File size: 29,546 Bytes
093b0a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from time import sleep\n",
"import datetime\n",
"import os\n",
"from utils.ipynb_helpers import read_data, write_df, convert_tz, add_tz\n",
"from dotenv import load_dotenv\n",
"\n",
"# Create a .env file and add your keys\n",
"load_dotenv()\n",
"\n",
"# Location to save raw data from data providers\n",
"DATA_RAW = \"data/raw\"\n",
"\n",
"\n",
"equities = [\"XOM\", \"CVX\", \"COP\", \"BP\", \"PBR\", \"WTI\", \"TTE\", \"EQNR\", \"EOG\", \"ENB\", \"SLB\"]\n",
"more_equities = []\n",
"\n",
"crude_oil = [\"CL=F\", \"BZ=F\"] # wti, brent,\n",
"random = [\"TSLA\", \"AAPL\"]\n",
"\n",
"materials_equities = [\"BHP\", \"LIN\", \"RIO\", \"VALE\", \"APD\", \"FCX\", \"SHW\", \"SCCO\", \"CTVA\", \"ECL\", \"NUE\", \"NTR\"]\n",
"\n",
"\n",
"# https://en.wikipedia.org/wiki/List_of_countries_by_oil_production\n",
"# https://www.weforum.org/agenda/2016/05/which-economies-are-most-reliant-on-oil/\n",
"# OPEC: Iran, Iraq, Kuwait, Saudi Arabia, Venezuela\n",
"# fx_opec = [_, \"C:USDIQD\", \"C:USDKWD\", \"C:USDSAR\", \"C:USDVEF\"]\n",
"\n",
"# OPEC+: Algeria, Angola, Congo, Equatorial Guinea, Gabon, Libya, Nigeria, United Arab Emirates\n",
"# fx_opec_pp = [\"C:USDDZD\",_, \"C:USDCDF\", \"C:USDGNF\", _, \"C:USDLYD\", \"C:USDNGN\", \"C:USDAED\"]\n",
"\n",
"# Large: US, Russia, China, Canada, Norway\n",
"# Other important: Qatar, Kazakhstan\n",
"# fx_other= [\"C:USDQAR\", \"C:USDKZT\"]\n",
"\n",
"fx = [\"C:USDSAR\", \"C:USDAED\"]\n",
"\n",
"tickers = equities # + crude_oil"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Get Data From Data Provider"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Y Finance\n",
"\n",
"import yfinance as yf\n",
"\n",
"\n",
"def use_yfinance(\n",
" tickers, out_file, timeframe=\"day\", start=\"2000-01-01\", end=\"2023-01-01\"\n",
"):\n",
" assert timeframe == \"day\", \"Use day timeframe for day\"\n",
"\n",
" data = yf.download(tickers, start=start, end=end, group_by=\"ticker\")\n",
"\n",
" if len(tickers) == 1:\n",
" data = pd.concat([data], axis=1, keys=[tickers[0]])\n",
"\n",
" data.index.rename(\"date\", inplace=True)\n",
" data.rename(columns=lambda x: str.lower(x), level=1, inplace=True)\n",
"\n",
" if data.index.to_series().dt.tz is None:\n",
" print(\"Adding time\")\n",
" data = add_tz(data, time_zone=\"UTC\")\n",
"\n",
" if out_file is not None:\n",
" write_df(data, out_file)\n",
"\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# Alpha Vantage\n",
"\n",
"\n",
"def csv_str_to_df(decoded_content, ticker):\n",
" \"\"\"CSV string to df\"\"\"\n",
" print(decoded_content[:1000])\n",
" lines = decoded_content.splitlines()\n",
" print(len(lines), lines[0].split(\",\")[1:])\n",
" print(lines[2])\n",
" #while(1):pass\n",
" data = pd.DataFrame(\n",
" [row.split(\",\") for row in lines[1:]],\n",
" columns=[\"date\", *lines[0].split(\",\")[1:]],\n",
" )\n",
" \n",
"\n",
" data = data.reset_index(drop=True).set_index(\"date\")\n",
" data.index = pd.to_datetime(data.index)\n",
"\n",
" # Add timezome -- we assume it is sent in with unlabled eastern time\n",
" if data.index.to_series().dt.tz is None:\n",
" print(\"CONVERTING TIME\")\n",
" data = add_tz(data, time_zone=\"US/Eastern\")\n",
" data = convert_tz(data, time_zone=\"UTC\")\n",
" data = pd.concat([data], axis=1, keys=[ticker])\n",
" return data\n",
"\n",
"\n",
"def alpha_vantage_get_ticker_data(ticker, time=\"1min\", year=1, month=1):\n",
" \"\"\"Function to get (ticker, year, month) data using alpha vantage's time series intraday extended API\"\"\"\n",
" ALPHA_VANTAGE_API_KEY = os.environ.get(\"ALPHA_VANTAGE_API_KEY\")\n",
" import requests\n",
"\n",
" CSV_URL = f\"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={ticker}&interval={time}&month={2026-year}-{11-month:02d}&outputsize=full&apikey={ALPHA_VANTAGE_API_KEY}\"\n",
"\n",
" while True:\n",
" with requests.Session() as s:\n",
" download = s.get(CSV_URL)\n",
" decoded_content = download.content.decode(\"utf-8\")\n",
" print(\n",
" f\"ticker: {ticker}, y{year} m{month}; response length: {len(decoded_content)}\"\n",
" )\n",
"\n",
" if len(decoded_content) == 236:\n",
" # API too many requests\n",
" sleep(60)\n",
" elif len(decoded_content) <= 243:\n",
" # Token doesn't exist or something\n",
" print(f\"Error getting {ticker}, y{year}, m{month}. We are skipping\")\n",
" print(decoded_content)\n",
" return None\n",
" else:\n",
" return csv_str_to_df(decoded_content, ticker)\n",
"\n",
"\n",
"def use_alpha_vantage(tickers, out_file, time=\"1min\"):\n",
" \"\"\"Function to get multiple full tickers data using alpha vantage's time series intraday extended API\"\"\"\n",
"\n",
" dfs = []\n",
" for ticker in tickers:\n",
" t_dfs = []\n",
" for year in range(1, 3):\n",
" for month in range(1, 13):\n",
" df_temp = alpha_vantage_get_ticker_data(\n",
" ticker, time=time, year=year, month=month\n",
" )\n",
" if df_temp is not None:\n",
" t_dfs.append(df_temp)\n",
"\n",
" if len(t_dfs):\n",
" dfs.append(pd.concat(t_dfs, axis=0))\n",
" else:\n",
" print(f\"Skipped {ticker}.\")\n",
" df = pd.concat(dfs, axis=1, sort=True)\n",
" df.index.rename(\"date\", inplace=True)\n",
"\n",
" write_df(df, out_file)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Alpaca\n",
"\n",
"\n",
"def use_alpaca(tickers, out_file, timeframe=\"1Minute\", start=\"2017-01-01\"):\n",
" APCA_API_BASE_URL = os.environ.get(\"APCA_API_BASE_URL\")\n",
" APCA_API_KEY_ID = os.environ.get(\"APCA_API_KEY_ID\")\n",
" APCA_API_SECRET_KEY = os.environ.get(\"APCA_API_SECRET_KEY\")\n",
" import alpaca_trade_api as tradeapi\n",
"\n",
" alpaca = tradeapi.REST(\n",
" key_id=APCA_API_KEY_ID,\n",
" secret_key=APCA_API_SECRET_KEY,\n",
" base_url=APCA_API_BASE_URL,\n",
" )\n",
" account = alpaca.get_account()\n",
" print(account.status)\n",
"\n",
" dfs = []\n",
" for ticker in tickers:\n",
" print(\"Getting\", ticker)\n",
" df = alpaca.get_bars(ticker, timeframe, start).df\n",
" print(\"Recieved\", ticker)\n",
" df.index.name = \"date\"\n",
" df = pd.concat([df], axis=1, keys=[ticker])\n",
" dfs.append(df)\n",
" df = pd.concat(dfs, axis=1, sort=True)\n",
" df.index.rename(\"date\", inplace=True)\n",
"\n",
" if out_file is not None:\n",
" write_df(df, out_file)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Polygon\n",
"\n",
"\n",
"def use_polygon(tickers, out_file, multiplier=1, timespan=\"minute\", start=\"2000-01-01\"):\n",
" POLYGON_API_KEY = os.environ.get(\"POLYGON_API_KEY\")\n",
" from polygon import RESTClient\n",
"\n",
" client = RESTClient(POLYGON_API_KEY)\n",
" dfs = []\n",
" end = datetime.datetime.utcnow()\n",
" start_og = start\n",
" for ticker in tickers:\n",
" start = start_og\n",
" df_agg = None\n",
" response_len = None\n",
" i = 0\n",
" print(\"Getting\", ticker)\n",
" while response_len != 1:\n",
" i += 1\n",
" aggs = client.get_aggs(\n",
" ticker,\n",
" multiplier,\n",
" timespan,\n",
" start,\n",
" end,\n",
" adjusted=True,\n",
" sort=\"asc\",\n",
" limit=50000,\n",
" )\n",
" df = pd.DataFrame(aggs)\n",
" df.index = pd.DatetimeIndex(\n",
" pd.to_datetime(df[\"timestamp\"], unit=\"ms\", utc=True)\n",
" )\n",
" df.index.name = \"date\"\n",
" df = df.filter([\"open\", \"high\", \"low\", \"close\", \"volume\", \"vwap\"], axis=1)\n",
" response_len = len(df.index)\n",
" start = df.last_valid_index()\n",
" print(i, response_len)\n",
" if df_agg is not None:\n",
" df_agg.drop(index=df_agg.index[-1], axis=0, inplace=True)\n",
" df_agg = pd.merge(df_agg.reset_index(), df.reset_index(), how=\"outer\")\n",
" df_agg = df_agg.set_index(\"date\")\n",
" else:\n",
" df_agg = df\n",
" sleep(1) # Attempt to be nice\n",
" df_agg = pd.concat([df_agg], axis=1, keys=[ticker])\n",
" dfs.append(df_agg)\n",
" print(\"Recieved\", ticker)\n",
"\n",
" df = pd.concat(dfs, axis=1, sort=True)\n",
" df.index.rename(\"date\", inplace=True)\n",
"\n",
" if out_file is not None:\n",
" write_df(df, out_file)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_7521/3255818553.py:11: FutureWarning: YF.download() has changed argument auto_adjust default to True\n",
" data = yf.download(tickers, start=start, end=end, group_by=\"ticker\")\n",
"[*********************100%***********************] 2 of 2 completed"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Adding time\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Yahoo Finance\n",
"df = use_yfinance(\n",
" [\"AAPL\", \"TSLA\"], os.path.join(DATA_RAW, \"aapl_day_full.csv\"), start=\"1970-01-01\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ticker: XOM, y1 m1; response length: 2481441\n",
"{\n",
" \"Meta Data\": {\n",
" \"1. Information\": \"Intraday (1min) open, high, low, close prices and volume\",\n",
" \"2. Symbol\": \"XOM\",\n",
" \"3. Last Refreshed\": \"2025-10-22 19:59:00\",\n",
" \"4. Interval\": \"1min\",\n",
" \"5. Output Size\": \"Full size\",\n",
" \"6. Time Zone\": \"US/Eastern\"\n",
" },\n",
" \"Time Series (1min)\": {\n",
" \"2025-10-22 19:59:00\": {\n",
" \"1. open\": \"115.2100\",\n",
" \"2. high\": \"115.3900\",\n",
" \"3. low\": \"115.2100\",\n",
" \"4. close\": \"115.3900\",\n",
" \"5. volume\": \"105\"\n",
" },\n",
" \"2025-10-22 19:58:00\": {\n",
" \"1. open\": \"115.4800\",\n",
" \"2. high\": \"115.4800\",\n",
" \"3. low\": \"115.2000\",\n",
" \"4. close\": \"115.2000\",\n",
" \"5. volume\": \"6\"\n",
" },\n",
" \"2025-10-22 19:57:00\": {\n",
" \"1. open\": \"115.4800\",\n",
" \"2. high\": \"115.4800\",\n",
" \"3. low\": \"115.3800\",\n",
" \"4. close\": \"115.3800\",\n",
" \"5. volume\": \"170\"\n",
" },\n",
" \"2025-10-22 19:56:00\n",
"80589 []\n",
" \"1. Information\": \"Intraday (1min) open, high, low, close prices and volume\",\n"
]
},
{
"ename": "ValueError",
"evalue": "1 columns passed, passed data had 5 columns",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/internals/construction.py:939\u001b[0m, in \u001b[0;36m_finalize_columns_and_data\u001b[0;34m(content, columns, dtype)\u001b[0m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 939\u001b[0m columns \u001b[38;5;241m=\u001b[39m \u001b[43m_validate_or_indexify_columns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontents\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 941\u001b[0m \u001b[38;5;66;03m# GH#26429 do not raise user-facing AssertionError\u001b[39;00m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/internals/construction.py:986\u001b[0m, in \u001b[0;36m_validate_or_indexify_columns\u001b[0;34m(content, columns)\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_mi_list \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(columns) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(content): \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m 985\u001b[0m \u001b[38;5;66;03m# caller's responsibility to check for this...\u001b[39;00m\n\u001b[0;32m--> 986\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m(\n\u001b[1;32m 987\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(columns)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m columns passed, passed data had \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(content)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m columns\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 989\u001b[0m )\n\u001b[1;32m 990\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_mi_list:\n\u001b[1;32m 991\u001b[0m \u001b[38;5;66;03m# check if nested list column, length of each sub-list should be equal\u001b[39;00m\n",
"\u001b[0;31mAssertionError\u001b[0m: 1 columns passed, passed data had 5 columns",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[25], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Alpha Vantage\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43muse_alpha_vantage\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtickers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDATA_RAW\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrealdata.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[22], line 64\u001b[0m, in \u001b[0;36muse_alpha_vantage\u001b[0;34m(tickers, out_file, time)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m year \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m):\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m month \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m13\u001b[39m):\n\u001b[0;32m---> 64\u001b[0m df_temp \u001b[38;5;241m=\u001b[39m \u001b[43malpha_vantage_get_ticker_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mticker\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myear\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myear\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmonth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmonth\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m df_temp \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 68\u001b[0m t_dfs\u001b[38;5;241m.\u001b[39mappend(df_temp)\n",
"Cell \u001b[0;32mIn[22], line 53\u001b[0m, in \u001b[0;36malpha_vantage_get_ticker_data\u001b[0;34m(ticker, time, year, month)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcsv_str_to_df\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdecoded_content\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mticker\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[22], line 11\u001b[0m, in \u001b[0;36mcsv_str_to_df\u001b[0;34m(decoded_content, ticker)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(lines[\u001b[38;5;241m2\u001b[39m])\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m#while(1):pass\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDataFrame\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrow\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mlines\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mlines\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mreset_index(drop\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mset_index(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 18\u001b[0m data\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data\u001b[38;5;241m.\u001b[39mindex)\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/frame.py:851\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 849\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 850\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[0;32m--> 851\u001b[0m arrays, columns, index \u001b[38;5;241m=\u001b[39m \u001b[43mnested_data_to_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 852\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# error: Argument 3 to \"nested_data_to_arrays\" has incompatible\u001b[39;49;00m\n\u001b[1;32m 853\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type \"Optional[Collection[Any]]\"; expected \"Optional[Index]\"\u001b[39;49;00m\n\u001b[1;32m 854\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 855\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 856\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m 857\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 859\u001b[0m mgr \u001b[38;5;241m=\u001b[39m arrays_to_mgr(\n\u001b[1;32m 860\u001b[0m arrays,\n\u001b[1;32m 861\u001b[0m columns,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 864\u001b[0m typ\u001b[38;5;241m=\u001b[39mmanager,\n\u001b[1;32m 865\u001b[0m )\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/internals/construction.py:520\u001b[0m, in \u001b[0;36mnested_data_to_arrays\u001b[0;34m(data, columns, index, dtype)\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_named_tuple(data[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;129;01mand\u001b[39;00m columns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 518\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(data[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39m_fields)\n\u001b[0;32m--> 520\u001b[0m arrays, columns \u001b[38;5;241m=\u001b[39m \u001b[43mto_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m columns \u001b[38;5;241m=\u001b[39m ensure_index(columns)\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/internals/construction.py:845\u001b[0m, in \u001b[0;36mto_arrays\u001b[0;34m(data, columns, dtype)\u001b[0m\n\u001b[1;32m 842\u001b[0m data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mtuple\u001b[39m(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data]\n\u001b[1;32m 843\u001b[0m arr \u001b[38;5;241m=\u001b[39m _list_to_arrays(data)\n\u001b[0;32m--> 845\u001b[0m content, columns \u001b[38;5;241m=\u001b[39m \u001b[43m_finalize_columns_and_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 846\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m content, columns\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/core/internals/construction.py:942\u001b[0m, in \u001b[0;36m_finalize_columns_and_data\u001b[0;34m(content, columns, dtype)\u001b[0m\n\u001b[1;32m 939\u001b[0m columns \u001b[38;5;241m=\u001b[39m _validate_or_indexify_columns(contents, columns)\n\u001b[1;32m 940\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAssertionError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 941\u001b[0m \u001b[38;5;66;03m# GH#26429 do not raise user-facing AssertionError\u001b[39;00m\n\u001b[0;32m--> 942\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(contents) \u001b[38;5;129;01mand\u001b[39;00m contents[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mobject_:\n\u001b[1;32m 945\u001b[0m contents \u001b[38;5;241m=\u001b[39m convert_object_array(contents, dtype\u001b[38;5;241m=\u001b[39mdtype)\n",
"\u001b[0;31mValueError\u001b[0m: 1 columns passed, passed data had 5 columns"
]
}
],
"source": [
"# Alpha Vantage\n",
"df = use_alpha_vantage(tickers, os.path.join(DATA_RAW, \"realdata.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Alpaca\n",
"df = use_alpaca(\n",
" tickers + random, os.path.join(DATA_RAW, \"realdata_alp_1h.csv\"), timeframe=\"1Hour\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Polygon\n",
"df = use_polygon(\n",
" materials_equities,\n",
" os.path.join(DATA_RAW, \"materials_1h.csv\"),\n",
" multiplier=1,\n",
" timespan=\"hour\",\n",
" start=\"2000-01-01\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Read Data From All-Data CSV (Multi Index Columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_all = read_data(os.path.join(DATA_RAW, \"realdata.csv\"))\n",
"# df = read_data(\"tsla_aapl.csv\")\n",
"print(df_all.head())\n",
"print(df.head())\n",
"print(df_all.columns)\n",
"print(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Concatenate two datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run = False\n",
"if run and not df.columns.equals(df_all.columns):\n",
" df_new = write_df(\n",
" pd.concat([df_all, df], axis=1), os.path.join(DATA_RAW, \"realdata.csv\")\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Remove rows with a lot of NANs\n",
"This is important when using FX data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_f = df.copy()\n",
"df_f = df_f.dropna(axis=0, thresh=50) #80\n",
"write_df(df_f, os.path.join(DATA_RAW, \"realdata_pol_1h.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.tail(80)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
},
"vscode": {
"interpreter": {
"hash": "51980e48e269f7c05efac26b22569386591d7f1d45336266d53ed7fc3ab7efc6"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|