\n Flag --links will display one additional column with all coingecko urls for listed coins.\\n {category} will display: rank, name, price_usd, price_btc\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"name\\\", \\\"price_usd\\\", \\\"price_btc\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will additional column with urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.discover_coins(category=category)\\n df.index = df.index + 1\\n df.reset_index(inplace=True)\\n df.rename(columns={\\\"index\\\": \\\"rank\\\"}, inplace=True)\\n\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n if not ns_parser.links:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".5f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef news(other_args: List[str]):\\n \\\"\\\"\\\"Shows latest crypto news from www.coingecko.com\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"news\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Shows latest crypto news from CoinGecko. \\\"\\n \\\"You will see index, title, author, posted columns. \\\"\\n \\\"You can sort by each of column above, using --sort parameter and also do it descending with --descend flag\\\"\\n \\\"To display urls to news use --links flag.\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=int,\\n help=\\\"top N number of news >=10\\\",\\n default=50,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: index\\\",\\n default=\\\"index\\\",\\n choices=[\\\"index\\\", \\\"title\\\", \\\"author\\\", \\\"posted\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will additional column with urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_news(n=ns_parser.top).sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n df[\\\"title\\\"] = df[\\\"title\\\"].apply(\\n lambda x: \\\"\\\\n\\\".join(textwrap.wrap(x, width=50)) if isinstance(x, str) else x\\n )\\n\\n if not ns_parser.links:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n else:\\n df = df[[\\\"index\\\", \\\"url\\\"]]\\n\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef categories(other_args: List[str]):\\n \\\"\\\"\\\"Shows top cryptocurrency categories by market capitalization from https://www.coingecko.com/en/categories\\n\\n The cryptocurrency category ranking is based on market capitalization.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"categories\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows top cryptocurrency categories by market capitalization. It includes categories like:\\n stablecoins, defi, solana ecosystem, polkadot ecosystem and many others.\\n \\\"You can sort by each of column above, using --sort parameter and also do it descending with --descend flag\\\"\\n \\\"To display urls to news use --links flag.\\\",\\n Displays: rank, name, change_1h, change_24h, change_7d, market_cap, volume_24h, n_of_coins\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number of news >=10\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"change_1h\\\",\\n \\\"change_24h\\\",\\n \\\"change_7d\\\",\\n \\\"market_cap\\\",\\n \\\"volume_24h\\\",\\n \\\"n_of_coins\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls. If you will use that flag you will additional column with urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_top_crypto_categories().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if not ns_parser.links:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n else:\\n df = df[[\\\"rank\\\", \\\"name\\\", \\\"url\\\"]]\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef recently_added(other_args: List[str]):\\n \\\"\\\"\\\"Shows recently added coins from \\\"https://www.coingecko.com/en/coins/recently_added\\\"\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"recently\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows recently added coins on CoinGecko. You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, change_24h, change_1h, added with --sort\\n and also with --descend flag to sort descending.\\n Flag --links will display urls\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"name\\\", \\\"symbol\\\", \\\"price\\\", \\\"change_24h\\\", \\\"change_1h\\\", \\\"added\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_recently_added_coins().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"symbol\\\", \\\"added\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef stablecoins(other_args: List[str]):\\n \\\"\\\"\\\"Shows stablecoins data from \\\"https://www.coingecko.com/en/stablecoins\\\"\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"stables\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows stablecoins by market capitalization.\\n Stablecoins are cryptocurrencies that attempt to peg their market value to some external reference\\n like the U.S. dollar or to a commodity's price such as gold.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, change_24h, exchanges, market_cap, change_30d with --sort\\n and also with --descend flag to sort descending.\\n Flag --links will display stablecoins urls\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"change_24h\\\",\\n \\\"exchanges\\\",\\n \\\"market_cap\\\",\\n \\\"change_30d\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_stable_coins().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"name\\\", \\\"symbol\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef yfarms(other_args: List[str]):\\n \\\"\\\"\\\"Shows Top Yield Farming Pools by Value Locked from \\\"https://www.coingecko.com/en/yield-farming\\\"\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"yfarms\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top Yield Farming Pools by Value Locked\\n Yield farming, also referred to as liquidity mining, is a way to generate rewards with cryptocurrency holdings.\\n In simple terms, it means locking up cryptocurrencies and getting rewards.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, value_locked, return_year with --sort parameter\\n and also with --descend flag to sort descending.\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"Top N of records. Default 20\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"name\\\", \\\"value_locked\\\", \\\"return_year\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_yield_farms()\\n df = df.sort_values(by=ns_parser.sortby, ascending=ns_parser.descend)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".0f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef top_volume_coins(other_args: List[str]):\\n \\\"\\\"\\\"Shows Top 100 Coins by Trading Volume from \\\"https://www.coingecko.com/en/yield-farming\\\"\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"top_volume\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top Coins by Trading Volume.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by on of columns rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h ,\\n market_cap with --sort parameter and also with --descend flag to sort descending.\\n Displays columns: rank, name, symbol, price, change_1h, change_24h, change_7d , volume_24h ,market_cap\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"Top N of records. Default 20\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"change_1h\\\",\\n \\\"change_24h\\\",\\n \\\"change_7d\\\",\\n \\\"volume_24h\\\",\\n \\\"market_cap\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_top_volume_coins().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef top_defi_coins(other_args: List[str]):\\n \\\"\\\"\\\"Shows Top 100 DeFi Coins by Market Capitalization from \\\"https://www.coingecko.com/en/defi\\\"\\n DeFi or Decentralized Finance refers to financial services that are built\\n on top of distributed networks with no central intermediaries.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"top_defi\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top DeFi Coins by Market Capitalization\\n DeFi or Decentralized Finance refers to financial services that are built\\n on top of distributed networks with no central intermediaries.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, change_24h, change_1h, change_7d,\\n volume_24h, market_cap with --sort and also with --descend flag to sort descending.\\n Flag --links will display urls\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"change_1h\\\",\\n \\\"change_24h\\\",\\n \\\"change_7d\\\",\\n \\\"volume_24h\\\",\\n \\\"market_cap\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_top_defi_coins().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"name\\\", \\\"symbol\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef top_dex(other_args: List[str]):\\n \\\"\\\"\\\"Shows Top Decentralized Exchanges on CoinGecko by Trading Volume from \\\"https://www.coingecko.com/en/dex\\\"\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"top_dex\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows Top Decentralized Exchanges on CoinGecko by Trading Volume\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\\n most_traded_pairs, market_share_by_volume with --sort and also with --descend flag to sort descending.\\n Display columns:\\n rank, name, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"volume_24h\\\",\\n \\\"n_coins\\\",\\n \\\"visits\\\",\\n \\\"most_traded\\\",\\n \\\"market_share_by_vol\\\",\\n \\\"n_pairs\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_top_dexes().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef top_nft(other_args: List[str]):\\n \\\"\\\"\\\"Shows Top 100 NFT Coins by Market Capitalization from \\\"https://www.coingecko.com/en/nft\\\"\\n Top 100 NFT Coins by Market Capitalization\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"top_nft\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top NFT Coins by Market Capitalization\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, symbol, price, change_24h, change_1h, change_7d,\\n volume_24h, market_cap with --sort and also with --descend flag to sort descending.\\n Flag --links will display urls\\n Displays : rank, name, symbol, price, change_1h, change_24h, change_7d, market_cap, url\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"change_1h\\\",\\n \\\"change_24h\\\",\\n \\\"change_7d\\\",\\n \\\"volume_24h\\\",\\n \\\"market_cap\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_top_nfts().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"name\\\", \\\"symbol\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".4f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef nft_of_the_day(other_args: List[str]):\\n \\\"\\\"\\\"Shows NFT of the day \\\"https://www.coingecko.com/en/nft\\\"\\n\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"nft_today\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows NFT of the day\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n With nft_today command you will display:\\n author, description, url, img url for NFT which was chosen on CoinGecko as a nft of the day.\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_nft_of_the_day()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef nft_market_status(other_args: List[str]):\\n \\\"\\\"\\\"Shows overview data of nft markets \\\"https://www.coingecko.com/en/nft\\\"\\n\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"nft_market\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows NFT market status\\n NFT (Non-fungible Token) refers to digital assets with unique characteristics.\\n Examples of NFT include crypto artwork, collectibles, game items, financial products, and more.\\n Displays: NFT Market Cap, 24h Trading Volume, NFT Dominance vs Global market, Theta Network NFT Dominance\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_nft_market_status()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef exchanges(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of top exchanges from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"exchanges\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top Crypto Exchanges\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, trust_score, id, name, country, established, trade_volume_24h_btc with --sort\\n and also with --descend flag to sort descending.\\n Flag --links will display urls.\\n Displays: rank, trust_score, id, name, country, established, trade_volume_24h_btc\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"trust_score\\\",\\n \\\"id\\\",\\n \\\"name\\\",\\n \\\"country\\\",\\n \\\"year_established\\\",\\n \\\"trade_volume_24h_btc\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--links\\\",\\n dest=\\\"links\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Flag to show urls\\\",\\n default=False,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_exchanges().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n if ns_parser.links is True:\\n df = df[[\\\"rank\\\", \\\"name\\\", \\\"url\\\"]]\\n else:\\n df.drop(\\\"url\\\", axis=1, inplace=True)\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef platforms(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of financial platforms from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"platforms\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top Crypto Financial Platforms in which you can borrow or lend your crypto.\\n e.g Celsius, Nexo, Crypto.com, Aave and others.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, category, centralized with --sort\\n and also with --descend flag to sort descending.\\n Displays: rank, name, category, centralized, website_url\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"name\\\", \\\"category\\\", \\\"centralized\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_financial_platforms().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef products(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of financial products from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"products\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows Top Crypto Financial Products with which you can earn yield, borrow or lend your crypto.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage with --sort\\n and also with --descend flag to sort descending.\\n Displays: rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"platform\\\",\\n \\\"identifier\\\",\\n \\\"supply_rate_percentage\\\",\\n \\\"borrow_rate_percentage\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_finance_products().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef indexes(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of crypto indexes from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"indexes\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows list of crypto indexes from CoinGecko.\\n Each crypto index is made up of a selection of cryptocurrencies, grouped together and weighted by market cap.\\n You can display only top N number of coins with --top parameter.\\n You can sort data by rank, name, id, market, last, is_multi_asset_composite with --sort\\n and also with --descend flag to sort descending.\\n Displays: rank, name, id, market, last, is_multi_asset_composite\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\\"rank\\\", \\\"name\\\", \\\"id\\\", \\\"market\\\", \\\"last\\\", \\\"is_multi_asset_composite\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_indexes().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef derivatives(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of crypto derivatives from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"derivatives\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows list of crypto derivatives from CoinGecko\\n Crypto derivatives are secondary contracts or financial tools that derive their value from a primary\\n underlying asset. In this case, the primary asset would be a cryptocurrency such as Bitcoin.\\n The most popular crypto derivatives are crypto futures, crypto options, and perpetual contracts.\\n You can look on only top N number of records with --top,\\n You can sort by rank, market, symbol, price, pct_change_24h, contract_type, basis, spread,\\n funding_rate, volume_24h with --sort and also with --descend flag to set it to sort descending.\\n Displays:\\n rank, market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: rank\\\",\\n default=\\\"rank\\\",\\n choices=[\\n \\\"rank\\\",\\n \\\"market\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"pct_change_24h\\\",\\n \\\"contract_type\\\",\\n \\\"basis\\\",\\n \\\"spread\\\",\\n \\\"funding_rate\\\",\\n \\\"volume_24h\\\",\\n ],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_derivatives().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef exchange_rates(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of crypto, fiats, commodity exchange rates from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"ex_rates\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Shows list of crypto, fiats, commodity exchange rates from CoinGecko\\n You can look on only top N number of records with --top,\\n You can sort by index,name,unit, value, type, and also use --descend flag to sort descending.\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n dest=\\\"top\\\",\\n type=check_positive,\\n help=\\\"top N number records\\\",\\n default=20,\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--sort\\\",\\n dest=\\\"sortby\\\",\\n type=str,\\n help=\\\"Sort by given column. Default: index\\\",\\n default=\\\"index\\\",\\n choices=[\\\"index\\\", \\\"name\\\", \\\"unit\\\", \\\"value\\\", \\\"type\\\"],\\n )\\n parser.add_argument(\\n \\\"--descend\\\",\\n action=\\\"store_false\\\",\\n help=\\\"Flag to sort in descending order (lowest first)\\\",\\n dest=\\\"descend\\\",\\n default=True,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_exchange_rates().sort_values(\\n by=ns_parser.sortby, ascending=ns_parser.descend\\n )\\n print(\\n tabulate(\\n df.head(ns_parser.top),\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef global_market_info(other_args: List[str]):\\n \\\"\\\"\\\"Shows global statistics about crypto from CoinGecko\\n - market cap change\\n - number of markets\\n - icos\\n - number of active crypto\\n - market_cap_pct\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"global\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows global statistics about Crypto Market like:\\n active_cryptocurrencies, upcoming_icos, ongoing_icos, ended_icos, markets, market_cap_change_percentage_24h,\\n eth_market_cap_in_pct, btc_market_cap_in_pct, altcoin_market_cap_in_pct\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_global_info()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".2f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef global_defi_info(other_args: List[str]):\\n \\\"\\\"\\\"Shows global statistics about Decentralized Finances from CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"defi\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"Shows global DeFi statistics\\n DeFi or Decentralized Finance refers to financial services that are built\\n on top of distributed networks with no central intermediaries.\\n Displays metrics like:\\n defi_market_cap, eth_market_cap, defi_to_eth_ratio, trading_volume_24h, defi_dominance, top_coin_name,\\n top_coin_defi_dominance\\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_global_defi_info()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef coin_list(other_args: List[str]):\\n \\\"\\\"\\\"Shows list of coins available on CoinGecko\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"coins\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Shows list of coins available on CoinGecko\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--skip\\\",\\n default=0,\\n dest=\\\"skip\\\",\\n help=\\\"Skip n of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=100,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n parser.add_argument(\\\"-l\\\", \\\"--letter\\\", dest=\\\"letter\\\", help=\\\"First letters\\\", type=str)\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--key\\\",\\n dest=\\\"key\\\",\\n help=\\\"Search in column symbol, name, id\\\",\\n type=str,\\n choices=[\\\"id\\\", \\\"symbol\\\", \\\"name\\\"],\\n default=\\\"symbol\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n df = gecko.get_coin_list()\\n\\n letter = ns_parser.letter\\n if letter and isinstance(letter, str):\\n if letter.isalpha():\\n letter = letter.lower()\\n df = df[df[ns_parser.key].str.startswith(letter)]\\n\\n try:\\n df = df[ns_parser.skip : ns_parser.skip + ns_parser.top]\\n except Exception:\\n df = gecko.get_coin_list()\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef find(other_args: List[str]):\\n \\\"\\\"\\\"Find similar coin by coin name,symbol or id.\\n\\n If you don't remember exact name or id of the Coin at CoinGecko,\\n you can use this command to display coins with similar name, symbol or id to your search query.\\n Example of usage: coin name is something like \\\"polka\\\". So I can try: find -c polka -k name -t 25\\n It will search for coin that has similar name to polka and display top 25 matches.\\n -c, --coin stands for coin - you provide here your search query\\n -k, --key it's a searching key. You can search by symbol, id or name of coin\\n -t, --top it displays top N number of records.\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Arguments to pass to argparse\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"find\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"\\\"\\\"\\n Find similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at CoinGecko,\\n you can use this command to display coins with similar name, symbol or id to your search query.\\n Example of usage: coin name is something like \\\"polka\\\". So I can try: find -c polka -k name -t 25\\n It will search for coin that has similar name to polka and display top 25 matches.\\n -c, --coin stands for coin - you provide here your search query\\n -k, --key it's a searching key. You can search by symbol, id or name of coin\\n -t, --top it displays top N number of records.\\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--coin\\\",\\n help=\\\"Symbol Name or Id of Coin\\\",\\n dest=\\\"coin\\\",\\n required=True,\\n type=str,\\n )\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--key\\\",\\n dest=\\\"key\\\",\\n help=\\\"Specify by which column you would like to search: symbol, name, id\\\",\\n type=str,\\n choices=[\\\"id\\\", \\\"symbol\\\", \\\"name\\\"],\\n default=\\\"symbol\\\",\\n )\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--top\\\",\\n default=10,\\n dest=\\\"top\\\",\\n help=\\\"Limit of records\\\",\\n type=check_positive,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n coins_df = gecko.get_coin_list()\\n coins_list = coins_df[ns_parser.key].to_list()\\n sim = difflib.get_close_matches(ns_parser.coin, coins_list, ns_parser.top)\\n df = pd.Series(sim).to_frame().reset_index()\\n df.columns = [\\\"index\\\", ns_parser.key]\\n coins_df.drop(\\\"index\\\", axis=1, inplace=True)\\n df = df.merge(coins_df, on=ns_parser.key)\\n print(\\n tabulate(\\n df,\\n headers=df.columns,\\n floatfmt=\\\".1f\\\",\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5443524718284607,\n \"alphanum_fraction\": 0.5479006767272949,\n \"avg_line_length\": 26.721311569213867,\n \"blob_id\": \"9113afdc10388394b2a7f85e7e3938379529f898\",\n \"content_id\": \"4c799719f4928689be9ea69d5000789f1e076a7f\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3382,\n \"license_type\": \"permissive\",\n \"max_line_length\": 86,\n \"num_lines\": 122,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinmarketcap/coinmarketcap_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Cryptocurrency Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n# pylint: disable=R0904, C0302, W0622\\nimport argparse\\nimport os\\nimport pandas as pd\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.cryptocurrency.coinmarketcap import (\\n coinmarketcap_view as cmc_view,\\n)\\n\\n\\nclass CMCController:\\n\\n CHOICES = [\\n \\\"?\\\",\\n \\\"cls\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n \\\"top\\\",\\n ]\\n\\n def __init__(self):\\n \\\"\\\"\\\"CONSTRUCTOR\\\"\\\"\\\"\\n\\n self._cmc_parser = argparse.ArgumentParser(add_help=False, prog=\\\"cmc\\\")\\n self._cmc_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n self.current_coin = None\\n self.current_currency = None\\n self.current_df = pd.DataFrame()\\n self.source = \\\"\\\"\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\\"\\\\nCoinmarketcap:\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\" top view top coins from coinmarketcap\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self._cmc_parser.parse_known_args(an_input.split())\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu.\\\"\\\"\\\"\\n print(\\\"Moving back to (crypto) menu\\\")\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program.\\\"\\\"\\\"\\n return True\\n\\n def call_top(self, other_args):\\n \\\"\\\"\\\"Process top command\\\"\\\"\\\"\\n cmc_view.get_cmc_top_n(other_args)\\n\\n\\ndef menu():\\n cmc_controller = CMCController()\\n cmc_controller.print_help()\\n\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in cmc_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (crypto)>(cmc)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (crypto)>(cmc)> \\\")\\n\\n try:\\n process_input = cmc_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.6182795763015747,\n \"alphanum_fraction\": 0.6182795763015747,\n \"avg_line_length\": 7.08695650100708,\n \"blob_id\": \"c874e7669251014a1a49d68bb08b8b4f3d0c92e1\",\n \"content_id\": \"4429740a9885cea0385e096310e21ade00344e63\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 186,\n \"license_type\": \"permissive\",\n \"max_line_length\": 10,\n \"num_lines\": 23,\n \"path\": \"/gamestonk_terminal/etf/etf_config.ini\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[Price]\\nMIN = None\\nMAX = None\\n\\n[Assets]\\nMIN = None\\nMAX = None\\n\\n[NAV]\\nMIN = None\\nMAX = None\\n\\n[Expense]\\nMIN = None\\nMAX = None\\n\\n[PE]\\nMIN = None\\nMAX = None\\n\\n[DivYield]\\nMIN = None\\nMAX = None\\n\"\n },\n {\n \"alpha_fraction\": 0.5868619084358215,\n \"alphanum_fraction\": 0.5876533389091492,\n \"avg_line_length\": 26.467391967773438,\n \"blob_id\": \"3fd8b1f43c7915cfaa88df009dc8590d9f0fb26a\",\n \"content_id\": \"6dc74c9e27802bd7f9777a10a3118981ed45593b\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2527,\n \"license_type\": \"permissive\",\n \"max_line_length\": 106,\n \"num_lines\": 92,\n \"path\": \"/gamestonk_terminal/stocks/due_diligence/csimarket_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Stockgrid DD View\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom bs4 import BeautifulSoup\\nimport requests\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n)\\n\\n\\ndef suppliers(ticker: str, other_args: List[str]):\\n \\\"\\\"\\\"Print suppliers from ticker provided\\n\\n Parameters\\n ----------\\n ticker: str\\n Ticker to select suppliers from\\n other_args : List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"supplier\\\",\\n add_help=False,\\n description=\\\"List of suppliers from ticker provided. [Source: CSIMarket]\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n url_supply_chain = f\\\"https://csimarket.com/stocks/competitionNO3.php?supply&code={ticker.upper()}\\\"\\n text_supplier_chain = BeautifulSoup(requests.get(url_supply_chain).text, \\\"lxml\\\")\\n\\n l_supplier = list()\\n for supplier in text_supplier_chain.findAll(\\n \\\"td\\\", {\\\"class\\\": \\\"plavat svjetlirub dae al\\\"}\\n ):\\n l_supplier.append(supplier.text)\\n\\n if l_supplier:\\n print(\\\"List of Suppliers: \\\" + \\\", \\\".join(l_supplier) + \\\"\\\\n\\\")\\n else:\\n print(\\\"No suppliers found.\\\\n\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef customers(ticker: str, other_args: List[str]):\\n \\\"\\\"\\\"Print customers from ticker provided\\n\\n Parameters\\n ----------\\n ticker: str\\n Ticker to select customers from\\n other_args : List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"customer\\\",\\n add_help=False,\\n description=\\\"List of customers from ticker provided. [Source: CSIMarket]\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n url_customer_chain = (\\n f\\\"https://csimarket.com/stocks/custexNO.php?markets&code={ticker.upper()}\\\"\\n )\\n text_customer_chain = BeautifulSoup(\\n requests.get(url_customer_chain).text, \\\"lxml\\\"\\n )\\n\\n l_customer = list()\\n for customer in text_customer_chain.findAll(\\n \\\"td\\\", {\\\"class\\\": \\\"plava svjetlirub\\\"}\\n ):\\n l_customer.append(customer.text)\\n\\n if l_customer:\\n print(\\\"List of Customers: \\\" + \\\", \\\".join(l_customer) + \\\"\\\\n\\\")\\n else:\\n print(\\\"No customers found.\\\\n\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5344297885894775,\n \"alphanum_fraction\": 0.5435711145401001,\n \"avg_line_length\": 26.460905075073242,\n \"blob_id\": \"c0702410468a5c2e6defbc76a5ad6a163b881c29\",\n \"content_id\": \"76e3448ca87434fc655cedab7d7b012bcacb710d\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 26692,\n \"license_type\": \"permissive\",\n \"max_line_length\": 104,\n \"num_lines\": 972,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_overview_model.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\nimport requests\\nimport pandas as pd\\nimport numpy as np\\nfrom bs4 import BeautifulSoup\\nfrom pycoingecko import CoinGeckoAPI\\nfrom gamestonk_terminal.cryptocurrency.cryptocurrency_helpers import (\\n wrap_text_in_df,\\n percent_to_float,\\n create_df_index,\\n)\\nfrom gamestonk_terminal.cryptocurrency.coingecko.pycoingecko_helpers import (\\n changes_parser,\\n replace_qm,\\n clean_row,\\n collateral_auditors_parse,\\n swap_columns,\\n)\\n\\nPERIODS = {\\n \\\"1h\\\": \\\"?time=h1\\\",\\n \\\"24h\\\": \\\"?time=h24\\\",\\n \\\"7d\\\": \\\"?time=d7\\\",\\n \\\"14d\\\": \\\"?time=d14\\\",\\n \\\"30d\\\": \\\"?time=d30\\\",\\n \\\"60d\\\": \\\"?time=d60\\\",\\n \\\"1y\\\": \\\"?time=y1\\\",\\n}\\n\\nCATEGORIES = {\\n \\\"trending\\\": 0,\\n \\\"most_voted\\\": 1,\\n \\\"positive_sentiment\\\": 2,\\n \\\"recently_added\\\": 3,\\n \\\"most_visited\\\": 4,\\n}\\n\\nCOLUMNS = {\\n \\\"id\\\": \\\"id\\\",\\n \\\"rank\\\": \\\"rank\\\",\\n \\\"name\\\": \\\"name\\\",\\n \\\"symbol\\\": \\\"symbol\\\",\\n \\\"price\\\": \\\"price\\\",\\n \\\"change_1h\\\": \\\"change_1h\\\",\\n \\\"change_24h\\\": \\\"change_24h\\\",\\n \\\"change_7d\\\": \\\"change_7d\\\",\\n \\\"volume_24h\\\": \\\"volume_24h\\\",\\n \\\"market_cap\\\": \\\"market_cap\\\",\\n \\\"country\\\": \\\"country\\\",\\n \\\"total_market_cap\\\": \\\"total_market_cap\\\",\\n \\\"total_volume\\\": \\\"total_volume\\\",\\n \\\"market_cap_percentage\\\": \\\"market_cap_percentage\\\",\\n \\\"company\\\": \\\"company\\\",\\n \\\"ticker\\\": \\\"ticker\\\",\\n \\\"last_added\\\": \\\"added\\\",\\n \\\"title\\\": \\\"title\\\",\\n \\\"author\\\": \\\"author\\\",\\n \\\"posted\\\": \\\"posted\\\",\\n \\\"article\\\": \\\"article\\\",\\n \\\"url\\\": \\\"url\\\",\\n \\\"price_btc\\\": \\\"price_btc\\\",\\n \\\"price_usd\\\": \\\"price_usd\\\",\\n \\\"n_of_coins\\\": \\\"n_of_coins\\\",\\n \\\"exchanges\\\": \\\"exchanges\\\",\\n \\\"change_30d\\\": \\\"change_30d\\\",\\n}\\n\\nCHANNELS = {\\n \\\"telegram_channel_identifier\\\": \\\"telegram\\\",\\n \\\"twitter_screen_name\\\": \\\"twitter\\\",\\n \\\"subreddit_url\\\": \\\"subreddit\\\",\\n \\\"bitcointalk_thread_identifier\\\": \\\"bitcointalk\\\",\\n \\\"facebook_username\\\": \\\"facebook\\\",\\n \\\"discord\\\": \\\"discord\\\",\\n}\\n\\nBASE_INFO = [\\n \\\"id\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"asset_platform_id\\\",\\n \\\"description\\\",\\n \\\"contract_address\\\",\\n \\\"market_cap_rank\\\",\\n \\\"public_interest_score\\\",\\n]\\n\\nDENOMINATION = (\\\"usd\\\", \\\"btc\\\", \\\"eth\\\")\\n\\nclient = CoinGeckoAPI()\\n\\nGECKO_BASE_URL = \\\"https://www.coingecko.com\\\"\\n\\n\\ndef scrape_gecko_data(url: str) -> BeautifulSoup:\\n \\\"\\\"\\\"Helper method that scrape Coin Gecko site.\\n\\n Parameters\\n ----------\\n url : str\\n coin gecko url to scrape e.g: \\\"https://www.coingecko.com/en/discover\\\"\\n\\n Returns\\n -------\\n BeautifulSoup object\\n \\\"\\\"\\\"\\n\\n req = requests.get(url)\\n return BeautifulSoup(req.text, features=\\\"lxml\\\")\\n\\n\\ndef get_holdings_overview(endpoint: str = \\\"bitcoin\\\"):\\n \\\"\\\"\\\"Scrapes overview of public companies that holds ethereum or bitcoin\\n from \\\"https://www.coingecko.com/en/public-companies-{bitcoin/ethereum}\\\"\\n\\n Parameters\\n ----------\\n endpoint : str\\n \\\"bitcoin\\\" or \\\"ethereum\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n url = f\\\"https://www.coingecko.com/en/public-companies-{endpoint}\\\"\\n rows = scrape_gecko_data(url).find_all(\\n \\\"span\\\", class_=\\\"overview-box d-inline-block p-3 mr-2\\\"\\n )\\n kpis = {}\\n for row in rows:\\n row_cleaned = clean_row(row)\\n if row_cleaned:\\n value, *kpi = row_cleaned\\n name = \\\" \\\".join(kpi)\\n kpis[name] = value\\n\\n df = pd.Series(kpis).to_frame().reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef get_companies_assets(endpoint=\\\"bitcoin\\\"):\\n \\\"\\\"\\\"Scrapes list of companies that holds ethereum or bitcoin\\n from \\\"https://www.coingecko.com/en/public-companies-{bitcoin/ethereum}\\\"\\n\\n Parameters\\n ----------\\n endpoint : str\\n \\\"bitcoin\\\" or \\\"ethereum\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, company, ticker, country, total_btc, entry_value, today_value, pct_of_supply, url\\n \\\"\\\"\\\"\\n url = f\\\"https://www.coingecko.com/en/public-companies-{endpoint}\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n link = row.find(\\\"a\\\")[\\\"href\\\"]\\n row_cleaned = clean_row(row)\\n row_cleaned.append(link)\\n results.append(row_cleaned)\\n df = pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"company\\\"],\\n COLUMNS[\\\"ticker\\\"],\\n COLUMNS[\\\"country\\\"],\\n \\\"total_btc\\\",\\n \\\"entry_value\\\",\\n \\\"today_value\\\",\\n \\\"pct_of_supply\\\",\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n return df\\n\\n\\ndef get_gainers_or_losers(period=\\\"1h\\\", typ=\\\"gainers\\\"):\\n \\\"\\\"\\\"Scrape data about top gainers - coins which gain the most in given period and\\n top losers - coins that lost the most in given period of time.\\n\\n Parameters\\n ----------\\n period: str\\n One from [1h, 24h, 7d, 14d, 30d, 60d, 1y]\\n typ: str\\n Either \\\"gainers\\\" or \\\"losers\\\"\\n Returns\\n -------\\n pandas.DataFrame\\n symbol, name, volume, price, change_{period}, url\\n \\\"\\\"\\\"\\n category = {\\n \\\"gainers\\\": 0,\\n \\\"losers\\\": 1,\\n }\\n\\n if period not in PERIODS:\\n raise ValueError(\\n f\\\"Wrong time period\\\\nPlease chose one from list: {PERIODS.keys()}\\\"\\n )\\n\\n url = f\\\"https://www.coingecko.com/en/coins/trending{PERIODS.get(period)}\\\"\\n rows = scrape_gecko_data(url).find_all(\\\"tbody\\\")[category.get(typ)].find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n url = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n symbol, name, *_, volume, price, change = clean_row(row)\\n try:\\n change = percent_to_float(change)\\n except (ValueError, TypeError):\\n ...\\n results.append([symbol, name, volume, price, change, url])\\n df = pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"name\\\"],\\n \\\"volume\\\",\\n COLUMNS[\\\"price\\\"],\\n f\\\"%change_{period}\\\",\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n df.index = df.index + 1\\n df.reset_index(inplace=True)\\n df = df.rename(columns={\\\"index\\\": \\\"rank\\\"})\\n return df\\n\\n\\ndef get_btc_price():\\n \\\"\\\"\\\"Get BTC/USD price from CoinGecko API\\n\\n Returns\\n -------\\n str\\n latest bitcoin price in usd.\\n \\\"\\\"\\\"\\n req = requests.get(\\n \\\"https://api.coingecko.com/api/v3/simple/\\\"\\n \\\"price?ids=bitcoin&vs_currencies=usd&include_market_cap\\\"\\n \\\"=false&include_24hr_vol\\\"\\n \\\"=false&include_24hr_change=false&include_last_updated_at=false\\\"\\n )\\n return req.json()[\\\"bitcoin\\\"][\\\"usd\\\"]\\n\\n\\ndef discover_coins(category: str = \\\"trending\\\") -> pd.DataFrame:\\n \\\"\\\"\\\"Scrapes data from \\\"https://www.coingecko.com/en/discover\\\"\\n - Most voted coins\\n - Most popular coins\\n - Recently added coins\\n - Most positive sentiment coins\\n Parameters\\n ----------\\n category: str\\n - one from list: [trending, most_voted, positive_sentiment,recently_added, most_visited]\\n Returns\\n -------\\n pandas.DataFrame:\\n name, price_btc, price_usd, url\\n \\\"\\\"\\\"\\n if category not in CATEGORIES:\\n raise ValueError(\\n f\\\"Wrong category name\\\\nPlease chose one from list: {CATEGORIES.keys()}\\\"\\n )\\n url = \\\"https://www.coingecko.com/en/discover\\\"\\n popular = scrape_gecko_data(url).find_all(\\n \\\"div\\\", class_=\\\"col-12 col-sm-6 col-md-6 col-lg-4\\\"\\n )[CATEGORIES[category]]\\n rows = popular.find_all(\\\"a\\\")\\n results = []\\n btc_price = get_btc_price()\\n\\n for row in rows:\\n name, *_, price = clean_row(row)\\n url = GECKO_BASE_URL + row[\\\"href\\\"]\\n if price.startswith(\\\"BTC\\\"):\\n price = price.replace(\\\"BTC\\\", \\\"\\\").replace(\\\",\\\", \\\".\\\")\\n\\n price_usd = (int(btc_price) * float(price)) if btc_price else None\\n results.append([name, price, price_usd, url])\\n return pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"price_btc\\\"],\\n COLUMNS[\\\"price_usd\\\"],\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n\\n\\ndef get_news(n: int = 100) -> pd.DataFrame:\\n \\\"\\\"\\\"Scrapes news from \\\"https://www.coingecko.com/en/news?page={}\\\"\\n\\n Parameters\\n ----------\\n n: int\\n Number of news, by default n=100, one page has 25 news, so 4 pages are scraped.\\n Returns\\n -------\\n pandas.DataFrame:\\n title, author, posted, article\\n \\\"\\\"\\\"\\n\\n n_of_pages = (math.ceil(n / 25) + 1) if n else 2\\n dfs = []\\n for page in range(1, n_of_pages):\\n url = f\\\"https://www.coingecko.com/en/news?page={page}\\\"\\n rows = scrape_gecko_data(url).find_all(COLUMNS[\\\"article\\\"])\\n results = []\\n for row in rows:\\n header = row.find(\\\"header\\\")\\n link = header.find(\\\"a\\\")[\\\"href\\\"]\\n text = [t for t in header.text.strip().split(\\\"\\\\n\\\") if t not in [\\\"\\\", \\\" \\\"]]\\n article = row.find(\\\"div\\\", class_=\\\"post-body\\\").text.strip()\\n title, *by_who = text\\n author, posted = \\\" \\\".join(by_who).split(\\\"(\\\")\\n posted = posted.strip().replace(\\\")\\\", \\\"\\\")\\n results.append([title, author.strip(), posted, article, link])\\n dfs.append(\\n pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"title\\\"],\\n COLUMNS[\\\"author\\\"],\\n COLUMNS[\\\"posted\\\"],\\n COLUMNS[\\\"article\\\"],\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n )\\n df = pd.concat(dfs, ignore_index=True).head(n)\\n df.drop(\\\"article\\\", axis=1, inplace=True)\\n df.index = df.index + 1\\n df.reset_index(inplace=True)\\n return df\\n\\n\\ndef get_top_crypto_categories():\\n \\\"\\\"\\\"Scrapes top crypto categories from \\\"https://www.coingecko.com/en/categories\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, change_1h, change_7d, market_cap, volume_24h, n_of_coins, url\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"change_1h\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"change_7d\\\"],\\n COLUMNS[\\\"market_cap\\\"],\\n COLUMNS[\\\"volume_24h\\\"],\\n COLUMNS[\\\"n_of_coins\\\"],\\n COLUMNS[\\\"url\\\"],\\n ]\\n url = \\\"https://www.coingecko.com/en/categories\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n url = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n (\\n rank,\\n *names,\\n change_1h,\\n change_24h,\\n change_7d,\\n market_cap,\\n volume,\\n n_of_coins,\\n ) = row.text.strip().split()\\n results.append(\\n [\\n rank,\\n \\\" \\\".join(names),\\n change_1h,\\n change_24h,\\n change_7d,\\n market_cap,\\n volume,\\n n_of_coins,\\n url,\\n ]\\n )\\n\\n df = pd.DataFrame(results, columns=columns)\\n df[\\\"rank\\\"] = df[\\\"rank\\\"].astype(int)\\n return df\\n\\n\\ndef get_recently_added_coins():\\n \\\"\\\"\\\"Scrape recently added coins on CoinGecko from \\\"https://www.coingecko.com/en/coins/recently_added\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n name, symbol, price, change_1h, change_24h, last_added\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"price\\\"],\\n COLUMNS[\\\"change_1h\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"last_added\\\"],\\n COLUMNS[\\\"url\\\"],\\n ]\\n\\n url = \\\"https://www.coingecko.com/en/coins/recently_added\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n\\n for row in rows:\\n url = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n row_cleaned = clean_row(row)\\n (\\n name,\\n symbol,\\n _,\\n price,\\n *changes,\\n _,\\n _volume,\\n last_added,\\n ) = row_cleaned\\n change_1h, change_24h, _ = changes_parser(changes)\\n results.append([name, symbol, price, change_1h, change_24h, last_added, url])\\n df = replace_qm(pd.DataFrame(results, columns=columns))\\n df.index = df.index + 1\\n df.reset_index(inplace=True)\\n df.rename(columns={\\\"index\\\": \\\"rank\\\"}, inplace=True)\\n return df\\n\\n\\ndef get_stable_coins():\\n \\\"\\\"\\\"Scrapes stable coins data from \\\"https://www.coingecko.com/en/stablecoins\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, change_24h, exchanges, market_cap, change_30, url\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"price\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"exchanges\\\"],\\n COLUMNS[\\\"market_cap\\\"],\\n COLUMNS[\\\"change_30d\\\"],\\n COLUMNS[\\\"url\\\"],\\n ]\\n url = \\\"https://www.coingecko.com/en/stablecoins\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n link = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n row_cleaned = clean_row(row)\\n if len(row_cleaned) == 8:\\n row_cleaned.append(None)\\n\\n (\\n rank,\\n name,\\n *symbols,\\n price,\\n volume_24h,\\n exchanges,\\n market_cap,\\n change_30d,\\n ) = row_cleaned\\n symbol = symbols[0] if symbols else symbols\\n results.append(\\n [\\n rank,\\n name,\\n symbol,\\n price,\\n volume_24h,\\n exchanges,\\n market_cap,\\n change_30d,\\n link,\\n ]\\n )\\n df = replace_qm(pd.DataFrame(results, columns=columns))\\n df.drop(\\\"rank\\\", axis=1, inplace=True)\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_yield_farms():\\n \\\"\\\"\\\"Scrapes yield farms data from \\\"https://www.coingecko.com/en/yield-farming\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, pool, audits, collateral,value_locked, returns_year, returns_hour\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n \\\"pool\\\",\\n \\\"audits\\\",\\n \\\"collateral\\\",\\n \\\"value_locked\\\",\\n \\\"return_year\\\",\\n ]\\n url = \\\"https://www.coingecko.com/en/yield-farming\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n row_cleaned = clean_row(row)[:-2]\\n if \\\" New\\\" in row_cleaned: # find better way to fix it in future\\n row_cleaned.remove(\\\" New\\\")\\n\\n if len(row_cleaned) == 7:\\n row_cleaned.insert(2, None)\\n (\\n rank,\\n name,\\n pool,\\n *others,\\n _,\\n value_locked,\\n apy1,\\n _, # hourly removed for most cases it's 0.00 so it doesn't bring any value for user\\n ) = row_cleaned\\n auditors, collateral = collateral_auditors_parse(others)\\n auditors = \\\", \\\".join(aud.strip() for aud in auditors)\\n collateral = \\\", \\\".join(coll.strip() for coll in collateral)\\n results.append(\\n [\\n rank,\\n name,\\n pool,\\n auditors,\\n collateral,\\n value_locked,\\n apy1,\\n ]\\n )\\n df = pd.DataFrame(results, columns=columns).replace({\\\"\\\": None})\\n for col in [\\\"return_year\\\"]:\\n df[col] = df[col].apply(\\n lambda x: x.replace(\\\" Yearly\\\", \\\"\\\") if isinstance(x, str) else x\\n )\\n df[\\\"rank\\\"] = df[\\\"rank\\\"].astype(int)\\n df = wrap_text_in_df(df, w=30)\\n return df\\n\\n\\ndef get_top_volume_coins():\\n \\\"\\\"\\\"Scrapes top coins by trading volume \\\"https://www.coingecko.com/en/coins/high_volume\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, change_1h, change_24h, change_7d, volume_24h, market_cap\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"price\\\"],\\n COLUMNS[\\\"change_1h\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"change_7d\\\"],\\n COLUMNS[\\\"volume_24h\\\"],\\n COLUMNS[\\\"market_cap\\\"],\\n ]\\n url = \\\"https://www.coingecko.com/en/coins/high_volume\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n row_cleaned = clean_row(row)\\n if len(row_cleaned) == 9:\\n row_cleaned.insert(0, \\\"?\\\")\\n row_cleaned.pop(3)\\n results.append(row_cleaned)\\n df = replace_qm(pd.DataFrame(results, columns=columns))\\n df.drop(\\\"rank\\\", axis=1, inplace=True)\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_top_defi_coins():\\n \\\"\\\"\\\"Scrapes top decentralized finance coins \\\"https://www.coingecko.com/en/defi\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, change_1h, change_24h, change_7d, volume_24h, market_cap, url\\n \\\"\\\"\\\"\\n url = \\\"https://www.coingecko.com/en/defi\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n\\n row_cleaned = clean_row(row)\\n row_cleaned.pop(2)\\n url = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n row_cleaned.append(url)\\n if len(row_cleaned) == 11:\\n row_cleaned.insert(4, \\\"?\\\")\\n results.append(row_cleaned)\\n\\n df = pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"price\\\"],\\n COLUMNS[\\\"change_1h\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"change_7d\\\"],\\n COLUMNS[\\\"volume_24h\\\"],\\n COLUMNS[\\\"market_cap\\\"],\\n \\\"fully_diluted_market_cap\\\",\\n \\\"market_cap_to_tvl_ratio\\\",\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n df.drop(\\n [\\\"fully_diluted_market_cap\\\", \\\"market_cap_to_tvl_ratio\\\"],\\n axis=1,\\n inplace=True,\\n )\\n df[\\\"rank\\\"] = df[\\\"rank\\\"].astype(int)\\n return df\\n\\n\\ndef get_top_dexes():\\n \\\"\\\"\\\"Scrapes top decentralized exchanges from \\\"https://www.coingecko.com/en/dex\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n name, rank, volume_24h, n_coins, n_pairs, visits, most_traded, market_share_by_vol\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"volume_24h\\\"],\\n \\\"n_coins\\\",\\n \\\"n_pairs\\\",\\n \\\"visits\\\",\\n \\\"most_traded\\\",\\n \\\"market_share_by_vol\\\",\\n ]\\n url = \\\"https://www.coingecko.com/en/dex\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n row_cleaned = clean_row(row)\\n if \\\" Trading Incentives\\\" in row_cleaned:\\n row_cleaned.remove(\\\" Trading Incentives\\\")\\n if len(row_cleaned) == 8:\\n row_cleaned.insert(-3, \\\"N/A\\\")\\n results.append(row_cleaned)\\n df = pd.DataFrame(results)\\n df[COLUMNS[\\\"name\\\"]] = df.iloc[:, 1] + \\\" \\\" + df.iloc[:, 2].replace(\\\"N/A\\\", \\\"\\\")\\n df.drop(df.columns[1:3], axis=1, inplace=True)\\n df = swap_columns(df)\\n df.columns = columns\\n df[\\\"most_traded\\\"] = (\\n df[\\\"most_traded\\\"]\\n .apply(lambda x: x.split(\\\"$\\\")[0])\\n .str.replace(\\\",\\\", \\\"\\\", regex=True)\\n .str.replace(\\\".\\\", \\\"\\\", regex=True)\\n )\\n df[\\\"most_traded\\\"] = df[\\\"most_traded\\\"].apply(lambda x: None if x.isdigit() else x)\\n df[\\\"rank\\\"] = df[\\\"rank\\\"].astype(int)\\n df.set_index(\\\"rank\\\", inplace=True)\\n return df.reset_index()\\n\\n\\ndef get_top_nfts():\\n \\\"\\\"\\\"Scrapes top nfts from \\\"https://www.coingecko.com/en/nft\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, change_1d, change_24h, change_7d, market_cap, url\\n \\\"\\\"\\\"\\n url = \\\"https://www.coingecko.com/en/nft\\\"\\n rows = scrape_gecko_data(url).find(\\\"tbody\\\").find_all(\\\"tr\\\")\\n results = []\\n for row in rows:\\n link = GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"]\\n row_cleaned = clean_row(row)\\n if len(row_cleaned) == 9:\\n row_cleaned.insert(5, \\\"N/A\\\")\\n row_cleaned.append(link)\\n row_cleaned.pop(3)\\n results.append(row_cleaned)\\n df = pd.DataFrame(\\n results,\\n columns=[\\n COLUMNS[\\\"rank\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"symbol\\\"],\\n COLUMNS[\\\"price\\\"],\\n COLUMNS[\\\"change_1h\\\"],\\n COLUMNS[\\\"change_24h\\\"],\\n COLUMNS[\\\"change_7d\\\"],\\n COLUMNS[\\\"volume_24h\\\"],\\n COLUMNS[\\\"market_cap\\\"],\\n COLUMNS[\\\"url\\\"],\\n ],\\n )\\n df[\\\"rank\\\"] = df[\\\"rank\\\"].astype(int)\\n return df\\n\\n\\ndef get_nft_of_the_day():\\n \\\"\\\"\\\"Scrapes data about nft of the day.\\n\\n Returns\\n -------\\n pandas.DataFrame\\n metric, value\\n \\\"\\\"\\\"\\n url = \\\"https://www.coingecko.com/en/nft\\\"\\n soup = scrape_gecko_data(url)\\n row = soup.find(\\\"div\\\", class_=\\\"tw-px-4 tw-py-5 sm:tw-p-6\\\")\\n try:\\n *author, description, _ = clean_row(row)\\n if len(author) > 3:\\n author, description = author[:3], author[3]\\n except (ValueError, IndexError):\\n return {}\\n df = (\\n pd.Series(\\n {\\n COLUMNS[\\\"author\\\"]: \\\" \\\".join(author),\\n \\\"desc\\\": description,\\n COLUMNS[\\\"url\\\"]: GECKO_BASE_URL + row.find(\\\"a\\\")[\\\"href\\\"],\\n \\\"img\\\": row.find(\\\"img\\\")[\\\"src\\\"],\\n }\\n )\\n .to_frame()\\n .reset_index()\\n )\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n df = wrap_text_in_df(df, w=100)\\n return df\\n\\n\\ndef get_nft_market_status():\\n \\\"\\\"\\\"Scrapes overview data of nft markets from \\\"https://www.coingecko.com/en/nft\\\"\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n url = \\\"https://www.coingecko.com/en/nft\\\"\\n rows = scrape_gecko_data(url).find_all(\\n \\\"span\\\", class_=\\\"overview-box d-inline-block p-3 mr-2\\\"\\n )\\n kpis = {}\\n for row in rows:\\n value, *kpi = clean_row(row)\\n name = \\\" \\\".join(kpi)\\n kpis[name] = value\\n df = pd.Series(kpis).to_frame().reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef get_exchanges():\\n \\\"\\\"\\\"Get list of top exchanges from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n trust_score, id, name, country, year_established, trade_volume_24h_btc, url\\n \\\"\\\"\\\"\\n df = pd.DataFrame(client.get_exchanges_list(per_page=250))\\n df.replace({float(np.NaN): None}, inplace=True)\\n df = df[\\n [\\n \\\"trust_score\\\",\\n COLUMNS[\\\"id\\\"],\\n COLUMNS[\\\"name\\\"],\\n COLUMNS[\\\"country\\\"],\\n \\\"year_established\\\",\\n \\\"trade_volume_24h_btc\\\",\\n COLUMNS[\\\"url\\\"],\\n ]\\n ]\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_financial_platforms():\\n \\\"\\\"\\\"Get list of financial platforms from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, category, centralized, website_url\\n \\\"\\\"\\\"\\n df = pd.DataFrame(client.get_finance_platforms())\\n df.drop(\\\"facts\\\", axis=1, inplace=True)\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_finance_products():\\n \\\"\\\"\\\"Get list of financial products from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, platform, identifier, supply_rate_percentage, borrow_rate_percentage\\n \\\"\\\"\\\"\\n df = pd.DataFrame(\\n client.get_finance_products(per_page=250),\\n columns=[\\n \\\"platform\\\",\\n \\\"identifier\\\",\\n \\\"supply_rate_percentage\\\",\\n \\\"borrow_rate_percentage\\\",\\n ],\\n )\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_indexes():\\n \\\"\\\"\\\"Get list of crypto indexes from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n name, id, market, last, is_multi_asset_composite\\n \\\"\\\"\\\"\\n df = pd.DataFrame(client.get_indexes(per_page=250))\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_derivatives():\\n \\\"\\\"\\\"Get list of crypto derivatives from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n market, symbol, price, pct_change_24h, contract_type, basis, spread, funding_rate, volume_24h\\n \\\"\\\"\\\"\\n df = pd.DataFrame(client.get_derivatives(include_tickers=\\\"unexpired\\\"))\\n df.drop(\\n [\\\"index\\\", \\\"last_traded_at\\\", \\\"expired_at\\\", \\\"index_id\\\", \\\"open_interest\\\"],\\n axis=1,\\n inplace=True,\\n )\\n\\n df.rename(columns={\\\"price_percentage_change_24h\\\": \\\"pct_change_24h\\\"}, inplace=True)\\n create_df_index(df, \\\"rank\\\")\\n return df\\n\\n\\ndef get_exchange_rates():\\n \\\"\\\"\\\"Get list of crypto, fiats, commodity exchange rates from CoinGecko API\\n\\n Returns\\n -------\\n pandas.DataFrame\\n index, name, unit, value, type\\n \\\"\\\"\\\"\\n df = pd.DataFrame(client.get_exchange_rates()[\\\"rates\\\"]).T.reset_index()\\n df.drop(\\\"index\\\", axis=1, inplace=True)\\n create_df_index(df, \\\"index\\\")\\n return df\\n\\n\\ndef get_global_info():\\n \\\"\\\"\\\"Get global statistics about crypto from CoinGecko API like:\\n - market cap change\\n - number of markets\\n - icos\\n - number of active crypto\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n results = client.get_global()\\n\\n total_mcap = results.pop(\\\"market_cap_percentage\\\")\\n eth, btc = total_mcap.get(\\\"btc\\\"), total_mcap.get(\\\"eth\\\")\\n for key in [COLUMNS[\\\"total_market_cap\\\"], COLUMNS[\\\"total_volume\\\"], \\\"updated_at\\\"]:\\n del results[key]\\n results[\\\"eth_market_cap_in_pct\\\"] = eth\\n results[\\\"btc_market_cap_in_pct\\\"] = btc\\n results[\\\"altcoin_market_cap_in_pct\\\"] = 100 - (float(eth) + float(btc))\\n df = pd.Series(results).reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef get_global_markets_info():\\n \\\"\\\"\\\"Get global statistics about crypto markets from CoinGecko API like:\\n - total_market_cap\\n - total_volume\\n - market_cap_percentage\\n\\n Returns\\n -------\\n pandas.DataFrame\\n total_market_cap, total_volume, market_cap_percentage\\n \\\"\\\"\\\"\\n columns = [\\n COLUMNS[\\\"total_market_cap\\\"],\\n COLUMNS[\\\"total_volume\\\"],\\n COLUMNS[\\\"market_cap_percentage\\\"],\\n ]\\n data = []\\n results = client.get_global()\\n for key in columns:\\n data.append(results.get(key))\\n df = pd.DataFrame(data).T\\n df.columns = columns\\n df.replace({float(\\\"nan\\\"): None}, inplace=True)\\n return df.reset_index()\\n\\n\\ndef get_global_defi_info():\\n \\\"\\\"\\\"Get global statistics about Decentralized Finances from CoinGecko API like:\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n results = client.get_global_decentralized_finance_defi()\\n for key, value in results.items():\\n try:\\n results[key] = round(float(value), 4)\\n except (ValueError, TypeError):\\n pass\\n\\n df = pd.Series(results).reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef get_coin_list():\\n \\\"\\\"\\\"Get list of coins available on CoinGecko\\n\\n Returns\\n -------\\n pandas.DataFrame\\n id, symbol, name\\n \\\"\\\"\\\"\\n\\n return pd.DataFrame(\\n client.get_coins_list(),\\n columns=[COLUMNS[\\\"id\\\"], COLUMNS[\\\"symbol\\\"], COLUMNS[\\\"name\\\"]],\\n ).reset_index()\\n\"\n },\n {\n \"alpha_fraction\": 0.7459016442298889,\n \"alphanum_fraction\": 0.7704917788505554,\n \"avg_line_length\": 23.399999618530273,\n \"blob_id\": \"2d51823dbc6a59d7823f49c8c099034e34b8a4d2\",\n \"content_id\": \"b9c6074028a8b0246effa54f3045fc16012290a9\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 122,\n \"license_type\": \"permissive\",\n \"max_line_length\": 57,\n \"num_lines\": 5,\n \"path\": \"/docker/gst-deps.dockerfile\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM gst/gst-python:1.0.0\\n\\nCOPY --chown=python:python requirements.txt /home/python/\\n\\nRUN pip install -r requirements.txt\\n\"\n },\n {\n \"alpha_fraction\": 0.5154705047607422,\n \"alphanum_fraction\": 0.5336523056030273,\n \"avg_line_length\": 30.989795684814453,\n \"blob_id\": \"cc39ce87547640763bc2d6ce0372eef94ea99c25\",\n \"content_id\": \"ff44f326049022f7df05df459afd66e085089eb5\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9405,\n \"license_type\": \"permissive\",\n \"max_line_length\": 100,\n \"num_lines\": 294,\n \"path\": \"/gamestonk_terminal/common/technical_analysis/volume.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Volume Technical Analysis\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom datetime import timedelta\\n\\nimport matplotlib.pyplot as plt\\nimport pandas_ta as ta\\nimport pandas as pd\\n\\nfrom pandas.plotting import register_matplotlib_converters\\nfrom gamestonk_terminal.helper_funcs import (\\n check_positive,\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\nregister_matplotlib_converters()\\n\\n\\ndef ad(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Accumulation Dictribution Line\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock data interval\\n df_stock: pd.DataFrame\\n Dataframe of stock prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ad\\\",\\n description=\\\"\\\"\\\"\\n The Accumulation/Distribution Line is similar to the On Balance\\n Volume (OBV), which sums the volume times +1/-1 based on whether the close is\\n higher than the previous close. The Accumulation/Distribution indicator, however\\n multiplies the volume by the close location value (CLV). The CLV is based on the\\n movement of the issue within a single bar and can be +1, -1 or zero. \\\\n \\\\n\\n The Accumulation/Distribution Line is interpreted by looking for a divergence in\\n the direction of the indicator relative to price. If the Accumulation/Distribution\\n Line is trending upward it indicates that the price may follow. Also, if the\\n Accumulation/Distribution Line becomes flat while the price is still rising (or falling)\\n then it signals an impending flattening of the price.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n parser.add_argument(\\n \\\"--open\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"b_use_open\\\",\\n help=\\\"uses open value of stock\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n bar_colors = [\\n \\\"r\\\" if x[1].Open < x[1].Close else \\\"g\\\" for x in df_stock.iterrows()\\n ]\\n\\n if s_interval == \\\"1440min\\\":\\n bar_width = timedelta(days=1)\\n else:\\n bar_width = timedelta(minutes=int(s_interval.split(\\\"m\\\")[0]))\\n\\n if ns_parser.b_use_open:\\n df_ta = ta.ad(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Adj Close\\\"],\\n volume=df_stock[\\\"Volume\\\"],\\n offset=ns_parser.n_offset,\\n open_=df_stock[\\\"Open\\\"],\\n ).dropna()\\n # Do not use open stock values\\n else:\\n # Note this should always be Close not Adj Close\\n df_ta = ta.ad(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Close\\\"],\\n volume=df_stock[\\\"Volume\\\"],\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(\\n 3,\\n 1,\\n gridspec_kw={\\\"height_ratios\\\": [2, 1, 1]},\\n figsize=plot_autoscale(),\\n dpi=PLOT_DPI,\\n )\\n ax = axes[0]\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n ax.set_title(f\\\"{s_ticker} AD\\\")\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n ax2 = axes[1]\\n ax2.set_ylabel(\\\"Volume\\\")\\n if s_interval == \\\"1440min\\\":\\n ax2.bar(\\n df_stock.index,\\n df_stock[\\\"Volume\\\"].values,\\n color=bar_colors,\\n alpha=0.8,\\n width=0.3,\\n )\\n else:\\n ax2.bar(\\n df_stock.index,\\n df_stock[\\\"Volume\\\"].values,\\n color=bar_colors,\\n alpha=0.8,\\n width=bar_width,\\n )\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n\\n ax3 = axes[2]\\n ax3.plot(df_ta.index, df_ta.values, \\\"b\\\", lw=1)\\n ax3.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax3.axhline(0, linewidth=2, color=\\\"k\\\", ls=\\\"--\\\")\\n ax3.set_ylabel(\\\"A/D\\\")\\n ax3.set_xlabel(\\\"Time\\\")\\n ax3.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax3.minorticks_on()\\n ax3.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef obv(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"On Balance Volume\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock data interval\\n df_stock: pd.DataFrame\\n Dataframe of stock prices\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"obv\\\",\\n description=\\\"\\\"\\\"\\n The On Balance Volume (OBV) is a cumulative total of the up and\\n down volume. When the close is higher than the previous close, the volume is added\\n to the running total, and when the close is lower than the previous close, the volume\\n is subtracted from the running total. \\\\n \\\\n To interpret the OBV, look for the OBV\\n to move with the price or precede price moves. If the price moves before the OBV,\\n then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the\\n OBV indicates a strong trend. If the OBV is flat, then the market is not trending.\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n bar_colors = [\\n \\\"r\\\" if x[1].Open < x[1].Close else \\\"g\\\" for x in df_stock.iterrows()\\n ]\\n\\n if s_interval == \\\"1440min\\\":\\n bar_width = timedelta(days=1)\\n else:\\n bar_width = timedelta(minutes=int(s_interval.split(\\\"m\\\")[0]))\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.obv(\\n close=df_stock[\\\"Adj Close\\\"],\\n volume=df_stock[\\\"Volume\\\"],\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.obv(\\n close=df_stock[\\\"Close\\\"],\\n volume=df_stock[\\\"Volume\\\"],\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(\\n 3,\\n 1,\\n gridspec_kw={\\\"height_ratios\\\": [2, 1, 1]},\\n figsize=plot_autoscale(),\\n dpi=PLOT_DPI,\\n )\\n ax = axes[0]\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n\\n ax.set_title(f\\\"{s_ticker} OBV\\\")\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2 = axes[1]\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n\\n if s_interval == \\\"1440min\\\":\\n ax2.bar(\\n df_stock.index,\\n df_stock[\\\"Volume\\\"].values,\\n color=bar_colors,\\n alpha=0.8,\\n width=bar_width,\\n )\\n else:\\n ax2.bar(\\n df_stock.index,\\n df_stock[\\\"Volume\\\"].values,\\n color=bar_colors,\\n alpha=0.8,\\n width=bar_width,\\n )\\n ax3 = axes[2]\\n ax3.plot(df_ta.index, df_ta.values, \\\"b\\\", lw=1)\\n ax3.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax3.set_xlabel(\\\"Time\\\")\\n ax3.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax3.minorticks_on()\\n ax3.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5352464318275452,\n \"alphanum_fraction\": 0.5370139479637146,\n \"avg_line_length\": 22.34465980529785,\n \"blob_id\": \"71258097abcf1448c8e627da8acc5459faf57677\",\n \"content_id\": \"f701fae7234155f95bb3a662b5f3cf38b6fbec3f\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9618,\n \"license_type\": \"permissive\",\n \"max_line_length\": 104,\n \"num_lines\": 412,\n \"path\": \"/gamestonk_terminal/options/op_helpers.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Option helper functions\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\n\\nimport pandas as pd\\nimport numpy as np\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n check_non_negative,\\n)\\n\\n# pylint: disable=R1710\\n\\n\\ndef load(other_args: List[str]) -> str:\\n \\\"\\\"\\\"Load ticker into object\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Agrparse arguments\\n\\n Returns\\n -------\\n str:\\n Ticker\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"opload\\\",\\n description=\\\"Load a ticker into option menu\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--ticker\\\",\\n action=\\\"store\\\",\\n dest=\\\"ticker\\\",\\n required=\\\"-h\\\" not in other_args,\\n help=\\\"Stock ticker\\\",\\n )\\n\\n try:\\n if other_args:\\n if \\\"-t\\\" not in other_args and \\\"-h\\\" not in other_args:\\n other_args.insert(0, \\\"-t\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return \\\"\\\"\\n print(\\\"\\\")\\n return ns_parser.ticker\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return \\\"\\\"\\n except SystemExit:\\n print(\\\"\\\")\\n return \\\"\\\"\\n\\n\\n# pylint: disable=no-else-return\\n\\n\\ndef select_option_date(avalaiable_dates: List[str], other_args: List[str]) -> str:\\n \\\"\\\"\\\"Select an option date out of a supplied list\\n\\n Parameters\\n ----------\\n avalaiable_dates: List[str]\\n Possible date options\\n other_args: List[str]\\n Arparse arguments\\n Returns\\n -------\\n expiry_date: str\\n Selected expiry date\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"exp\\\",\\n description=\\\"See and set expiration date\\\",\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--date\\\",\\n dest=\\\"n_date\\\",\\n action=\\\"store\\\",\\n type=int,\\n default=-1,\\n choices=range(len(avalaiable_dates)),\\n help=\\\"Select index for expiry date.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-D\\\",\\n dest=\\\"date\\\",\\n type=str,\\n choices=avalaiable_dates + [\\\"\\\"],\\n help=\\\"Select date (YYYY-MM-DD)\\\",\\n default=\\\"\\\",\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-d\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return \\\"\\\"\\n\\n # Print possible expiry dates\\n if ns_parser.n_date == -1 and not ns_parser.date:\\n print(\\\"\\\\nAvailable expiry dates:\\\")\\n for i, d in enumerate(avalaiable_dates):\\n print(f\\\" {(2 - len(str(i))) * ' '}{i}. {d}\\\")\\n print(\\\"\\\")\\n return \\\"\\\"\\n\\n # It means an expiry date was correctly selected\\n else:\\n if ns_parser.date:\\n if ns_parser.date in avalaiable_dates:\\n print(f\\\"Expiraration set to {ns_parser.date} \\\\n\\\")\\n return ns_parser.date\\n else:\\n print(\\\"Expiration not an option\\\")\\n return \\\"\\\"\\n else:\\n expiry_date = avalaiable_dates[ns_parser.n_date]\\n print(f\\\"Expiraration set to {expiry_date} \\\\n\\\")\\n return expiry_date\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return \\\"\\\"\\n\\n\\ndef get_loss_at_strike(strike: float, chain: pd.DataFrame) -> float:\\n \\\"\\\"\\\"Function to get the loss at the given expiry\\n\\n Parameters\\n ----------\\n strike: Union[int,float]\\n Value to calculate total loss at\\n chain: Dataframe:\\n Dataframe containing at least strike and openInterest\\n\\n Returns\\n -------\\n loss: Union[float,int]\\n Total loss\\n \\\"\\\"\\\"\\n\\n itm_calls = chain[chain.index < strike][[\\\"OI_call\\\"]]\\n itm_calls[\\\"loss\\\"] = (strike - itm_calls.index) * itm_calls[\\\"OI_call\\\"]\\n call_loss = itm_calls[\\\"loss\\\"].sum()\\n\\n itm_puts = chain[chain.index > strike][[\\\"OI_put\\\"]]\\n itm_puts[\\\"loss\\\"] = (itm_puts.index - strike) * itm_puts[\\\"OI_put\\\"]\\n put_loss = itm_puts.loss.sum()\\n loss = call_loss + put_loss\\n\\n return loss\\n\\n\\ndef calculate_max_pain(chain: pd.DataFrame) -> int:\\n \\\"\\\"\\\"Returns the max pain for a given call/put dataframe\\n\\n Parameters\\n ----------\\n chain: DataFrame\\n Dataframe to calculate value from\\n\\n Returns\\n -------\\n max_pain : int\\n Max pain value\\n \\\"\\\"\\\"\\n\\n strikes = np.array(chain.index)\\n if (\\\"OI_call\\\" not in chain.columns) or (\\\"OI_put\\\" not in chain.columns):\\n print(\\\"Incorrect columns. Unable to parse max pain\\\")\\n return np.nan\\n\\n loss = []\\n for price_at_exp in strikes:\\n loss.append(get_loss_at_strike(price_at_exp, chain))\\n\\n chain[\\\"loss\\\"] = loss\\n max_pain = chain[\\\"loss\\\"].idxmin()\\n\\n return max_pain\\n\\n\\ndef vol(other_args: List[str]):\\n \\\"\\\"\\\"Parse volume argparse\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n\\n Returns\\n -------\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"vol\\\",\\n description=\\\"Plot volume. Volume refers to the number of contracts traded today.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--min\\\",\\n default=-1,\\n type=check_non_negative,\\n help=\\\"Min strike to plot\\\",\\n dest=\\\"min\\\",\\n )\\n parser.add_argument(\\n \\\"-M\\\",\\n \\\"--max\\\",\\n default=-1,\\n type=check_non_negative,\\n help=\\\"Max strike to plot\\\",\\n dest=\\\"max\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--calls\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"calls\\\",\\n help=\\\"Flag to plot call options only\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--puts\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"puts\\\",\\n help=\\\"Flag to plot put options only\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--source\\\",\\n type=str,\\n default=\\\"tr\\\",\\n choices=[\\\"tr\\\", \\\"yf\\\"],\\n dest=\\\"source\\\",\\n help=\\\"Source to get data from\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n return ns_parser\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef voi(other_args: List[str]):\\n \\\"\\\"\\\"Parse Volume + open interest argparse\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n\\n Returns\\n -------\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"voi\\\",\\n description=\\\"\\\"\\\"\\n Plots Volume + Open Interest of calls vs puts.\\n \\\"\\\"\\\",\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--minv\\\",\\n dest=\\\"min_vol\\\",\\n type=check_non_negative,\\n default=-1,\\n help=\\\"minimum volume (considering open interest) threshold of the plot.\\\",\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--min\\\",\\n dest=\\\"min_sp\\\",\\n type=check_non_negative,\\n default=-1,\\n help=\\\"minimum strike price to consider in the plot.\\\",\\n )\\n parser.add_argument(\\n \\\"-M\\\",\\n \\\"--max\\\",\\n dest=\\\"max_sp\\\",\\n type=check_non_negative,\\n default=-1,\\n help=\\\"maximum strike price to consider in the plot.\\\",\\n )\\n parser.add_argument(\\n \\\"--source\\\",\\n type=str,\\n default=\\\"tr\\\",\\n choices=[\\\"tr\\\", \\\"yf\\\"],\\n dest=\\\"source\\\",\\n help=\\\"Source to get data from\\\",\\n )\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return None\\n return ns_parser\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return None\\n\\n\\ndef oi(other_args: List[str]):\\n \\\"\\\"\\\"Parse Open Interest argparse\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n\\n Returns\\n -------\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"oi\\\",\\n description=\\\"Plot open interest. Open interest represents the number of contracts that exist.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--min\\\",\\n default=-1,\\n type=check_non_negative,\\n help=\\\"Min strike to plot\\\",\\n dest=\\\"min\\\",\\n )\\n parser.add_argument(\\n \\\"-M\\\",\\n \\\"--max\\\",\\n default=-1,\\n type=check_non_negative,\\n help=\\\"Max strike to plot\\\",\\n dest=\\\"max\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--calls\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"calls\\\",\\n help=\\\"Flag to plot call options only\\\",\\n )\\n\\n parser.add_argument(\\n \\\"--puts\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"puts\\\",\\n help=\\\"Flag to plot put options only\\\",\\n )\\n parser.add_argument(\\n \\\"--source\\\",\\n type=str,\\n default=\\\"tr\\\",\\n choices=[\\\"tr\\\", \\\"yf\\\"],\\n dest=\\\"source\\\",\\n help=\\\"Source to get data from\\\",\\n )\\n\\n try:\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return None\\n\\n return ns_parser\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return None\\n\"\n },\n {\n \"alpha_fraction\": 0.5389886498451233,\n \"alphanum_fraction\": 0.5440404415130615,\n \"avg_line_length\": 27.35886573791504,\n \"blob_id\": \"cda83353de8dc95c1f3dc52ada708ea40a4ea410\",\n \"content_id\": \"4043b2bd43f1b75bdcf0ff0f39e99e9991e627ab\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 19993,\n \"license_type\": \"permissive\",\n \"max_line_length\": 120,\n \"num_lines\": 705,\n \"path\": \"/gamestonk_terminal/options/tradier_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Tradier options view\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nfrom bisect import bisect_left\\nimport os\\n\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\nimport mplfinance as mpf\\nfrom colorama import Fore, Style\\nfrom tabulate import tabulate\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n check_non_negative,\\n export_data,\\n plot_autoscale,\\n patch_pandas_text_adjustment,\\n)\\nfrom gamestonk_terminal.options import op_helpers, tradier_model\\nfrom gamestonk_terminal import config_plot as cfp\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\ncolumn_map = {\\\"mid_iv\\\": \\\"iv\\\", \\\"open_interest\\\": \\\"oi\\\", \\\"volume\\\": \\\"vol\\\"}\\n\\n\\ndef red_highlight(val):\\n \\\"\\\"\\\"Red highlight\\n\\n Parameters\\n ----------\\n val\\n dataframe values to color\\n\\n Returns\\n ----------\\n str\\n colored dataframes values\\n \\\"\\\"\\\"\\n return f\\\"{Fore.RED}{val}{Style.RESET_ALL}\\\"\\n\\n\\ndef green_highlight(val):\\n \\\"\\\"\\\"Green highlight\\n\\n Parameters\\n ----------\\n values : List[str]\\n dataframe values to color\\n\\n Returns\\n ----------\\n List[str]\\n colored dataframes values\\n \\\"\\\"\\\"\\n return f\\\"{Fore.GREEN}{val}{Style.RESET_ALL}\\\"\\n\\n\\ndef check_valid_option_chains_headers(headers: str) -> List[str]:\\n \\\"\\\"\\\"Check valid option chains headers\\n\\n Parameters\\n ----------\\n headers : str\\n Option chains headers\\n\\n Returns\\n ----------\\n List[str]\\n List of columns string\\n \\\"\\\"\\\"\\n columns = [str(item) for item in headers.split(\\\",\\\")]\\n\\n for header in columns:\\n if header not in tradier_model.df_columns:\\n raise argparse.ArgumentTypeError(\\\"Invalid option chains header selected!\\\")\\n\\n return columns\\n\\n\\ndef display_chains(ticker: str, expiry: str, other_args: List[str]):\\n \\\"\\\"\\\"Display option chain\\n\\n Parameters\\n ----------\\n ticker: str\\n Stock ticker\\n expiry: str\\n Expiration date of option\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n prog=\\\"chains\\\",\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n description=\\\"Display option chains\\\",\\n )\\n parser.add_argument(\\n \\\"--calls\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"calls_only\\\",\\n help=\\\"Flag to show calls only\\\",\\n )\\n parser.add_argument(\\n \\\"--puts\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n dest=\\\"puts_only\\\",\\n help=\\\"Flag to show puts only\\\",\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--min\\\",\\n dest=\\\"min_sp\\\",\\n type=check_non_negative,\\n default=-1,\\n help=\\\"minimum strike price to consider.\\\",\\n )\\n parser.add_argument(\\n \\\"-M\\\",\\n \\\"--max\\\",\\n dest=\\\"max_sp\\\",\\n type=check_non_negative,\\n default=-1,\\n help=\\\"maximum strike price to consider.\\\",\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--display\\\",\\n dest=\\\"to_display\\\",\\n default=tradier_model.default_columns,\\n type=check_valid_option_chains_headers,\\n help=\\\"columns to look at. Columns can be: {bid, ask, strike, bidsize, asksize, volume, open_interest, delta, \\\"\\n \\\"gamma, theta, vega, ask_iv, bid_iv, mid_iv} \\\",\\n )\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n chains_df = tradier_model.get_option_chains(ticker, expiry)\\n columns = ns_parser.to_display + [\\\"strike\\\", \\\"option_type\\\"]\\n chains_df = chains_df[columns].rename(columns=column_map)\\n\\n if ns_parser.min_sp == -1:\\n min_strike = np.percentile(chains_df[\\\"strike\\\"], 25)\\n else:\\n min_strike = ns_parser.min_sp\\n\\n if ns_parser.max_sp == -1:\\n max_strike = np.percentile(chains_df[\\\"strike\\\"], 75)\\n else:\\n max_strike = ns_parser.max_sp\\n\\n print(f\\\"The strike prices are displayed between {min_strike} and {max_strike}\\\")\\n\\n chains_df = chains_df[chains_df[\\\"strike\\\"] >= min_strike]\\n chains_df = chains_df[chains_df[\\\"strike\\\"] <= max_strike]\\n\\n if ns_parser.export:\\n # Note the extra dirname needed due to the subfolder in options\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.dirname(os.path.abspath(__file__))),\\n f\\\"chains_{ticker}_{expiry}\\\",\\n chains_df,\\n )\\n\\n calls_df = chains_df[chains_df.option_type == \\\"call\\\"].drop(\\n columns=[\\\"option_type\\\"]\\n )\\n puts_df = chains_df[chains_df.option_type == \\\"put\\\"].drop(\\n columns=[\\\"option_type\\\"]\\n )\\n\\n if ns_parser.calls_only:\\n print(\\n tabulate(\\n calls_df,\\n headers=calls_df.columns,\\n tablefmt=\\\"grid\\\",\\n showindex=False,\\n floatfmt=\\\".2f\\\",\\n )\\n )\\n\\n elif ns_parser.puts_only:\\n print(\\n tabulate(\\n puts_df,\\n headers=puts_df.columns,\\n tablefmt=\\\"grid\\\",\\n showindex=False,\\n floatfmt=\\\".2f\\\",\\n )\\n )\\n\\n else:\\n puts_df = puts_df[puts_df.columns[::-1]]\\n chain_table = calls_df.merge(puts_df, on=\\\"strike\\\")\\n\\n if gtff.USE_COLOR:\\n call_cols = [col for col in chain_table if col.endswith(\\\"_x\\\")]\\n put_cols = [col for col in chain_table if col.endswith(\\\"_y\\\")]\\n patch_pandas_text_adjustment()\\n pd.set_option(\\\"display.max_colwidth\\\", 0)\\n pd.set_option(\\\"display.max_rows\\\", None)\\n for cc in call_cols:\\n chain_table[cc] = chain_table[cc].astype(str).apply(green_highlight)\\n for pc in put_cols:\\n chain_table[pc] = chain_table[pc].astype(str).apply(red_highlight)\\n headers = [\\n col.strip(\\\"_x\\\")\\n if col.endswith(\\\"_x\\\")\\n else col.strip(\\\"_y\\\")\\n if col.endswith(\\\"_y\\\")\\n else col\\n for col in chain_table.columns\\n ]\\n print(\\n tabulate(\\n chain_table,\\n headers=headers,\\n tablefmt=\\\"fancy_grid\\\",\\n showindex=False,\\n floatfmt=\\\".2f\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef plot_oi(\\n options: pd.DataFrame,\\n ticker: str,\\n expiry: str,\\n ns_parser: argparse.Namespace,\\n):\\n \\\"\\\"\\\"Plot open interest\\n\\n Parameters\\n ----------\\n options: pd.DataFrame\\n Options dataframe with both calls and puts\\n ticker: str\\n Ticker\\n expiry: str\\n Expiry date for options\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n\\n current_price = tradier_model.last_price(ticker)\\n\\n if ns_parser.min == -1:\\n min_strike = 0.75 * current_price\\n else:\\n min_strike = ns_parser.min\\n\\n if ns_parser.max == -1:\\n max_strike = 1.25 * current_price\\n else:\\n max_strike = ns_parser.max\\n\\n if ns_parser.calls and ns_parser.puts:\\n print(\\\"Both flags selected, please select one\\\", \\\"\\\\n\\\")\\n return\\n\\n calls = options[options.option_type == \\\"call\\\"][[\\\"strike\\\", \\\"open_interest\\\"]]\\n puts = options[options.option_type == \\\"put\\\"][[\\\"strike\\\", \\\"open_interest\\\"]]\\n call_oi = calls.set_index(\\\"strike\\\")[\\\"open_interest\\\"] / 1000\\n put_oi = puts.set_index(\\\"strike\\\")[\\\"open_interest\\\"] / 1000\\n\\n df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)\\n df_opt = df_opt.rename(\\n columns={\\\"open_interest_x\\\": \\\"OI_call\\\", \\\"open_interest_y\\\": \\\"OI_put\\\"}\\n )\\n\\n max_pain = op_helpers.calculate_max_pain(df_opt)\\n plt.style.use(\\\"classic\\\")\\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\\n\\n if not ns_parser.calls:\\n put_oi.plot(\\n x=\\\"strike\\\",\\n y=\\\"open_interest\\\",\\n label=\\\"Puts\\\",\\n ax=ax,\\n marker=\\\"o\\\",\\n ls=\\\"-\\\",\\n c=\\\"r\\\",\\n )\\n if not ns_parser.puts:\\n call_oi.plot(\\n x=\\\"strike\\\",\\n y=\\\"open_interest\\\",\\n label=\\\"Calls\\\",\\n ax=ax,\\n marker=\\\"o\\\",\\n ls=\\\"-\\\",\\n c=\\\"g\\\",\\n )\\n ax.axvline(\\n current_price, lw=2, c=\\\"k\\\", ls=\\\"--\\\", label=\\\"Current Price\\\", alpha=0.7\\n )\\n ax.axvline(max_pain, lw=3, c=\\\"k\\\", label=f\\\"Max Pain: {max_pain}\\\", alpha=0.7)\\n ax.grid(\\\"on\\\")\\n ax.set_xlabel(\\\"Strike Price\\\")\\n ax.set_ylabel(\\\"Open Interest (1k) \\\")\\n ax.set_xlim(min_strike, max_strike)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n ax.set_title(f\\\"Open Interest for {ticker.upper()} expiring {expiry}\\\")\\n plt.legend(loc=0)\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n plt.style.use(\\\"default\\\")\\n print(\\\"\\\")\\n\\n\\ndef plot_vol(\\n options: pd.DataFrame,\\n ticker: str,\\n expiry: str,\\n ns_parser: argparse.Namespace,\\n):\\n \\\"\\\"\\\"Plot volume\\n\\n Parameters\\n ----------\\n options: pd.DataFrame\\n Options dataframe with both calls and puts\\n ticker: str\\n Ticker\\n expiry: str\\n Expiry date for options\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n\\n current_price = tradier_model.last_price(ticker)\\n\\n if ns_parser.min == -1:\\n min_strike = 0.75 * current_price\\n else:\\n min_strike = ns_parser.min\\n\\n if ns_parser.max == -1:\\n max_strike = 1.25 * current_price\\n else:\\n max_strike = ns_parser.max\\n\\n if ns_parser.calls and ns_parser.puts:\\n print(\\\"Both flags selected, please select one\\\", \\\"\\\\n\\\")\\n return\\n\\n calls = options[options.option_type == \\\"call\\\"][[\\\"strike\\\", \\\"volume\\\"]]\\n puts = options[options.option_type == \\\"put\\\"][[\\\"strike\\\", \\\"volume\\\"]]\\n call_v = calls.set_index(\\\"strike\\\")[\\\"volume\\\"] / 1000\\n put_v = puts.set_index(\\\"strike\\\")[\\\"volume\\\"] / 1000\\n plt.style.use(\\\"classic\\\")\\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\\n\\n if not ns_parser.calls:\\n put_v.plot(\\n x=\\\"strike\\\",\\n y=\\\"volume\\\",\\n label=\\\"Puts\\\",\\n ax=ax,\\n marker=\\\"o\\\",\\n ls=\\\"-\\\",\\n c=\\\"r\\\",\\n )\\n if not ns_parser.puts:\\n call_v.plot(\\n x=\\\"strike\\\",\\n y=\\\"volume\\\",\\n label=\\\"Calls\\\",\\n ax=ax,\\n marker=\\\"o\\\",\\n ls=\\\"-\\\",\\n c=\\\"g\\\",\\n )\\n ax.axvline(current_price, lw=2, c=\\\"k\\\", ls=\\\"--\\\", label=\\\"Current Price\\\", alpha=0.7)\\n ax.grid(\\\"on\\\")\\n ax.set_xlabel(\\\"Strike Price\\\")\\n ax.set_ylabel(\\\"Volume (1k) \\\")\\n ax.set_xlim(min_strike, max_strike)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n ax.set_title(f\\\"Volume for {ticker.upper()} expiring {expiry}\\\")\\n plt.legend(loc=0)\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n plt.style.use(\\\"default\\\")\\n print(\\\"\\\")\\n\\n\\ndef plot_volume_open_interest(\\n ticker: str, exp_date: str, options: pd.DataFrame, ns_parser: argparse.Namespace\\n):\\n \\\"\\\"\\\"Plot volume and open interest\\n\\n Parameters\\n ----------\\n ticker : str\\n Main ticker to compare income\\n exp_date : str\\n Expiry date of the option\\n Options: pd.DataFrame\\n Options dataframe with both calls and puts\\n ns_parser: argparse.Namespace\\n Parsed namespace\\n \\\"\\\"\\\"\\n\\n current_price = tradier_model.last_price(ticker)\\n calls = options[options.option_type == \\\"call\\\"][\\n [\\\"strike\\\", \\\"volume\\\", \\\"open_interest\\\"]\\n ]\\n puts = options[options.option_type == \\\"put\\\"][[\\\"strike\\\", \\\"volume\\\", \\\"open_interest\\\"]]\\n\\n # Process Calls Data\\n df_calls = calls.pivot_table(\\n index=\\\"strike\\\", values=[\\\"volume\\\", \\\"open_interest\\\"], aggfunc=\\\"sum\\\"\\n ).reindex()\\n df_calls[\\\"strike\\\"] = df_calls.index\\n df_calls[\\\"type\\\"] = \\\"calls\\\"\\n df_calls[\\\"open_interest\\\"] = df_calls[\\\"open_interest\\\"]\\n df_calls[\\\"volume\\\"] = df_calls[\\\"volume\\\"]\\n df_calls[\\\"oi+v\\\"] = df_calls[\\\"open_interest\\\"] + df_calls[\\\"volume\\\"]\\n df_calls[\\\"spot\\\"] = round(current_price, 2)\\n\\n df_puts = puts.pivot_table(\\n index=\\\"strike\\\", values=[\\\"volume\\\", \\\"open_interest\\\"], aggfunc=\\\"sum\\\"\\n ).reindex()\\n df_puts[\\\"strike\\\"] = df_puts.index\\n df_puts[\\\"type\\\"] = \\\"puts\\\"\\n df_puts[\\\"open_interest\\\"] = df_puts[\\\"open_interest\\\"]\\n df_puts[\\\"volume\\\"] = -df_puts[\\\"volume\\\"]\\n df_puts[\\\"open_interest\\\"] = -df_puts[\\\"open_interest\\\"]\\n df_puts[\\\"oi+v\\\"] = df_puts[\\\"open_interest\\\"] + df_puts[\\\"volume\\\"]\\n df_puts[\\\"spot\\\"] = round(current_price, 2)\\n\\n call_oi = calls.set_index(\\\"strike\\\")[\\\"open_interest\\\"] / 1000\\n put_oi = puts.set_index(\\\"strike\\\")[\\\"open_interest\\\"] / 1000\\n\\n df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)\\n df_opt = df_opt.rename(\\n columns={\\\"open_interest_x\\\": \\\"OI_call\\\", \\\"open_interest_y\\\": \\\"OI_put\\\"}\\n )\\n\\n max_pain = op_helpers.calculate_max_pain(df_opt)\\n\\n if ns_parser.min_vol == -1 and ns_parser.min_sp == -1 and ns_parser.max_sp == -1:\\n # If no argument provided, we use the percentile 50 to get 50% of upper volume data\\n volume_percentile_threshold = 50\\n min_vol_calls = np.percentile(df_calls[\\\"oi+v\\\"], volume_percentile_threshold)\\n min_vol_puts = np.percentile(df_puts[\\\"oi+v\\\"], volume_percentile_threshold)\\n\\n df_calls = df_calls[df_calls[\\\"oi+v\\\"] > min_vol_calls]\\n df_puts = df_puts[df_puts[\\\"oi+v\\\"] < min_vol_puts]\\n\\n else:\\n if ns_parser.min_vol > -1:\\n df_calls = df_calls[df_calls[\\\"oi+v\\\"] > ns_parser.min_vol]\\n df_puts = df_puts[df_puts[\\\"oi+v\\\"] < -ns_parser.min_vol]\\n\\n if ns_parser.min_sp > -1:\\n df_calls = df_calls[df_calls[\\\"strike\\\"] > ns_parser.min_sp]\\n df_puts = df_puts[df_puts[\\\"strike\\\"] > ns_parser.min_sp]\\n\\n if ns_parser.max_sp > -1:\\n df_calls = df_calls[df_calls[\\\"strike\\\"] < ns_parser.max_sp]\\n df_puts = df_puts[df_puts[\\\"strike\\\"] < ns_parser.max_sp]\\n\\n if df_calls.empty and df_puts.empty:\\n print(\\n \\\"The filtering applied is too strong, there is no data available for such conditions.\\\\n\\\"\\n )\\n return\\n\\n # Initialize the matplotlib figure\\n _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)\\n\\n # make x axis symmetric\\n axis_origin = max(abs(max(df_puts[\\\"oi+v\\\"])), abs(max(df_calls[\\\"oi+v\\\"])))\\n ax.set_xlim(-axis_origin, +axis_origin)\\n\\n sns.set_style(style=\\\"darkgrid\\\")\\n\\n g = sns.barplot(\\n x=\\\"oi+v\\\",\\n y=\\\"strike\\\",\\n data=df_calls,\\n label=\\\"Calls: Open Interest\\\",\\n color=\\\"lightgreen\\\",\\n orient=\\\"h\\\",\\n )\\n\\n g = sns.barplot(\\n x=\\\"volume\\\",\\n y=\\\"strike\\\",\\n data=df_calls,\\n label=\\\"Calls: Volume\\\",\\n color=\\\"green\\\",\\n orient=\\\"h\\\",\\n )\\n\\n g = sns.barplot(\\n x=\\\"oi+v\\\",\\n y=\\\"strike\\\",\\n data=df_puts,\\n label=\\\"Puts: Open Interest\\\",\\n color=\\\"pink\\\",\\n orient=\\\"h\\\",\\n )\\n\\n g = sns.barplot(\\n x=\\\"volume\\\",\\n y=\\\"strike\\\",\\n data=df_puts,\\n label=\\\"Puts: Volume\\\",\\n color=\\\"red\\\",\\n orient=\\\"h\\\",\\n )\\n\\n # draw spot line\\n s = [float(strike.get_text()) for strike in ax.get_yticklabels()]\\n spot_index = bisect_left(s, current_price) # find where the spot is on the graph\\n spot_line = ax.axhline(spot_index, ls=\\\"--\\\", color=\\\"dodgerblue\\\", alpha=0.3)\\n\\n # draw max pain line\\n max_pain_index = bisect_left(s, max_pain)\\n max_pain_line = ax.axhline(max_pain_index, ls=\\\"-\\\", color=\\\"black\\\", alpha=0.3)\\n max_pain_line.set_linewidth(3)\\n\\n # format ticklabels without - for puts\\n g.set_xticks(g.get_xticks())\\n xlabels = [f\\\"{x:,.0f}\\\".replace(\\\"-\\\", \\\"\\\") for x in g.get_xticks()]\\n g.set_xticklabels(xlabels)\\n\\n plt.title(\\n f\\\"{ticker} volumes for {exp_date} (open interest displayed only during market hours)\\\"\\n )\\n ax.invert_yaxis()\\n\\n _ = ax.legend()\\n handles, _ = ax.get_legend_handles_labels()\\n handles.append(spot_line)\\n handles.append(max_pain_line)\\n\\n # create legend labels + add to graph\\n labels = [\\n \\\"Calls open interest\\\",\\n \\\"Calls volume \\\",\\n \\\"Puts open interest\\\",\\n \\\"Puts volume\\\",\\n \\\"Current stock price\\\",\\n f\\\"Max pain = {max_pain}\\\",\\n ]\\n\\n plt.legend(handles=handles[:], labels=labels)\\n sns.despine(left=True, bottom=True)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n plt.show()\\n plt.style.use(\\\"default\\\")\\n print(\\\"\\\")\\n\\n\\ndef display_historical(ticker: str, expiry: str, other_args: List[str]):\\n \\\"\\\"\\\"Plot historical option data\\n\\n Parameters\\n ----------\\n ticker: str\\n Ticker\\n expiry: str\\n Expiration of option\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"hist\\\",\\n description=\\\"Gets historical quotes for given option chain\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--strike\\\",\\n dest=\\\"strike\\\",\\n type=float,\\n required=\\\"--chain\\\" not in other_args or \\\"-h\\\" not in other_args,\\n help=\\\"Strike price to look at\\\",\\n )\\n parser.add_argument(\\n \\\"--put\\\",\\n dest=\\\"put\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"Flag for showing put option\\\",\\n )\\n parser.add_argument(\\\"--chain\\\", dest=\\\"chain_id\\\", type=str, help=\\\"OCC option symbol\\\")\\n\\n parser.add_argument(\\n \\\"--raw\\\", dest=\\\"raw\\\", action=\\\"store_true\\\", default=False, help=\\\"Display raw data\\\"\\n )\\n\\n parser.add_argument(\\n \\\"--export\\\",\\n choices=[\\\"csv\\\", \\\"json\\\", \\\"xlsx\\\"],\\n default=\\\"\\\",\\n dest=\\\"export\\\",\\n help=\\\"Export dataframe data to csv,json,xlsx file\\\",\\n )\\n\\n try:\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if not ns_parser.chain_id:\\n strike = float(ns_parser.strike)\\n op_type = [\\\"call\\\", \\\"put\\\"][ns_parser.put]\\n chain = tradier_model.get_option_chains(ticker, expiry)\\n try:\\n symbol = chain[\\n (chain.strike == strike) & (chain.option_type == op_type)\\n ][\\\"symbol\\\"].values[0]\\n except IndexError:\\n print(f\\\"Strike: {strike}, Option type: {op_type} not not found \\\\n\\\")\\n return\\n else:\\n symbol = ns_parser.chain_id\\n\\n df_hist = tradier_model.historical_prices(symbol)\\n df_hist.index = pd.DatetimeIndex(df_hist.index)\\n\\n if ns_parser.raw:\\n print(df_hist)\\n\\n mc = mpf.make_marketcolors(\\n up=\\\"green\\\", down=\\\"red\\\", edge=\\\"black\\\", wick=\\\"black\\\", volume=\\\"in\\\", ohlc=\\\"i\\\"\\n )\\n\\n s = mpf.make_mpf_style(marketcolors=mc, gridstyle=\\\":\\\", y_on_right=True)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n mpf.plot(\\n df_hist,\\n type=\\\"candle\\\",\\n volume=True,\\n title=f\\\"\\\\n{ticker.upper()} {strike} {op_type} expiring {expiry} Historical\\\",\\n style=s,\\n figratio=(10, 7),\\n figscale=1.10,\\n figsize=(plot_autoscale()),\\n update_width_config=dict(\\n candle_linewidth=1.0, candle_width=0.8, volume_linewidth=1.0\\n ),\\n )\\n\\n print(\\\"\\\")\\n\\n if ns_parser.export:\\n export_data(\\n ns_parser.export,\\n os.path.dirname(os.path.abspath(__file__)),\\n f\\\"historical_op_{ticker}_{expiry}_{str(strike).replace('.','p')}_{op_type}\\\",\\n df_hist,\\n )\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.588744580745697,\n \"alphanum_fraction\": 0.6132755875587463,\n \"avg_line_length\": 21.354839324951172,\n \"blob_id\": \"0cf50c5e56d3edbc718086652e631f84a56a5e08\",\n \"content_id\": \"d5cbce63305263fd5a63378373d45cb4dc03edf0\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"YAML\",\n \"length_bytes\": 693,\n \"license_type\": \"permissive\",\n \"max_line_length\": 51,\n \"num_lines\": 31,\n \"path\": \"/docker/docker-compose.yaml\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"version: '3.8'\\nservices:\\n gst-python:\\n build:\\n context: ..\\n dockerfile: docker/gst-python.dockerfile\\n image: gst/gst-python:1.0.0\\n gst-deps:\\n build:\\n context: ..\\n dockerfile: docker/gst-deps.dockerfile\\n image: gst/gst-deps:1.0.0\\n gst-poetry-deps:\\n build:\\n context: ..\\n dockerfile: docker/gst-poetry-deps.dockerfile\\n image: gst/gst-poetry-deps:1.0.0\\n gst:\\n build:\\n context: ..\\n dockerfile: docker/gst.dockerfile\\n image: gst/gst:1.1.0\\n tty: true\\n env_file: setenv\\n gst-poetry:\\n build:\\n context: ..\\n dockerfile: docker/gst-poetry.dockerfile\\n image: gst/gst-poetry:1.1.0\\n tty: true\\n env_file: setenv\\n\"\n },\n {\n \"alpha_fraction\": 0.5253863334655762,\n \"alphanum_fraction\": 0.531776487827301,\n \"avg_line_length\": 27.405941009521484,\n \"blob_id\": \"fafa83f1591f2d470626f0ec9824b587bfb6ea44\",\n \"content_id\": \"29c365fa513ddf5e88dcd05123cc9b158df1adcf\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8607,\n \"license_type\": \"permissive\",\n \"max_line_length\": 88,\n \"num_lines\": 303,\n \"path\": \"/gamestonk_terminal/etf/stockanalysis_model.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Stockanalysis.com/etf Model\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport webbrowser\\nimport requests\\nimport pandas as pd\\nfrom bs4 import BeautifulSoup as bs\\nfrom tabulate import tabulate\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n)\\n\\n# Run this when called to get all available etfs and names\\nr = requests.get(\\\"https://stockanalysis.com/etf/\\\")\\nsoup = bs(r.text, \\\"html.parser\\\").findAll(\\\"table\\\", {\\\"class\\\": \\\"SymbolTable_table__3Q2qq\\\"})\\nall_links = soup[0].findAll(\\\"tr\\\")\\n\\netf_symbols = []\\netf_names = []\\nfor link in all_links:\\n data = link.findAll(\\\"td\\\")\\n if data:\\n etf_symbols.append(data[0].find(\\\"a\\\").get_text())\\n etf_names.append(data[1].find(\\\"span\\\").get_text())\\n\\n\\ndef limit_number_of_holdings(num: str) -> int:\\n if int(num) > 200:\\n raise argparse.ArgumentTypeError(\\\"Asking for too many holdings\\\")\\n return int(num)\\n\\n\\ndef open_web(other_args: List[str]):\\n \\\"\\\"\\\"Opens webbrowser to the website page\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"web\\\",\\n description=\\\"Opens webbrowser to the website page\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", type=str, dest=\\\"name\\\", help=\\\"Symbol to look for\\\", required=False\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-n\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if not ns_parser.name:\\n webbrowser.open(\\\"https://stockanalysis.com/etf/\\\")\\n return\\n\\n if ns_parser.name.upper() in etf_symbols:\\n webbrowser.open(f\\\"https://stockanalysis.com/etf/{ns_parser.name.lower()}\\\")\\n else:\\n print(\\\"ETF symbol not available\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef name_search(other_args: List[str]):\\n \\\"\\\"\\\"Search all available etfs for matching input\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"search\\\",\\n description=\\\"Search all available etfs for matching input\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", type=str, dest=\\\"name\\\", help=\\\"Name to search for\\\", required=True\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-n\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args[:2])\\n if not ns_parser:\\n return\\n\\n matching_etfs = [\\n etf_symbols[idx] + \\\" - \\\" + etf\\n for idx, etf in enumerate(etf_names)\\n if \\\" \\\".join(other_args[1:]).lower() in etf.lower()\\n ]\\n print(*matching_etfs, sep=\\\"\\\\n\\\")\\n if len(matching_etfs) == 0:\\n print(\\\"No matches found\\\")\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef etf_overview(other_args: List[str]):\\n \\\"\\\"\\\"\\n Get overview data for selected etf\\n Parameters\\n ----------\\n other_args : List[str]\\n Argparse arguments\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"overview\\\",\\n description=\\\"Get overview data for selected etf\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", type=str, dest=\\\"name\\\", help=\\\"Symbol to look for\\\", required=True\\n )\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-n\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if ns_parser.name.upper() not in etf_symbols:\\n print(\\\"ETF symbol not available\\\")\\n return\\n\\n r1 = requests.get(f\\\"https://stockanalysis.com/etf/{ns_parser.name}\\\")\\n soup1 = bs(r1.text, \\\"html.parser\\\").find(\\\"div\\\", {\\\"class\\\": \\\"info\\\"}).findAll(\\\"td\\\")\\n column = []\\n value = []\\n column.append(\\\"Last Price\\\")\\n value.append(\\n bs(r1.text, \\\"html.parser\\\")\\n .find(\\\"div\\\", {\\\"class\\\": \\\"quote\\\"})\\n .find(\\\"td\\\", {\\\"id\\\": \\\"qLast\\\"})\\n .text\\n )\\n for row in soup1[:-4:2]:\\n column.append(row.text)\\n for row in soup1[1:-4:2]:\\n value.append(row.text)\\n df = pd.DataFrame(value, index=column, columns=[ns_parser.name.upper()])\\n print(tabulate(df, headers=df.columns, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"\\\")\\n return\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef etf_holdings(other_args: List[str]):\\n \\\"\\\"\\\"Look at ETF holdings\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"holdings\\\",\\n description=\\\"Look at ETF holdings\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--name\\\",\\n type=str,\\n dest=\\\"name\\\",\\n help=\\\"ETF to get holdings for\\\",\\n required=True,\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--limit\\\",\\n type=limit_number_of_holdings,\\n dest=\\\"limit\\\",\\n help=\\\"Number of holdings to get\\\",\\n default=20,\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-n\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n r1 = requests.get(f\\\"https://stockanalysis.com/etf/{ns_parser.name}/holdings\\\")\\n s1 = (\\n bs(r1.text, \\\"html.parser\\\")\\n .find(\\\"table\\\", {\\\"class\\\": \\\"fullholdings\\\"})\\n .find(\\\"tbody\\\")\\n )\\n tick, percent, shares = [], [], []\\n for idx, entry in enumerate(s1.findAll(\\\"tr\\\"), 1):\\n tick.append(entry.findAll(\\\"td\\\")[1].text)\\n percent.append(entry.findAll(\\\"td\\\")[3].text)\\n shares.append(entry.findAll(\\\"td\\\")[4].text)\\n if idx >= ns_parser.limit:\\n break\\n\\n df = pd.DataFrame(data=[tick, percent, shares]).T\\n print(\\n tabulate(\\n df, headers=[\\\"Ticker\\\", \\\"% of ETF\\\", \\\"Shares\\\"], tablefmt=\\\"fancy_grid\\\"\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef compare_etfs(other_args: List[str]):\\n \\\"\\\"\\\"Compare selected ETFs\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n Argparse arguments\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"compare\\\",\\n description=\\\"Compare selected ETFs\\\",\\n )\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--names\\\",\\n type=str,\\n dest=\\\"names\\\",\\n help=\\\"Symbols to compare\\\",\\n required=True,\\n )\\n\\n try:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-n\\\")\\n\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n to_compare = [name.upper() for name in ns_parser.names.split(\\\",\\\")]\\n df = pd.DataFrame(columns=to_compare)\\n for etf in to_compare:\\n if etf in etf_symbols:\\n r1 = requests.get(f\\\"https://stockanalysis.com/etf/{etf}\\\")\\n soup1 = (\\n bs(r1.text, \\\"html.parser\\\")\\n .find(\\\"div\\\", {\\\"class\\\": \\\"info\\\"})\\n .findAll(\\\"td\\\")\\n )\\n column = []\\n value = []\\n column.append(\\\"Last Price\\\")\\n value.append(\\n bs(r1.text, \\\"html.parser\\\")\\n .find(\\\"div\\\", {\\\"class\\\": \\\"quote\\\"})\\n .find(\\\"td\\\", {\\\"id\\\": \\\"qLast\\\"})\\n .text\\n )\\n for row in soup1[:-4:2]:\\n column.append(row.text)\\n for row in soup1[1:-4:2]:\\n value.append(row.text)\\n df[etf] = value\\n else:\\n print(f\\\"{etf} not found\\\")\\n df = df.drop(etf, axis=1)\\n df.index = column\\n print(tabulate(df, headers=df.columns, tablefmt=\\\"fancy_grid\\\"))\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5581395626068115,\n \"alphanum_fraction\": 0.5581395626068115,\n \"avg_line_length\": 9.75,\n \"blob_id\": \"3584ecd7188e7e9e3f0573d4579353bbed6d6bd4\",\n \"content_id\": \"d640f2996a1e0839f00fd3b27e6f088ae9624487\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 43,\n \"license_type\": \"permissive\",\n \"max_line_length\": 14,\n \"num_lines\": 4,\n \"path\": \"/.coveragerc\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[coverage:run]\\nomit =\\n env/*\\nsource = .\\n\"\n },\n {\n \"alpha_fraction\": 0.5176047682762146,\n \"alphanum_fraction\": 0.5307784676551819,\n \"avg_line_length\": 26.287582397460938,\n \"blob_id\": \"a4f35b7e54b2d45012b1bb1cf55ad2a4a3e985fc\",\n \"content_id\": \"a849e20d618a38840c80159e97e4781da5a30581\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4175,\n \"license_type\": \"permissive\",\n \"max_line_length\": 87,\n \"num_lines\": 153,\n \"path\": \"/gamestonk_terminal/common/technical_analysis/custom_indicators_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Custom TA indicators\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nfrom tabulate import tabulate\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n plot_autoscale,\\n valid_date,\\n)\\n\\nfrom gamestonk_terminal import config_plot as cfp\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\n\\ndef fibinocci_retracement(other_args: List[str], data: pd.DataFrame, ticker: str):\\n \\\"\\\"\\\"Calculate fibinocci retracement levels\\n\\n Parameters\\n ----------\\n other_args:List[str]\\n Argparse arguments\\n data: pd.DataFrame\\n Stock data\\n ticker:str\\n Stock ticker\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"fib\\\",\\n description=\\\"Calculates the fibinocci retracement levels\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--period\\\",\\n dest=\\\"period\\\",\\n type=int,\\n help=\\\"Days to lookback for retracement\\\",\\n default=120,\\n )\\n\\n parser.add_argument(\\n \\\"--start\\\",\\n dest=\\\"start\\\",\\n type=valid_date,\\n help=\\\"Starting date to select\\\",\\n required=\\\"--end\\\" in other_args,\\n )\\n\\n parser.add_argument(\\n \\\"--end\\\",\\n dest=\\\"end\\\",\\n type=valid_date,\\n help=\\\"Ending date to select\\\",\\n required=\\\"--start\\\" in other_args,\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n if ns_parser.start and ns_parser.end:\\n\\n if ns_parser.start not in data.index:\\n date0 = data.index[\\n data.index.get_loc(ns_parser.start, method=\\\"nearest\\\")\\n ]\\n print(f\\\"Start date not in data. Using nearest: {date0}\\\")\\n else:\\n date0 = ns_parser.start\\n if ns_parser.end not in data.index:\\n date1 = data.index[data.index.get_loc(ns_parser.end, method=\\\"nearest\\\")]\\n print(f\\\"End date not in data. Using nearest: {date1}\\\")\\n else:\\n date1 = ns_parser.end\\n\\n data0 = data.loc[date0, \\\"Adj Close\\\"]\\n data1 = data.loc[date1, \\\"Adj Close\\\"]\\n\\n min_pr = min(data0, data1)\\n max_pr = max(data0, data1)\\n\\n if min_pr == data0:\\n min_date = date0\\n max_date = date1\\n else:\\n min_date = date1\\n max_date = date0\\n\\n else:\\n data_to_use = data.iloc[-ns_parser.period :][\\\"Adj Close\\\"]\\n\\n min_pr = data_to_use.min()\\n min_date = data_to_use.idxmin()\\n max_pr = data_to_use.max()\\n max_date = data_to_use.idxmax()\\n\\n fib_levels = [0, 0.235, 0.382, 0.5, 0.618, 1]\\n price_dif = max_pr - min_pr\\n\\n levels = [round(max_pr - price_dif * f_lev, 2) for f_lev in fib_levels]\\n\\n df = pd.DataFrame()\\n df[\\\"Level\\\"] = fib_levels\\n df[\\\"Level\\\"] = df[\\\"Level\\\"].apply(lambda x: str(x * 100) + \\\"%\\\")\\n df[\\\"Price\\\"] = levels\\n\\n fig, ax = plt.subplots(figsize=(plot_autoscale()), dpi=cfp.PLOT_DPI)\\n\\n ax.plot(data[\\\"Adj Close\\\"], \\\"b\\\")\\n ax.plot([min_date, max_date], [min_pr, max_pr], c=\\\"k\\\")\\n\\n for i in levels:\\n ax.axhline(y=i, c=\\\"g\\\", alpha=0.5)\\n\\n for i in range(5):\\n ax.fill_between(data.index, levels[i], levels[i + 1], alpha=0.6)\\n\\n ax.set_ylabel(\\\"Price\\\")\\n ax.set_title(f\\\"Fibonacci Support for {ticker.upper()}\\\")\\n ax.set_xlim(data.index[0], data.index[-1])\\n\\n ax1 = ax.twinx()\\n ax1.set_ylim(ax.get_ylim())\\n ax1.set_yticks(levels)\\n ax1.set_yticklabels(fib_levels)\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n plt.show()\\n\\n print(\\n tabulate(\\n df,\\n headers=[\\\"Fib Level\\\", \\\"Price\\\"],\\n showindex=False,\\n tablefmt=\\\"fancy_grid\\\",\\n )\\n )\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5439841747283936,\n \"alphanum_fraction\": 0.5671164393424988,\n \"avg_line_length\": 29.646974563598633,\n \"blob_id\": \"0bb84306047c71abbb83e5457122e134999d7900\",\n \"content_id\": \"9ca8b000447abd97a12c7c57f8a0d1e943abe264\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 21269,\n \"license_type\": \"permissive\",\n \"max_line_length\": 118,\n \"num_lines\": 694,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_model.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime, timedelta\\nimport textwrap\\nimport requests\\nimport pandas as pd\\nfrom dateutil import parser\\nfrom requests.adapters import HTTPAdapter\\n\\nENDPOINTS = {\\n \\\"global\\\": \\\"/global\\\",\\n \\\"coin\\\": \\\"/coins/{}\\\",\\n \\\"coins\\\": \\\"/coins\\\",\\n \\\"coin_tweeter\\\": \\\"/coins/{}/twitter\\\",\\n \\\"coin_events\\\": \\\"/coins/{}/events\\\",\\n \\\"coin_exchanges\\\": \\\"/coins/{}/exchanges\\\",\\n \\\"coin_markets\\\": \\\"/coins/{}/markets\\\",\\n \\\"ohlcv\\\": \\\"/coins/{}/ohlcv/latest\\\",\\n \\\"ohlcv_hist\\\": \\\"/coins/{}/ohlcv/historical\\\",\\n \\\"people\\\": \\\"/people/{}\\\",\\n \\\"tickers\\\": \\\"/tickers\\\",\\n \\\"ticker_info\\\": \\\"/tickers/{}\\\",\\n \\\"exchanges\\\": \\\"/exchanges\\\",\\n \\\"exchange_info\\\": \\\"/exchanges/{}\\\",\\n \\\"exchange_markets\\\": \\\"/exchanges/{}/markets\\\",\\n \\\"contract_platforms\\\": \\\"/contracts\\\",\\n \\\"contract_platform_addresses\\\": \\\"/contracts/{}\\\",\\n \\\"search\\\": \\\"/search\\\",\\n}\\n\\nPAPRIKA_BASE_URL = \\\"https://api.coinpaprika.com/v1\\\"\\n\\n# Mount a session with adapter. It's solution for time to time timeouts\\nsession = requests.Session()\\nsession.mount(PAPRIKA_BASE_URL, HTTPAdapter(max_retries=5))\\n\\n\\ndef make_request(endpoint, payload=None, **kwargs):\\n \\\"\\\"\\\"Helper method that handles request for coinpaprika api.\\n It prepares URL for given endpoint and payload if it's part of requests\\n\\n Parameters\\n ----------\\n endpoint: str,\\n it's an endpoint that we want to query. e.g. to get twitter data for given coin we need to use:\\n https://api.coinpaprika.com/v1/coins/{}/twitter\\n payload: dict\\n the body of your request. Contains the data send to the CoinPaprika API when making an API request\\n kwargs:\\n additional parameters that will be added to payload\\n Returns\\n -------\\n dict with response data\\n\\n \\\"\\\"\\\"\\n url = f\\\"{PAPRIKA_BASE_URL}{endpoint}\\\"\\n if payload is None:\\n payload = {}\\n if kwargs:\\n payload.update(kwargs)\\n return session.get(url, params=payload).json()\\n\\n\\ndef get_global_market():\\n \\\"\\\"\\\"Return data frame with most important global crypto statistics like:\\n market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\\n market_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date,\\n market_cap_change_24h, volume_24h_change_24h, last_updated,\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n global_markets = make_request(ENDPOINTS[\\\"global\\\"])\\n global_markets[\\\"last_updated\\\"] = datetime.fromtimestamp(\\n global_markets[\\\"last_updated\\\"]\\n )\\n\\n for key, date in global_markets.items():\\n if \\\"date\\\" in key:\\n try:\\n global_markets[key] = parser.parse(date).strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n except (KeyError, ValueError, TypeError):\\n ...\\n df = pd.Series(global_markets).to_frame().reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef get_list_of_coins():\\n \\\"\\\"\\\"Get list of all available coins on CoinPaprika\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, id, name, symbol, type\\n \\\"\\\"\\\"\\n coins = make_request(ENDPOINTS[\\\"coins\\\"])\\n df = pd.DataFrame(coins)\\n df = df[df[\\\"is_active\\\"]]\\n return df[[\\\"rank\\\", \\\"id\\\", \\\"name\\\", \\\"symbol\\\", \\\"type\\\"]]\\n\\n\\ndef get_coin(coin_id=\\\"eth-ethereum\\\"):\\n \\\"\\\"\\\"Get coin by id\\n Parameters\\n ----------\\n coin_id: str\\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\\n Returns\\n -------\\n dict with response\\n \\\"\\\"\\\"\\n coin = make_request(ENDPOINTS[\\\"coin\\\"].format(coin_id))\\n return coin\\n\\n\\ndef get_coin_twitter_timeline(coin_id=\\\"eth-ethereum\\\"):\\n \\\"\\\"\\\"Get twitter timeline for given coin id. Not more than last 50 tweets\\n\\n Parameters\\n ----------\\n coin_id: str\\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\\n Returns\\n -------\\n pandas.DataFrame\\n date, user_name, status, retweet_count, like_count\\n\\n \\\"\\\"\\\"\\n res = make_request(ENDPOINTS[\\\"coin_tweeter\\\"].format(coin_id))\\n if \\\"error\\\" in res:\\n print(res)\\n return pd.DataFrame()\\n if isinstance(res, list) and len(res) == 0:\\n return pd.DataFrame()\\n df = pd.DataFrame(res)[\\n [\\\"date\\\", \\\"user_name\\\", \\\"status\\\", \\\"retweet_count\\\", \\\"like_count\\\"]\\n ]\\n\\n df = df.applymap(\\n lambda x: \\\"\\\\n\\\".join(textwrap.wrap(x, width=80)) if isinstance(x, str) else x\\n )\\n df[\\\"status\\\"] = df[\\\"status\\\"].apply(lambda x: x.replace(\\\" \\\", \\\"\\\"))\\n df[\\\"date\\\"] = df[\\\"date\\\"].apply(lambda x: x.replace(\\\"T\\\", \\\"\\\\n\\\"))\\n df[\\\"date\\\"] = df[\\\"date\\\"].apply(lambda x: x.replace(\\\"Z\\\", \\\"\\\"))\\n return df\\n\\n\\ndef get_coin_events_by_id(coin_id=\\\"eth-ethereum\\\"):\\n \\\"\\\"\\\"\\n Example of response from API:\\n {\\n \\\"id\\\": \\\"17398-cme-april-first-trade\\\",\\n \\\"date\\\": \\\"2018-04-02T00:00:00Z\\\",\\n \\\"date_to\\\": \\\"string\\\",\\n \\\"name\\\": \\\"CME: April First Trade\\\",\\n \\\"description\\\": \\\"First trade of Bitcoin futures contract for April 2018.\\\",\\n \\\"is_conference\\\": false,\\n \\\"link\\\": \\\"http://www.cmegroup.com/trading/equity-index/us-index/bitcoin_product_calendar_futures.html\\\",\\n \\\"proof_image_link\\\": \\\"https://static.coinpaprika.com/storage/cdn/event_images/16635.jpg\\\"\\n }\\n\\n Parameters\\n ----------\\n coin_id: str\\n id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'\\n Returns\\n -------\\n pandas.DataFrame\\n id, date , date_to, name, description, is_conference, link, proof_image_link\\n\\n \\\"\\\"\\\"\\n res = make_request(ENDPOINTS[\\\"coin_events\\\"].format(coin_id))\\n if not res:\\n return pd.DataFrame()\\n data = pd.DataFrame(res)\\n data[\\\"description\\\"] = data[\\\"description\\\"].apply(\\n lambda x: \\\"\\\\n\\\".join(textwrap.wrap(x, width=40)) if isinstance(x, str) else x\\n )\\n data.drop([\\\"id\\\", \\\"proof_image_link\\\"], axis=1, inplace=True)\\n for col in [\\\"date\\\", \\\"date_to\\\"]:\\n data[col] = data[col].apply(lambda x: x.replace(\\\"T\\\", \\\"\\\\n\\\"))\\n data[col] = data[col].apply(lambda x: x.replace(\\\"Z\\\", \\\"\\\"))\\n return data\\n\\n\\ndef get_coin_exchanges_by_id(coin_id=\\\"eth-ethereum\\\"):\\n \\\"\\\"\\\"Get all exchanges for given coin id.\\n\\n Parameters\\n ----------\\n coin_id: Identifier of Coin from CoinPaprika\\n\\n Returns\\n -------\\n pandas.DataFrame\\n id, name, adjusted_volume_24h_share, fiats\\n \\\"\\\"\\\"\\n res = make_request(ENDPOINTS[\\\"coin_exchanges\\\"].format(coin_id))\\n df = pd.DataFrame(res)\\n df[\\\"fiats\\\"] = df[\\\"fiats\\\"].copy().apply(lambda x: len([i[\\\"symbol\\\"] for i in x if x]))\\n return df\\n\\n\\ndef get_coin_markets_by_id(coin_id=\\\"eth-ethereum\\\", quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"\\n\\n Parameters\\n ----------\\n coin_id: Coin Parpika identifier of coin e.g. eth-ethereum\\n quotes: Comma separated list of quotes to return.\\n Example: quotes=USD,BTC\\n Allowed values:\\n BTC, ETH, USD, EUR, PLN, KRW, GBP, CAD, JPY, RUB, TRY, NZD, AUD, CHF, UAH, HKD, SGD, NGN, PHP, MXN, BRL,\\n THB, CLP, CNY, CZK, DKK, HUF, IDR, ILS, INR, MYR, NOK, PKR, SEK, TWD, ZAR, VND, BOB, COP, PEN, ARS, ISK\\n\\n Returns\\n -------\\n pandas.DataFrame\\n \\\"\\\"\\\"\\n\\n markets = make_request(ENDPOINTS[\\\"coin_markets\\\"].format(coin_id), quotes=quotes)\\n if \\\"error\\\" in markets:\\n print(markets)\\n return pd.DataFrame()\\n\\n data = []\\n for r in markets:\\n dct = {\\n \\\"exchange\\\": r.get(\\\"exchange_name\\\"),\\n \\\"pair\\\": r.get(\\\"pair\\\"),\\n \\\"trust_score\\\": r.get(\\\"trust_score\\\"),\\n \\\"pct_volume_share\\\": r.get(\\\"adjusted_volume_24h_share\\\"),\\n }\\n quotes = r.get(\\\"quotes\\\")\\n for k, v in quotes.items():\\n dct[f\\\"{k.lower()}_price\\\"] = v.get(\\\"price\\\")\\n dct[f\\\"{k.lower()}_volume\\\"] = v.get(\\\"volume_24h\\\")\\n dct[\\\"market_url\\\"] = r.get(\\\"market_url\\\")\\n data.append(dct)\\n\\n return pd.DataFrame(data)\\n\\n\\ndef get_ohlc_historical(coin_id=\\\"eth-ethereum\\\", quotes=\\\"USD\\\", days=90):\\n \\\"\\\"\\\"\\n Open/High/Low/Close values with volume and market_cap.\\n Request example: https://api.coinpaprika.com/v1/coins/btc-bitcoin/ohlcv/historical?start=2019-01-01&end=2019-01-20\\n if the last day is current day it can an change with every request until actual close of the day at 23:59:59\\n\\n\\n Parameters\\n ----------\\n coin_id: str\\n Paprika coin identifier e.g. eth-ethereum\\n quotes: str\\n returned data quote (available values: usd btc)\\n days: int\\n time range for chart in days. Maximum 365\\n\\n Returns\\n -------\\n pandas.DataFrame\\n\\n \\\"\\\"\\\"\\n if quotes.lower() not in [\\\"usd\\\", \\\"btc\\\"]:\\n quotes = \\\"USD\\\"\\n\\n if abs(int(days)) > 365:\\n days = 365\\n\\n end = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n start = (datetime.now() - timedelta(days=days)).strftime(\\\"%Y-%m-%d\\\")\\n\\n data = make_request(\\n ENDPOINTS[\\\"ohlcv_hist\\\"].format(coin_id), quotes=quotes, start=start, end=end\\n )\\n if \\\"error\\\" in data:\\n print(data)\\n return pd.DataFrame()\\n return pd.DataFrame(data)\\n\\n\\ndef _get_coins_info_helper(quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"Helper method that call /tickers endpoint which returns for all coins quoted in provided currency/crypto\\n\\n {\\n \\\"id\\\": \\\"btc-bitcoin\\\",\\n \\\"name\\\": \\\"Bitcoin\\\",\\n \\\"symbol\\\": \\\"BTC\\\",\\n \\\"rank\\\": 1,\\n \\\"circulating_supply\\\": 17007062,\\n \\\"total_supply\\\": 17007062,\\n \\\"max_supply\\\": 21000000,\\n \\\"beta_value\\\": 0.735327,\\n \\\"first_data_at\\\": \\\"2010-11-14T07:20:41Z\\\",\\n \\\"last_updated\\\": \\\"2018-11-14T07:20:41Z\\\",\\n \\\"quotes\\\" : {\\n \\\"USD\\\": {\\n \\\"price\\\": 5162.15941296,\\n \\\"volume_24h\\\": 7304207651.1585,\\n \\\"volume_24h_change_24h\\\": -2.5,\\n \\\"market_cap\\\": 91094433242,\\n \\\"market_cap_change_24h\\\": 1.6,\\n \\\"percent_change_15m\\\": 0,\\n \\\"percent_change_30m\\\": 0,\\n \\\"percent_change_1h\\\": 0,\\n \\\"percent_change_6h\\\": 0,\\n \\\"percent_change_12h\\\": -0.09,\\n \\\"percent_change_24h\\\": 1.59,\\n \\\"percent_change_7d\\\": 0.28,\\n \\\"percent_change_30d\\\": 27.39,\\n \\\"percent_change_1y\\\": -37.99,\\n \\\"ath_price\\\": 20089,\\n \\\"ath_date\\\": \\\"2017-12-17T12:19:00Z\\\",\\n \\\"percent_from_price_ath\\\": -74.3\\n }\\n }\\n }\\n\\n Parameters\\n ----------\\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n id, name, symbol, rank, circulating_supply, total_supply, max_supply, beta_value, first_data_at,\\n last_updated, price, volume_24h, volume_24h_change_24h, market_cap, market_cap_change_24h,\\n percent_change_15m, percent_change_30m, percent_change_1h, percent_change_6h, percent_change_12h,\\n percent_change_24h, percent_change_7d, percent_change_30d, percent_change_1y,\\n ath_price, ath_date, percent_from_price_ath\\n \\\"\\\"\\\"\\n tickers = make_request(ENDPOINTS[\\\"tickers\\\"], quotes=quotes)\\n data = pd.json_normalize(tickers)\\n try:\\n # data.columns = [col.replace(f\\\"quotes.{quotes}.\\\", f\\\"{quotes.lower()}_\\\") for col in data.columns.tolist()]\\n data.columns = [\\n col.replace(f\\\"quotes.{quotes}.\\\", \\\"\\\") for col in data.columns.tolist()\\n ]\\n data.columns = [col.replace(\\\"percent\\\", \\\"pct\\\") for col in data.columns.tolist()]\\n except KeyError as e:\\n print(e)\\n data.rename(\\n columns={\\n \\\"market_cap_change_24h\\\": \\\"mcap_change_24h\\\",\\n \\\"pct_from_price_ath\\\": \\\"pct_from_ath\\\",\\n },\\n inplace=True,\\n )\\n return data\\n\\n\\ndef get_coins_info(quotes=\\\"USD\\\"): # > format big numbers fix\\n \\\"\\\"\\\"Returns basic coin information for all coins from CoinPaprika API\\n Parameters\\n ----------\\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, volume_24h, circulating_supply, total_supply,\\n max_supply, market_cap, beta_value, ath_price,\\n \\\"\\\"\\\"\\n cols = [\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"volume_24h\\\",\\n \\\"circulating_supply\\\",\\n \\\"total_supply\\\",\\n \\\"max_supply\\\",\\n \\\"market_cap\\\",\\n \\\"beta_value\\\",\\n \\\"ath_price\\\",\\n ]\\n return _get_coins_info_helper(quotes)[cols].sort_values(by=\\\"rank\\\")\\n\\n\\ndef get_coins_market_info(quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"Returns basic coin information for all coins from CoinPaprika API\\n Parameters\\n ----------\\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, symbol, price, volume_24h, mcap_change_24h,\\n pct_change_1h, pct_change_24h, ath_price, pct_from_ath,\\n \\\"\\\"\\\"\\n cols = [\\n \\\"rank\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"price\\\",\\n \\\"volume_24h\\\",\\n \\\"mcap_change_24h\\\",\\n \\\"pct_change_1h\\\",\\n \\\"pct_change_24h\\\",\\n # \\\"pct_change_7d\\\",\\n # \\\"pct_change_30d\\\",\\n \\\"ath_price\\\",\\n \\\"pct_from_ath\\\",\\n ]\\n return _get_coins_info_helper(quotes=quotes)[cols].sort_values(by=\\\"rank\\\")\\n\\n\\ndef get_list_of_exchanges(quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"\\n List exchanges from CoinPaprika API\\n Parameters\\n ----------\\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n rank, name, currencies, markets, fiats, confidence_score, reported_volume_24h,\\n reported_volume_7d ,reported_volume_30d, sessions_per_month,\\n \\\"\\\"\\\"\\n exchanges = make_request(ENDPOINTS[\\\"exchanges\\\"], quotes=quotes)\\n df = pd.json_normalize(exchanges)\\n try:\\n df.columns = [\\n col.replace(f\\\"quotes.{quotes}.\\\", \\\"\\\") for col in df.columns.tolist()\\n ]\\n except KeyError as e:\\n print(e)\\n df = df[df[\\\"active\\\"]]\\n cols = [\\n \\\"adjusted_rank\\\",\\n \\\"id\\\",\\n \\\"name\\\",\\n \\\"currencies\\\",\\n \\\"markets\\\",\\n \\\"fiats\\\",\\n \\\"confidence_score\\\",\\n \\\"reported_volume_24h\\\",\\n \\\"reported_volume_7d\\\",\\n \\\"reported_volume_30d\\\",\\n \\\"sessions_per_month\\\",\\n ]\\n df.loc[:, \\\"fiats\\\"] = df[\\\"fiats\\\"].apply(lambda x: len([i[\\\"symbol\\\"] for i in x if x]))\\n df = df[cols]\\n df = df.applymap(\\n lambda x: \\\"\\\\n\\\".join(textwrap.wrap(x, width=28)) if isinstance(x, str) else x\\n )\\n df.rename(\\n columns={\\\"adjusted_rank\\\": \\\"rank\\\", \\\"confidence_score\\\": \\\"confidence\\\"},\\n inplace=True,\\n )\\n df.columns = [x.replace(\\\"reported_\\\", \\\"\\\") for x in df.columns]\\n return df.sort_values(by=\\\"rank\\\")\\n\\n\\ndef get_exchanges_market(exchange_id=\\\"binance\\\", quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"List markets by exchange ID\\n Parameters\\n ----------\\n exchange_id: identifier of exchange e.g for Binance Exchange -> binance\\n quotes: Coma separated quotes to return e.g quotes=USD,BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n pair, base_currency_name, quote_currency_name, market_url,\\n category, reported_volume_24h_share, trust_score,\\n \\\"\\\"\\\"\\n data = make_request(\\n ENDPOINTS[\\\"exchange_markets\\\"].format(exchange_id), quotes=quotes\\n )\\n if \\\"error\\\" in data:\\n print(data)\\n return pd.DataFrame()\\n cols = [\\n \\\"exchange_id\\\",\\n \\\"pair\\\",\\n \\\"base_currency_name\\\",\\n \\\"quote_currency_name\\\",\\n \\\"category\\\",\\n \\\"reported_volume_24h_share\\\",\\n \\\"trust_score\\\",\\n \\\"market_url\\\",\\n ]\\n df = pd.DataFrame(data)\\n df[\\\"exchange_id\\\"] = exchange_id\\n return df[cols]\\n\\n\\ndef get_tickers_info_for_coin(coin_id=\\\"btc-bitcoin\\\", quotes=\\\"USD\\\"):\\n \\\"\\\"\\\"Get all most important ticker related information for given coin id\\n {\\n \\\"id\\\": \\\"btc-bitcoin\\\",\\n \\\"name\\\": \\\"Bitcoin\\\",\\n \\\"symbol\\\": \\\"BTC\\\",\\n \\\"rank\\\": 1,\\n \\\"circulating_supply\\\": 17007062,\\n \\\"total_supply\\\": 17007062,\\n \\\"max_supply\\\": 21000000,\\n \\\"beta_value\\\": 0.735327,\\n \\\"first_data_at\\\": \\\"2010-11-14T07:20:41Z\\\",\\n \\\"last_updated\\\": \\\"2018-11-14T07:20:41Z\\\",\\n \\\"quotes\\\": {\\n \\\"USD\\\": {\\n \\\"price\\\": 5162.15941296,\\n \\\"volume_24h\\\": 7304207651.1585,\\n \\\"volume_24h_change_24h\\\": -2.5,\\n \\\"market_cap\\\": 91094433242,\\n \\\"market_cap_change_24h\\\": 1.6,\\n \\\"percent_change_15m\\\": 0,\\n \\\"percent_change_30m\\\": 0,\\n \\\"percent_change_1h\\\": 0,\\n \\\"percent_change_6h\\\": 0,\\n \\\"percent_change_12h\\\": -0.09,\\n \\\"percent_change_24h\\\": 1.59,\\n \\\"percent_change_7d\\\": 0.28,\\n \\\"percent_change_30d\\\": 27.39,\\n \\\"percent_change_1y\\\": -37.99,\\n \\\"ath_price\\\": 20089,\\n \\\"ath_date\\\": \\\"2017-12-17T12:19:00Z\\\",\\n \\\"percent_from_price_ath\\\": -74.3\\n }\\n }\\n }\\n\\n Parameters\\n ----------\\n coin_id: Id of coin from CoinPaprika\\n quotes: Coma separated quotes to return e.g quotes = USD, BTC\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n tickers = make_request(ENDPOINTS[\\\"ticker_info\\\"].format(coin_id), quotes=quotes)\\n\\n for key, date in tickers.items():\\n if \\\"date\\\" in key or \\\"data\\\" in key:\\n try:\\n tickers[key] = parser.parse(date).strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n except (KeyError, ValueError, TypeError):\\n ...\\n if key == \\\"quotes\\\":\\n try:\\n tickers[key][quotes][\\\"ath_date\\\"] = parser.parse(\\n tickers[key][quotes][\\\"ath_date\\\"]\\n ).strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n except (KeyError, ValueError, TypeError) as e:\\n print(e)\\n\\n df = pd.json_normalize(tickers)\\n try:\\n df.columns = [col.replace(\\\"quotes.\\\", \\\"\\\") for col in list(df.columns)]\\n df.columns = [col.replace(\\\".\\\", \\\"_\\\").lower() for col in list(df.columns)]\\n except KeyError as e:\\n print(e)\\n df = df.T.reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n return df\\n\\n\\ndef search(q, c=None, modifier=None):\\n \\\"\\\"\\\"Search CoinPaprika\\n Parameters\\n ----------\\n q: phrase for search\\n c: one or more categories (comma separated) to search.\\n Available options: currencies|exchanges|icos|people|tags\\n Default: currencies,exchanges,icos,people,tags\\n modifier: set modifier for search results. Available options: symbol_search -\\n search only by symbol (works for currencies only)\\n\\n Returns\\n -------\\n pandas.DataFrame\\n Metric, Value\\n \\\"\\\"\\\"\\n if c is None:\\n c = \\\"currencies,exchanges,icos,people,tags\\\"\\n data = make_request(ENDPOINTS[\\\"search\\\"], q=q, c=c, modifier=modifier, limit=100)\\n results = []\\n for item in data:\\n category = data[item]\\n for r in category:\\n results.append(\\n {\\n \\\"id\\\": r.get(\\\"id\\\"),\\n \\\"name\\\": r.get(\\\"name\\\"),\\n \\\"category\\\": item,\\n }\\n )\\n return pd.DataFrame(results)\\n\\n\\ndef get_all_contract_platforms():\\n \\\"\\\"\\\"List all smart contract platforms like ethereum, solana, cosmos, polkadot, kusama ...\\n\\n Returns\\n -------\\n pandas.DataFrame\\n index, platform_id\\n \\\"\\\"\\\"\\n\\n contract_platforms = make_request(ENDPOINTS[\\\"contract_platforms\\\"])\\n df = pd.DataFrame(contract_platforms).reset_index()\\n df.columns = [\\\"index\\\", \\\"platform_id\\\"]\\n df[\\\"index\\\"] = df[\\\"index\\\"] + 1\\n return df\\n\\n\\ndef get_contract_platform(platform_id=\\\"eth-ethereum\\\"):\\n \\\"\\\"\\\"Gets all contract addresses for given platform\\n Parameters\\n ----------\\n platform_id: Blockchain platform like eth-ethereum\\n\\n Returns\\n -------\\n pandas.DataFrame\\n id, type, active, address\\n \\\"\\\"\\\"\\n contract_platforms = make_request(\\n ENDPOINTS[\\\"contract_platform_addresses\\\"].format(platform_id)\\n )\\n\\n return pd.DataFrame(contract_platforms)[[\\\"id\\\", \\\"type\\\", \\\"active\\\", \\\"address\\\"]]\\n\\n\\ndef validate_coin(coin: str, coins_dct: dict):\\n \\\"\\\"\\\"Helper method that validates if proper coin id or symbol was provided\\n\\n Parameters\\n ----------\\n coin: id or symbol of coin for CoinPaprika\\n coins_dct: dictionary of coins\\n\\n Returns\\n -------\\n coin id, coin symbol\\n\\n \\\"\\\"\\\"\\n coin_found, symbol = None, None\\n if coin in coins_dct:\\n coin_found = coin\\n symbol = coins_dct.get(coin_found)\\n else:\\n for key, value in coins_dct.items():\\n if coin.upper() == value:\\n coin_found = key\\n symbol = value\\n\\n if not coin_found:\\n raise ValueError(f\\\"Could not find coin with given id: {coin}\\\\n\\\")\\n print(f\\\"Coin found : {coin_found} with symbol {symbol}\\\")\\n return coin_found, symbol\\n\\n\\ndef basic_coin_info(coin_id: str):\\n coin = get_coin(coin_id)\\n tags = coin.get(\\\"tags\\\") or []\\n keys = [\\n \\\"id\\\",\\n \\\"name\\\",\\n \\\"symbol\\\",\\n \\\"rank\\\",\\n \\\"type\\\",\\n \\\"description\\\",\\n \\\"platform\\\",\\n \\\"proof_type\\\",\\n \\\"contract\\\",\\n ]\\n results = {}\\n for key in keys:\\n results[key] = coin.get(key)\\n\\n try:\\n tags = \\\", \\\".join(t.get(\\\"name\\\") for t in tags)\\n parent = coin.get(\\\"parent\\\") or {}\\n except (KeyError, IndexError):\\n tags, parent = [], {}\\n\\n results[\\\"tags\\\"] = tags\\n results[\\\"parent\\\"] = parent.get(\\\"id\\\")\\n df = pd.Series(results).reset_index()\\n df.columns = [\\\"Metric\\\", \\\"Value\\\"]\\n df[\\\"Value\\\"] = df[\\\"Value\\\"].apply(\\n lambda x: \\\"\\\\n\\\".join(textwrap.wrap(x, width=80)) if isinstance(x, str) else x\\n )\\n df.dropna(subset=[\\\"Value\\\"], inplace=True)\\n return df\\n\"\n },\n {\n \"alpha_fraction\": 0.5414535403251648,\n \"alphanum_fraction\": 0.5488064885139465,\n \"avg_line_length\": 28.696203231811523,\n \"blob_id\": \"467e16c500509c2729232d4d33f2dfb62fd38bbe\",\n \"content_id\": \"dd1e8eb28f50242c557418542e013119bcc14d5a\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9384,\n \"license_type\": \"permissive\",\n \"max_line_length\": 96,\n \"num_lines\": 316,\n \"path\": \"/gamestonk_terminal/common/technical_analysis/overlap.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Overlap Technical Analysis\\\"\\\"\\\"\\n___docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\n\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\nimport pandas_ta as ta\\nfrom pandas.plotting import register_matplotlib_converters\\nimport mplfinance as mpf\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n check_positive,\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\nregister_matplotlib_converters()\\n\\n\\ndef ema(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Plots exponential moving average (EMA) over stock\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Data interval\\n df_stock: pd.DataFrame\\n Dataframe of dates and prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"ema\\\",\\n description=\\\"\\\"\\\"\\n The Exponential Moving Average is a staple of technical\\n analysis and is used in countless technical indicators. In a Simple Moving\\n Average, each value in the time period carries equal weight, and values outside\\n of the time period are not included in the average. However, the Exponential\\n Moving Average is a cumulative calculation, including all data. Past values have\\n a diminishing contribution to the average, while more recent values have a greater\\n contribution. This method allows the moving average to be more responsive to changes\\n in the data.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--length\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_length\\\",\\n type=check_positive,\\n default=20,\\n help=\\\"length\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.ema(\\n df_stock[\\\"Adj Close\\\"],\\n length=ns_parser.n_length,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.ema(\\n df_stock[\\\"Close\\\"],\\n length=ns_parser.n_length,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, _ = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n plt.title(f\\\"{ns_parser.n_length} Day EMA on {s_ticker}\\\")\\n\\n if s_interval == \\\"1440min\\\":\\n plt.plot(\\n df_stock[\\\"Adj Close\\\"].index,\\n df_stock[\\\"Adj Close\\\"].values,\\n \\\"k\\\",\\n lw=3,\\n )\\n plt.xlim(\\n df_stock[\\\"Adj Close\\\"].index[0],\\n df_stock[\\\"Adj Close\\\"].index[-1],\\n )\\n else:\\n plt.plot(df_stock[\\\"Close\\\"].index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=3)\\n plt.xlim(df_stock[\\\"Close\\\"].index[0], df_stock[\\\"Close\\\"].index[-1])\\n\\n plt.xlabel(\\\"Time\\\")\\n plt.ylabel(f\\\"{s_ticker} Price($)\\\")\\n plt.plot(df_ta.index, df_ta.values, c=\\\"tab:blue\\\")\\n l_legend = list()\\n l_legend.append(s_ticker)\\n # Pandas series\\n if len(df_ta.shape) == 1:\\n l_legend.append(f\\\"EMA {ns_parser.n_length}\\\")\\n # Pandas dataframe\\n else:\\n l_legend.append(df_ta.columns.tolist())\\n\\n plt.legend(l_legend)\\n plt.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n plt.minorticks_on()\\n plt.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef sma(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Plots simple moving average (SMA) over stock\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Data interval\\n df_stock: pd.DataFrame\\n Dataframe of dates and prices\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"sma\\\",\\n description=\\\"\\\"\\\"\\n Moving Averages are used to smooth the data in an array to\\n help eliminate noise and identify trends. The Simple Moving Average is literally\\n the simplest form of a moving average. Each output value is the average of the\\n previous n values. In a Simple Moving Average, each value in the time period carries\\n equal weight, and values outside of the time period are not included in the average.\\n This makes it less responsive to recent changes in the data, which can be useful for\\n filtering out those changes.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--length\\\",\\n dest=\\\"l_length\\\",\\n type=lambda s: [int(item) for item in s.split(\\\",\\\")],\\n default=[20, 50],\\n help=\\\"length of MA window\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n fig, _ = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n if s_interval == \\\"1440min\\\":\\n plt.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, color=\\\"k\\\")\\n else:\\n plt.plot(df_stock.index, df_stock[\\\"Close\\\"].values, color=\\\"k\\\")\\n l_legend = list()\\n l_legend.append(s_ticker)\\n for length in ns_parser.l_length:\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.sma(\\n df_stock[\\\"Adj Close\\\"],\\n length=length,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n else:\\n df_ta = ta.sma(\\n df_stock[\\\"Close\\\"], length=length, offset=ns_parser.n_offset\\n ).dropna()\\n plt.plot(df_ta.index, df_ta.values)\\n l_legend.append(f\\\"SMA{length} \\\")\\n\\n plt.title(f\\\"SMA on {s_ticker}\\\")\\n plt.xlim(df_stock.index[0], df_stock.index[-1])\\n plt.xlabel(\\\"Time\\\")\\n plt.ylabel(f\\\"{s_ticker} Price ($)\\\")\\n plt.legend(l_legend)\\n plt.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n plt.minorticks_on()\\n plt.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef vwap(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Plots volume weighted average price (VWAP)\\n\\n Parameters\\n ----------\\n other_args: List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Data interval\\n df_stock: pd.DataFrame\\n Dataframe of dates and prices\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"vwap\\\",\\n description=\\\"\\\"\\\"\\n The Volume Weighted Average Price that measures the average typical price\\n by volume. It is typically used with intraday charts to identify general direction.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n print(\\\"VWAP should be used with intraday data. \\\\n\\\")\\n return\\n\\n df_stock.index = df_stock.index.tz_localize(None)\\n df_stock[\\\"Day\\\"] = [idx.date() for idx in df_stock.index]\\n\\n day_df = df_stock[df_stock.Day == df_stock.Day[-1]]\\n\\n df_vwap = ta.vwap(\\n high=day_df[\\\"High\\\"],\\n low=day_df[\\\"Low\\\"],\\n close=day_df[\\\"Close\\\"],\\n volume=day_df[\\\"Volume\\\"],\\n )\\n\\n mc = mpf.make_marketcolors(\\n up=\\\"green\\\", down=\\\"red\\\", edge=\\\"black\\\", wick=\\\"black\\\", volume=\\\"in\\\", ohlc=\\\"i\\\"\\n )\\n\\n s = mpf.make_mpf_style(marketcolors=mc, gridstyle=\\\":\\\", y_on_right=True)\\n apdict = mpf.make_addplot(df_vwap, color=\\\"k\\\")\\n mpf.plot(\\n day_df,\\n style=s,\\n type=\\\"candle\\\",\\n addplot=apdict,\\n volume=True,\\n title=f\\\"\\\\n{s_ticker} {s_interval} VWAP\\\",\\n )\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6302816867828369,\n \"alphanum_fraction\": 0.6332159638404846,\n \"avg_line_length\": 24.432836532592773,\n \"blob_id\": \"137a9dd92c4529274b09c26d4379ba452f8dd9d6\",\n \"content_id\": \"99dd5646dfe4f9928a1c204b4bec9debcde63149\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1704,\n \"license_type\": \"permissive\",\n \"max_line_length\": 76,\n \"num_lines\": 67,\n \"path\": \"/gamestonk_terminal/stocks/technical_analysis/finviz_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Finviz View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport io\\nfrom typing import List\\nimport requests\\nimport matplotlib.pyplot as plt\\nfrom finvizfinance.quote import finvizfinance\\nfrom finvizfinance.util import headers\\nfrom PIL import Image\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\n\\ndef view(other_args: List[str], ticker: str):\\n \\\"\\\"\\\"View historical price with trendlines. [Source: Finviz]\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse other args\\n ticker: str\\n stock ticker\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"view\\\",\\n description=\\\"\\\"\\\"\\n View historical price with trendlines. [Source: Finviz]\\n \\\"\\\"\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n stock = finvizfinance(ticker)\\n image_url = stock.TickerCharts(urlonly=True)\\n\\n r = requests.get(image_url, stream=True, headers=headers, timeout=5)\\n r.raise_for_status()\\n r.raw.decode_content = True\\n\\n dataBytesIO = io.BytesIO(r.content)\\n im = Image.open(dataBytesIO)\\n\\n fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax.set_axis_off()\\n fig.subplots_adjust(left=0, right=1, top=1, bottom=0)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.imshow(im)\\n plt.show()\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6856231093406677,\n \"alphanum_fraction\": 0.7020165324211121,\n \"avg_line_length\": 41.94392395019531,\n \"blob_id\": \"7efd8fc0a2b4f9cb26a66e0023a049a4f6454fd3\",\n \"content_id\": \"9f5f9787a53fda7c3c2ae481182c88602d55ab44\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 13786,\n \"license_type\": \"permissive\",\n \"max_line_length\": 244,\n \"num_lines\": 321,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinpaprika/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Coinpaprika\\n\\nThis menu aims to explore crypto world with [Coinpaprika](#https://coinpaprika.com/)\\nThe usage of the following commands along with an example will be exploited below.\\n\\nCoinpaprika research platform was founded in April 2018. Coinpaprika mission is to provide comprehensive, reliable, transparent\\nand objective access to information about crypto projects from all around the world. [Source: See https://coinpaprika.com/about-us/]\\n\\n\\n[COIN](#COIN)\\n* [find](#find)\\n * find similar coin by coin name,symbol or id\\n* [load](#load)\\n * load a given coin, you can use either coin symbol or coin id\\n* [clear](#clear)\\n * clear loaded coin\\n* [chart](#chart)\\n * plot the loaded crypto chart\\n* [ta](technical_analysis/README.md)\\n * open technical analysis menu\\n* [basic](#basic)\\n * show coin basic information\\n* [ps](#ps)\\n * show coin price, supply, market cap related metrics.\\n* [mkt](#mkt)\\n * show all markets for loaded coin\\n* [ex](#ex)\\n * show all exchanges where loaded coin is listed\\n* [twitter](#twitter)\\n * show up to 50 last tweets for loaded coin\\n* [events](#events )\\n * show events related to loaded coin\\n\\n\\n[OVERVIEW](#OVERVIEW)\\n* [global](#global)\\n * show global crypto market info\\n* [coins](#coins)\\n * show coins available on CoinGecko\\n* [info](#info)\\n * show basic info about all coins available on CoinPaprika\\n* [markets](#markets)\\n * show market related info about all coins available on CoinPaprika\\n* [search](#search)\\n * search for coins, exchanges, people on CoinPaprika\\n* [exchanges](#exchanges)\\n * list all exchanges available on CoinPaprika\\n* [ex_markets](#ex_markets)\\n * show all available markets on given exchange\\n* [platforms](#platforms)\\n * list blockchain platforms/ecosystems eg. ethereum, solana, kusama, terra\\n* [contracts](#contracts)\\n * show all smart contracts for given platform\\n\\n\\n\\n\\n# COIN <a name=\\\"COIN\\\"></a>\\n## load <a name=\\\"load\\\"></a>\\n\\n````\\nusage: load [-c --coin]\\n````\\n\\nLoad a given coin. The current crypto data is [Powered by Coinpaprika API](#https://coinpaprika.com/en/api/), which is an awesome service that currently requires no API Key!\\nBy loading a coin you will have access to a lot of statistics on that coin like price data, available markets, exchanges where coin is listed and also open access to technical analysis menu\\n\\n\\n* -c/--coin: The coin you wish to load. This can either be the symbol or the name. `load -c BTC` and `load -c btc-bitcoin`\\n will load. The -c flag is optional, the above is equivalent to `load btc`. If you not sure about coin symbol or coin id. You can use either `search`, `find` or `coins` commands and look for id of coin that you are interested in.\\n\\n\\n## chart <a name=\\\"chart\\\"></a>\\n\\n````\\nusage: chart [-d --days] [--vs]\\n````\\n\\nDisplay chart for loaded coin. You can specify currency vs which you want to show chart and also number of days to get data for.\\nMaximum range for chart is 1 year (365 days). If you use bigger range it will be automatically converted to 365 limit.\\nBy default currency: usd and days: 30. E.g. if you loaded in previous step Bitcoin and you want to see it's price vs USD in last 90 days range use `chart --vs USD --days 90`\\n\\n\\n* -d/--days: The number of days to look. Maximum 365 days. Default: 30\\n* --vs: The currency to look against. Available options are: `USD, BTC`. Default: `USD`\\n\\n\\n## ta <a name=\\\"ta\\\"></a>\\n\\n````\\nusage: ta [-d --days] [--vs]\\n````\\n\\nOpen Technical Analysis menu for loaded coin\\nLoads data for technical analysis. You can specify currency vs which you want to show chart and also number of days to get data for.\\nE.g. if you loaded in previous step Bitcoin and you want to see it's price vs USDin last 90 days range use `ta --vs USD --days 90`\\n\\n\\n* -d/--days: The number of days to look. Maximum 365 days. Default: 30\\n* --vs: The currency to look against. Available options are: `USD, BTC`. Default: `USD`\\n\\n## basic <a name=\\\"basic\\\"></a>\\n\\n````\\nusage: basic\\n````\\n\\nShows basic information about loaded coin like: name, symbol, rank, type, description, platform, proof_type, contract, tags, parent\\n\\n\\n## ps <a name=\\\"ps\\\"></a>\\n\\n````\\nusage: ps [--vs]\\n````\\n\\nPrice, supply related data for loaded coin. There you find metrics like:\\nname, symbol, rank, supply, volume, ath, market cap, price change related metrics\\n\\n\\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\\n\\n## mkt <a name=\\\"mkt\\\"></a>\\n\\n````\\nusage: mkt [--vs] [-t --top] [-s --sort] [--descend] [-l --links]\\n````\\n\\nGet all markets found for given coin.\\n\\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\\n* -t/--top - number of markets to display. To display top 10 markets: `mkt --top 10 --sort trust_score`\\n* -s/--sort - sort by given column. You can chose on from `pct_volume_share, exchange, pair, trust_score, volume,price`.\\n* --descend - flag to sort in descending order (lowest first)\\n* -l/--links - flag to show urls. Using this flag will add additional column with urls e.g. `mkt --links`\\n\\n## ex <a name=\\\"ex\\\"></a>\\n\\n````\\nusage: ex [-t --top] [-s --sort] [--descend]\\n````\\n\\nGet all exchanges found for given coin.\\n\\n* -t/--top: Number of exchanges to display. To display top 10 exchanges: `ex --top 10 --sort fiats`\\n* -s/--sort: Sort by given column. You can chose on from `id, name, adjusted_volume_24h_share, fiats`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## twitter <a name=\\\"twitter\\\"></a>\\n\\n````\\nusage: twitter [-t --top] [-s --sort] [--descend]\\n````\\n\\nShow last tweets for given coin.\\n\\n* -t/--top: Number of tweets to display. To display top 10 tweets: `twitter --top 10 --sort date`\\n* -s/--sort: Sort by given column. You can chose on from `date, user_name, status, retweet_count, like_count`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## events <a name=\\\"events\\\"></a>\\n\\n````\\nusage: events [-t --top] [-s --sort] [--descend] [-l --links]\\n````\\n\\nShow information about most important coins events.\\n\\n* -t/--top: Number of events to display. To display top 10 events: `events --top 10 --sort date`\\n* -s/--sort: Sort by given column. You can chose on from `date, date_to, name, description, is_conference`.\\n* --descend: Flag to sort in descending order (lowest first)\\n* -l/--links: Flag to show urls. Using this flag will add additional column with urls e.g. `events --links`. If you will use this flag url column will be displayed.\\n\\n## clear <a name=\\\"clear\\\"></a>\\n\\n````\\nusage: clear\\n````\\n\\nJust remove previously loaded coin. (set coin = None)\\n\\n\\n## find <a name=\\\"find\\\"></a>\\n\\n````\\nusage: find [-c --coin] [-t --top] [-k --key]\\n````\\n\\nFind similar coin by coin name,symbol or id. If you don't remember exact name or id of the Coin at Coinpaprika, you can use this command to display coins with similar name, symbol or id to your search query.\\nExample of usage: coin name is something like `polka`. So you can try: `find -c polka -k name -t 25`\\nIt will search for coin that has similar name to polka and display top 25 matches.\\n\\n* -c, --coin: Stands for coin - you provide here your search query\\n* -k, --key: It's a searching key. You can search by symbol, id or name of coin\\n* -t, --top: It displays top N number of records.\\n\\n\\n\\n\\n# OVERVIEW <a name=\\\"OVERVIEW\\\"></a>\\n\\n## global <a name=\\\"global\\\"></a>\\n\\n````\\nusage: global\\n````\\n\\nShow most important global crypto statistics like: market_cap_usd, volume_24h_usd, bitcoin_dominance_percentage, cryptocurrencies_number,\\nmarket_cap_ath_value, market_cap_ath_date, volume_24h_ath_value, volume_24h_ath_date, market_cap_change_24h, volume_24h_change_24h, last_updated\\n\\n## coins <a name=\\\"coins\\\"></a>\\n\\n````\\nusage: coins [-s --skip] [-t --top] [-l --letter] [-k --key]\\n````\\n\\nDisplay all available coins in coinpaprika. You can search with pagination mechanism with `coins --skip [num] --top [num]` or you can just search by letter like\\n`coins --letter d --key name --top 10` it will display top 10 matches for coins which name starts with letter `d` and search will be done in column `name`\\n\\n* -s/--skip: Number of records to skip. Default is 0. There are thousands of coins, so there is mechanism to paginate through them.\\ne.q if you want to see only records from 500-750 you should use `coins --skip 500 --top 250` or if you want to see 1200-1880 you should use `coins --skip 1200 --top 680`.\\n* -t/--top: Number of news to display. One page of news contains 25 news, so to get 250 news script needs to scrape 10 pages (it can take some time). Default 100. E.g `news --top 150`\\n* -l/--letter: First letters of coin by which you want to search\\n* -k/--key: Search key. With this parameter you can specify in which column you would like to search. Choose on from `name, id, symbol`\\n\\n## info <a name=\\\"info\\\"></a>\\n\\n````\\nusage: info [-t --top] [-s --sort] [--descend]\\n````\\nShow basic coin information for all coins from CoinPaprika\\n\\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\\n* -t/--top: Number of coins to display. To display top 10 coins: `info --top 10 --sort rank`\\n* -s/--sort: Sort by given column. You can chose on from `rank, name, symbol, price, volume_24h, circulating_supply, total_supply, max_supply, ath_price,market_cap, beta_value`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## markets <a name=\\\"markets\\\"></a>\\n\\n````\\nusage: markets [--vs] [-t --top] [-s --sort] [--descend]\\n````\\n\\nShow market related (price, supply, volume) coin information for all coins on CoinPaprika\\n\\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\\n* -t/--top: Number of coins to display. To display top 10 coins: `markets --top 10 --sort rank`\\n* -s/--sort: Sort by given column. You can chose on from `rank, name, symbol, price, volume_24h, mcap_change_24h, pct_change_1h, pct_change_24h, ath_price, pct_from_ath`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## exchanges <a name=\\\"exchanges\\\"></a>\\n\\n````\\nusage: exchanges [--vs] [-t --top] [-s --sort] [--descend]\\n````\\n\\nShow all exchanges from CoinPaprika\\n\\n* --vs: The currency to look against. Available options are: `BTC,ETH,USD,EUR,PLN,KRW,GBP,CAD,JPY,RUB,TRY,NZD,AUD,CHF,UAH,HKD,SGD,NGN,PHP,MXN,BRL,THB,CLP,CNY,CZK,DKK,HUF,IDR,ILS,INR,MYR,NOK,PKR,SEK,TWD,ZAR,VND,BOB,COP,PEN,ARS,ISK`. Default: USD\\n* -t/--top: Number of coins to display. To display top 10 coins: `exchanges --top 10 --sort rank`\\n* -s/--sort: Sort by given column. You can chose on from `rank, name, currencies, markets, fiats, confidence, volume_24h, volume_7d, volume_30d, sessions_per_month`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## ex_markets <a name=\\\"ex_markets\\\"></a>\\n\\n````\\nusage: ex_markets [-e --exchange] [-t --top] [-s --sort] [--descend] [-l --links]\\n````\\n\\nGet all exchange markets found for given exchange\\n\\n* -e/--exchange: Identifier of exchange e.g for Binance Exchange: `binance`\\n* -t/--top: Number of markets to display. To display top 10 markets: `ex_markets --top 10 --sort reported_volume_24h_share`\\n* -s/--sort: Sort by given column. You can chose on from `pair , base_currency_name, quote_currency_name, category, reported_volume_24h_share, trust_score, market_url`.\\n* --descend: Flag to sort in descending order (lowest first)\\n* -l/--links: Flag to show urls. Using this flag will add additional column with urls e.g. `ex_markets --links`. If you will use this flag url column will be displayed.\\n\\n## platforms <a name=\\\"platforms\\\"></a>\\n\\n````\\nusage: platforms\\n````\\n\\nList all smart contract platforms like ethereum, solana, cosmos, eos\\n\\n\\n## contracts <a name=\\\"contracts\\\"></a>\\n\\n````\\nusage: contracts [-p --platform] [-t --top] [-s --sort] [--descend]\\n````\\n\\nGets all contract addresses for given blockchain platform\\n\\n* -p/--platform: smart contract platform id. Available coins:\\n```\\nbtc-bitcoin, eos-eos, eth-ethereum, xrp-xrp, bch-bitcoin-cash, xem-nem, neo-neo, xlm-stellar, etc-ethereum-classic,\\nqtum-qtum, zec-zcash, bts-bitshares, waves-waves, nxt-nxt, act-achain, ubq-ubiq, xcp-counterparty, etp-metaverse-etp,\\nburst-burst, omni-omni, trx-tron, bnb-binance-coin, ardr-ardor, ht-huobi-token, blvr-believer, cake-pa\\nncakeswap, fsxu-flashx-ultra, chik-chickenkebab-finance, jgn-juggernaut7492, crx-cryptex, whirl-whirl-finance,\\n eubi-eubi-token, swam-swapmatic-token, shells-shells\\n```\\n* -t/--top: Number of smart contracts to display. To display 10 smart contracts: `contracts -p eth-ethereum --top 10 --sort index`\\n* -s/--sort: Sort by given column. You can chose on from `index, id, type, active, address`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n## search <a name=\\\"search\\\"></a>\\n\\n````\\nusage: search [-q --query] [-c --cat] [-s --sort] [-t --top] [--descend]\\n````\\n\\nSearch over CoinPaprika API\\n\\n* -q/--query: Phrase for search\\n* -c/--cat: Categories to search: currencies|exchanges|icos|people|tags|all. Default: all\\n* -t/--top: Number of found records to display.\\n* -s/--sort: Sort by given column. You can chose on from `category, id, name`.\\n* --descend: Flag to sort in descending order (lowest first)\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6315789222717285,\n \"alphanum_fraction\": 0.6631578803062439,\n \"avg_line_length\": 18,\n \"blob_id\": \"4090b8d46cb98f24a729a2b6bdcfa8f55a61ae31\",\n \"content_id\": \"e7c66172a3deed61c1fc71fff5c2611805754bd3\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Dockerfile\",\n \"length_bytes\": 95,\n \"license_type\": \"permissive\",\n \"max_line_length\": 37,\n \"num_lines\": 5,\n \"path\": \"/docker/gst.dockerfile\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"FROM gst/gst-deps:1.0.0\\n\\nCOPY --chown=python:python . .\\n\\nCMD [\\\"bash\\\", \\\"docker/entry-point.sh\\\"]\\n\"\n },\n {\n \"alpha_fraction\": 0.5145893096923828,\n \"alphanum_fraction\": 0.5369328260421753,\n \"avg_line_length\": 30.200716018676758,\n \"blob_id\": \"1814d862a23e0172c10cb0c9937c60c86882af67\",\n \"content_id\": \"65e003ff91503bfaa7308ea2bf4582a826392523\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 17410,\n \"license_type\": \"permissive\",\n \"max_line_length\": 97,\n \"num_lines\": 558,\n \"path\": \"/gamestonk_terminal/common/technical_analysis/momentum.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Momentum Technical Analysis\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\n\\nimport matplotlib.pyplot as plt\\nimport pandas_ta as ta\\nimport pandas as pd\\nfrom pandas.plotting import register_matplotlib_converters\\n\\nfrom gamestonk_terminal.helper_funcs import (\\n check_positive,\\n parse_known_args_and_warn,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\nregister_matplotlib_converters()\\n\\n\\ndef cci(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Commodity channel index\\n\\n Parameters\\n ----------\\n other_args:List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock time interval\\n df_stock: pd.DataFrame\\n Dataframe of prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"cci\\\",\\n description=\\\"\\\"\\\"\\n The CCI is designed to detect beginning and ending market trends.\\n The range of 100 to -100 is the normal trading range. CCI values outside of this\\n range indicate overbought or oversold conditions. You can also look for price\\n divergence in the CCI. If the price is making new highs, and the CCI is not,\\n then a price correction is likely.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--length\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_length\\\",\\n type=check_positive,\\n default=14,\\n help=\\\"length\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--scalar\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_scalar\\\",\\n type=check_positive,\\n default=0.015,\\n help=\\\"scalar\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.cci(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Adj Close\\\"],\\n length=ns_parser.n_length,\\n scalar=ns_parser.n_scalar,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.cci(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Close\\\"],\\n length=ns_parser.n_length,\\n scalar=ns_parser.n_scalar,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax = axes[0]\\n ax.set_title(f\\\"{s_ticker} CCI\\\")\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2 = axes[1]\\n ax2.plot(df_ta.index, df_ta.values, \\\"b\\\", lw=2)\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax2.axhspan(100, plt.gca().get_ylim()[1], facecolor=\\\"r\\\", alpha=0.2)\\n ax2.axhspan(plt.gca().get_ylim()[0], -100, facecolor=\\\"g\\\", alpha=0.2)\\n ax2.axhline(100, linewidth=3, color=\\\"r\\\", ls=\\\"--\\\")\\n ax2.axhline(-100, linewidth=3, color=\\\"g\\\", ls=\\\"--\\\")\\n ax2.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax2.minorticks_on()\\n ax2.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n ax3 = ax2.twinx()\\n ax3.set_ylim(ax2.get_ylim())\\n ax3.set_yticks([-100, 100])\\n ax3.set_yticklabels([\\\"OVERSOLD\\\", \\\"OVERBOUGHT\\\"])\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef macd(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Moving average convergence divergence\\n\\n Parameters\\n ----------\\n other_args:List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock time interval\\n df_stock: pd.DataFrame\\n Dataframe of prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"macd\\\",\\n description=\\\"\\\"\\\"\\n The Moving Average Convergence Divergence (MACD) is the difference\\n between two Exponential Moving Averages. The Signal line is an Exponential Moving\\n Average of the MACD. \\\\n \\\\n The MACD signals trend changes and indicates the start\\n of new trend direction. High values indicate overbought conditions, low values\\n indicate oversold conditions. Divergence with the price indicates an end to the\\n current trend, especially if the MACD is at extreme high or low values. When the MACD\\n line crosses above the signal line a buy signal is generated. When the MACD crosses\\n below the signal line a sell signal is generated. To confirm the signal, the MACD\\n should be above zero for a buy, and below zero for a sell.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--fast\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_fast\\\",\\n type=check_positive,\\n default=12,\\n help=\\\"The short period.\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--slow\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_slow\\\",\\n type=check_positive,\\n default=26,\\n help=\\\"The long period.\\\",\\n )\\n parser.add_argument(\\n \\\"--signal\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_signal\\\",\\n type=check_positive,\\n default=9,\\n help=\\\"The signal period.\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"How many periods to offset the result.\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.macd(\\n df_stock[\\\"Adj Close\\\"],\\n fast=ns_parser.n_fast,\\n slow=ns_parser.n_slow,\\n signal=ns_parser.n_signal,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.macd(\\n df_stock[\\\"Close\\\"],\\n fast=ns_parser.n_fast,\\n slow=ns_parser.n_slow,\\n signal=ns_parser.n_signal,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax = axes[0]\\n ax.set_title(f\\\"{s_ticker} MACD\\\")\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2 = axes[1]\\n ax2.plot(df_ta.index, df_ta.iloc[:, 0].values, \\\"b\\\", lw=2)\\n ax2.plot(df_ta.index, df_ta.iloc[:, 2].values, \\\"r\\\", lw=2)\\n ax2.bar(df_ta.index, df_ta.iloc[:, 1].values, color=\\\"g\\\")\\n ax2.legend(\\n [\\n f\\\"MACD Line {df_ta.columns[0]}\\\",\\n f\\\"Signal Line {df_ta.columns[2]}\\\",\\n f\\\"Histogram {df_ta.columns[1]}\\\",\\n ],\\n loc=\\\"upper left\\\",\\n )\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax2.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax2.minorticks_on()\\n ax2.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef rsi(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):\\n \\\"\\\"\\\"Relative strength index\\n\\n Parameters\\n ----------\\n other_args:List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock time interval\\n df_stock: pd.DataFrame\\n Dataframe of prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"rsi\\\",\\n description=\\\"\\\"\\\"\\n The Relative Strength Index (RSI) calculates a ratio of the\\n recent upward price movements to the absolute price movement. The RSI ranges\\n from 0 to 100. The RSI is interpreted as an overbought/oversold indicator when\\n the value is over 70/below 30. You can also look for divergence with price. If\\n the price is making new highs/lows, and the RSI is not, it indicates a reversal.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--length\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_length\\\",\\n type=check_positive,\\n default=14,\\n help=\\\"length\\\",\\n )\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--scalar\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_scalar\\\",\\n type=check_positive,\\n default=100,\\n help=\\\"scalar\\\",\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--drift\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_drift\\\",\\n type=check_positive,\\n default=1,\\n help=\\\"drift\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.rsi(\\n df_stock[\\\"Adj Close\\\"],\\n length=ns_parser.n_length,\\n scalar=ns_parser.n_scalar,\\n drift=ns_parser.n_drift,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.rsi(\\n df_stock[\\\"Close\\\"],\\n length=ns_parser.n_length,\\n scalar=ns_parser.n_scalar,\\n drift=ns_parser.n_drift,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax = axes[0]\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n ax.set_title(f\\\" {s_ticker} RSI{ns_parser.n_length} \\\")\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2 = axes[1]\\n ax2.plot(df_ta.index, df_ta.values, \\\"b\\\", lw=2)\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax2.axhspan(70, 100, facecolor=\\\"r\\\", alpha=0.2)\\n ax2.axhspan(0, 30, facecolor=\\\"g\\\", alpha=0.2)\\n ax2.axhline(70, linewidth=3, color=\\\"r\\\", ls=\\\"--\\\")\\n ax2.axhline(30, linewidth=3, color=\\\"g\\\", ls=\\\"--\\\")\\n ax2.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax2.minorticks_on()\\n ax2.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2.set_ylim([0, 100])\\n ax3 = ax2.twinx()\\n ax3.set_ylim(ax2.get_ylim())\\n ax3.set_yticks([30, 70])\\n ax3.set_yticklabels([\\\"OVERSOLD\\\", \\\"OVERBOUGHT\\\"])\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n\\n plt.show()\\n\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\\n\\ndef stoch(\\n other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame\\n):\\n \\\"\\\"\\\"Stochastic oscillator\\n\\n Parameters\\n ----------\\n other_args:List[str]\\n Argparse arguments\\n s_ticker: str\\n Ticker\\n s_interval: str\\n Stock time interval\\n df_stock: pd.DataFrame\\n Dataframe of prices\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n prog=\\\"stoch\\\",\\n description=\\\"\\\"\\\"\\n The Stochastic Oscillator measures where the close is in relation\\n to the recent trading range. The values range from zero to 100. %D values over 75\\n indicate an overbought condition; values under 25 indicate an oversold condition.\\n When the Fast %D crosses above the Slow %D, it is a buy signal; when it crosses\\n below, it is a sell signal. The Raw %K is generally considered too erratic to use\\n for crossover signals.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-k\\\",\\n \\\"--fastkperiod\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_fastkperiod\\\",\\n type=check_positive,\\n default=14,\\n help=\\\"The time period of the fastk moving average\\\",\\n )\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--slowdperiod\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_slowdperiod\\\",\\n type=check_positive,\\n default=3,\\n help=\\\"The time period of the slowd moving average\\\",\\n )\\n parser.add_argument(\\n \\\"--slowkperiod\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_slowkperiod\\\",\\n type=check_positive,\\n default=3,\\n help=\\\"The time period of the slowk moving average\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--offset\\\",\\n action=\\\"store\\\",\\n dest=\\\"n_offset\\\",\\n type=check_positive,\\n default=0,\\n help=\\\"offset\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n # Daily\\n if s_interval == \\\"1440min\\\":\\n df_ta = ta.stoch(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Adj Close\\\"],\\n k=ns_parser.n_fastkperiod,\\n d=ns_parser.n_slowdperiod,\\n smooth_k=ns_parser.n_slowkperiod,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n # Intraday\\n else:\\n df_ta = ta.stoch(\\n high=df_stock[\\\"High\\\"],\\n low=df_stock[\\\"Low\\\"],\\n close=df_stock[\\\"Close\\\"],\\n k=ns_parser.n_fastkperiod,\\n d=ns_parser.n_slowdperiod,\\n smooth_k=ns_parser.n_slowkperiod,\\n offset=ns_parser.n_offset,\\n ).dropna()\\n\\n fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax = axes[0]\\n if s_interval == \\\"1440min\\\":\\n ax.plot(df_stock.index, df_stock[\\\"Adj Close\\\"].values, \\\"k\\\", lw=2)\\n else:\\n ax.plot(df_stock.index, df_stock[\\\"Close\\\"].values, \\\"k\\\", lw=2)\\n ax.set_title(f\\\"Stochastic Relative Strength Index (STOCH RSI) on {s_ticker}\\\")\\n ax.set_xlim(df_stock.index[0], df_stock.index[-1])\\n ax.set_xticklabels([])\\n ax.set_ylabel(\\\"Share Price ($)\\\")\\n ax.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax.minorticks_on()\\n ax.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n\\n ax2 = axes[1]\\n ax2.plot(df_ta.index, df_ta.iloc[:, 0].values, \\\"k\\\", lw=2)\\n ax2.plot(df_ta.index, df_ta.iloc[:, 1].values, \\\"b\\\", lw=2, ls=\\\"--\\\")\\n ax2.set_xlim(df_stock.index[0], df_stock.index[-1])\\n\\n ax2.axhspan(80, 100, facecolor=\\\"r\\\", alpha=0.2)\\n ax2.axhspan(0, 20, facecolor=\\\"g\\\", alpha=0.2)\\n ax2.axhline(80, linewidth=3, color=\\\"r\\\", ls=\\\"--\\\")\\n ax2.axhline(20, linewidth=3, color=\\\"g\\\", ls=\\\"--\\\")\\n\\n ax2.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n ax2.minorticks_on()\\n ax2.grid(b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2)\\n ax2.set_ylim([0, 100])\\n\\n ax3 = ax2.twinx()\\n ax3.set_ylim(ax2.get_ylim())\\n ax3.set_yticks([20, 80])\\n ax3.set_yticklabels([\\\"OVERSOLD\\\", \\\"OVERBOUGHT\\\"])\\n ax2.legend(\\n [f\\\"%K {df_ta.columns[0]}\\\", f\\\"%D {df_ta.columns[1]}\\\"], loc=\\\"lower left\\\"\\n )\\n\\n if gtff.USE_ION:\\n plt.ion()\\n\\n plt.gcf().autofmt_xdate()\\n fig.tight_layout(pad=1)\\n plt.show()\\n\\n print(\\\"\\\")\\n\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5603761672973633,\n \"alphanum_fraction\": 0.561191201210022,\n \"avg_line_length\": 33.97806930541992,\n \"blob_id\": \"41795a878ffeaf375e615587050ef608cffd6d72\",\n \"content_id\": \"798814600c55cb795f5d948f62ae7c22180fb170\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 15950,\n \"license_type\": \"permissive\",\n \"max_line_length\": 118,\n \"num_lines\": 456,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coingecko/pycoingecko_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Cryptocurrency Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n# pylint: disable=R0904, C0302, W0622\\nimport argparse\\nimport os\\nimport pandas as pd\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.cryptocurrency.coingecko import pycoingecko_view\\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\\n\\n\\nclass GeckoController:\\n\\n CHOICES = [\\n \\\"?\\\",\\n \\\"cls\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n \\\"global\\\",\\n \\\"find\\\",\\n \\\"coins\\\",\\n \\\"defi\\\",\\n \\\"news\\\",\\n \\\"trending\\\",\\n \\\"most_voted\\\",\\n \\\"most_visited\\\",\\n \\\"top_volume\\\",\\n \\\"recently\\\",\\n \\\"sentiment\\\",\\n \\\"gainers\\\",\\n \\\"losers\\\",\\n \\\"stables\\\",\\n \\\"yfarms\\\",\\n \\\"top_defi\\\",\\n \\\"top_dex\\\",\\n \\\"top_nft\\\",\\n \\\"nft_today\\\",\\n \\\"nft_market\\\",\\n \\\"exchanges\\\",\\n \\\"ex_rates\\\",\\n \\\"platforms\\\",\\n \\\"products\\\",\\n \\\"indexes\\\",\\n \\\"derivatives\\\",\\n \\\"categories\\\",\\n \\\"hold\\\",\\n \\\"hold_comp\\\",\\n \\\"load\\\",\\n \\\"clear\\\",\\n \\\"chart\\\",\\n \\\"ta\\\",\\n \\\"info\\\",\\n \\\"market\\\",\\n \\\"ath\\\",\\n \\\"atl\\\",\\n \\\"score\\\",\\n \\\"web\\\",\\n \\\"social\\\",\\n \\\"bc\\\",\\n \\\"dev\\\",\\n ]\\n\\n def __init__(self):\\n \\\"\\\"\\\"CONSTRUCTOR\\\"\\\"\\\"\\n\\n self._gecko_parser = argparse.ArgumentParser(add_help=False, prog=\\\"cg\\\")\\n self._gecko_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n self.current_coin = None\\n self.current_currency = None\\n self.current_df = pd.DataFrame()\\n self.source = \\\"\\\"\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\\"\\\\nCoinGecko:\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\")\\n print(\\\"Coin\\\")\\n print(\\\" find find coin by name, symbol or id\\\")\\n print(\\\" load load cryptocurrency data\\\")\\n print(\\\" clear remove loaded coin\\\")\\n print(\\\"\\\")\\n print(f\\\"Loaded coin: {self.current_coin}\\\")\\n print(\\\"\\\")\\n print(\\\" chart price chart for loaded coin\\\")\\n print(\\\"> ta technical analysis menu for loaded coin\\\")\\n print(\\\" info nasic information about loaded coin\\\")\\n print(\\\" market market stats about loaded coin\\\")\\n print(\\\" ath all time high related stats for loaded coin\\\")\\n print(\\\" atl all time low related stats for loaded coin\\\")\\n print(\\\" web found websites for loaded coin e.g forum, homepage\\\")\\n print(\\n \\\" social social portals urls for loaded coin, e.g reddit, twitter\\\"\\n )\\n print(\\n \\\" score different kind of scores for loaded coin, e.g developer score, sentiment score\\\"\\n )\\n print(\\\" dev github, bitbucket coin development statistics\\\")\\n print(\\\" bc links to blockchain explorers for loaded coin\\\")\\n print(\\\"\\\")\\n print(\\\"Overview:\\\")\\n print(\\\" global global crypto market info\\\")\\n print(\\\" coins coins available on CoinGecko\\\")\\n print(\\\" defi global DeFi market info\\\")\\n print(\\\" trending trending coins on CoinGecko\\\")\\n print(\\\" most_voted most voted coins on CoinGecko\\\")\\n print(\\\" most_visited most visited coins on CoinGecko\\\")\\n print(\\\" top_volume coins with highest volume on CoinGecko\\\")\\n print(\\\" recently recently added on CoinGecko\\\")\\n print(\\\" sentiment coins with most positive sentiment\\\")\\n print(\\n \\\" gainers top gainers - coins which price gained the most in given period \\\"\\n )\\n print(\\n \\\" losers top losers - coins which price dropped the most in given period \\\"\\n )\\n print(\\\" stables stablecoins\\\")\\n print(\\\" yfarms top yield farms\\\")\\n print(\\\" top_defi top defi protocols\\\")\\n print(\\\" top_dex top decentralized exchanges\\\")\\n print(\\\" top_nft top non fungible tokens\\\")\\n print(\\\" nft_today non fungible token of the day\\\")\\n print(\\\" nft_market non fungible token market status\\\")\\n print(\\\" exchanges top crypto exchanges\\\")\\n print(\\\" ex_rates coin exchange rates\\\")\\n print(\\\" platforms crypto financial platforms\\\")\\n print(\\\" products crypto financial products\\\")\\n print(\\\" indexes crypto indexes\\\")\\n print(\\\" derivatives crypto derivatives\\\")\\n print(\\\" categories crypto categories\\\")\\n print(\\\" hold ethereum, bitcoin holdings overview statistics\\\")\\n print(\\\" hold_comp ethereum, bitcoin holdings by public companies\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self._gecko_parser.parse_known_args(an_input.split())\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu.\\\"\\\"\\\"\\n print(\\\"Moving back to (crypto) menu\\\")\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program.\\\"\\\"\\\"\\n return True\\n\\n def call_find(self, other_args):\\n \\\"\\\"\\\"Process find command\\\"\\\"\\\"\\n pycoingecko_view.find(other_args=other_args)\\n\\n def call_load(self, other_args):\\n \\\"\\\"\\\"Process load command\\\"\\\"\\\"\\n self.current_coin = pycoingecko_view.load(other_args)\\n\\n def call_chart(self, other_args):\\n \\\"\\\"\\\"Process chart command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.chart(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_info(self, other_args):\\n \\\"\\\"\\\"Process info command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.info(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_market(self, other_args):\\n \\\"\\\"\\\"Process market command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.market(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_web(self, other_args):\\n \\\"\\\"\\\"Process web command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.web(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_social(self, other_args):\\n \\\"\\\"\\\"Process social command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.social(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_dev(self, other_args):\\n \\\"\\\"\\\"Process dev command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.dev(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_ath(self, other_args):\\n \\\"\\\"\\\"Process ath command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.ath(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_atl(self, other_args):\\n \\\"\\\"\\\"Process atl command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.atl(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_score(self, other_args):\\n \\\"\\\"\\\"Process score command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.score(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_bc(self, other_args):\\n \\\"\\\"\\\"Process bc command\\\"\\\"\\\"\\n if self.current_coin:\\n pycoingecko_view.bc(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_clear(self, _):\\n \\\"\\\"\\\"Process clear command\\\"\\\"\\\"\\n if self.current_coin:\\n print(\\n f\\\"Current coin {self.current_coin.coin_symbol} was removed. You can load new coin with load -c <coin>\\\"\\n )\\n self.current_coin = None\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n # pylint: disable=inconsistent-return-statements\\n def call_ta(self, other_args):\\n \\\"\\\"\\\"Process ta command\\\"\\\"\\\"\\n if self.current_coin:\\n self.current_df, self.current_currency = pycoingecko_view.ta(\\n self.current_coin, other_args\\n )\\n if self.current_df is not None:\\n try:\\n self.current_df = self.current_df[[\\\"price\\\"]].rename(\\n columns={\\\"price\\\": \\\"Close\\\"}\\n )\\n self.current_df.index.name = \\\"date\\\"\\n quit = ta_controller.menu(\\n stock=self.current_df,\\n ticker=self.current_coin.coin_symbol,\\n start=self.current_df.index[0],\\n interval=\\\"\\\",\\n )\\n print(\\\"\\\")\\n if quit is not None:\\n if quit is True:\\n return quit\\n\\n except (ValueError, KeyError) as e:\\n print(e)\\n else:\\n return\\n else:\\n print(\\\"Please load a coin through either load - coin\\\", \\\"\\\\n\\\")\\n return\\n\\n def call_hold(self, other_args):\\n \\\"\\\"\\\"Process hold command\\\"\\\"\\\"\\n pycoingecko_view.holdings_overview(other_args)\\n\\n def call_hold_comp(self, other_args):\\n \\\"\\\"\\\"Process hold_comp command\\\"\\\"\\\"\\n pycoingecko_view.holdings_companies_list(other_args)\\n\\n def call_gainers(self, other_args):\\n \\\"\\\"\\\"Process hold_comp command\\\"\\\"\\\"\\n pycoingecko_view.gainers(other_args)\\n\\n def call_losers(self, other_args):\\n \\\"\\\"\\\"Process hold_comp command\\\"\\\"\\\"\\n pycoingecko_view.losers(other_args)\\n\\n def call_trending(self, other_args):\\n \\\"\\\"\\\"Process trending command\\\"\\\"\\\"\\n pycoingecko_view.discover(category=\\\"trending\\\", other_args=other_args)\\n\\n def call_most_voted(self, other_args):\\n \\\"\\\"\\\"Process most_voted command\\\"\\\"\\\"\\n pycoingecko_view.discover(category=\\\"most_voted\\\", other_args=other_args)\\n\\n def call_recently(self, other_args):\\n \\\"\\\"\\\"Process recently command\\\"\\\"\\\"\\n pycoingecko_view.recently_added(other_args=other_args)\\n\\n def call_most_visited(self, other_args):\\n \\\"\\\"\\\"Process most_voted command\\\"\\\"\\\"\\n pycoingecko_view.discover(category=\\\"most_visited\\\", other_args=other_args)\\n\\n def call_sentiment(self, other_args):\\n \\\"\\\"\\\"Process sentiment command\\\"\\\"\\\"\\n pycoingecko_view.discover(category=\\\"positive_sentiment\\\", other_args=other_args)\\n\\n def call_news(self, other_args):\\n \\\"\\\"\\\"Process news command\\\"\\\"\\\"\\n pycoingecko_view.news(other_args=other_args)\\n\\n def call_categories(self, other_args):\\n \\\"\\\"\\\"Process top_categories command\\\"\\\"\\\"\\n pycoingecko_view.categories(other_args=other_args)\\n\\n def call_stables(self, other_args):\\n \\\"\\\"\\\"Process stables command\\\"\\\"\\\"\\n pycoingecko_view.stablecoins(other_args=other_args)\\n\\n def call_yfarms(self, other_args):\\n \\\"\\\"\\\"Process yield_farms command\\\"\\\"\\\"\\n pycoingecko_view.yfarms(other_args=other_args)\\n\\n def call_top_volume(self, other_args):\\n \\\"\\\"\\\"Process top_volume command\\\"\\\"\\\"\\n pycoingecko_view.top_volume_coins(other_args=other_args)\\n\\n def call_top_defi(self, other_args):\\n \\\"\\\"\\\"Process top_volume command\\\"\\\"\\\"\\n pycoingecko_view.top_defi_coins(other_args=other_args)\\n\\n def call_top_dex(self, other_args):\\n \\\"\\\"\\\"Process top_volume command\\\"\\\"\\\"\\n pycoingecko_view.top_dex(other_args=other_args)\\n\\n def call_top_nft(self, other_args):\\n \\\"\\\"\\\"Process top_volume command\\\"\\\"\\\"\\n pycoingecko_view.top_dex(other_args=other_args)\\n\\n def call_nft_market(self, other_args):\\n \\\"\\\"\\\"Process top_volume command\\\"\\\"\\\"\\n pycoingecko_view.nft_market_status(other_args=other_args)\\n\\n def call_nft_today(self, other_args):\\n \\\"\\\"\\\"Process nft_today command\\\"\\\"\\\"\\n pycoingecko_view.nft_of_the_day(other_args=other_args)\\n\\n def call_products(self, other_args):\\n \\\"\\\"\\\"Process products command\\\"\\\"\\\"\\n pycoingecko_view.products(other_args=other_args)\\n\\n def call_platforms(self, other_args):\\n \\\"\\\"\\\"Process platforms command\\\"\\\"\\\"\\n pycoingecko_view.platforms(other_args=other_args)\\n\\n def call_exchanges(self, other_args):\\n \\\"\\\"\\\"Process exchanges command\\\"\\\"\\\"\\n pycoingecko_view.exchanges(other_args=other_args)\\n\\n def call_ex_rates(self, other_args):\\n \\\"\\\"\\\"Process exchange_rates command\\\"\\\"\\\"\\n pycoingecko_view.exchange_rates(other_args=other_args)\\n\\n def call_indexes(self, other_args):\\n \\\"\\\"\\\"Process indexes command\\\"\\\"\\\"\\n pycoingecko_view.indexes(other_args=other_args)\\n\\n def call_derivatives(self, other_args):\\n \\\"\\\"\\\"Process derivatives command\\\"\\\"\\\"\\n pycoingecko_view.derivatives(other_args=other_args)\\n\\n def call_global(self, other_args):\\n \\\"\\\"\\\"Process global command\\\"\\\"\\\"\\n pycoingecko_view.global_market_info(other_args=other_args)\\n\\n def call_defi(self, other_args):\\n \\\"\\\"\\\"Process defi command\\\"\\\"\\\"\\n pycoingecko_view.global_defi_info(other_args=other_args)\\n\\n def call_coins(self, other_args):\\n \\\"\\\"\\\"Process coins command\\\"\\\"\\\"\\n pycoingecko_view.coin_list(other_args=other_args)\\n\\n\\ndef menu():\\n gecko_controller = GeckoController()\\n gecko_controller.print_help()\\n\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in gecko_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (crypto)>(cg)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (crypto)>(cg)> \\\")\\n\\n try:\\n process_input = gecko_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.6789960265159607,\n \"alphanum_fraction\": 0.7384412288665771,\n \"avg_line_length\": 43.52941131591797,\n \"blob_id\": \"7fa811c406c65045d13f4400f271f1750e211817\",\n \"content_id\": \"0a0ad2b3c0e5dadfbc37a984d54c551b95f58e66\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 757,\n \"license_type\": \"permissive\",\n \"max_line_length\": 138,\n \"num_lines\": 17,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinmarketcap/README.md\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# COINMARKETCAP\\n\\nThis menu aims to explore CoinMarketCap API and provide to user meaningful crypto currency data and different kind of metrics.\\n\\n## top <a name=\\\"top\\\"></a>\\n\\n````\\ntop [-n] [-s --sort SORTBY] [--descend]\\n````\\n\\nThis command displays the top n cryptocurrencies from coinmarketcap.com.\\n\\n* -n: Number of coins to look at (after sort). Defaults to 10.\\n* -s/--sort : Column to sort by. One of {Symbol,CMC_Rank,LastPrice,DayPctChange,MarketCap}. Defaults to sorting by the CMC rank.\\n* --descend : Flag to change the sorting order. Sorts in ascending order (since default order is the Rank which starts at 1).\\n\\n<img width=\\\"990\\\" alt=\\\"crypto\\\" src=\\\"https://user-images.githubusercontent.com/25267873/115787544-4746d100-a3ba-11eb-9433-b7cb9142404a.png\\\">\\n\"\n },\n {\n \"alpha_fraction\": 0.5500816106796265,\n \"alphanum_fraction\": 0.5501776337623596,\n \"avg_line_length\": 32.48231506347656,\n \"blob_id\": \"63ca7c208235a8dcb80b91d39ec0becb93de7c0b\",\n \"content_id\": \"9ac4c58429e263a4df8531a761b674ff1482d00d\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10413,\n \"license_type\": \"permissive\",\n \"max_line_length\": 108,\n \"num_lines\": 311,\n \"path\": \"/gamestonk_terminal/cryptocurrency/coinpaprika/coinpaprika_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"CoinPaprika Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\n# pylint: disable=inconsistent-return-statements, too-many-public-methods, too-many-lines, redefined-builtin\\n\\nimport argparse\\nimport os\\nimport pandas as pd\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_view\\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\\n\\n\\nclass PaprikaController:\\n\\n CHOICES = [\\n \\\"?\\\",\\n \\\"cls\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n \\\"clear\\\",\\n \\\"global\\\",\\n \\\"search\\\",\\n \\\"coins\\\",\\n \\\"info\\\",\\n \\\"markets\\\",\\n \\\"exchanges\\\",\\n \\\"ex_markets\\\",\\n \\\"platforms\\\",\\n \\\"contracts\\\",\\n \\\"events\\\",\\n \\\"twitter\\\",\\n \\\"ex\\\",\\n \\\"mkt\\\",\\n \\\"chart\\\",\\n \\\"ta\\\",\\n \\\"ps\\\",\\n \\\"basic\\\",\\n \\\"load\\\",\\n \\\"find\\\",\\n ]\\n\\n def __init__(self):\\n \\\"\\\"\\\"CONSTRUCTOR\\\"\\\"\\\"\\n\\n self._paprika_parser = argparse.ArgumentParser(add_help=False, prog=\\\"cp\\\")\\n self._paprika_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n self.current_coin = None\\n self.current_currency = None\\n self.current_df = pd.DataFrame()\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\\"\\\\nCoinPaprika:\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\")\\n print(\\\"Coin\\\")\\n print(\\\" find alternate way to search for coins\\\")\\n print(\\\" load load cryptocurrency data\\\")\\n print(\\\" clear remove loaded coin\\\")\\n print(\\\"\\\")\\n print(f\\\"Loaded coin: {self.current_coin}\\\")\\n print(\\\"\\\")\\n print(\\\" chart price chart for loaded coin\\\")\\n print(\\\"> ta technical analysis menu for loaded coin\\\")\\n print(\\\" basic basic information about loaded coin\\\")\\n print(\\\" ps price and supply related metrics for loaded coin\\\")\\n print(\\\" mkt all markets for loaded coin\\\")\\n print(\\\" ex all exchanges where loaded coin is listed\\\")\\n print(\\\" twitter tweets for loaded coin\\\")\\n print(\\\" events events related to loaded coin\\\")\\n print(\\\"\\\")\\n print(\\\"Overview:\\\")\\n print(\\\" global global crypto market info\\\")\\n print(\\\" coins coins available on CoinPaprika\\\")\\n print(\\\" info basic info about all coins available on CoinPaprika\\\")\\n print(\\n \\\" markets market related info about all coins available on CoinPaprika\\\"\\n )\\n print(\\\" search search for coins, exchanges, people on CoinPaprika\\\")\\n print(\\\" exchanges list all exchanges\\\")\\n print(\\\" ex_markets all available markets on given exchange\\\")\\n print(\\n \\\" platforms list blockchain platforms eg. ethereum, solana, kusama, terra\\\"\\n )\\n print(\\\" contracts all smart contracts for given platform\\\")\\n\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self._paprika_parser.parse_known_args(\\n an_input.split()\\n )\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu.\\\"\\\"\\\"\\n print(\\\"Moving back to (crypto) menu\\\")\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program.\\\"\\\"\\\"\\n return True\\n\\n def call_search(self, other_args):\\n \\\"\\\"\\\"Process search command\\\"\\\"\\\"\\n coinpaprika_view.search(other_args=other_args)\\n\\n def call_load(self, other_args):\\n \\\"\\\"\\\"Process load command\\\"\\\"\\\"\\n self.current_coin = coinpaprika_view.load(other_args)\\n\\n def call_chart(self, other_args):\\n \\\"\\\"\\\"Process chart command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.chart(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_ps(self, other_args):\\n \\\"\\\"\\\"Process ps command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.price_supply(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_basic(self, other_args):\\n \\\"\\\"\\\"Process ps command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.basic(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_mkt(self, other_args):\\n \\\"\\\"\\\"Process market command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.markets(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_ex(self, other_args):\\n \\\"\\\"\\\"Process ex command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.exchanges(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_events(self, other_args):\\n \\\"\\\"\\\"Process events command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.events(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_twitter(self, other_args):\\n \\\"\\\"\\\"Process twitter command\\\"\\\"\\\"\\n if self.current_coin:\\n coinpaprika_view.twitter(self.current_coin, other_args)\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n def call_clear(self, _):\\n \\\"\\\"\\\"Process clear command\\\"\\\"\\\"\\n if self.current_coin:\\n print(\\n f\\\"Current coin {self.current_coin} was removed. You can load new coin with load -c <coin>\\\"\\n )\\n print(\\\"\\\")\\n self.current_coin = None\\n self.current_df = pd.DataFrame()\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n # pylint: disable=inconsistent-return-statements\\n def call_ta(self, other_args):\\n \\\"\\\"\\\"Process ta command\\\"\\\"\\\"\\n if self.current_coin:\\n self.current_df, self.current_currency = coinpaprika_view.ta(\\n self.current_coin, other_args\\n )\\n if self.current_df is not None:\\n try:\\n quit = ta_controller.menu(\\n stock=self.current_df,\\n ticker=self.current_coin,\\n start=self.current_df.index[0],\\n interval=\\\"\\\",\\n )\\n print(\\\"\\\")\\n if quit is not None:\\n if quit is True:\\n return quit\\n\\n except (ValueError, KeyError) as e:\\n print(e)\\n else:\\n return\\n else:\\n print(\\\"Please load a coin through either load - coin\\\", \\\"\\\\n\\\")\\n return\\n\\n def call_global(self, other_args):\\n \\\"\\\"\\\"Process global command\\\"\\\"\\\"\\n coinpaprika_view.global_market(other_args=other_args)\\n\\n def call_coins(self, other_args):\\n \\\"\\\"\\\"Process coins command\\\"\\\"\\\"\\n coinpaprika_view.coins(other_args=other_args)\\n\\n def call_markets(self, other_args):\\n \\\"\\\"\\\"Process markets command\\\"\\\"\\\"\\n coinpaprika_view.all_coins_market_info(other_args=other_args)\\n\\n def call_ex_markets(self, other_args):\\n \\\"\\\"\\\"Process ex_markets command\\\"\\\"\\\"\\n coinpaprika_view.exchange_markets(other_args=other_args)\\n\\n def call_info(self, other_args):\\n \\\"\\\"\\\"Process info command\\\"\\\"\\\"\\n coinpaprika_view.all_coins_info(other_args=other_args)\\n\\n def call_exchanges(self, other_args):\\n \\\"\\\"\\\"Process coins_market command\\\"\\\"\\\"\\n coinpaprika_view.all_exchanges(other_args=other_args)\\n\\n def call_platforms(self, other_args):\\n \\\"\\\"\\\"Process platforms command\\\"\\\"\\\"\\n coinpaprika_view.all_platforms(other_args=other_args)\\n\\n def call_contracts(self, other_args):\\n \\\"\\\"\\\"Process contracts command\\\"\\\"\\\"\\n coinpaprika_view.contracts(other_args=other_args)\\n\\n def call_find(self, other_args):\\n \\\"\\\"\\\"Process find command\\\"\\\"\\\"\\n coinpaprika_view.find(other_args=other_args)\\n\\n\\ndef menu():\\n paprika_controller = PaprikaController()\\n paprika_controller.print_help()\\n\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in paprika_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (crypto)>(cp)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (crypto)>(cp)> \\\")\\n\\n try:\\n process_input = paprika_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.5619261860847473,\n \"alphanum_fraction\": 0.5619261860847473,\n \"avg_line_length\": 27.848648071289062,\n \"blob_id\": \"2bcae65395369791e1cf1727bb1679b09f8a7920\",\n \"content_id\": \"7de9aa21654accb99af6d6be9f769cc3ccd9057e\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5337,\n \"license_type\": \"permissive\",\n \"max_line_length\": 101,\n \"num_lines\": 185,\n \"path\": \"/gamestonk_terminal/etf/etf_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"ETF Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nimport os\\nfrom typing import List\\nimport matplotlib.pyplot as plt\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.etf.stockanalysis_model import (\\n name_search,\\n open_web,\\n etf_overview,\\n compare_etfs,\\n etf_holdings,\\n)\\nfrom gamestonk_terminal.etf.screener_model import etf_screener\\nfrom gamestonk_terminal.etf import wsj_view\\n\\n\\nclass ETFController:\\n \\\"\\\"\\\"ETF Controller class\\\"\\\"\\\"\\n\\n CHOICES = [\\n \\\"cls\\\",\\n \\\"?\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n ]\\n\\n CHOICES_COMMANDS = [\\n \\\"web\\\",\\n \\\"search\\\",\\n \\\"overview\\\",\\n \\\"compare\\\",\\n \\\"holdings\\\",\\n \\\"screener\\\",\\n \\\"gainers\\\",\\n \\\"decliners\\\",\\n \\\"active\\\",\\n ]\\n\\n CHOICES += CHOICES_COMMANDS\\n\\n def __init__(self):\\n \\\"\\\"\\\"Constructor\\\"\\\"\\\"\\n self.etf_parser = argparse.ArgumentParser(add_help=False, prog=\\\"etf\\\")\\n self.etf_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\n \\\"https://github.com/GamestonkTerminal/GamestonkTerminal/tree/main/gamestonk_terminal/etf\\\"\\n )\\n print(\\\"\\\")\\n print(\\\">> ETF <<\\\")\\n print(\\\"\\\")\\n print(\\\"What do you want to do?\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\\nStockAnalysis.com\\\")\\n print(\\\" web open StockAnalysis.com/etf\\\")\\n print(\\\" search search ETFs matching name (i.e. BlackRock or Invesco)\\\")\\n print(\\\" overview get overview of ETF symbol\\\")\\n print(\\\" holdings get top holdings for ETF\\\")\\n print(\\\" compare compare overview of multiple ETF\\\")\\n print(\\\" screener screen etfs based on overview data\\\")\\n print(\\\"\\\\nWall St. Journal\\\")\\n print(\\\" gainers show top gainers\\\")\\n print(\\\" decliners show top decliners\\\")\\n print(\\\" active show most active\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self.etf_parser.parse_known_args(an_input.split())\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu\\\"\\\"\\\"\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program\\\"\\\"\\\"\\n return True\\n\\n def call_web(self, other_args: List[str]):\\n \\\"\\\"\\\"Process web command\\\"\\\"\\\"\\n open_web(other_args)\\n\\n def call_search(self, other_args: List[str]):\\n \\\"\\\"\\\"Process search command\\\"\\\"\\\"\\n name_search(other_args)\\n\\n def call_overview(self, other_args: List[str]):\\n \\\"\\\"\\\"Process overview command\\\"\\\"\\\"\\n etf_overview(other_args)\\n\\n def call_holdings(self, other_args: List[str]):\\n \\\"\\\"\\\"Process holdings command\\\"\\\"\\\"\\n etf_holdings(other_args)\\n\\n def call_compare(self, other_args):\\n \\\"\\\"\\\"Process compare command\\\"\\\"\\\"\\n compare_etfs(other_args)\\n\\n def call_screener(self, other_args):\\n \\\"\\\"\\\"Process screener command\\\"\\\"\\\"\\n etf_screener(other_args)\\n\\n def call_gainers(self, other_args):\\n \\\"\\\"\\\"Process gainers command\\\"\\\"\\\"\\n wsj_view.show_top_mover(\\\"gainers\\\", other_args)\\n\\n def call_decliners(self, other_args):\\n \\\"\\\"\\\"Process decliners command\\\"\\\"\\\"\\n wsj_view.show_top_mover(\\\"decliners\\\", other_args)\\n\\n def call_active(self, other_args):\\n \\\"\\\"\\\"Process gainers command\\\"\\\"\\\"\\n wsj_view.show_top_mover(\\\"active\\\", other_args)\\n\\n\\ndef menu():\\n etf_controller = ETFController()\\n etf_controller.print_help()\\n plt.close(\\\"all\\\")\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in etf_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (etf)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (etf)> \\\")\\n\\n try:\\n process_input = etf_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.49760839343070984,\n \"alphanum_fraction\": 0.5079463124275208,\n \"avg_line_length\": 32.4072151184082,\n \"blob_id\": \"5f3a8300689f4cb62a78df001713837e897aada4\",\n \"content_id\": \"e5dbdd142efde5e1147066b72de966284f3eb189\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6481,\n \"license_type\": \"permissive\",\n \"max_line_length\": 113,\n \"num_lines\": 194,\n \"path\": \"/gamestonk_terminal/economy/fred_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\" Fred View \\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n\\nimport argparse\\nfrom typing import List\\nimport pandas as pd\\nfrom pandas.plotting import register_matplotlib_converters\\nimport matplotlib.pyplot as plt\\nfrom fredapi import Fred\\nfrom gamestonk_terminal.helper_funcs import (\\n parse_known_args_and_warn,\\n valid_date,\\n plot_autoscale,\\n)\\nfrom gamestonk_terminal.config_plot import PLOT_DPI\\nfrom gamestonk_terminal import config_terminal as cfg\\nfrom gamestonk_terminal import feature_flags as gtff\\n\\nregister_matplotlib_converters()\\n\\n\\nfred_series_description = {\\n \\\"VIXCLS\\\": \\\"Volatility Index\\\",\\n \\\"GDP\\\": \\\"Gross Domestic Product\\\",\\n \\\"UNRATE\\\": \\\"Unemployment Rate\\\",\\n \\\"DGS1\\\": \\\"1-Year Treasury Constant Maturity Rate\\\",\\n \\\"DGS5\\\": \\\"5-Year Treasury Constant Maturity Rate\\\",\\n \\\"DGS10\\\": \\\"10-Year Treasury Constant Maturity Rate\\\",\\n \\\"DGS30\\\": \\\"30-Year Treasury Constant Maturity Rate\\\",\\n \\\"MORTGAGE30US\\\": \\\"30-Year Mortgage Rate\\\",\\n \\\"FEDFUNDS\\\": \\\"Effective Federal Funds Rate\\\",\\n \\\"AAA\\\": \\\"Moody's Seasoned AAA Corporate Bond Yield\\\",\\n \\\"DEXCAUS\\\": \\\"Canada / U.S. Foreign Exchange Rate\\\",\\n}\\n\\n\\ndef display_fred(other_args: List[str], choice: str):\\n \\\"\\\"\\\"Display customized Federal Reserve Economic Data (FRED) from https://fred.stlouisfed.org.\\n It is possible to display multiple series.\\n\\n Parameters\\n ----------\\n other_args : List[str]\\n argparse other args\\n choice : str\\n Fred data series: \\\"VIXCLS\\\", \\\"GDP\\\", \\\"DGS1\\\", \\\"DGS5\\\", \\\"DGS10\\\", \\\"DGS30\\\", \\\"MORTGAGE30US\\\", \\\"FEDFUNDS\\\",\\n \\\"AAA\\\", \\\"DEXCAUS\\\", \\\"UNRATE\\\",\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n prog=choice if choice else \\\"fred\\\",\\n description=\\\"\\\"\\\"\\n Display customized Federal Reserve Economic Data (FRED) from https://fred.stlouisfed.org.\\n It is possible to display multiple series.\\n \\\"\\\"\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-i\\\",\\n \\\"--id\\\",\\n dest=\\\"series_id\\\",\\n required=not bool(choice) and \\\"-h\\\" not in other_args,\\n type=str,\\n default=choice.upper(),\\n help=\\\"FRED Series ID from https://fred.stlouisfed.org. For multiple series use: series1,series2,series3\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-s\\\",\\n dest=\\\"start_date\\\",\\n type=valid_date,\\n default=\\\"2019-01-01\\\",\\n help=\\\"Starting date (YYYY-MM-DD) of data\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--text\\\",\\n action=\\\"store_true\\\",\\n dest=\\\"text\\\",\\n help=\\\"Only output text data\\\",\\n )\\n\\n try:\\n if not choice:\\n if other_args:\\n if \\\"-\\\" not in other_args[0]:\\n other_args.insert(0, \\\"-i\\\")\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n\\n if not ns_parser:\\n return\\n\\n fred = Fred(api_key=cfg.API_FRED_KEY)\\n\\n if \\\",\\\" in ns_parser.series_id:\\n if not ns_parser.text:\\n l_colors = [\\n \\\"tab:blue\\\",\\n \\\"tab:orange\\\",\\n \\\"tab:green\\\",\\n \\\"tab:red\\\",\\n \\\"tab:purple\\\",\\n \\\"tab:brown\\\",\\n \\\"tab:pink\\\",\\n \\\"tab:gray\\\",\\n \\\"tab:olive\\\",\\n \\\"tab:cyan\\\",\\n ]\\n l_ts_start = list()\\n l_ts_end = list()\\n p = {}\\n _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n ax.axes.get_yaxis().set_visible(False)\\n plt.subplots_adjust(right=0.9 - ns_parser.series_id.count(\\\",\\\") * 0.1)\\n\\n series = ns_parser.series_id.split(\\\",\\\")\\n\\n success = -1\\n success_series = list()\\n for series_id in series:\\n try:\\n d_data = fred.get_series(series_id, ns_parser.start_date)\\n success += 1\\n success_series.append(series_id.upper())\\n except Exception:\\n print(f\\\"Series '{series_id}' doesn't exist!\\\")\\n continue\\n\\n df_fred = pd.DataFrame(d_data, columns=[f\\\"{series_id}\\\"])\\n df_fred.index.name = \\\"Date\\\"\\n\\n if ns_parser.text:\\n print(df_fred.dropna().to_string(), \\\"\\\\n\\\")\\n\\n else:\\n axes = ax.twinx()\\n axes.spines[\\\"right\\\"].set_position((\\\"axes\\\", 1 + success * 0.15))\\n axes.spines[\\\"right\\\"].set_color(l_colors[success])\\n (p[success],) = axes.plot(\\n df_fred.index,\\n df_fred.iloc[:, 0],\\n c=l_colors[success],\\n label=series_id.upper(),\\n )\\n\\n axes.yaxis.label.set_color(l_colors[success])\\n\\n l_ts_start.append(df_fred.index[0])\\n l_ts_end.append(df_fred.index[-1])\\n\\n if not ns_parser.text and success:\\n plt.title(\\\"FRED: \\\" + \\\", \\\".join(success_series))\\n plt.xlim(min(l_ts_start), max(l_ts_end))\\n plt.gcf().autofmt_xdate()\\n plt.xlabel(\\\"Time\\\")\\n plt.legend([val for _, val in p.items()], success_series)\\n plt.gca().spines[\\\"left\\\"].set_visible(False)\\n plt.show()\\n print(\\\"\\\")\\n\\n else:\\n d_data = fred.get_series(ns_parser.series_id, ns_parser.start_date)\\n\\n df_fred = pd.DataFrame(d_data, columns=[f\\\"{ns_parser.series_id}\\\"])\\n df_fred.index.name = \\\"Date\\\"\\n\\n if ns_parser.text:\\n print(df_fred.to_string())\\n\\n else:\\n plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)\\n plt.plot(df_fred.index, df_fred.iloc[:, 0])\\n plt.xlabel(\\\"Time\\\")\\n plt.xlim(df_fred.index[0], df_fred.index[-1])\\n plt.ylabel(f\\\"{ns_parser.series_id.upper()}\\\")\\n plt.grid(b=True, which=\\\"major\\\", color=\\\"#666666\\\", linestyle=\\\"-\\\")\\n plt.minorticks_on()\\n plt.grid(\\n b=True, which=\\\"minor\\\", color=\\\"#999999\\\", linestyle=\\\"-\\\", alpha=0.2\\n )\\n if choice:\\n plt.title(fred_series_description[ns_parser.series_id])\\n if gtff.USE_ION:\\n plt.ion()\\n plt.show()\\n print(\\\"\\\")\\n\\n except SystemExit:\\n print(\\\"\\\")\\n except Exception as e:\\n print(e, \\\"\\\\n\\\")\\n return\\n\"\n },\n {\n \"alpha_fraction\": 0.6118078231811523,\n \"alphanum_fraction\": 0.6121937036514282,\n \"avg_line_length\": 32.655845642089844,\n \"blob_id\": \"f826646e67c14571e6ba7a40eedef074571cd01e\",\n \"content_id\": \"652309dc107b700c2b4efa04ed67b2c4413f87ca\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5183,\n \"license_type\": \"permissive\",\n \"max_line_length\": 74,\n \"num_lines\": 154,\n \"path\": \"/tests/test_cryptocurrency/test_coinpaprika_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from unittest import TestCase, mock\\nimport sys\\nimport io\\n\\nfrom gamestonk_terminal.cryptocurrency.coinpaprika import coinpaprika_view\\n\\n\\nclass TestCoinPaprikaView(TestCase):\\n def test_global_markets(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.global_market([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"market_cap_usd\\\", capt)\\n\\n def test_coins(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.coins([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"rank\\\", capt)\\n\\n def test_all_coins_market_info(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.all_coins_market_info([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Displaying data vs USD\\\", capt)\\n\\n def test_all_coins_info(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.all_coins_info([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Displaying data vs USD\\\", capt)\\n\\n def test_all_exchanges(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.all_exchanges([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Displaying data vs USD\\\", capt)\\n\\n def test_search(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.search([\\\"-q\\\", \\\"bt\\\"])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"category\\\", capt)\\n\\n def test_all_platforms(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.all_platforms([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"platform_id\\\", capt)\\n\\n def test_contracts(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.contracts([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"active\\\", capt)\\n\\n def test_find(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.find([\\\"-c\\\", \\\"BTC\\\"])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"index\\\", capt)\\n\\n def test_twitter(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.twitter(\\\"eth-ethereum\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Couldn't find\\\", capt)\\n\\n def test_events(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.events(\\\"eth-ethereum\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"description\\\", capt)\\n\\n def test_exchanges(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.exchanges(\\\"btc-bitcoin\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"name\\\", capt)\\n\\n def test_markets(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.markets(\\\"eth-ethereum\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"exchange\\\", capt)\\n\\n @mock.patch(\\\"matplotlib.pyplot.show\\\")\\n def test_chart(self, mock_matplot):\\n # pylint: disable=unused-argument\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.chart(\\\"btc-bitcoin\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"\\\\n\\\", capt)\\n\\n def test_exchange_markets(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.exchange_markets([])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"base_currency_name\\\", capt)\\n\\n def price_supply(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.price_supply(\\\"btc\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"asset_platform_id\\\", capt)\\n\\n def test_load(self):\\n value = coinpaprika_view.load([\\\"-c\\\", \\\"BTC\\\"])\\n self.assertEqual(value, \\\"btc-bitcoin\\\")\\n\\n def test_ta(self):\\n value = coinpaprika_view.ta(\\\"eth-ethereum\\\", [])\\n print(value[0])\\n self.assertIn(\\\"Open\\\", value[0])\\n\\n def test_basic(self):\\n capturedOutput = io.StringIO()\\n sys.stdout = capturedOutput\\n coinpaprika_view.basic(\\\"BTC\\\", [])\\n sys.stdout = sys.__stdout__\\n capt = capturedOutput.getvalue()\\n self.assertIn(\\\"Metric\\\", capt)\\n\"\n },\n {\n \"alpha_fraction\": 0.5311464667320251,\n \"alphanum_fraction\": 0.5333002209663391,\n \"avg_line_length\": 29.18000030517578,\n \"blob_id\": \"380d2d21531b2d4340e47974bbab6996a4ca91db\",\n \"content_id\": \"3b29dc81f7089c2ebce54bccbe359d51b674148d\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6036,\n \"license_type\": \"permissive\",\n \"max_line_length\": 106,\n \"num_lines\": 200,\n \"path\": \"/gamestonk_terminal/cryptocurrency/binance/binance_controller.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Cryptocurrency Controller\\\"\\\"\\\"\\n__docformat__ = \\\"numpy\\\"\\n# pylint: disable=R0904, C0302, W0622\\nimport argparse\\nimport os\\nimport pandas as pd\\nfrom prompt_toolkit.completion import NestedCompleter\\nfrom gamestonk_terminal import feature_flags as gtff\\nfrom gamestonk_terminal.helper_funcs import get_flair\\nfrom gamestonk_terminal.menu import session\\nfrom gamestonk_terminal.cryptocurrency.binance import binance_model\\nfrom gamestonk_terminal.cryptocurrency.technical_analysis import ta_controller\\n\\n\\nclass BinanceController:\\n\\n CHOICES = [\\n \\\"?\\\",\\n \\\"cls\\\",\\n \\\"help\\\",\\n \\\"q\\\",\\n \\\"quit\\\",\\n \\\"book\\\",\\n \\\"candle\\\",\\n \\\"balance\\\",\\n \\\"load\\\",\\n \\\"ta\\\",\\n \\\"clear\\\",\\n ]\\n\\n def __init__(self):\\n \\\"\\\"\\\"CONSTRUCTOR\\\"\\\"\\\"\\n\\n self._binance_parser = argparse.ArgumentParser(add_help=False, prog=\\\"bin\\\")\\n self._binance_parser.add_argument(\\\"cmd\\\", choices=self.CHOICES)\\n self.current_coin = None\\n self.current_currency = None\\n self.current_df = pd.DataFrame()\\n self.source = \\\"\\\"\\n\\n def print_help(self):\\n \\\"\\\"\\\"Print help\\\"\\\"\\\"\\n print(\\\"\\\\nBinance:\\\")\\n print(\\\" cls clear screen\\\")\\n print(\\\" ?/help show this menu again\\\")\\n print(\\\" q quit this menu, and shows back to main menu\\\")\\n print(\\\" quit quit to abandon program\\\")\\n print(\\\"\\\")\\n print(f\\\"Loaded coin: {self.current_coin}\\\")\\n print(\\\"\\\")\\n print(\\\" load load coin/currency to use and load candle data\\\")\\n print(\\\" clear clear loaded coin\\\")\\n print(\\\" book show order book\\\")\\n print(\\\" candle show candles\\\")\\n print(\\\" balance show coin balance\\\")\\n print(\\\"> ta open technical analysis menu\\\")\\n print(\\\"\\\")\\n\\n def switch(self, an_input: str):\\n \\\"\\\"\\\"Process and dispatch input\\n\\n Returns\\n -------\\n True, False or None\\n False - quit the menu\\n True - quit the program\\n None - continue in the menu\\n \\\"\\\"\\\"\\n\\n # Empty command\\n if not an_input:\\n print(\\\"\\\")\\n return None\\n\\n (known_args, other_args) = self._binance_parser.parse_known_args(\\n an_input.split()\\n )\\n\\n # Help menu again\\n if known_args.cmd == \\\"?\\\":\\n self.print_help()\\n return None\\n\\n # Clear screen\\n if known_args.cmd == \\\"cls\\\":\\n os.system(\\\"cls||clear\\\")\\n return None\\n\\n return getattr(\\n self, \\\"call_\\\" + known_args.cmd, lambda: \\\"Command not recognized!\\\"\\n )(other_args)\\n\\n def call_help(self, _):\\n \\\"\\\"\\\"Process Help command\\\"\\\"\\\"\\n self.print_help()\\n\\n def call_q(self, _):\\n \\\"\\\"\\\"Process Q command - quit the menu.\\\"\\\"\\\"\\n print(\\\"Moving back to (crypto) menu\\\")\\n return False\\n\\n def call_quit(self, _):\\n \\\"\\\"\\\"Process Quit command - quit the program.\\\"\\\"\\\"\\n return True\\n\\n def call_book(self, other_args):\\n \\\"\\\"\\\"Process book command\\\"\\\"\\\"\\n binance_model.order_book(other_args, self.current_coin, self.current_currency)\\n\\n def call_candle(self, _):\\n \\\"\\\"\\\"Process candle command\\\"\\\"\\\"\\n if self.current_coin is None and self.current_df.empty:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n else:\\n binance_model.show_candles(\\n self.current_df, self.current_coin, self.current_currency\\n )\\n\\n def call_balance(self, _):\\n \\\"\\\"\\\"Process balance command\\\"\\\"\\\"\\n binance_model.balance(self.current_coin)\\n\\n def call_load(self, other_args):\\n \\\"\\\"\\\"Process select command\\\"\\\"\\\"\\n (\\n self.current_coin,\\n self.current_currency,\\n self.current_df,\\n ) = binance_model.select_binance_coin(other_args)\\n self.source = \\\"BIN\\\"\\n print(\\\"\\\")\\n\\n def call_clear(self, _):\\n \\\"\\\"\\\"Process clear command\\\"\\\"\\\"\\n if self.current_coin:\\n print(\\n f\\\"Current coin {self.current_coin} was removed. You can load new coin with load -c <coin>\\\"\\n )\\n print(\\\"\\\")\\n self.current_coin = None\\n self.current_currency = None\\n self.current_df = pd.DataFrame()\\n else:\\n print(\\\"No coin selected. Use 'load' to load the coin you want to look at.\\\")\\n print(\\\"\\\")\\n\\n # pylint: disable=inconsistent-return-statements\\n def call_ta(self, _):\\n \\\"\\\"\\\"Process ta command\\\"\\\"\\\"\\n if not self.current_coin:\\n print(\\\"Please load a coin through either load -c \\\", \\\"\\\\n\\\")\\n\\n elif self.current_df.empty:\\n print(\\\"Price dataframe is empty\\\")\\n\\n else:\\n\\n try:\\n quit = ta_controller.menu(\\n stock=self.current_df,\\n ticker=self.current_coin,\\n start=self.current_df.index[0],\\n interval=\\\"\\\",\\n )\\n print(\\\"\\\")\\n if quit is not None:\\n if quit is True:\\n return quit\\n\\n except (ValueError, KeyError) as e:\\n print(e)\\n\\n\\ndef menu():\\n binance_controller = BinanceController()\\n binance_controller.print_help()\\n\\n while True:\\n # Get input command from user\\n if session and gtff.USE_PROMPT_TOOLKIT:\\n completer = NestedCompleter.from_nested_dict(\\n {c: None for c in binance_controller.CHOICES}\\n )\\n an_input = session.prompt(\\n f\\\"{get_flair()} (crypto)>(bin)> \\\",\\n completer=completer,\\n )\\n else:\\n an_input = input(f\\\"{get_flair()} (crypto)>(bin)> \\\")\\n\\n try:\\n process_input = binance_controller.switch(an_input)\\n\\n if process_input is not None:\\n return process_input\\n\\n except SystemExit:\\n print(\\\"The command selected doesn't exist\\\\n\\\")\\n continue\\n\"\n },\n {\n \"alpha_fraction\": 0.6536450386047363,\n \"alphanum_fraction\": 0.6576437950134277,\n \"avg_line_length\": 34.72527313232422,\n \"blob_id\": \"389908adb15d957951617b74616673e0223e1621\",\n \"content_id\": \"a13dd7b14591d54930150bf01aa8d98b7dfa5eef\",\n \"detected_licenses\": [\n \"LicenseRef-scancode-free-unknown\",\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3251,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 91,\n \"path\": \"/gamestonk_terminal/stocks/discovery/sentimentinvestor_view.py\",\n \"repo_name\": \"Meg1211/GamestonkTerminal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import argparse\\nimport logging\\nimport textwrap\\nfrom typing import Any, List\\n\\nfrom colorama import Style\\nfrom sentipy.sentipy import Sentipy\\nfrom tabulate import tabulate\\n\\nfrom gamestonk_terminal import config_terminal as cfg\\nfrom gamestonk_terminal.helper_funcs import parse_known_args_and_warn\\n\\nsentipy: Sentipy = Sentipy(\\n token=cfg.API_SENTIMENTINVESTOR_TOKEN, key=cfg.API_SENTIMENTINVESTOR_KEY\\n)\\n\\\"\\\"\\\"Initialise SentiPy with the user's API token and key\\\"\\\"\\\"\\n\\n\\n__command_descriptions = {\\n \\\"popular\\\": f\\\"\\\"\\\"\\n The {Style.BRIGHT}popular{Style.RESET_ALL} command prints the stocks with highest Average Hype Index right now.\\n\\n {Style.BRIGHT}AHI (Absolute Hype Index){Style.RESET_ALL}\\n ---\\n AHI is a measure of how much people are talking about a stock on social media.\\n It is calculated by dividing the total number of mentions for the chosen stock\\n on a social network by the mean number of mentions any stock receives on that\\n social medium.\\n\\n ===\\n\\n {Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to\\n generate hourly metrics on over 2,000 stocks. Sentiment provides volume and\\n sentiment metrics powered by proprietary NLP models.\\n \\\"\\\"\\\",\\n \\\"emerging\\\": f\\\"\\\"\\\"\\n The {Style.BRIGHT}emerging{Style.RESET_ALL} command prints the stocks with highest Relative Hype Index right now.\\n\\n {Style.BRIGHT}RHI (Relative Hype Index){Style.RESET_ALL}\\n ---\\n RHI is a measure of whether people are talking about a stock more or less than\\n usual, calculated by dividing the mean AHI for the past day by the mean AHI for\\n for the past week for that stock.\\n\\n ===\\n\\n {Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to\\n generate hourly metrics on over 2,000 stocks. Sentiment provides volume and\\n sentiment metrics powered by proprietary NLP models.\\n \\\"\\\"\\\",\\n}\\n\\n\\ndef sort(metric: str, other_args: List[str], command_name: str) -> None:\\n parser = argparse.ArgumentParser(\\n add_help=False,\\n prog=command_name,\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=textwrap.dedent(__command_descriptions[command_name]),\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--limit\\\",\\n action=\\\"store\\\",\\n dest=\\\"limit\\\",\\n type=int,\\n default=10,\\n help=\\\"the maximum number of stocks to retrieve\\\",\\n )\\n\\n try:\\n ns_parser = parse_known_args_and_warn(parser, other_args)\\n if not ns_parser:\\n return\\n\\n data = sentipy.sort(metric, ns_parser.limit)\\n\\n table: List[List[Any]] = []\\n for index, stock in enumerate(data):\\n if not hasattr(stock, \\\"symbol\\\") or not hasattr(stock, metric):\\n logging.warning(\\\"data for stock %s is incomplete, ignoring\\\", index + 1)\\n table.append([])\\n else:\\n table.append([index + 1, stock.symbol, stock.__getattribute__(metric)])\\n\\n print(tabulate(table, headers=[\\\"Rank\\\", \\\"Ticker\\\", metric], floatfmt=\\\".3f\\\"))\\n print()\\n except Exception as e:\\n logging.error(e)\\n print(e, \\\"\\\\n\\\")\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":49,"string":"49"}}},{"rowIdx":2975796,"cells":{"repo_name":{"kind":"string","value":"Paluthyst/rpsgame"},"repo_url":{"kind":"string","value":"https://github.com/Paluthyst/rpsgame"},"snapshot_id":{"kind":"string","value":"6f6613b79aed7bd611ca149c21c5e07febde72f7"},"revision_id":{"kind":"string","value":"5e4f6d85eea33759786a90e9620aaf59031ae730"},"directory_id":{"kind":"string","value":"b6d66ff942ea3280630097e94ce96077b69d51df"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-11-18T05:03:48.619250","string":"2022-11-18T05:03:48.619250"},"revision_date":{"kind":"timestamp","value":"2020-07-22T21:11:13","string":"2020-07-22T21:11:13"},"committer_date":{"kind":"timestamp","value":"2020-07-22T21:11:13","string":"2020-07-22T21:11:13"},"github_id":{"kind":"number","value":281783949,"string":"281,783,949"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5412765741348267,"alphanum_fraction":0.5446808338165283,"avg_line_length":30.783782958984375,"blob_id":"968aca5c7f9d9309d10f30d1e7652427ea8ac9dd","content_id":"427184333ed8048f2be9f0675506c2cc0dca3ca4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1175,"license_type":"no_license","max_line_length":63,"num_lines":37,"path":"/rpsMAIN.py","repo_name":"Paluthyst/rpsgame","src_encoding":"UTF-8","text":"from random import randint\nt = [\"Rock\", \"Paper\", \"Scissors\"]\ncomputer = t[randint(0, 2)]\nplayer = False\nwhile player == False:\n\n player = input(\"Choose Rock, Paper or Scissors: \")\n\n if player == computer:\n print(\"You tied with the computer :/\")\n elif player == \"Rock\".lower():\n if computer == \"Paper\":\n print(\"You loose\", computer, \"beats\", player)\n else:\n print(\"YOU WIN :)\", computer, \"looses to\", player)\n\n elif player == \"Paper\".lower():\n if computer == \"Scissors\":\n print(\"You loose\", computer, \"beats\", player)\n else:\n print(\"YOU WIN :)\", computer, \"looses to\", player)\n\n elif player == \"Scissors\".lower():\n if computer == \"Rock\":\n print(\"You loose\", computer, \"beats\", player)\n else:\n print(\"YOU WIN :)\", computer, \"looses to\", player)\n else:\n print(\"ah ah ah that's not an option friend\")\n player = input(\"Would you like to play again? Yes or No: \")\n if player == \"Yes\":\n print(\"OK here we go!\")\n computer = t[randint(0, 2)]\n player = False\n else:\n print(\"Ok see ya\")\n player == True"}],"string":"[\n {\n \"alpha_fraction\": 0.5412765741348267,\n \"alphanum_fraction\": 0.5446808338165283,\n \"avg_line_length\": 30.783782958984375,\n \"blob_id\": \"968aca5c7f9d9309d10f30d1e7652427ea8ac9dd\",\n \"content_id\": \"427184333ed8048f2be9f0675506c2cc0dca3ca4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1175,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 37,\n \"path\": \"/rpsMAIN.py\",\n \"repo_name\": \"Paluthyst/rpsgame\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from random import randint\\nt = [\\\"Rock\\\", \\\"Paper\\\", \\\"Scissors\\\"]\\ncomputer = t[randint(0, 2)]\\nplayer = False\\nwhile player == False:\\n\\n player = input(\\\"Choose Rock, Paper or Scissors: \\\")\\n\\n if player == computer:\\n print(\\\"You tied with the computer :/\\\")\\n elif player == \\\"Rock\\\".lower():\\n if computer == \\\"Paper\\\":\\n print(\\\"You loose\\\", computer, \\\"beats\\\", player)\\n else:\\n print(\\\"YOU WIN :)\\\", computer, \\\"looses to\\\", player)\\n\\n elif player == \\\"Paper\\\".lower():\\n if computer == \\\"Scissors\\\":\\n print(\\\"You loose\\\", computer, \\\"beats\\\", player)\\n else:\\n print(\\\"YOU WIN :)\\\", computer, \\\"looses to\\\", player)\\n\\n elif player == \\\"Scissors\\\".lower():\\n if computer == \\\"Rock\\\":\\n print(\\\"You loose\\\", computer, \\\"beats\\\", player)\\n else:\\n print(\\\"YOU WIN :)\\\", computer, \\\"looses to\\\", player)\\n else:\\n print(\\\"ah ah ah that's not an option friend\\\")\\n player = input(\\\"Would you like to play again? Yes or No: \\\")\\n if player == \\\"Yes\\\":\\n print(\\\"OK here we go!\\\")\\n computer = t[randint(0, 2)]\\n player = False\\n else:\\n print(\\\"Ok see ya\\\")\\n player == True\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2975797,"cells":{"repo_name":{"kind":"string","value":"hooss-only/sakeL-comment-analysis"},"repo_url":{"kind":"string","value":"https://github.com/hooss-only/sakeL-comment-analysis"},"snapshot_id":{"kind":"string","value":"050bf2a06030645def2e4af656c253adaf6046ad"},"revision_id":{"kind":"string","value":"18cadbccf7155e6f8475fd9e8ffa51cc7ba961da"},"directory_id":{"kind":"string","value":"d89e5e5d77c10107b2eab5f5a1645d9a67baa5f4"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-02-19T22:33:30.881192","string":"2023-02-19T22:33:30.881192"},"revision_date":{"kind":"timestamp","value":"2021-01-24T11:46:41","string":"2021-01-24T11:46:41"},"committer_date":{"kind":"timestamp","value":"2021-01-24T11:46:41","string":"2021-01-24T11:46:41"},"github_id":{"kind":"number","value":332168992,"string":"332,168,992"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.557655930519104,"alphanum_fraction":0.5746691823005676,"avg_line_length":25.450000762939453,"blob_id":"0f5ed26c6a3969111987a2d0246edd6cd0509452","content_id":"b437c3778136d539a5630856a7392c4cf71f156d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":565,"license_type":"no_license","max_line_length":80,"num_lines":20,"path":"/main.py","repo_name":"hooss-only/sakeL-comment-analysis","src_encoding":"UTF-8","text":"import json\n\ncomments = {}\n\nwith open(\"coments.json\", \"r\", encoding='UTF8') as json_file:\n comments = json.load(json_file)\n\nb = 0\nfor i in range(30001):\n\n if comments[str(i)]['comment'].startswith(\"sake L\"):\n # print(f\"{comments[str(i)]['author']} : {comments[str(i)]['comment']}\")\n b = b+1\n\n elif comments[str(i)]['comment'].startswith(\"Sake L\"):\n # print(f\"{comments[str(i)]['author']} : {comments[str(i)]['comment']}\")\n b = b+1\n\n\nprint(f\"{i}개의 댓글 중 {b}개의 sake L / Sake L 로 시작하는 찬양 글 발견됨\")\n"},{"alpha_fraction":0.6929824352264404,"alphanum_fraction":0.7368420958518982,"avg_line_length":37,"blob_id":"a10e8871d93b0bc8bd4d3986f517c96bb4f52479","content_id":"f713ceb4978edd69ecfa45bd1f13350b47057de7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":172,"license_type":"no_license","max_line_length":96,"num_lines":3,"path":"/README.md","repo_name":"hooss-only/sakeL-comment-analysis","src_encoding":"UTF-8","text":"# What is this?\n\nsake L에 있는 [댓글 정보](https://github.com/jinpyojoo/sakeL-archive) 약 30002개를 분석해서 나온 결과를 출력해주는 코드입니다\n"}],"string":"[\n {\n \"alpha_fraction\": 0.557655930519104,\n \"alphanum_fraction\": 0.5746691823005676,\n \"avg_line_length\": 25.450000762939453,\n \"blob_id\": \"0f5ed26c6a3969111987a2d0246edd6cd0509452\",\n \"content_id\": \"b437c3778136d539a5630856a7392c4cf71f156d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 565,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 20,\n \"path\": \"/main.py\",\n \"repo_name\": \"hooss-only/sakeL-comment-analysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\n\\ncomments = {}\\n\\nwith open(\\\"coments.json\\\", \\\"r\\\", encoding='UTF8') as json_file:\\n comments = json.load(json_file)\\n\\nb = 0\\nfor i in range(30001):\\n\\n if comments[str(i)]['comment'].startswith(\\\"sake L\\\"):\\n # print(f\\\"{comments[str(i)]['author']} : {comments[str(i)]['comment']}\\\")\\n b = b+1\\n\\n elif comments[str(i)]['comment'].startswith(\\\"Sake L\\\"):\\n # print(f\\\"{comments[str(i)]['author']} : {comments[str(i)]['comment']}\\\")\\n b = b+1\\n\\n\\nprint(f\\\"{i}개의 댓글 중 {b}개의 sake L / Sake L 로 시작하는 찬양 글 발견됨\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6929824352264404,\n \"alphanum_fraction\": 0.7368420958518982,\n \"avg_line_length\": 37,\n \"blob_id\": \"a10e8871d93b0bc8bd4d3986f517c96bb4f52479\",\n \"content_id\": \"f713ceb4978edd69ecfa45bd1f13350b47057de7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 172,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"hooss-only/sakeL-comment-analysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# What is this?\\n\\nsake L에 있는 [댓글 정보](https://github.com/jinpyojoo/sakeL-archive) 약 30002개를 분석해서 나온 결과를 출력해주는 코드입니다\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2975798,"cells":{"repo_name":{"kind":"string","value":"vk-98/quora_clone"},"repo_url":{"kind":"string","value":"https://github.com/vk-98/quora_clone"},"snapshot_id":{"kind":"string","value":"6ac77ea344548f96653d517e20e06c01cf079618"},"revision_id":{"kind":"string","value":"d0ff553320ff0ef2d2a7d968301e638ff3e00d78"},"directory_id":{"kind":"string","value":"ff6e2ae0bc90861492d17e76ee11677fc21d3cb5"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-12-20T21:13:44.470285","string":"2020-12-20T21:13:44.470285"},"revision_date":{"kind":"timestamp","value":"2020-03-17T19:17:26","string":"2020-03-17T19:17:26"},"committer_date":{"kind":"timestamp","value":"2020-03-17T19:17:26","string":"2020-03-17T19:17:26"},"github_id":{"kind":"number","value":236208427,"string":"236,208,427"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5599021911621094,"alphanum_fraction":0.6063569784164429,"avg_line_length":20.526315689086914,"blob_id":"86f578b7a2cfa06d54aaf0d25b9a8b4f3dbc8a5b","content_id":"4ebc0907be96d8235befcc840e73ca52549127d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":409,"license_type":"no_license","max_line_length":60,"num_lines":19,"path":"/users/migrations/0003_profile_about.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# Generated by Django 3.0.2 on 2020-02-21 14:56\n\nimport ckeditor.fields\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('users', '0002_remove_profile_about'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='profile',\n name='about',\n field=ckeditor.fields.RichTextField(blank=True),\n ),\n ]\n"},{"alpha_fraction":0.4909638464450836,"alphanum_fraction":0.5843373537063599,"avg_line_length":18.52941131591797,"blob_id":"b36740c561a218f1231958f7987431f944730409","content_id":"ce1733b850e3644d4eebbfb8728b86f3ecb07213","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":332,"license_type":"no_license","max_line_length":49,"num_lines":17,"path":"/questions/migrations/0005_remove_thread_question.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# Generated by Django 3.0.2 on 2020-02-21 09:21\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('questions', '0004_auto_20200221_0748'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='thread',\n name='question',\n ),\n ]\n"},{"alpha_fraction":0.6790637969970703,"alphanum_fraction":0.6813080906867981,"avg_line_length":29.58823585510254,"blob_id":"930fe959ce817e848ccdb2f74b190381f661edca","content_id":"5fb9d37e11990e499d196ab5cb157945eca924fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3119,"license_type":"no_license","max_line_length":89,"num_lines":102,"path":"/questions/views.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.urls import reverse\nfrom .models import Question, Answer, Thread, Tag\nfrom .forms import NewQuestionForm\nfrom django.views.generic import ListView, DetailView, CreateView, UpdateView, DeleteView\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\nfrom django.contrib.auth.decorators import login_required\nfrom django import forms\n\n\n# Create your views here.\ndef home(request):\n questions = Question.objects.all()\n context = {\n 'questions' : questions,\n }\n return render(request, 'questions/home.html', context)\n\n\nclass QuestionCreationView(LoginRequiredMixin, CreateView):\n model = Question\n fields = ['question_text']\n\n class Meta:\n widgets = { 'question_text': forms.TextInput()}\n \n def form_valid(self, form, *args, **kwargs):\n form.instance.author = self.request.user\n return super().form_valid(form)\n\nclass QuestionDetailView(DetailView):\n model = Question\n\n\nclass QuestionUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\n model = Question\n fields = ['question_text']\n\n def form_valid(self, form):\n form.instance.author = self.request.user\n return super().form_valid(form)\n \n def test_func(self):\n question = self.get_object()\n return self.request.user == question.author\n\n \nclass QuestionDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = Question\n success_url = '/'\n\n def test_func(self):\n question = self.get_object()\n return self.request.user == question.author\n \n\nclass AnswerCreationView(LoginRequiredMixin, CreateView):\n model = Answer\n fields = ['answer_text']\n class Meta:\n widgets = { 'answer_text': forms.TextInput()}\n \n def form_valid(self, form, *args, **kwargs):\n form.instance.author = self.request.user\n form.instance.question_id = self.kwargs['pk']\n return super().form_valid(form)\n\n\nclass AnswerUpdateView(LoginRequiredMixin,UserPassesTestMixin,UpdateView):\n model = Answer\n fields = ['answer_text']\n pk_url_kwarg = \"pk1\"\n\n def form_valid(self, form, *args, **kwargs):\n form.instance.author = self.request.user\n form.instance.question_id = self.kwargs['pk']\n return super().form_valid(form)\n\n def test_func(self):\n answer = self.get_object()\n return self.request.user == answer.author\n\nclass AnswerDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\n model = Answer\n success_url = '/'\n pk_url_kwarg = \"pk1\"\n\n def test_func(self):\n answer = self.get_object()\n return self.request.user == answer.author\n\n\n \n@login_required\ndef vote(request, pk, pk1):\n\n answer = Answer.objects.get(pk = pk1)\n if Thread.objects.filter(answer = answer, userUpVoted = request.user):\n Thread.objects.filter(answer = answer, userUpVoted = request.user).delete()\n else :\n Thread.objects.create(answer = answer, userUpVoted = request.user)\n return redirect(f'/question/{pk}/')"},{"alpha_fraction":0.7142857313156128,"alphanum_fraction":0.7142857313156128,"avg_line_length":13,"blob_id":"f8144492f4ef15918f89619029b9a497a4c4294b","content_id":"f49767c68d2b604b28b6374814d08af23555a1e5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":14,"license_type":"no_license","max_line_length":13,"num_lines":1,"path":"/README.md","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# quora clone\n"},{"alpha_fraction":0.5893442630767822,"alphanum_fraction":0.6024590134620667,"avg_line_length":20.803571701049805,"blob_id":"da11514fe37ce34fe85076bebb07ae7a6cf1e59c","content_id":"471d7ef8ab226bd7dd375ab8815ca6729c6e574c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":1220,"license_type":"no_license","max_line_length":80,"num_lines":56,"path":"/quora-clone-frontend/src/Ques.js","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"import React from \"react\"\n\n\nimport CssBaseline from '@material-ui/core/CssBaseline';\nimport Typography from '@material-ui/core/Typography';\nimport Container from '@material-ui/core/Container';\nimport { makeStyles } from '@material-ui/core/styles';\nimport TextField from '@material-ui/core/TextField';\n\n\nimport Button from '@material-ui/core/Button';\n\n\n\n\nconst useStyles = makeStyles(theme => ({\n container: {\n display: 'flex',\n flexWrap: 'wrap',\n },\n textField: {\n marginLeft: theme.spacing(1),\n marginRight: theme.spacing(1),\n width: 2000,\n },\n root: {\n '& > *': {\n margin: theme.spacing(1),\n width: 200,\n },\n },\n }));\n \n\nfunction Ques() {\n const classes = useStyles();\n return (\n\n \n <React.Fragment>\n <CssBaseline />\n <Container maxWidth=\"600px\" maxHeight=\"300px\">\n \n <form className={classes.root} noValidate autoComplete=\"off\">\n <TextField id=\"outlined-basic\" label=\"Ask Question\" variant=\"outlined\" />\n <Button variant=\"contained\" color=\"primary\" disableElevation>\n Submit\n </Button>\n \n </form>\n </Container>\n </React.Fragment>\n )\n}\n\nexport default Ques"},{"alpha_fraction":0.5356200337409973,"alphanum_fraction":0.5857519507408142,"avg_line_length":20.05555534362793,"blob_id":"4c63ef4e78ef5393170dffc63a0423da4c66d195","content_id":"4409e811298812d3b3c21be6f673cb30f3d82243","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":379,"license_type":"no_license","max_line_length":53,"num_lines":18,"path":"/questions/migrations/0006_auto_20200221_0944.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# Generated by Django 3.0.2 on 2020-02-21 09:44\n\nfrom django.db import migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('questions', '0005_remove_thread_question'),\n ]\n\n operations = [\n migrations.RenameField(\n model_name='thread',\n old_name='userUpvoted',\n new_name='userUpVoted',\n ),\n ]\n"},{"alpha_fraction":0.6896316409111023,"alphanum_fraction":0.6909959316253662,"avg_line_length":33.11627960205078,"blob_id":"86bf1cbbc8f5c705419dfb35f959c94aa7c21654","content_id":"682a23efcfb12db1a89d618f5d580b88b526d239","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1466,"license_type":"no_license","max_line_length":87,"num_lines":43,"path":"/questions/models.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom ckeditor.fields import RichTextField\nfrom django.urls import reverse\nfrom django.utils import timezone\n\n# Create your models here.\nclass Question(models.Model):\n question_text = RichTextField()\n author = models.ForeignKey( User, on_delete = models.CASCADE )\n date_posted = models.DateTimeField( default = timezone.now )\n\n def __str__(self):\n return self.question_text\n \n def get_absolute_url(self):\n return reverse('question_detail', kwargs = {'pk': self.pk})\n\nclass Answer(models.Model):\n answer_text = RichTextField()\n author = models.ForeignKey(User, on_delete = models.CASCADE, related_name='author')\n question = models.ForeignKey(Question, on_delete = models.CASCADE)\n date_posted = models.DateTimeField( default = timezone.now )\n\n def __str__(self):\n return self.answer_text\n \n def get_absolute_url(self):\n return reverse('question_detail', kwargs = {'pk': self.question.id})\n\nclass Tag(models.Model):\n question = models.ForeignKey( Question, on_delete = models.CASCADE )\n tag_name = models.CharField(max_length = 50)\n\n def __str__(self):\n return self.tag_name\n\nclass Thread(models.Model):\n answer = models.ForeignKey(Answer, on_delete = models.CASCADE )\n userUpVoted = models.ForeignKey( User, on_delete = models.CASCADE )\n\n def __str__(self):\n return f'Thread for {self.answer.id}'"},{"alpha_fraction":0.5888077616691589,"alphanum_fraction":0.6088807582855225,"avg_line_length":35.53333282470703,"blob_id":"69bdf825bb707c7d6a2a1de55cc346ae84aa5e25","content_id":"b7f39651c76093df6aee5393742ea289e52141ee","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1644,"license_type":"no_license","max_line_length":146,"num_lines":45,"path":"/questions/migrations/0003_auto_20200221_0731.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# Generated by Django 3.0.2 on 2020-02-21 07:31\n\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\nimport django.utils.timezone\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('questions', '0002_auto_20200206_1941'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='answer',\n name='date_posted',\n field=models.DateTimeField(default=django.utils.timezone.now),\n ),\n migrations.AddField(\n model_name='answer',\n name='upVoters',\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, related_name='upVoters', to=settings.AUTH_USER_MODEL),\n ),\n migrations.AddField(\n model_name='question',\n name='date_posted',\n field=models.DateTimeField(default=django.utils.timezone.now),\n ),\n migrations.AlterField(\n model_name='answer',\n name='author',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='author', to=settings.AUTH_USER_MODEL),\n ),\n migrations.CreateModel(\n name='Tag',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('tag_name', models.CharField(max_length=50)),\n ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Question')),\n ],\n ),\n ]\n"},{"alpha_fraction":0.6636670231819153,"alphanum_fraction":0.6704161763191223,"avg_line_length":27.70967674255371,"blob_id":"dd74a8369fd4896a9a6e34638a18f9c6094d9377","content_id":"5a0cf30dcd0f1cc1392a60be01d023310f2603b5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":889,"license_type":"no_license","max_line_length":78,"num_lines":31,"path":"/users/views.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom .forms import UserRegisterForm\nfrom django.contrib import messages\nfrom django.contrib.auth.models import User\n\n# Create your views here.\ndef register(request):\n\n if request.method == 'POST':\n form = UserRegisterForm(request.POST)\n if form.is_valid():\n form.save()\n username = form.cleaned_data.get('username')\n messages.success(request, f'{username} Registered successfully')\n return redirect('home')\n\n else:\n form = UserRegisterForm()\n\n context = {\n 'form': form\n }\n return render(request, 'users/register.html', context)\n\n\n\ndef DisplayProfile(request, uname):\n\n userProfile = get_object_or_404(User, username = uname)\n print(userProfile.email)\n return render(request, 'users/profile.html', {'userProfile': userProfile})"},{"alpha_fraction":0.5958254337310791,"alphanum_fraction":0.6252371668815613,"avg_line_length":35.344825744628906,"blob_id":"837bcf9a8288a6d1b3f9624c14fb9bfa4ee026cb","content_id":"c2fbeef8fd9e380b4172e03987e68d7c628b6c33","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1054,"license_type":"no_license","max_line_length":125,"num_lines":29,"path":"/questions/migrations/0004_auto_20200221_0748.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"# Generated by Django 3.0.2 on 2020-02-21 07:48\n\nfrom django.conf import settings\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('questions', '0003_auto_20200221_0731'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='answer',\n name='upVoters',\n ),\n migrations.CreateModel(\n name='Thread',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('answer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Answer')),\n ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Question')),\n ('userUpvoted', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.6769759654998779,"alphanum_fraction":0.6804123520851135,"avg_line_length":61.42856979370117,"blob_id":"e04ae867e7ae0f4b1b8eb2245df7b623839bf26c","content_id":"11700ce5688dbdf06ccafe48b36c889bdaad7224","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":873,"license_type":"no_license","max_line_length":112,"num_lines":14,"path":"/questions/urls.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.home, name = 'home'),\n path('question/<int:pk>/', views.QuestionDetailView.as_view(), name = 'question_detail'),\n path('create/', views.QuestionCreationView.as_view(), name='create'),\n path('question/<int:pk>/answer', views.AnswerCreationView.as_view(), name = 'answer'),\n path('question/<int:pk>/update', views.QuestionUpdateView.as_view(), name = 'question_update'),\n path('question/<int:pk>/delete', views.QuestionDeleteView.as_view(), name = 'question_delete'),\n path('question/<int:pk>/answer/<int:pk1>/update', views.AnswerUpdateView.as_view(), name = 'answer_update'),\n path('question/<int:pk>/answer/<int:pk1>/delete', views.AnswerDeleteView.as_view(), name = 'answer_delete'),\n path('question/<int:pk>/answer/<int:pk1>/vote', views.vote, name = 'vote'),\n]"},{"alpha_fraction":0.6217765212059021,"alphanum_fraction":0.6217765212059021,"avg_line_length":25.923076629638672,"blob_id":"72a9277db36b5f2d91c878a684d1db2088d8820c","content_id":"5a0560ca3c581b838652c4f87da85d5722dc4c1f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":349,"license_type":"no_license","max_line_length":62,"num_lines":13,"path":"/questions/forms.py","repo_name":"vk-98/quora_clone","src_encoding":"UTF-8","text":"from django import forms\nfrom .models import Question, Answer\n\nclass NewQuestionForm(forms.ModelForm):\n\n class Meta:\n model = Question\n fields = ['question_text']\n widgets = { 'question_text': forms.TextInput()\n }\n\n def __init__(self, *args, **kwargs):\n super(NewQuestionForm, self).__init__(*args, **kwargs)"}],"string":"[\n {\n \"alpha_fraction\": 0.5599021911621094,\n \"alphanum_fraction\": 0.6063569784164429,\n \"avg_line_length\": 20.526315689086914,\n \"blob_id\": \"86f578b7a2cfa06d54aaf0d25b9a8b4f3dbc8a5b\",\n \"content_id\": \"4ebc0907be96d8235befcc840e73ca52549127d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 409,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 19,\n \"path\": \"/users/migrations/0003_profile_about.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 3.0.2 on 2020-02-21 14:56\\n\\nimport ckeditor.fields\\nfrom django.db import migrations\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('users', '0002_remove_profile_about'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='profile',\\n name='about',\\n field=ckeditor.fields.RichTextField(blank=True),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.4909638464450836,\n \"alphanum_fraction\": 0.5843373537063599,\n \"avg_line_length\": 18.52941131591797,\n \"blob_id\": \"b36740c561a218f1231958f7987431f944730409\",\n \"content_id\": \"ce1733b850e3644d4eebbfb8728b86f3ecb07213\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 332,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 17,\n \"path\": \"/questions/migrations/0005_remove_thread_question.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 3.0.2 on 2020-02-21 09:21\\n\\nfrom django.db import migrations\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('questions', '0004_auto_20200221_0748'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='thread',\\n name='question',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6790637969970703,\n \"alphanum_fraction\": 0.6813080906867981,\n \"avg_line_length\": 29.58823585510254,\n \"blob_id\": \"930fe959ce817e848ccdb2f74b190381f661edca\",\n \"content_id\": \"5fb9d37e11990e499d196ab5cb157945eca924fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3119,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 102,\n \"path\": \"/questions/views.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render, redirect, get_object_or_404\\nfrom django.urls import reverse\\nfrom .models import Question, Answer, Thread, Tag\\nfrom .forms import NewQuestionForm\\nfrom django.views.generic import ListView, DetailView, CreateView, UpdateView, DeleteView\\nfrom django.contrib.auth.mixins import LoginRequiredMixin, UserPassesTestMixin\\nfrom django.contrib.auth.decorators import login_required\\nfrom django import forms\\n\\n\\n# Create your views here.\\ndef home(request):\\n questions = Question.objects.all()\\n context = {\\n 'questions' : questions,\\n }\\n return render(request, 'questions/home.html', context)\\n\\n\\nclass QuestionCreationView(LoginRequiredMixin, CreateView):\\n model = Question\\n fields = ['question_text']\\n\\n class Meta:\\n widgets = { 'question_text': forms.TextInput()}\\n \\n def form_valid(self, form, *args, **kwargs):\\n form.instance.author = self.request.user\\n return super().form_valid(form)\\n\\nclass QuestionDetailView(DetailView):\\n model = Question\\n\\n\\nclass QuestionUpdateView(LoginRequiredMixin, UserPassesTestMixin, UpdateView):\\n model = Question\\n fields = ['question_text']\\n\\n def form_valid(self, form):\\n form.instance.author = self.request.user\\n return super().form_valid(form)\\n \\n def test_func(self):\\n question = self.get_object()\\n return self.request.user == question.author\\n\\n \\nclass QuestionDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\\n model = Question\\n success_url = '/'\\n\\n def test_func(self):\\n question = self.get_object()\\n return self.request.user == question.author\\n \\n\\nclass AnswerCreationView(LoginRequiredMixin, CreateView):\\n model = Answer\\n fields = ['answer_text']\\n class Meta:\\n widgets = { 'answer_text': forms.TextInput()}\\n \\n def form_valid(self, form, *args, **kwargs):\\n form.instance.author = self.request.user\\n form.instance.question_id = self.kwargs['pk']\\n return super().form_valid(form)\\n\\n\\nclass AnswerUpdateView(LoginRequiredMixin,UserPassesTestMixin,UpdateView):\\n model = Answer\\n fields = ['answer_text']\\n pk_url_kwarg = \\\"pk1\\\"\\n\\n def form_valid(self, form, *args, **kwargs):\\n form.instance.author = self.request.user\\n form.instance.question_id = self.kwargs['pk']\\n return super().form_valid(form)\\n\\n def test_func(self):\\n answer = self.get_object()\\n return self.request.user == answer.author\\n\\nclass AnswerDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):\\n model = Answer\\n success_url = '/'\\n pk_url_kwarg = \\\"pk1\\\"\\n\\n def test_func(self):\\n answer = self.get_object()\\n return self.request.user == answer.author\\n\\n\\n \\n@login_required\\ndef vote(request, pk, pk1):\\n\\n answer = Answer.objects.get(pk = pk1)\\n if Thread.objects.filter(answer = answer, userUpVoted = request.user):\\n Thread.objects.filter(answer = answer, userUpVoted = request.user).delete()\\n else :\\n Thread.objects.create(answer = answer, userUpVoted = request.user)\\n return redirect(f'/question/{pk}/')\"\n },\n {\n \"alpha_fraction\": 0.7142857313156128,\n \"alphanum_fraction\": 0.7142857313156128,\n \"avg_line_length\": 13,\n \"blob_id\": \"f8144492f4ef15918f89619029b9a497a4c4294b\",\n \"content_id\": \"f49767c68d2b604b28b6374814d08af23555a1e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 14,\n \"license_type\": \"no_license\",\n \"max_line_length\": 13,\n \"num_lines\": 1,\n \"path\": \"/README.md\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# quora clone\\n\"\n },\n {\n \"alpha_fraction\": 0.5893442630767822,\n \"alphanum_fraction\": 0.6024590134620667,\n \"avg_line_length\": 20.803571701049805,\n \"blob_id\": \"da11514fe37ce34fe85076bebb07ae7a6cf1e59c\",\n \"content_id\": \"471d7ef8ab226bd7dd375ab8815ca6729c6e574c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 1220,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 56,\n \"path\": \"/quora-clone-frontend/src/Ques.js\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import React from \\\"react\\\"\\n\\n\\nimport CssBaseline from '@material-ui/core/CssBaseline';\\nimport Typography from '@material-ui/core/Typography';\\nimport Container from '@material-ui/core/Container';\\nimport { makeStyles } from '@material-ui/core/styles';\\nimport TextField from '@material-ui/core/TextField';\\n\\n\\nimport Button from '@material-ui/core/Button';\\n\\n\\n\\n\\nconst useStyles = makeStyles(theme => ({\\n container: {\\n display: 'flex',\\n flexWrap: 'wrap',\\n },\\n textField: {\\n marginLeft: theme.spacing(1),\\n marginRight: theme.spacing(1),\\n width: 2000,\\n },\\n root: {\\n '& > *': {\\n margin: theme.spacing(1),\\n width: 200,\\n },\\n },\\n }));\\n \\n\\nfunction Ques() {\\n const classes = useStyles();\\n return (\\n\\n \\n <React.Fragment>\\n <CssBaseline />\\n <Container maxWidth=\\\"600px\\\" maxHeight=\\\"300px\\\">\\n \\n <form className={classes.root} noValidate autoComplete=\\\"off\\\">\\n <TextField id=\\\"outlined-basic\\\" label=\\\"Ask Question\\\" variant=\\\"outlined\\\" />\\n <Button variant=\\\"contained\\\" color=\\\"primary\\\" disableElevation>\\n Submit\\n </Button>\\n \\n </form>\\n </Container>\\n </React.Fragment>\\n )\\n}\\n\\nexport default Ques\"\n },\n {\n \"alpha_fraction\": 0.5356200337409973,\n \"alphanum_fraction\": 0.5857519507408142,\n \"avg_line_length\": 20.05555534362793,\n \"blob_id\": \"4c63ef4e78ef5393170dffc63a0423da4c66d195\",\n \"content_id\": \"4409e811298812d3b3c21be6f673cb30f3d82243\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 379,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 18,\n \"path\": \"/questions/migrations/0006_auto_20200221_0944.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 3.0.2 on 2020-02-21 09:44\\n\\nfrom django.db import migrations\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('questions', '0005_remove_thread_question'),\\n ]\\n\\n operations = [\\n migrations.RenameField(\\n model_name='thread',\\n old_name='userUpvoted',\\n new_name='userUpVoted',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6896316409111023,\n \"alphanum_fraction\": 0.6909959316253662,\n \"avg_line_length\": 33.11627960205078,\n \"blob_id\": \"86bf1cbbc8f5c705419dfb35f959c94aa7c21654\",\n \"content_id\": \"682a23efcfb12db1a89d618f5d580b88b526d239\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1466,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 43,\n \"path\": \"/questions/models.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\nfrom django.contrib.auth.models import User\\nfrom ckeditor.fields import RichTextField\\nfrom django.urls import reverse\\nfrom django.utils import timezone\\n\\n# Create your models here.\\nclass Question(models.Model):\\n question_text = RichTextField()\\n author = models.ForeignKey( User, on_delete = models.CASCADE )\\n date_posted = models.DateTimeField( default = timezone.now )\\n\\n def __str__(self):\\n return self.question_text\\n \\n def get_absolute_url(self):\\n return reverse('question_detail', kwargs = {'pk': self.pk})\\n\\nclass Answer(models.Model):\\n answer_text = RichTextField()\\n author = models.ForeignKey(User, on_delete = models.CASCADE, related_name='author')\\n question = models.ForeignKey(Question, on_delete = models.CASCADE)\\n date_posted = models.DateTimeField( default = timezone.now )\\n\\n def __str__(self):\\n return self.answer_text\\n \\n def get_absolute_url(self):\\n return reverse('question_detail', kwargs = {'pk': self.question.id})\\n\\nclass Tag(models.Model):\\n question = models.ForeignKey( Question, on_delete = models.CASCADE )\\n tag_name = models.CharField(max_length = 50)\\n\\n def __str__(self):\\n return self.tag_name\\n\\nclass Thread(models.Model):\\n answer = models.ForeignKey(Answer, on_delete = models.CASCADE )\\n userUpVoted = models.ForeignKey( User, on_delete = models.CASCADE )\\n\\n def __str__(self):\\n return f'Thread for {self.answer.id}'\"\n },\n {\n \"alpha_fraction\": 0.5888077616691589,\n \"alphanum_fraction\": 0.6088807582855225,\n \"avg_line_length\": 35.53333282470703,\n \"blob_id\": \"69bdf825bb707c7d6a2a1de55cc346ae84aa5e25\",\n \"content_id\": \"b7f39651c76093df6aee5393742ea289e52141ee\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1644,\n \"license_type\": \"no_license\",\n \"max_line_length\": 146,\n \"num_lines\": 45,\n \"path\": \"/questions/migrations/0003_auto_20200221_0731.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 3.0.2 on 2020-02-21 07:31\\n\\nfrom django.conf import settings\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\nimport django.utils.timezone\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\\n ('questions', '0002_auto_20200206_1941'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='answer',\\n name='date_posted',\\n field=models.DateTimeField(default=django.utils.timezone.now),\\n ),\\n migrations.AddField(\\n model_name='answer',\\n name='upVoters',\\n field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, related_name='upVoters', to=settings.AUTH_USER_MODEL),\\n ),\\n migrations.AddField(\\n model_name='question',\\n name='date_posted',\\n field=models.DateTimeField(default=django.utils.timezone.now),\\n ),\\n migrations.AlterField(\\n model_name='answer',\\n name='author',\\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='author', to=settings.AUTH_USER_MODEL),\\n ),\\n migrations.CreateModel(\\n name='Tag',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('tag_name', models.CharField(max_length=50)),\\n ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Question')),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6636670231819153,\n \"alphanum_fraction\": 0.6704161763191223,\n \"avg_line_length\": 27.70967674255371,\n \"blob_id\": \"dd74a8369fd4896a9a6e34638a18f9c6094d9377\",\n \"content_id\": \"5a0cf30dcd0f1cc1392a60be01d023310f2603b5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 889,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 31,\n \"path\": \"/users/views.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render, redirect, get_object_or_404\\nfrom .forms import UserRegisterForm\\nfrom django.contrib import messages\\nfrom django.contrib.auth.models import User\\n\\n# Create your views here.\\ndef register(request):\\n\\n if request.method == 'POST':\\n form = UserRegisterForm(request.POST)\\n if form.is_valid():\\n form.save()\\n username = form.cleaned_data.get('username')\\n messages.success(request, f'{username} Registered successfully')\\n return redirect('home')\\n\\n else:\\n form = UserRegisterForm()\\n\\n context = {\\n 'form': form\\n }\\n return render(request, 'users/register.html', context)\\n\\n\\n\\ndef DisplayProfile(request, uname):\\n\\n userProfile = get_object_or_404(User, username = uname)\\n print(userProfile.email)\\n return render(request, 'users/profile.html', {'userProfile': userProfile})\"\n },\n {\n \"alpha_fraction\": 0.5958254337310791,\n \"alphanum_fraction\": 0.6252371668815613,\n \"avg_line_length\": 35.344825744628906,\n \"blob_id\": \"837bcf9a8288a6d1b3f9624c14fb9bfa4ee026cb\",\n \"content_id\": \"c2fbeef8fd9e380b4172e03987e68d7c628b6c33\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1054,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 29,\n \"path\": \"/questions/migrations/0004_auto_20200221_0748.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 3.0.2 on 2020-02-21 07:48\\n\\nfrom django.conf import settings\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\\n ('questions', '0003_auto_20200221_0731'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='answer',\\n name='upVoters',\\n ),\\n migrations.CreateModel(\\n name='Thread',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('answer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Answer')),\\n ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='questions.Question')),\\n ('userUpvoted', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6769759654998779,\n \"alphanum_fraction\": 0.6804123520851135,\n \"avg_line_length\": 61.42856979370117,\n \"blob_id\": \"e04ae867e7ae0f4b1b8eb2245df7b623839bf26c\",\n \"content_id\": \"11700ce5688dbdf06ccafe48b36c889bdaad7224\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 873,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 14,\n \"path\": \"/questions/urls.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.urls import path\\nfrom . import views\\n\\nurlpatterns = [\\n path('', views.home, name = 'home'),\\n path('question/<int:pk>/', views.QuestionDetailView.as_view(), name = 'question_detail'),\\n path('create/', views.QuestionCreationView.as_view(), name='create'),\\n path('question/<int:pk>/answer', views.AnswerCreationView.as_view(), name = 'answer'),\\n path('question/<int:pk>/update', views.QuestionUpdateView.as_view(), name = 'question_update'),\\n path('question/<int:pk>/delete', views.QuestionDeleteView.as_view(), name = 'question_delete'),\\n path('question/<int:pk>/answer/<int:pk1>/update', views.AnswerUpdateView.as_view(), name = 'answer_update'),\\n path('question/<int:pk>/answer/<int:pk1>/delete', views.AnswerDeleteView.as_view(), name = 'answer_delete'),\\n path('question/<int:pk>/answer/<int:pk1>/vote', views.vote, name = 'vote'),\\n]\"\n },\n {\n \"alpha_fraction\": 0.6217765212059021,\n \"alphanum_fraction\": 0.6217765212059021,\n \"avg_line_length\": 25.923076629638672,\n \"blob_id\": \"72a9277db36b5f2d91c878a684d1db2088d8820c\",\n \"content_id\": \"5a0560ca3c581b838652c4f87da85d5722dc4c1f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 349,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 13,\n \"path\": \"/questions/forms.py\",\n \"repo_name\": \"vk-98/quora_clone\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django import forms\\nfrom .models import Question, Answer\\n\\nclass NewQuestionForm(forms.ModelForm):\\n\\n class Meta:\\n model = Question\\n fields = ['question_text']\\n widgets = { 'question_text': forms.TextInput()\\n }\\n\\n def __init__(self, *args, **kwargs):\\n super(NewQuestionForm, self).__init__(*args, **kwargs)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":12,"string":"12"}}},{"rowIdx":2975799,"cells":{"repo_name":{"kind":"string","value":"bsosis/rnn-fxpts"},"repo_url":{"kind":"string","value":"https://github.com/bsosis/rnn-fxpts"},"snapshot_id":{"kind":"string","value":"d030e5ec4306240f333013b9ae5507b299d35be4"},"revision_id":{"kind":"string","value":"6b40acae175af7227cc2174fb2f59b8b9c234b8d"},"directory_id":{"kind":"string","value":"60f79cfbf650b60f09c5ef4ff248a06fa41784f6"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-01T16:41:54.375373","string":"2021-01-01T16:41:54.375373"},"revision_date":{"kind":"timestamp","value":"2018-02-21T03:13:15","string":"2018-02-21T03:13:15"},"committer_date":{"kind":"timestamp","value":"2018-02-21T03:13:15","string":"2018-02-21T03:13:15"},"github_id":{"kind":"number","value":97892000,"string":"97,892,000"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2017-07-21T01:19:10","string":"2017-07-21T01:19:10"},"gha_updated_at":{"kind":"timestamp","value":"2016-12-09T18:38:17","string":"2016-12-09T18:38:17"},"gha_pushed_at":{"kind":"timestamp","value":"2017-07-16T15:12:11","string":"2017-07-16T15:12:11"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5294630527496338,"alphanum_fraction":0.5418685674667358,"avg_line_length":31.44968605041504,"blob_id":"8c682be7706b8b26c9cf006e6854d04b455e2bb3","content_id":"bc9d28563d35fc6f11d9faa4a52b4cf33261f9fe","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10318,"license_type":"permissive","max_line_length":157,"num_lines":318,"path":"/cont_hopnet_customizable.py","repo_name":"bsosis/rnn-fxpts","src_encoding":"UTF-8","text":"import numpy as np\nimport sys\n\n\n\ndef _update_sync(W, gain, a):\n \"\"\"Update the current activation\"\"\"\n\n return np.tanh(gain*np.dot(W, a))\n\ndef _energy_sync(W, gain, cur_act, prev_act):\n \"\"\"\n Uses eq. 3.4 in Soulie et al. 1989\n But modifies eq. 3.2 to include gain, and adds gain term to first sum in eq. 3.4 (equivalent to just multiplying the weights by the gain before use).\n If prev_act is None, tries to invert W to find the energy.\n \"\"\"\n\n if prev_act is None:\n W_inv = np.linalg.inv(W)\n # Test that W_inv can actually successfully invert W\n if not np.allclose(np.eye(W.shape[0]), np.matmul(W, W_inv), rtol=0, atol=1e-7):\n return float('nan') # TODO: pseudoinverse?\n prev_act = np.dot(W_inv, np.arctanh(cur_act)/gain)\n\n\n cur_field = gain*np.dot(W,cur_act)\n prev_field = gain*np.dot(W,prev_act)\n\n # summation = prev_act^T * W * cur_act\n summation = gain*np.matmul(prev_act[None,:], np.matmul(W, cur_act[:,None]))\n\n # Let c=0; note that log(cosh(0))=0\n\n # sum_int1 = sum(log(cosh(prev_field)))\n sum_int1 = np.sum(np.log(np.cosh(prev_field)))\n\n # sum_int2 = sum(log(cosh(cur_field)))\n sum_int2 = np.sum(np.log(np.cosh(cur_field)))\n\n return (summation - sum_int1 - sum_int2)[0,0]\n\ndef _jacobian_sync(W, gain, v):\n \"\"\" \n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv)\n f(v)[i] = tanh(gain*W[i,:].v)\n So df[i]/dv[j] = tanh'(gain*W[i,:].v)*gain*W[i,j]\n where tanh' = sech^2 = 1/cosh^2\n \"\"\"\n\n res = gain/np.cosh(gain*np.matmul(W, v))**2\n J = np.matmul(np.diag(res), W)\n return J\n\ndef _jacobian_sync_subtract_I(W, gain, v):\n \"\"\" \n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv)\n f(v)[i] = tanh(gain*W[i,:].v)\n So df[i]/dv[j] = tanh'(gain*W[i,:].v)*gain*W[i,j]\n where tanh' = sech^2 = 1/cosh^2\n \"\"\"\n\n res = gain/np.cosh(gain*np.matmul(W, v))**2\n J = np.matmul(np.diag(res), W)\n return J-np.eye(W.shape[0])\n\ndef _update_async_stochastic(W, gain, a):\n \"\"\"Update the current activation\"\"\"\n\n new_a = np.copy(a)\n for i in np.random.permutation(W.shape[0]):\n new_a[i] = np.tanh(gain*np.dot(W[i,:], new_a))\n return new_a\n\ndef _update_async_deterministic(W, gain, a):\n \"\"\"Update the current activation\"\"\"\n\n new_a = np.copy(a)\n for i in xrange(W.shape[0]):\n new_a[i] = np.tanh(gain*np.dot(W[i,:], new_a))\n return new_a\n\ndef _energy_async(W, gain, cur_act, prev_act):\n \"\"\"\n Uses eq. 4.3 in Soulie et al. 1989 with b_i=0\n But modifies eq. 4.2 to include gain, and adds gain term to first sum in eq. 4.3 (equivalent to just multiplying the weights by the gain before use).\n \"\"\"\n\n # summation = -0.5 * cur_act^T * W * cur_act\n summation = -0.5*gain*np.matmul(cur_act[None,:], np.matmul(W, cur_act[:,None]))\n\n # integral(arctanh(x)) = 0.5*log(1-x^2) + x*arctanh(x); note that this equals 0 at 0\n\n sum_int = np.sum(0.5*np.log(1 - cur_act**2) + cur_act * np.arctanh(cur_act))\n\n return (summation + sum_int)[0,0]\n\ndef _jacobian_async_deterministic(W, gain, v):\n \"\"\" \n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv).\n See Garrett's notes for derivation.\n TODO: optimize\n \"\"\"\n\n def partial_intermediate_partial_intermediate(v_prev,i,j,k):\n if i == j:\n return gain*W[i,k]/np.cosh(gain*np.dot(W[i,:],v_prev))**2\n elif j == k:\n return 1.0\n else:\n return 0.0\n\n n = W.shape[0]\n intermediate_v = [v]\n # Assume stochastic=False\n for i in xrange(n):\n prev = np.copy(intermediate_v[i])\n prev[i] = np.tanh(gain*np.dot(W[i,:], prev))\n intermediate_v.append(prev)\n # Note: intermediate_v is shifted: intermediate_v[i] = v^(i-1) in notes\n\n prev_M_prod = np.zeros((n,n))\n\n # M^(0)\n for j in xrange(n):\n for l in xrange(n):\n prev_M_prod[j,l] = partial_intermediate_partial_intermediate(v, 0, j, l)\n\n\n for i in xrange(1,n+1):\n cur_M_prod = np.zeros((n,n))\n\n for j in xrange(n):\n for l in xrange(n):\n for k in xrange(n):\n cur_M_prod[j,l] += partial_intermediate_partial_intermediate(intermediate_v[i], i, j, k)*prev_M_prod[k,l]\n prev_M_prod = cur_M_prod\n\n return prev_M_prod\n\ndef _jacobian_async_deterministic_subtract_I(W, gain, v):\n \"\"\" \n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv).\n See Garrett's notes for derivation.\n TODO: optimize\n \"\"\"\n\n def partial_intermediate_partial_intermediate(v_prev,i,j,k):\n if i == j:\n return gain*W[i,k]/np.cosh(gain*np.dot(W[i,:],v_prev))**2\n elif j == k:\n return 1.0\n else:\n return 0.0\n\n n = W.shape[0]\n intermediate_v = [v]\n # Assume stochastic=False\n for i in xrange(n):\n prev = np.copy(intermediate_v[i])\n prev[i] = np.tanh(gain*np.dot(W[i,:], prev))\n intermediate_v.append(prev)\n # Note: intermediate_v is shifted: intermediate_v[i] = v^(i-1) in notes\n\n prev_M_prod = np.zeros((n,n))\n\n # M^(0)\n for j in xrange(n):\n for l in xrange(n):\n prev_M_prod[j,l] = partial_intermediate_partial_intermediate(v, 0, j, l)\n\n\n for i in xrange(1,n+1):\n cur_M_prod = np.zeros((n,n))\n\n for j in xrange(n):\n for l in xrange(n):\n for k in xrange(n):\n cur_M_prod[j,l] += partial_intermediate_partial_intermediate(intermediate_v[i], i, j, k)*prev_M_prod[k,l]\n prev_M_prod = cur_M_prod\n\n return prev_M_prod-np.eye(n)\n\ndef _no_energy(W,g,a,p):\n \"\"\" Energy function to use if one isn't defined \"\"\"\n\n return float('nan')\n\ndef _no_jacobian(W,g,a):\n \"\"\" Jacobian function to use if one isn't defined \"\"\"\n\n raise NotImplementedError\n\ndef _hopfield_energy(W, gain, cur_act, prev_act):\n \"\"\" Calculate the energy using Hopfield's derivation. \"\"\"\n # TODO: I think this equals 0.5(Wv).v --> check this\n n = W.shape[0]\n e = 0\n for i in xrange(n):\n for j in xrange(n):\n e -= gain*W[i,j]*cur_act[i]*cur_act[j]\n return 0.5*e\n\n\n\nclass _Hopnet:\n def __init__(self, n, update_fun, energy_fun, jacobian_fun, gain=1.0):\n \"\"\"Initialize a customized Hopnet\"\"\"\n\n self.n = n\n self.a = np.zeros(n, dtype=np.float32)\n self.W = np.zeros((n,n), dtype=np.float32)\n self.e = 0\n self.gain = gain\n self.update_fun = update_fun\n self.energy_fun = energy_fun\n self.jacobian_fun = jacobian_fun\n\n def learn(self, data):\n \"\"\"Learn the data using Hebbian learning\"\"\"\n\n self.W = np.matmul(data, data.T)\n for i in range(self.n):\n self.W[i,i] = 0\n self.W *= (1.0/self.n)\n\n def update(self):\n return self.update_fun(self.W, self.gain, self.a)\n\n\n def simhop(self, a_init, tolerance=1e-05, max_steps=500, silent=False, fileid=sys.stdout):\n \"\"\"Simulate the Hopnet until termination conditions are reached\"\"\"\n\n if len(a_init.shape) == 1:\n a_init = a_init.flatten()\n if len(a_init) != self.n:\n raise ValueError('The given a_init does not have {} entries.'.format(self.n))\n\n self.a = np.copy(a_init)\n\n t = 0\n cont = True\n\n if silent: # More optimized mode without any output\n while cont and t < max_steps:\n prev_a = np.copy(self.a)\n self.a = self.update()\n cont = not np.allclose(prev_a, self.a, rtol=0, atol=tolerance)\n t += 1\n # Update the energy only at the very end\n self.e = self.energy(self.a, prev_a)\n else: # Slower mode that provides output at each step\n self.e = self.energy(self.a, None)\n while cont and t < max_steps:\n self.show_state(t, fileid=fileid)\n\n prev_a = np.copy(self.a)\n self.a = self.update()\n self.e = self.energy(self.a, prev_a)\n cont = not np.allclose(prev_a, self.a, rtol=0, atol=tolerance)\n t += 1\n # Show final state\n self.show_state(t, fileid=fileid)\n return t, self.a, self.e\n\n def energy(self, cur_act, prev_act):\n return self.energy_fun(self.W, self.gain, cur_act, prev_act)\n\n def jacobian(self, v):\n return self.jacobian_fun(self.W, self.gain, v)\n\n def show_state(self, t, fileid=sys.stdout):\n \"\"\"Print the current state\"\"\"\n\n fileid.write(\"t:{:4d} [\".format(t))\n for i in self.a:\n fileid.write(' {0: 5.3f}'.format(i))\n fileid.write(\" ] E: {}\\n\".format(self.e))\n\n def show_wts(self, fileid=sys.stdout):\n \"\"\"Print the weight matrix\"\"\"\n\n fileid.write(\"\\nWeights =\\n\")\n for i in range(self.n):\n for j in range(self.n):\n fileid.write(\" {0: 7.3f}\".format(self.W[i,j]))\n fileid.write(\"\\n\")\n\n\n\n\n\n\nmodes = {\n 'sync': (_update_sync, _energy_sync,_jacobian_sync),\n 'async_stochastic': (_update_async_stochastic, _energy_async, _no_jacobian),\n 'async_deterministic': (_update_async_deterministic, _energy_async, _jacobian_async_deterministic)\n}\n\n\ndef Hopnet(n, mode=modes['sync'], gain=1.0):\n \"\"\"\n Factory function for creating Hopnets.\n A mode is defined as a 3-tuple of functions (update,energy,Jacobian).\n The last two can be None.\n An update function is defined by W,gain,a-->a\n An energy function is defined by W,gain,a,prev_a-->e\n A Jacobian function is defined by W,gain,a-->J\n \"\"\"\n\n mode_lst = list(mode)\n if mode_lst[0] is None:\n raise ValueError('Invalid mode.')\n if mode_lst[1] is None:\n mode_lst[1] = _no_energy\n if mode_lst[2] is None:\n mode_lst[2] = _no_jacobian\n\n return _Hopnet(n, *mode_lst, gain=gain)"}],"string":"[\n {\n \"alpha_fraction\": 0.5294630527496338,\n \"alphanum_fraction\": 0.5418685674667358,\n \"avg_line_length\": 31.44968605041504,\n \"blob_id\": \"8c682be7706b8b26c9cf006e6854d04b455e2bb3\",\n \"content_id\": \"bc9d28563d35fc6f11d9faa4a52b4cf33261f9fe\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10318,\n \"license_type\": \"permissive\",\n \"max_line_length\": 157,\n \"num_lines\": 318,\n \"path\": \"/cont_hopnet_customizable.py\",\n \"repo_name\": \"bsosis/rnn-fxpts\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport sys\\n\\n\\n\\ndef _update_sync(W, gain, a):\\n \\\"\\\"\\\"Update the current activation\\\"\\\"\\\"\\n\\n return np.tanh(gain*np.dot(W, a))\\n\\ndef _energy_sync(W, gain, cur_act, prev_act):\\n \\\"\\\"\\\"\\n Uses eq. 3.4 in Soulie et al. 1989\\n But modifies eq. 3.2 to include gain, and adds gain term to first sum in eq. 3.4 (equivalent to just multiplying the weights by the gain before use).\\n If prev_act is None, tries to invert W to find the energy.\\n \\\"\\\"\\\"\\n\\n if prev_act is None:\\n W_inv = np.linalg.inv(W)\\n # Test that W_inv can actually successfully invert W\\n if not np.allclose(np.eye(W.shape[0]), np.matmul(W, W_inv), rtol=0, atol=1e-7):\\n return float('nan') # TODO: pseudoinverse?\\n prev_act = np.dot(W_inv, np.arctanh(cur_act)/gain)\\n\\n\\n cur_field = gain*np.dot(W,cur_act)\\n prev_field = gain*np.dot(W,prev_act)\\n\\n # summation = prev_act^T * W * cur_act\\n summation = gain*np.matmul(prev_act[None,:], np.matmul(W, cur_act[:,None]))\\n\\n # Let c=0; note that log(cosh(0))=0\\n\\n # sum_int1 = sum(log(cosh(prev_field)))\\n sum_int1 = np.sum(np.log(np.cosh(prev_field)))\\n\\n # sum_int2 = sum(log(cosh(cur_field)))\\n sum_int2 = np.sum(np.log(np.cosh(cur_field)))\\n\\n return (summation - sum_int1 - sum_int2)[0,0]\\n\\ndef _jacobian_sync(W, gain, v):\\n \\\"\\\"\\\" \\n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv)\\n f(v)[i] = tanh(gain*W[i,:].v)\\n So df[i]/dv[j] = tanh'(gain*W[i,:].v)*gain*W[i,j]\\n where tanh' = sech^2 = 1/cosh^2\\n \\\"\\\"\\\"\\n\\n res = gain/np.cosh(gain*np.matmul(W, v))**2\\n J = np.matmul(np.diag(res), W)\\n return J\\n\\ndef _jacobian_sync_subtract_I(W, gain, v):\\n \\\"\\\"\\\" \\n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv)\\n f(v)[i] = tanh(gain*W[i,:].v)\\n So df[i]/dv[j] = tanh'(gain*W[i,:].v)*gain*W[i,j]\\n where tanh' = sech^2 = 1/cosh^2\\n \\\"\\\"\\\"\\n\\n res = gain/np.cosh(gain*np.matmul(W, v))**2\\n J = np.matmul(np.diag(res), W)\\n return J-np.eye(W.shape[0])\\n\\ndef _update_async_stochastic(W, gain, a):\\n \\\"\\\"\\\"Update the current activation\\\"\\\"\\\"\\n\\n new_a = np.copy(a)\\n for i in np.random.permutation(W.shape[0]):\\n new_a[i] = np.tanh(gain*np.dot(W[i,:], new_a))\\n return new_a\\n\\ndef _update_async_deterministic(W, gain, a):\\n \\\"\\\"\\\"Update the current activation\\\"\\\"\\\"\\n\\n new_a = np.copy(a)\\n for i in xrange(W.shape[0]):\\n new_a[i] = np.tanh(gain*np.dot(W[i,:], new_a))\\n return new_a\\n\\ndef _energy_async(W, gain, cur_act, prev_act):\\n \\\"\\\"\\\"\\n Uses eq. 4.3 in Soulie et al. 1989 with b_i=0\\n But modifies eq. 4.2 to include gain, and adds gain term to first sum in eq. 4.3 (equivalent to just multiplying the weights by the gain before use).\\n \\\"\\\"\\\"\\n\\n # summation = -0.5 * cur_act^T * W * cur_act\\n summation = -0.5*gain*np.matmul(cur_act[None,:], np.matmul(W, cur_act[:,None]))\\n\\n # integral(arctanh(x)) = 0.5*log(1-x^2) + x*arctanh(x); note that this equals 0 at 0\\n\\n sum_int = np.sum(0.5*np.log(1 - cur_act**2) + cur_act * np.arctanh(cur_act))\\n\\n return (summation + sum_int)[0,0]\\n\\ndef _jacobian_async_deterministic(W, gain, v):\\n \\\"\\\"\\\" \\n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv).\\n See Garrett's notes for derivation.\\n TODO: optimize\\n \\\"\\\"\\\"\\n\\n def partial_intermediate_partial_intermediate(v_prev,i,j,k):\\n if i == j:\\n return gain*W[i,k]/np.cosh(gain*np.dot(W[i,:],v_prev))**2\\n elif j == k:\\n return 1.0\\n else:\\n return 0.0\\n\\n n = W.shape[0]\\n intermediate_v = [v]\\n # Assume stochastic=False\\n for i in xrange(n):\\n prev = np.copy(intermediate_v[i])\\n prev[i] = np.tanh(gain*np.dot(W[i,:], prev))\\n intermediate_v.append(prev)\\n # Note: intermediate_v is shifted: intermediate_v[i] = v^(i-1) in notes\\n\\n prev_M_prod = np.zeros((n,n))\\n\\n # M^(0)\\n for j in xrange(n):\\n for l in xrange(n):\\n prev_M_prod[j,l] = partial_intermediate_partial_intermediate(v, 0, j, l)\\n\\n\\n for i in xrange(1,n+1):\\n cur_M_prod = np.zeros((n,n))\\n\\n for j in xrange(n):\\n for l in xrange(n):\\n for k in xrange(n):\\n cur_M_prod[j,l] += partial_intermediate_partial_intermediate(intermediate_v[i], i, j, k)*prev_M_prod[k,l]\\n prev_M_prod = cur_M_prod\\n\\n return prev_M_prod\\n\\ndef _jacobian_async_deterministic_subtract_I(W, gain, v):\\n \\\"\\\"\\\" \\n Computes the Jacobian of f at v, where f(v)=tanh(gain*Wv).\\n See Garrett's notes for derivation.\\n TODO: optimize\\n \\\"\\\"\\\"\\n\\n def partial_intermediate_partial_intermediate(v_prev,i,j,k):\\n if i == j:\\n return gain*W[i,k]/np.cosh(gain*np.dot(W[i,:],v_prev))**2\\n elif j == k:\\n return 1.0\\n else:\\n return 0.0\\n\\n n = W.shape[0]\\n intermediate_v = [v]\\n # Assume stochastic=False\\n for i in xrange(n):\\n prev = np.copy(intermediate_v[i])\\n prev[i] = np.tanh(gain*np.dot(W[i,:], prev))\\n intermediate_v.append(prev)\\n # Note: intermediate_v is shifted: intermediate_v[i] = v^(i-1) in notes\\n\\n prev_M_prod = np.zeros((n,n))\\n\\n # M^(0)\\n for j in xrange(n):\\n for l in xrange(n):\\n prev_M_prod[j,l] = partial_intermediate_partial_intermediate(v, 0, j, l)\\n\\n\\n for i in xrange(1,n+1):\\n cur_M_prod = np.zeros((n,n))\\n\\n for j in xrange(n):\\n for l in xrange(n):\\n for k in xrange(n):\\n cur_M_prod[j,l] += partial_intermediate_partial_intermediate(intermediate_v[i], i, j, k)*prev_M_prod[k,l]\\n prev_M_prod = cur_M_prod\\n\\n return prev_M_prod-np.eye(n)\\n\\ndef _no_energy(W,g,a,p):\\n \\\"\\\"\\\" Energy function to use if one isn't defined \\\"\\\"\\\"\\n\\n return float('nan')\\n\\ndef _no_jacobian(W,g,a):\\n \\\"\\\"\\\" Jacobian function to use if one isn't defined \\\"\\\"\\\"\\n\\n raise NotImplementedError\\n\\ndef _hopfield_energy(W, gain, cur_act, prev_act):\\n \\\"\\\"\\\" Calculate the energy using Hopfield's derivation. \\\"\\\"\\\"\\n # TODO: I think this equals 0.5(Wv).v --> check this\\n n = W.shape[0]\\n e = 0\\n for i in xrange(n):\\n for j in xrange(n):\\n e -= gain*W[i,j]*cur_act[i]*cur_act[j]\\n return 0.5*e\\n\\n\\n\\nclass _Hopnet:\\n def __init__(self, n, update_fun, energy_fun, jacobian_fun, gain=1.0):\\n \\\"\\\"\\\"Initialize a customized Hopnet\\\"\\\"\\\"\\n\\n self.n = n\\n self.a = np.zeros(n, dtype=np.float32)\\n self.W = np.zeros((n,n), dtype=np.float32)\\n self.e = 0\\n self.gain = gain\\n self.update_fun = update_fun\\n self.energy_fun = energy_fun\\n self.jacobian_fun = jacobian_fun\\n\\n def learn(self, data):\\n \\\"\\\"\\\"Learn the data using Hebbian learning\\\"\\\"\\\"\\n\\n self.W = np.matmul(data, data.T)\\n for i in range(self.n):\\n self.W[i,i] = 0\\n self.W *= (1.0/self.n)\\n\\n def update(self):\\n return self.update_fun(self.W, self.gain, self.a)\\n\\n\\n def simhop(self, a_init, tolerance=1e-05, max_steps=500, silent=False, fileid=sys.stdout):\\n \\\"\\\"\\\"Simulate the Hopnet until termination conditions are reached\\\"\\\"\\\"\\n\\n if len(a_init.shape) == 1:\\n a_init = a_init.flatten()\\n if len(a_init) != self.n:\\n raise ValueError('The given a_init does not have {} entries.'.format(self.n))\\n\\n self.a = np.copy(a_init)\\n\\n t = 0\\n cont = True\\n\\n if silent: # More optimized mode without any output\\n while cont and t < max_steps:\\n prev_a = np.copy(self.a)\\n self.a = self.update()\\n cont = not np.allclose(prev_a, self.a, rtol=0, atol=tolerance)\\n t += 1\\n # Update the energy only at the very end\\n self.e = self.energy(self.a, prev_a)\\n else: # Slower mode that provides output at each step\\n self.e = self.energy(self.a, None)\\n while cont and t < max_steps:\\n self.show_state(t, fileid=fileid)\\n\\n prev_a = np.copy(self.a)\\n self.a = self.update()\\n self.e = self.energy(self.a, prev_a)\\n cont = not np.allclose(prev_a, self.a, rtol=0, atol=tolerance)\\n t += 1\\n # Show final state\\n self.show_state(t, fileid=fileid)\\n return t, self.a, self.e\\n\\n def energy(self, cur_act, prev_act):\\n return self.energy_fun(self.W, self.gain, cur_act, prev_act)\\n\\n def jacobian(self, v):\\n return self.jacobian_fun(self.W, self.gain, v)\\n\\n def show_state(self, t, fileid=sys.stdout):\\n \\\"\\\"\\\"Print the current state\\\"\\\"\\\"\\n\\n fileid.write(\\\"t:{:4d} [\\\".format(t))\\n for i in self.a:\\n fileid.write(' {0: 5.3f}'.format(i))\\n fileid.write(\\\" ] E: {}\\\\n\\\".format(self.e))\\n\\n def show_wts(self, fileid=sys.stdout):\\n \\\"\\\"\\\"Print the weight matrix\\\"\\\"\\\"\\n\\n fileid.write(\\\"\\\\nWeights =\\\\n\\\")\\n for i in range(self.n):\\n for j in range(self.n):\\n fileid.write(\\\" {0: 7.3f}\\\".format(self.W[i,j]))\\n fileid.write(\\\"\\\\n\\\")\\n\\n\\n\\n\\n\\n\\nmodes = {\\n 'sync': (_update_sync, _energy_sync,_jacobian_sync),\\n 'async_stochastic': (_update_async_stochastic, _energy_async, _no_jacobian),\\n 'async_deterministic': (_update_async_deterministic, _energy_async, _jacobian_async_deterministic)\\n}\\n\\n\\ndef Hopnet(n, mode=modes['sync'], gain=1.0):\\n \\\"\\\"\\\"\\n Factory function for creating Hopnets.\\n A mode is defined as a 3-tuple of functions (update,energy,Jacobian).\\n The last two can be None.\\n An update function is defined by W,gain,a-->a\\n An energy function is defined by W,gain,a,prev_a-->e\\n A Jacobian function is defined by W,gain,a-->J\\n \\\"\\\"\\\"\\n\\n mode_lst = list(mode)\\n if mode_lst[0] is None:\\n raise ValueError('Invalid mode.')\\n if mode_lst[1] is None:\\n mode_lst[1] = _no_energy\\n if mode_lst[2] is None:\\n mode_lst[2] = _no_jacobian\\n\\n return _Hopnet(n, *mode_lst, gain=gain)\"\n 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xydian/cleanup-digitalocean-registry | https://github.com/xydian/cleanup-digitalocean-registry | 370812f06047e237663aa083e1a5839baf289b02 | ac7e30589d217be68447441859e496818de4ba35 | 6fc943e190741ec5aba809a5004f9ad41b3d569b | refs/heads/main | 2023-04-24T05:07:37.935294 | 2021-05-13T12:18:27 | 2021-05-13T12:18:27 | 367,039,155 | 0 | 0 | null | null | null | null | null | [
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IvanRezv/TestTask | https://github.com/IvanRezv/TestTask | aa3af8be7fdb0e7b03cb2827aee38a8b639a694a | dd108030972a6b1d700faa1619aa79ef120843c0 | b85bdf35d01623c5dbcff3dbe93a54f852f28a84 | refs/heads/master | 2023-05-08T08:25:40.837189 | 2021-05-18T10:02:18 | 2021-05-18T10:02:18 | 368,343,435 | 0 | 0 | null | null | null | null | null | [
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JakenHerman/DailyProgrammer | https://github.com/JakenHerman/DailyProgrammer | a915fa96003d56872a6a210025133f7150b8413f | 86375c5d991262238c78058c7eb182c61082ecae | 88dc66317499e5ba0eff413bb3c6b8a9af2498b9 | refs/heads/master | 2020-05-24T14:51:43.811727 | 2015-03-28T03:01:02 | 2015-03-28T03:01:02 | 20,207,087 | 0 | 0 | null | null | null | null | null | [
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lindseylewallen/Group10_FinalProject | https://github.com/lindseylewallen/Group10_FinalProject | 04c6df658b99c10766d4496b2bc18014d5d920dd | c5e169d44ce560542e33db9a10b8a6743c316339 | a64f746878a21a14da7bdc13dba9a67568229878 | refs/heads/main | 2023-04-20T14:53:42.994565 | 2021-05-05T15:04:25 | 2021-05-05T15:04:25 | 362,687,451 | 0 | 0 | null | null | null | null | null | [
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kennynakamura/Similaridade-com-Word2Vec | https://github.com/kennynakamura/Similaridade-com-Word2Vec | afa14d2a81a4a6fedcca91d46381b4c77e65cdb5 | 960729574f8421b174131abcafec55d650cac0c5 | ce6d4e0e7f3ec3ff9cc167b20933a1184c1f48cc | refs/heads/main | 2023-03-26T09:48:22.494529 | 2021-02-24T12:47:29 | 2021-02-24T12:47:29 | 340,419,871 | 0 | 0 | null | null | null | null | null | [
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xXhectorXx/Construccion-de-Software | https://github.com/xXhectorXx/Construccion-de-Software | 7b3cce493987d8ebd1ae3945955f06153e02370c | badfa224bf917d4d60289a77c75b3f5cfa822ddd | 57c4d30bd06a12f7baff7da41040a84937eb90fc | refs/heads/main | 2023-08-22T19:45:29.647338 | 2021-10-13T01:49:00 | 2021-10-13T01:49:00 | null | 0 | 0 | null | null | null | null | null | [
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Nala7bd/sambuzgil_youtubeData | https://github.com/Nala7bd/sambuzgil_youtubeData | 3f07e477b8139f3e9cf13a2d149999f441a62b06 | ceefb64eec2fa194dc25c0feb589513c120d53fe | 40aa1e061f9c5a17b881cf3a99f6a580a127aa69 | refs/heads/main | 2023-01-21T22:26:37.168032 | 2020-12-06T20:25:21 | 2020-12-06T20:25:21 | 319,120,799 | 0 | 0 | null | null | null | null | null | [
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emfcamp/Volunteers2 | https://github.com/emfcamp/Volunteers2 | 14751077b4381d3d93fbb34530607f57e8fd2553 | 948c8ee99da7f1deb1e124ebb19e5a17564cb91c | fac3369df21aefe1d53d635f07e069e0a269a314 | refs/heads/master | 2020-03-22T12:15:49.299034 | 2018-07-07T01:48:10 | 2018-07-07T01:53:35 | 140,028,904 | 0 | 0 | null | 2018-07-06T20:56:46 | 2018-07-06T23:22:31 | 2018-07-06T23:55:13 | null | [
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hortont424/phiface | https://github.com/hortont424/phiface | f1239990423884c52b5f7170b2ec54d82033cdec | 04831c94d0ae1822b542294d9b206b8797a839ea | 5db8fab2a105ac85902635488e2ba34f5fcdc40b | refs/heads/master | 2020-12-24T13:17:20.134859 | 2010-05-07T15:41:36 | 2010-05-07T15:41:36 | 615,992 | 0 | 1 | null | null | null | null | null | [
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amitjoshi24/ElementSpeller | https://github.com/amitjoshi24/ElementSpeller | 4ed7a2d8f9f93c8eaf24de303961def356e23951 | c235f5ff6ed1d1af7f745f0d0e9c563b608d63c1 | a000c596b1d982b6baf250ec961971cfa7ceddcc | refs/heads/main | 2023-08-28T03:08:45.653770 | 2021-10-15T16:56:34 | 2021-10-15T16:56:34 | 417,341,646 | 0 | 0 | null | null | null | null | null | [
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velislavgerov/exercise03 | https://github.com/velislavgerov/exercise03 | 6dc707134db25695317dddbbf6d6d2ac95f9a002 | a416c6808128c3fe425a65ff3db5286db3db6a99 | c836b414b66be22f20814a7a76171620dfa97092 | refs/heads/master | 2023-06-19T05:38:12.688086 | 2021-07-15T15:38:17 | 2021-07-15T15:38:17 | null | 0 | 0 | null | null | null | null | null | [
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LadyGracie/testpractice | https://github.com/LadyGracie/testpractice | f2eece56af928232e997a6a5b2f5828b1b571083 | 967d9eced6eff019aee696f186110bd2a94a8264 | 3985ab579ecaaf924a1840ced6ea434ec8a6a783 | refs/heads/main | 2023-08-24T05:32:32.009854 | 2021-10-24T10:07:22 | 2021-10-24T10:07:22 | null | 0 | 0 | null | null | null | null | null | [
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julianfraiese/switchCalc | https://github.com/julianfraiese/switchCalc | 0b63d46b5d59090c1905b1019e02d628b56210ae | d9b03019f15e912028a22fb803afd554a770359f | 5d7f9b7e3d29fb0ea95a0f5c5a1b54938f259092 | refs/heads/master | 2016-09-13T08:13:06.807376 | 2016-05-27T18:40:49 | 2016-05-27T18:40:49 | 59,764,202 | 0 | 0 | null | null | null | null | null | [
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agt-the-walker/shogi-utils | https://github.com/agt-the-walker/shogi-utils | b5072a87ef74292feb9e8968b94d60d90a4b33d8 | b13c2d7d1bc91ad271c554cf144bf65ae195df7e | 85ccc5f9d527f3311932f210c9f860ede04f94e1 | refs/heads/master | 2022-06-01T19:32:59.986254 | 2022-05-26T14:21:34 | 2022-05-26T14:21:34 | 33,569,890 | 4 | 2 | MIT | 2015-04-07T21:43:49 | 2020-04-21T17:40:44 | 2020-05-01T09:08:19 | Python | [
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ayingxp/LeetCode | https://github.com/ayingxp/LeetCode | d60be2dbf7d8424ed0113d6e48e2f00e634d9c4d | 47e049c84a0c85b43ae8e353ee7985f23b94017b | eddb5e8f95a9639d1dfaed0f0bee31ea0ea4a8d4 | refs/heads/master | 2020-06-23T00:28:16.282907 | 2020-03-25T09:03:48 | 2020-03-25T09:03:48 | 198,445,381 | 0 | 0 | null | null | null | null | null | [
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Darpan28/PythonWallet | https://github.com/Darpan28/PythonWallet | ed1bf86c053868278e350c9ba1fc0d357fc82dd1 | 3452ae113b7b91237607527f4e775ec909edeb8b | 07092c39b0b07927f9423f3a47b3012ff198302c | refs/heads/master | 2020-04-25T18:16:27.110735 | 2019-02-27T19:44:09 | 2019-02-27T19:44:09 | 172,978,734 | 0 | 0 | null | null | null | null | null | [
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januniku812/Adventure_Game_Udacity_Project_INP | https://github.com/januniku812/Adventure_Game_Udacity_Project_INP | a54aee7c6005d0f16648062d26be5bbabaac49c8 | 5d2dd5a5d490fe6f581d83393213541606f9d288 | 0d2a466ecc67b1dd8d6e759bdae4b2a9e0c71cfd | refs/heads/master | 2022-11-24T19:24:39.067303 | 2020-08-02T17:29:38 | 2020-08-02T17:29:38 | 284,506,973 | 0 | 0 | null | null | null | null | null | [
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Arsalankhan-09/CopyandRename | https://github.com/Arsalankhan-09/CopyandRename | 2566de794bfcaccc305815f7a56b97565f8e079e | 56d3091d6d8d890600b2e3ca1713b3a5ea098f95 | a0f7f6e9cd4cc8463f79fb1212a3e9d0efa2e84e | refs/heads/main | 2023-06-09T10:36:20.251477 | 2021-07-06T21:01:32 | 2021-07-06T21:01:32 | 383,592,786 | 0 | 0 | null | null | null | null | null | [
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chadn737/Sbicolor-vascular | https://github.com/chadn737/Sbicolor-vascular | ffd38ec0574e7be42a712740a9dc81c58519b790 | cac682d829aabc9944aa2e506e0dee773e7b1d60 | a666604d60b36bf610f594fefd73efb3ed5a2c37 | refs/heads/master | 2020-03-05T03:27:36.441370 | 2017-02-11T23:29:02 | 2017-02-11T23:29:02 | 81,689,565 | 0 | 0 | null | null | null | null | null | [
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mkgalvao/aps-20109-2 | https://github.com/mkgalvao/aps-20109-2 | ccbe6ce6a079337b61e67b166f849e2e647f63af | cf9c0dc06e43eb4879aae503fb9c5c01d6c850b7 | 23571d8517512b2c4c57307c3f84504162f6e0cf | refs/heads/master | 2023-01-29T11:58:18.511908 | 2019-11-23T18:52:34 | 2019-11-23T18:52:34 | 223,067,958 | 0 | 1 | null | 2019-11-21T02:10:57 | 2019-11-23T18:52:51 | 2023-01-05T12:26:11 | JavaScript | [
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helloprasanta/google_sheet_api | https://github.com/helloprasanta/google_sheet_api | 7c727c5b0987f6263edb7f32513baf01b8c5bf98 | 2063c7a9d3f47d4326aa0cd62593e1dca64394ed | 3507f8c3cabca32746e4eb025a2929519b5c0d3b | refs/heads/master | 2023-07-02T11:51:54.959485 | 2023-02-11T10:44:11 | 2023-02-11T10:44:11 | 391,544,855 | 0 | 0 | null | null | null | null | null | [
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minkimpark/myblog | https://github.com/minkimpark/myblog | 08bd42b2e97c485636add2a5055d80255ea1d75c | eaee21df12a7eb11e738795373ec273890565077 | 4ccb5ebe2a8489525b8a4411f8bfb0e63c6801a9 | refs/heads/master | 2021-09-24T16:35:40.151954 | 2021-09-16T07:08:06 | 2021-09-16T07:08:06 | 144,540,431 | 0 | 0 | null | null | null | null | null | [
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pdean2012/SQLAlchHomework | https://github.com/pdean2012/SQLAlchHomework | fabf423b059c77abba52b0ff96d2834aa9a7a0a1 | 0f7d65c6973c0d8afaa4cf4f72c4396151f46ccd | 8465cb26fea4d4328724307de7c24be769de4f67 | refs/heads/master | 2020-07-02T18:47:23.154820 | 2019-08-11T18:48:47 | 2019-08-11T18:48:47 | 201,627,676 | 0 | 0 | null | null | null | null | null | [
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chaoshunh/PredictiveMANNs | https://github.com/chaoshunh/PredictiveMANNs | c9dfad64dc21cb9dc9e54dff8168f0ef334d194b | 0a624afc0021764a57c83b48867568c9e0092a8d | 618d754831eb23d6338b5bfb312567fc8e169120 | refs/heads/master | 2020-08-03T10:05:06.958628 | 2018-07-12T16:16:53 | 2018-07-12T16:16:53 | null | 0 | 0 | null | null | null | null | null | [
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schenkd/FlaskCookie | https://github.com/schenkd/FlaskCookie | 098f939f754af229e66a9d32a15f295e50a9e5a1 | dd65548a5c7ee8ded6007af5b97f74cf2586c899 | 16de4b0dc21737d382e7ac495d9a90faa8496288 | refs/heads/main | 2022-12-25T03:46:18.933705 | 2020-10-04T16:00:41 | 2020-10-04T16:00:41 | 300,841,729 | 20 | 1 | MIT | 2020-10-03T09:19:07 | 2020-10-04T12:27:40 | 2020-10-04T16:00:41 | Python | [
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hailtedhorch/Comparing-Genes-Proteins-and-Genomes-Bioinformatics-III- | https://github.com/hailtedhorch/Comparing-Genes-Proteins-and-Genomes-Bioinformatics-III- | c6a4b96d8c8c31047e2fa31ef5fc34dfc570f509 | fa39c80beacc6d5111d754e8f84e721560febffa | e920a03db06924865cd3d36e13fcbbdf4c0ee5ce | refs/heads/master | 2020-06-23T20:49:11.379944 | 2016-07-22T14:42:53 | 2016-07-22T14:42:53 | null | 0 | 0 | null | null | null | null | null | [
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nohmichel/wall_project | https://github.com/nohmichel/wall_project | 5cc3a49f26c976170ce18e100af1133586c4fc9f | f3a698e7b70478f8617d1361839d56446e8cd102 | bc3e500220159b5aa9ba5811e56b45a01ce94d96 | refs/heads/main | 2023-03-30T11:53:41.910552 | 2021-03-27T05:37:28 | 2021-03-27T05:37:28 | 351,991,695 | 0 | 0 | null | null | null | null | null | [
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MoldirZh/iterators_python | https://github.com/MoldirZh/iterators_python | 057f1f80822dfa7e64a9bf3c5ca73c68cac22815 | 756afa83d5ee1ed30c6c24d443892af50a6deda2 | 30de78d9e6d88e2a638f0de293459637874d1fc1 | refs/heads/main | 2023-01-08T08:02:17.854608 | 2020-11-12T16:13:56 | 2020-11-12T16:13:56 | 312,329,567 | 0 | 0 | null | null | null | null | null | [
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jeweldhali/insurance | https://github.com/jeweldhali/insurance | 6b01daa4e4a2a72b180679d2083407a14ceb5731 | 21fba0b1766ac8b7fa311c4ee5d0fd5c38dc2da2 | a1c96d34d61716c3cedf6c65bdeb0ac8bfb0f166 | refs/heads/master | 2022-11-04T18:21:56.645209 | 2020-05-17T19:05:56 | 2020-05-17T19:05:56 | 264,696,836 | 1 | 0 | null | 2020-05-17T15:22:51 | 2020-05-17T19:05:59 | 2022-09-30T20:18:10 | JavaScript | [
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debsk/Redes | https://github.com/debsk/Redes | 378248cd23848c6da75f62cd040d42b91fa4d7c3 | c9931d3548dbd5d53befdf8f675fbc3399160c18 | bd30cf696a32cecb1dc772d35d2282006fc5247f | refs/heads/master | 2023-01-11T05:16:37.418245 | 2020-07-15T17:19:56 | 2020-07-15T17:19:56 | 268,180,771 | 0 | 0 | null | null | null | null | null | [
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Yelloooowww/slam-surveying-collaboration | https://github.com/Yelloooowww/slam-surveying-collaboration | ba8816cf7325171e04b0f4c0912b08442df20d24 | 15da5299b01bbbc02112b70d3bb0cf75a833e957 | 6ed863a8bc273c66d2fb42e0b6b575867a4cf26f | refs/heads/main | 2023-08-18T13:18:13.643114 | 2021-10-14T21:53:04 | 2021-10-14T21:53:04 | 414,872,149 | 0 | 0 | null | null | null | null | null | [
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NiskuT/Tank-Master | https://github.com/NiskuT/Tank-Master | 39629540875f2bd56c277d81b53cc535daa2aa39 | 8f63c46841ddb89f0475e53075ec9515b58a6ee5 | ae94b8b110ea2d349f4845fa954417203dd32182 | refs/heads/master | 2021-10-26T20:21:00.463111 | 2021-10-18T09:49:07 | 2021-10-18T09:49:07 | 133,039,274 | 0 | 0 | null | null | null | null | null | [
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volkhin/BSc-thesis | https://github.com/volkhin/BSc-thesis | 14f01482e63936eec8d411776f98134df3741c6b | bf75f473c64df44d560b0d56e3b94418f8f4cd19 | 5f221b067d649a0638a25e650def0bbca32fb2a7 | refs/heads/master | 2016-08-07T11:23:35.687409 | 2013-07-10T20:04:43 | 2013-07-10T20:04:43 | null | 0 | 0 | null | null | null | null | null | [
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MarcoSCampos/testdata | https://github.com/MarcoSCampos/testdata | 166202e864e0c7cd43a3f902f6a8a190d557d230 | c85a92a0dcce1b91c5ce309a9e646b35eeb3cab0 | f398ac1240ca7e58d5a413e5e4ad55032719108d | refs/heads/master | 2021-01-17T14:05:45.506842 | 2018-05-13T20:26:04 | 2018-05-13T20:26:04 | 83,477,930 | 0 | 0 | null | null | null | null | null | [
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furqanfarooqui/AI-ATTENDENCE-PROJECT | https://github.com/furqanfarooqui/AI-ATTENDENCE-PROJECT | 0f7f185127964f23c3d3f622103c2ee357f75077 | b721d461fbdd8e3798a7e10728e3d8c3628d8a01 | f76d17d4ad652866338681eaef56819e7036312a | refs/heads/main | 2023-05-07T01:36:10.836263 | 2021-05-30T10:52:29 | 2021-05-30T10:52:29 | 359,969,843 | 0 | 0 | null | null | null | null | null | [
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xermicus/nsfeed | https://github.com/xermicus/nsfeed | 3b61cc2e745191ca1db1c11c87791580aa7e9c78 | 6886b9004f98f25f18393a3b72ac10085072306a | 50ae905e83354586117494eccc849e8adadb4ee6 | refs/heads/master | 2020-12-02T19:30:32.858937 | 2017-07-06T15:42:22 | 2017-07-06T15:42:22 | 96,352,841 | 1 | 0 | null | null | null | null | null | [
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Crisescode/fluent-python | https://github.com/Crisescode/fluent-python | 0ae184822fc1c347d5adfa01a7f3d0001f1fef1c | 7ff7c81641dbc33e1df43db5985e80a33dcd7c3c | 0d8359f978b122de52d6ca3e435ef36b9efd4333 | refs/heads/master | 2022-12-07T16:59:52.554633 | 2020-09-02T01:27:45 | 2020-09-02T01:27:45 | 256,255,219 | 0 | 0 | null | null | null | null | null | [
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andreacarriero/carriers_coverage_api | https://github.com/andreacarriero/carriers_coverage_api | d449397fdbcd4c7ac67934be0c149e86b7f465ca | 79d0f98a4b8b2c26b9dbc61025391f5b2a392423 | 747b7e05a789972b19e721ad1cc742f26eafdd5b | refs/heads/master | 2021-12-14T12:39:02.938680 | 2018-03-15T17:57:53 | 2018-03-15T17:57:53 | 124,270,815 | 0 | 0 | null | 2018-03-07T17:29:04 | 2018-03-15T17:58:01 | 2021-12-13T19:46:19 | Python | [
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ezequiel43878/app_farm | https://github.com/ezequiel43878/app_farm | deaee640b29429a85dba6d38de48a96256adaf8c | f928ea88d669b2e8a1647b4cc5a76ad05bb69a83 | 29784861284cbe8829e0c252022a5237da3b393a | refs/heads/master | 2021-08-16T23:40:40.311435 | 2017-11-20T13:35:32 | 2017-11-20T13:35:32 | 108,599,599 | 0 | 0 | null | null | null | null | null | [
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Deakin-mamort/urdf_info | https://github.com/Deakin-mamort/urdf_info | 50e8ae827a5578bd51144a403557011b099c5ce6 | 4ab71028844421938adb45fe7f49cd3ddf85e95b | df66d5efebe6e399350b978c5afd2c23a59ed466 | refs/heads/master | 2020-12-24T06:42:55.604803 | 2016-05-27T22:43:44 | 2016-05-27T22:43:44 | 28,587,574 | 0 | 0 | null | null | null | null | null | [
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riri31/OpenStreetProject_Rabaute | https://github.com/riri31/OpenStreetProject_Rabaute | 890b5a14bb97392a67b4dc95c64001e481f0ca35 | 40c1f283d53c6b93f213c629476d35b69f2d2621 | df88ae54561cbc000c6912f3dea194bf985561f6 | refs/heads/master | 2021-07-05T06:59:17.322976 | 2017-09-29T08:58:49 | 2017-09-29T08:58:49 | 104,890,822 | 0 | 0 | null | null | null | null | null | [
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quangndd/ems | https://github.com/quangndd/ems | 5f6f8fb127dabd0eb34b4f696f6425983cdbd674 | b7bfe64f0fbc1a584017234971de7278dab1a755 | 2a9dddecc5b23c69fe094dad7c9e05baf9bc6ddb | refs/heads/master | 2022-12-05T01:58:12.287715 | 2020-08-20T08:54:10 | 2020-08-20T08:54:10 | 288,952,663 | 0 | 0 | null | null | null | null | null | [
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ujpinom/some_python | https://github.com/ujpinom/some_python | 9d890b956939372a88cbd8e4a3088849e51bb8a6 | 00c7ae4ce54280a69be5fba5185110fb5b05b204 | 6f21d381675126bd3a46ca319d022789b10d1125 | refs/heads/main | 2023-02-16T12:48:22.530096 | 2022-07-14T23:37:06 | 2022-07-14T23:37:06 | 328,427,242 | 0 | 0 | null | null | null | null | null | [
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priyanshu2901/Store-Management-System-Python | https://github.com/priyanshu2901/Store-Management-System-Python | 1b372f3f747fbd2da28d994aae6c30e2952ce595 | e2f943a2b50a54815f71e43529e553ba801b8249 | 4dabd48f9fa7d03c51f5329c4e217851c09a45a7 | refs/heads/main | 2023-01-20T23:48:37.841949 | 2020-11-25T07:59:47 | 2020-11-25T07:59:47 | 315,867,139 | 2 | 0 | null | null | null | null | null | [
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xiaodao2020/mlflow | https://github.com/xiaodao2020/mlflow | 17b1c5e389f4013afd3396bbd9bcb1a084d72f92 | bc11255ba713a311f56c2cbb98e0b172028663c7 | 7520176e32cb328ba0e774ed8028504d0273344f | refs/heads/master | 2020-03-19T09:33:07.196354 | 2019-07-03T06:36:21 | 2019-07-03T06:36:21 | 136,298,174 | 0 | 0 | null | 2018-06-06T08:33:31 | 2018-06-06T08:33:27 | 2018-06-05T23:11:55 | null | [
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cforcross/SauceLabs | https://github.com/cforcross/SauceLabs | 068c07596b2e9b911ca2a2404e63cca87786dcdd | f8e37b36ec5143ddb50ddd2fb5bc381071619c8c | 42cac7069d900f38a51cbdb890317ea4a396c6fb | refs/heads/master | 2022-11-20T06:39:40.674273 | 2020-07-24T23:15:38 | 2020-07-24T23:15:38 | 243,093,501 | 0 | 0 | null | null | null | null | null | [
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DimetrikusK/leetcode | https://github.com/DimetrikusK/leetcode | 671b137c7e7d8b9640f14d00533f93f37c8301b1 | 76c428910014d85d5f32d919a64e0468f18877c5 | 3e3447fcfa9681ef1ef5c6de23f676beb9b914aa | refs/heads/main | 2023-04-28T20:46:40.121939 | 2021-05-28T12:26:33 | 2021-05-28T12:26:33 | 367,853,616 | 0 | 0 | null | null | null | null | null | [
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shakib500/api_consuming_using_django | https://github.com/shakib500/api_consuming_using_django | 4ce1d3cc3881e61f7325254bba566609bf07f9e7 | 1888525340f06b299a3b0b8ad0d231a53d8954c8 | 2fd3fef1e90121c781fed01da518f0a44c479b42 | refs/heads/master | 2020-05-21T17:03:33.229329 | 2019-05-20T19:02:20 | 2019-05-20T19:02:20 | 186,114,920 | 0 | 0 | null | null | null | null | null | [
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Bobur-oiligarh/CabinetBook | https://github.com/Bobur-oiligarh/CabinetBook | 070a0aa05900f6898669f836182bb39f4b1d6930 | da5475a720f101bc3b82a0bb98c240b64bbb8d3e | 9993a02f661e6cb2382ec207f2dc2c362a7b88ae | refs/heads/main | 2023-09-02T10:08:14.040525 | 2021-10-16T10:06:44 | 2021-10-16T10:06:44 | 416,995,526 | 0 | 0 | null | null | null | null | null | [
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abhishah901/Practical_python | https://github.com/abhishah901/Practical_python | 5bd61e3335e23173c7477e5df4aebda54baba17a | 829686ab6ae3f64c64a5e97550a16b589f6dac4a | bee6d2240c4de71c4d7df73c5e1f4853e832151e | refs/heads/master | 2023-07-12T12:34:43.903499 | 2021-08-21T23:53:47 | 2021-08-21T23:53:47 | 396,124,772 | 0 | 0 | null | null | null | null | null | [
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tylerbaker/controller-openstack-cinder | https://github.com/tylerbaker/controller-openstack-cinder | 5697980ab2cb84bea96ea067ff6b33fe480ae6ec | 89aad4baec4283b3894019e1d90d41d5e3a41fbe | 8b7e390070f50366244acd243f0b1822b1837abd | refs/heads/master | 2021-01-12T22:13:39.020765 | 2014-01-21T18:22:25 | 2014-01-21T18:22:25 | null | 0 | 0 | null | null | null | null | null | [
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AAT-SP2017/tweet-miner | https://github.com/AAT-SP2017/tweet-miner | 2e5a2cc8433aa04fdcbe603e3e12887d5e85d08a | 92f19e1bfaac848d1ab9210055adb3b4fb7230df | aa18720de2275982422dcb649e16339293306317 | refs/heads/master | 2019-12-22T20:25:34.502146 | 2019-10-29T11:06:22 | 2019-10-29T11:06:22 | 90,229,680 | 0 | 0 | MIT | 2017-05-04T06:34:33 | 2019-10-29T11:05:54 | 2019-10-29T11:06:23 | Python | [
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reskandary/RNA-Seq-in-the-Cloud | https://github.com/reskandary/RNA-Seq-in-the-Cloud | 254d31f8b99a5b137d2565cf0cea007d1ecef5f0 | 15ce9a9be72a4f16185ae6d2f07ead9a2d1d93b9 | 8aac0958c13ff3ee91a0104ba03be20982360f8b | refs/heads/master | 2020-04-28T18:59:16.916120 | 2019-03-13T20:45:27 | 2019-03-13T20:45:27 | 175,496,306 | 0 | 0 | null | 2019-03-13T20:45:34 | 2019-03-13T20:45:29 | 2019-03-13T20:45:28 | null | [
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Huud/freecad-nurbs | https://github.com/Huud/freecad-nurbs | 65cf669c02962ea484d1a26651d0fbae015d8f70 | ca1e3d54fd807e0db276b7d25a0337e195b71be8 | 6f5e15ed2367bc58a9e7e2e2e9fd6e9ade34e9f3 | refs/heads/master | 2020-04-18T17:28:26.807777 | 2019-01-07T16:55:02 | 2019-01-07T16:55:02 | null | 0 | 0 | null | null | null | null | null | [
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fasrc/User_Codes | https://github.com/fasrc/User_Codes | 8827335131925da4c47b1f00943895a6d20c84b5 | 3f0f2cae6a194f719a764293204e6cd3ccaf2f58 | 9fc8774d1c5a7d7f823de9f520822cfb9acd2e62 | refs/heads/master | 2023-08-31T07:10:34.249138 | 2023-08-15T17:40:13 | 2023-08-15T17:40:13 | 44,996,115 | 65 | 20 | null | 2015-10-26T20:43:56 | 2022-05-11T17:56:25 | 2022-06-01T14:41:08 | Jupyter Notebook | [
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Javierzx9r/Conexion_MongoDb | https://github.com/Javierzx9r/Conexion_MongoDb | c3f5706c63876c6070f44dd878af7bafd0ec3756 | 5691a0bcf81d5685a408d42425912b9bcca02bbb | b87220a551782be6b5879b729b2218ffb6fc65d7 | refs/heads/master | 2023-04-15T05:14:41.275522 | 2021-04-27T14:28:20 | 2021-04-27T14:28:20 | 360,812,397 | 0 | 0 | null | null | null | null | null | [
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dmitryzykovArtis/c4d_scripts | https://github.com/dmitryzykovArtis/c4d_scripts | 4f402f4f89f3e71a48dd6cf720b9fb4d65d737ad | 0d3c00b8a734f2b2dbfcf79e6ebf7838bd8b4763 | 6acecda841a092b249e2e80550c9e224234ed911 | refs/heads/master | 2020-05-26T13:05:36.402157 | 2019-05-23T11:11:49 | 2019-05-23T11:11:49 | null | 0 | 0 | null | null | null | null | null | [
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gopal-esri/Code-review | https://github.com/gopal-esri/Code-review | 87a4d88a5065075876f43d3c9e7b39ccb2ddd85d | cea72ba465681c47581cf62414aaa30410a69348 | a1ac0cd782ab9a7aeea421dc3f7587d81827a78e | refs/heads/main | 2023-03-29T08:07:15.605074 | 2021-03-29T02:28:50 | 2021-03-29T02:28:50 | 352,491,761 | 0 | 0 | null | null | null | null | null | [
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hith3107/fabnocci-in-python | https://github.com/hith3107/fabnocci-in-python | a03b4c628a894ea262e7ed27b9f51ee02eba414b | 96dda013c7521ef8cd5de9d039200b321c3add35 | 6dcbf8945295815f620635332fc5db71924610ef | refs/heads/main | 2023-05-28T04:48:41.939737 | 2021-06-17T12:49:12 | 2021-06-17T12:49:12 | 377,828,663 | 0 | 0 | null | null | null | null | null | [
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martinbuberl/pi | https://github.com/martinbuberl/pi | c772dd5287d6c0a54615f339abff6cd6bcdd7c54 | 39db6f15f1ad459ac715d555b38f825f12238c8e | 1c0fc080a9e21d543a349f5aacc24ba37c8a5e3b | refs/heads/master | 2022-01-24T03:43:35.054777 | 2019-07-13T16:24:10 | 2019-07-13T16:24:10 | 119,736,610 | 0 | 0 | null | null | null | null | null | [
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scMarth/ManaEggJunctions | https://github.com/scMarth/ManaEggJunctions | a612beb81b21db6d091dfd3e17dd4402caa1fedc | 1b592153efd06480709b5c35043dc875f75f537b | a689dd14c147ef00e2a7cef6cc332113a79ad1a3 | refs/heads/master | 2022-01-06T06:08:07.676141 | 2019-05-13T17:45:18 | 2019-05-13T17:45:18 | 106,351,054 | 0 | 0 | null | null | null | null | null | [
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hdh7485/soscon_windows | https://github.com/hdh7485/soscon_windows | adec55d33a36f52c0f64127c2035b207aa1902d7 | 046e019a2f8e79b1c142678cc32e1c23f863c246 | 25781c2e09f767d2bb7c9aed6c789f2e4a276c02 | refs/heads/master | 2020-04-02T05:21:30.249492 | 2018-10-22T01:25:58 | 2018-10-22T01:25:58 | null | 0 | 0 | null | null | null | null | null | [
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darkfiberiru/packet-explorer | https://github.com/darkfiberiru/packet-explorer | ba523ad4b29d87034448fa20b18d05cdf9f3551c | f85f1c5c82fd222318c715082dcbe84a5cb2fb99 | 63cfc9f4eeb25414acf78bf6a623f45582fa1ed4 | refs/heads/master | 2021-01-23T07:27:16.403576 | 2014-10-05T07:29:13 | 2014-10-05T07:29:13 | null | 0 | 0 | null | null | null | null | null | [
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Harshakarnan/FaceDetection_python | https://github.com/Harshakarnan/FaceDetection_python | 216b866765aa419f7394fae34282e987fc55d1ba | 65ac7272ed26b89a6c9f1de85ea18fd929ebc652 | b756940c11e79892cff87e96a877ce2e6b80ff9c | refs/heads/main | 2023-08-31T05:54:15.527997 | 2021-10-25T14:17:08 | 2021-10-25T14:17:08 | null | 0 | 0 | null | null | null | null | null | [
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GrowingDark/practice-stuff | https://github.com/GrowingDark/practice-stuff | 460b72e669a1f7407f13245d8b0f2f3e097227ae | efbe32f8293be7181cda72bec73a8d9514ec6150 | 44b93fe05ffff8c2d57df4026018fc3bbe2ceba2 | refs/heads/master | 2020-03-24T05:42:14.652038 | 2018-07-26T22:24:40 | 2018-07-26T22:24:40 | 142,498,519 | 0 | 0 | null | null | null | null | null | [
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Omer-Sella/molgym | https://github.com/Omer-Sella/molgym | ca1152312fedfe527738e34f72a30ae692d92345 | 684b7cfd20f2f195b0c90aa33ffde1363e3e850d | fa73ee7346e0fd121729d5f2537fd76e9cb56cc2 | refs/heads/master | 2023-05-20T02:37:37.042979 | 2021-05-07T11:39:20 | 2021-05-07T11:39:20 | null | 0 | 0 | null | null | null | null | null | [
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BrianBock/ENPM673_Project2 | https://github.com/BrianBock/ENPM673_Project2 | e36828cb093c68a5a428554418f1472c37b8b96b | f0aa6e72c858986edf03e98e413a082216db4939 | aba9ad011ce0b33f225da4a71cf90be37cb5fd35 | refs/heads/master | 2021-02-04T03:57:06.501078 | 2020-03-12T03:26:58 | 2020-03-12T03:26:58 | 243,614,534 | 0 | 0 | null | null | null | null | null | [
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GuilhermeJacobRech/projeto_django | https://github.com/GuilhermeJacobRech/projeto_django | b3edeb19142ac0c779f764b8d3760f7e919cb12a | 2e25793bd1fba4eba65e9fce21983d616cbb8d5f | 10d00988b1eced4bc42c7e2ca26660a50ea7442e | refs/heads/master | 2021-04-15T09:48:30.641683 | 2018-03-23T20:26:19 | 2018-03-23T20:26:19 | 126,536,323 | 2 | 0 | null | null | null | null | null | [
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jw122/poolside | https://github.com/jw122/poolside | 44a2b86495a0892d7cfb21acffdf0be58fda3083 | 1447cedfc3ccdbbaa7a50c24d12142a80607d188 | 3d27f1f7caf81cc401af8b54384523254e85edee | refs/heads/main | 2023-03-01T13:31:00.616153 | 2021-02-08T04:53:19 | 2021-02-08T04:53:19 | 330,797,768 | 2 | 0 | null | 2021-01-18T22:04:57 | 2021-02-08T04:53:22 | 2021-02-08T04:59:42 | Python | [
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Npasin/thoughts_flask | https://github.com/Npasin/thoughts_flask | b961d6945419c0f554381df6e8f2548342600f7b | 8a4c775a06b0e25aa71aef190a49589a84407acd | f5e69340d6dfc4937c0525c00ad74b4d5a7ec3bb | refs/heads/master | 2020-08-12T23:59:37.525169 | 2019-10-13T19:51:00 | 2019-10-13T19:51:00 | 214,869,679 | 0 | 0 | null | null | null | null | null | [
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nisheeth84/mock_test_aws | https://github.com/nisheeth84/mock_test_aws | a294e45966fbc6f2b9fd06494259a43ee3ccd99d | 7be25317e6e5ecc6cb111f9da5f76401535d5178 | d10d56262c030051d1594b039b034bbad9b82870 | refs/heads/master | 2022-12-31T00:39:33.187704 | 2020-09-11T15:54:29 | 2020-09-11T15:54:29 | null | 0 | 0 | null | null | null | null | null | [
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hupfyn/observer | https://github.com/hupfyn/observer | d743a2128700786c96afacff0b38f18a89211b23 | 4d60a09771d6dc13a846c0f521e7bb0cfb50e812 | 945e4c4a2f566e7401a6511e23da4436d01cb1d7 | refs/heads/master | 2020-12-20T16:48:29.730722 | 2020-01-24T16:55:39 | 2020-01-24T16:55:39 | null | 0 | 0 | null | null | null | null | null | [
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Meg1211/GamestonkTerminal | https://github.com/Meg1211/GamestonkTerminal | 917fccd8fa97b002b0cd744a080c8be5309c46b9 | 9e8b9fb631df1778795236b3ec2aa66725266c15 | baaa0e1caa34796d7e010ec277d94940c8b7ee72 | refs/heads/main | 2023-07-09T23:35:33.450603 | 2021-08-08T16:31:44 | 2021-08-08T16:31:44 | 393,953,964 | 0 | 0 | MIT | 2021-08-08T12:27:42 | 2021-08-08T12:27:41 | 2021-08-08T12:27:18 | null | [
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Paluthyst/rpsgame | https://github.com/Paluthyst/rpsgame | 6f6613b79aed7bd611ca149c21c5e07febde72f7 | 5e4f6d85eea33759786a90e9620aaf59031ae730 | b6d66ff942ea3280630097e94ce96077b69d51df | refs/heads/master | 2022-11-18T05:03:48.619250 | 2020-07-22T21:11:13 | 2020-07-22T21:11:13 | 281,783,949 | 0 | 0 | null | null | null | null | null | [
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hooss-only/sakeL-comment-analysis | https://github.com/hooss-only/sakeL-comment-analysis | 050bf2a06030645def2e4af656c253adaf6046ad | 18cadbccf7155e6f8475fd9e8ffa51cc7ba961da | d89e5e5d77c10107b2eab5f5a1645d9a67baa5f4 | refs/heads/master | 2023-02-19T22:33:30.881192 | 2021-01-24T11:46:41 | 2021-01-24T11:46:41 | 332,168,992 | 2 | 0 | null | null | null | null | null | [
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vk-98/quora_clone | https://github.com/vk-98/quora_clone | 6ac77ea344548f96653d517e20e06c01cf079618 | d0ff553320ff0ef2d2a7d968301e638ff3e00d78 | ff6e2ae0bc90861492d17e76ee11677fc21d3cb5 | refs/heads/master | 2020-12-20T21:13:44.470285 | 2020-03-17T19:17:26 | 2020-03-17T19:17:26 | 236,208,427 | 2 | 2 | null | null | null | null | null | [
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bsosis/rnn-fxpts | https://github.com/bsosis/rnn-fxpts | d030e5ec4306240f333013b9ae5507b299d35be4 | 6b40acae175af7227cc2174fb2f59b8b9c234b8d | 60f79cfbf650b60f09c5ef4ff248a06fa41784f6 | refs/heads/master | 2021-01-01T16:41:54.375373 | 2018-02-21T03:13:15 | 2018-02-21T03:13:15 | 97,892,000 | 0 | 0 | null | 2017-07-21T01:19:10 | 2016-12-09T18:38:17 | 2017-07-16T15:12:11 | null | [
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