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README.md

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----
-title: Finance Tracker
-emoji: 🐢
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.35.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Finance Tracker
+emoji: 🐢
+colorFrom: purple
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.35.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,5 +1,79 @@
-import streamlit as st
-
-
-x = st.slider("Select a value")
-st.write(x, "squared is", x * x)
+import streamlit as st
+from transformers import pipeline
+import google.generativeai as genai
+import yfinance as yf
+import matplotlib.pyplot as plt
+import pandas as pd
+import func
+import news_scraper
+import requests
+from bs4 import BeautifulSoup
+import json
+
+#
+
+# S E T U P
+
+#
+
+# TODO: deploy  
+
+fin_data = ""
+pipe = pipeline(
+    "text-classification",
+    model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis",
+)
+API_KEY = "AIzaSyDnRd4-UvV4U9oYcZfLXRT224pnU0KwEao"
+model = genai.GenerativeModel("gemini-1.5-flash")
+genai.configure(api_key=API_KEY)
+fig = plt.figure(figsize=(4, 4))
+
+
+st.title("Stock Analysis and Prediction")
+
+
+#  FIN INDICATOR CHARTS AND MODELS
+stock_name = st.text_input(label="enter the ticker name")
+# news_scraper
+history = yf.download(stock_name, start="2023-01-01")
+stck = yf.Ticker(stock_name)
+
+dict = stck.info
+# st.write(dict)
+df = pd.DataFrame.from_dict(dict, orient="index")
+df = df.reset_index()
+df_str = df.to_string()
+st.write(df_str)
+keywords = [stock_name, "finance", "news news news"]
+news_scraper.perform_search(keywords)
+
+with open("results.json", "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+text_descriptions = ""
+for frame in data:
+
+    text_descriptions += "Title: " + frame["Title"]
+    text_descriptions += "  " + (frame["Description"])
+
+st.write(text_descriptions)
+# SENTIMENT TRACKER
+# TODO : CONNECT THE SCRAPER TO THE SENTIMENT PIPELINE
+output_sentiment = pipe(text_descriptions)
+st.write(output_sentiment)
+
+prompt = f"You are a financial analyst, given relevant data provide only the pros and cons of the stock provide a buy reccomendation on a scale of 1 to 10. This is the financial data {df_str} . Consider the following news : {text_descriptions}, also here is a sentiment score of the recent news{output_sentiment}."
+
+
+# GEMINI API RESPONSE CODE
+response = model.generate_content(prompt)
+st.write(response.text)
+
+
+# st.line_chart(history["Close"])
+fig1 = func.plot_column(history, "Close")
+st.pyplot(fig1)
+st.write("% Change")
+fig2 = func.plot_column(history, "Volume")
+st.line_chart(history["Close"].pct_change())
+st.pyplot(fig2)

env.py

@@ -0,0 +1 @@
+global API_KEY

func.py

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+import matplotlib.pyplot as plt
+import numpy as np
+
+
+#  HELPER FUNCTIONS:
+def plot_column(dataframe, column_name):
+    plt.figure(figsize=(6, 3))
+    plt.plot(dataframe[column_name])
+    plt.title(f"Plot of {column_name}")
+    plt.xlabel("Index")
+    plt.ylabel(column_name)
+    plt.grid(True)
+    return plt
+
+
+# Calculate EMA
+def ema(close, period=20):
+    return close.ewm(span=period, adjust=False).mean()
+
+
+# Calculate RSI
+def rsi(close, period=14):
+    delta = close.diff()
+    gain, loss = delta.copy(), delta.copy()
+    gain[gain < 0] = 0
+    loss[loss > 0] = 0
+    avg_gain = gain.rolling(period).mean()
+    avg_loss = abs(loss.rolling(period).mean())
+    rs = avg_gain / avg_loss
+    rsi = 100.0 - (100.0 / (1.0 + rs))
+    return rsi
+
+
+# Calculate MACD
+def macd(close, fast_period=12, slow_period=26, signal_period=9):
+    fast_ema = close.ewm(span=fast_period, adjust=False).mean()
+    slow_ema = close.ewm(span=slow_period, adjust=False).mean()
+    macd_line = fast_ema - slow_ema
+    signal_line = macd_line.ewm(span=signal_period, adjust=False).mean()
+    histogram = macd_line - signal_line
+    return macd_line
+
+
+# Calculate OBV
+def obv(close, volume):
+    obv = np.where(
+        close > close.shift(), volume, np.where(close < close.shift(), -volume, 0)
+    ).cumsum()
+    return obv

functions.py

@@ -0,0 +1,49 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+#  HELPER FUNCTIONS:
+def plot_column(dataframe, column_name):
+    plt.figure(figsize=(6, 3))
+    plt.plot(dataframe[column_name])
+    plt.title(f"Plot of {column_name}")
+    plt.xlabel("Index")
+    plt.ylabel(column_name)
+    plt.grid(True)
+    return plt
+
+
+# Calculate EMA
+def ema(close, period=20):
+    return close.ewm(span=period, adjust=False).mean()
+
+
+# Calculate RSI
+def rsi(close, period=14):
+    delta = close.diff()
+    gain, loss = delta.copy(), delta.copy()
+    gain[gain < 0] = 0
+    loss[loss > 0] = 0
+    avg_gain = gain.rolling(period).mean()
+    avg_loss = abs(loss.rolling(period).mean())
+    rs = avg_gain / avg_loss
+    rsi = 100.0 - (100.0 / (1.0 + rs))
+    return rsi
+
+
+# Calculate MACD
+def macd(close, fast_period=12, slow_period=26, signal_period=9):
+    fast_ema = close.ewm(span=fast_period, adjust=False).mean()
+    slow_ema = close.ewm(span=slow_period, adjust=False).mean()
+    macd_line = fast_ema - slow_ema
+    signal_line = macd_line.ewm(span=signal_period, adjust=False).mean()
+    histogram = macd_line - signal_line
+    return macd_line
+
+
+# Calculate OBV
+def obv(close, volume):
+    obv = np.where(
+        close > close.shift(), volume, np.where(close < close.shift(), -volume, 0)
+    ).cumsum()
+    return obv

news_scraper.py

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+import requests
+from bs4 import BeautifulSoup
+import json
+
+excluded_urls = ["finance.yahoo.com", "google.com/finance"]
+
+
+def search_duckduckgo(keywords):
+    url = f"https://duckduckgo.com/html/?q={'+'.join(keywords)}"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"
+    }
+
+    try:
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        return response.text
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching search results: {e}")
+        return ""
+
+
+def parse_results(html, keywords):
+    soup = BeautifulSoup(html, "html.parser")
+    results = soup.select(".result")
+    parsed_results = []
+
+    for result in results:
+        try:
+            link = result.select_one(".result__a").get(
+                "href"
+            )  # Changed selector for link
+            title = result.select_one(".result__a").text  # Changed selector for title
+            description = result.select_one(
+                ".result__snippet"
+            )  # Kept the same selector for description
+
+            if description:
+                description = description.text
+            else:
+                description = ""
+
+            result_data = {
+                "Link": link,
+                "Title": title,
+                "Description": description,
+            }
+
+            # Check if the link is not in excluded URLs
+            if not any(excluded_url in link for excluded_url in excluded_urls):
+                # Check if any keyword is in title, description, or link
+                if any(
+                    keyword.lower() in result_data["Title"].lower()
+                    or keyword.lower() in result_data["Description"].lower()
+                    or keyword.lower() in result_data["Link"].lower()
+                    for keyword in keywords
+                ):
+                    print(result_data)
+                    parsed_results.append(result_data)
+        except Exception as e:
+            print(f"Error parsing result: {e}")
+
+    return parsed_results
+
+
+# keywords = ["tatasteel", "finance", "news"]
+
+
+def perform_search(keywords):
+    html = search_duckduckgo(keywords)
+
+    if html:
+        results = parse_results(html, keywords)
+        if results:
+            with open("results.json", "w", encoding="utf-8") as f:
+                json.dump(results, f, ensure_ascii=False, indent=4)
+        else:
+            print("No results found.")
+    else:
+        print("Failed to fetch search results.")
+
+
+# if __name__ == "__main__":
+# perform_search(keywords = keywords)