Update app.py

app.py

@@ -4,7 +4,7 @@ import yfinance as yf
 import requests
 from bs4 import BeautifulSoup
 import xml.etree.ElementTree as ET
-import re  # Add this import at the top of your code
+import numpy as np
 
 # Set up the models for Named Entity Recognition, Sentiment Analysis, and Text Generation
 ner_model = "Cassie-0121/fin-bert-finetuned-ner"
@@ -21,15 +21,16 @@ st.title("AI-Powered Financial Analysis App")
 # Sidebar for selecting stock
 st.sidebar.header("Select Stock for Analysis")
 top_100_stocks = [
-    "AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "META", "NVDA", "BRK-B", "JNJ", "JPM", 
-    "V", "PG", "UNH", "DIS", "MA", "HD", "BAC", "XOM", "VZ", "PFE", "KO", "PEP", "MRK", 
-    "WMT", "CSCO", "INTC", "NFLX", "NVDA", "AMD", "BABA", "T", "GS", "NKE", "WFC", "BA", 
-    "MCD", "CVX", "BMY", "GS", "IBM", "GE", "DELL", "CAT", "XLC", "LMT", "TMO", "RTX", 
-    "ZTS", "MDT", "AMAT", "SBUX", "SPGI", "PYPL", "CVS", "UNP", "MO", "CSX", "UPS", "LLY", 
-    "HUM", "C", "ADBE", "LULU", "PLD", "MS", "USB", "TGT", "CCI", "SYK", "BAX", "VRTX", 
-    "ADP", "FIS", "AMGN", "FDX", "ISRG", "AIG", "SCHW", "TRV", "ACN", "MCO", "AON", "EXC", 
-    "MDLZ", "REGN", "EOG", "INTU", "AMT", "AFL", "KLAC", "MELI", "COP", "CB", "ADSK", "LRCX", 
-    "F", "EQIX", "DHR", "TROW", "APD", "CHTR", "DOW", "NSC", "GE", "LHX", "ALL", "PXD"
+    "AAPL", "MSFT", "GOOGL", "AMZN", "TSLA", "META", "NVDA", "BRK-B", "JNJ", "JPM",
+    "V", "PG", "UNH", "DIS", "MA", "HD", "BAC", "XOM", "VZ", "PFE",
+    "KO", "PEP", "MRK", "WMT", "CSCO", "ABT", "CMCSA", "ADBE", "NKE", "CRM",
+    "T", "INTC", "ORCL", "IBM", "AVGO", "TXN", "LLY", "NFLX", "QCOM", "COST",
+    "MCD", "HON", "PM", "BMY", "WBA", "DHR", "UNP", "RTX", "ACN", "UPS",
+    "LOW", "CAT", "SCHW", "SPGI", "LIN", "NEE", "CVS", "MDT", "INTU", "BLK",
+    "GS", "AMT", "MS", "DE", "CI", "BA", "C", "ZTS", "PLD", "GE",
+    "NOW", "MO", "SBUX", "AMGN", "SYK", "MMM", "ADP", "TGT", "DUK", "SO",
+    "BKNG", "CB", "ANTM", "AXP", "BDX", "ISRG", "TJX", "MMC", "APD", "NSC",
+    "USB", "ECL", "CL", "CCI", "WM", "PGR", "MDLZ", "EQIX", "LMT", "ATVI"
 ]
 selected_stock = st.sidebar.selectbox("Choose a stock for analysis:", top_100_stocks)
 
@@ -112,60 +113,57 @@ else:
 # Investment advice for a specific stock
 st.header("Investment Advice or Strategy")
 
-# Get user input for stock symbol (e.g., "AAPL", "GOOGL", "MSFT")
-stock_symbol = selected_stock  # input("Enter the stock symbol (e.g., AAPL, GOOGL, MSFT): ").upper()
-
-# Fetch stock data for the selected symbol
-stock = yf.Ticker(stock_symbol)
-
-# Get historical data for the last month
+# Fetch stock data for the last month to calculate volatility
+stock = yf.Ticker(selected_stock)
 data = stock.history(period="1mo")
 
-# Debugging: Show the data to check if it's being fetched correctly
-st.write(data)
-
 # Ensure data is available
 if len(data) < 2:
-    st.warning(f"Not enough data available for {stock_symbol}. Please check the stock symbol.")
+    st.warning(f"Not enough data available for {selected_stock}. Please check the stock symbol.")
 else:
     # Get the latest closing price
     latest_close = data['Close'][-1]
 
-    # Get the 50-day moving average (optional, can be adjusted)
+    # Get the 50-day moving average (if enough data is available)
     moving_avg_50 = data['Close'].rolling(window=50).mean().iloc[-1] if len(data) > 50 else None
 
-    # Calculate stock volatility (standard deviation of returns)
-    returns = data['Close'].pct_change()
-    volatility = returns.std()
+    # Calculate daily returns
+    data['Daily Return'] = data['Close'].pct_change()
+
+    # Calculate daily volatility (standard deviation of daily returns)
+    daily_volatility = data['Daily Return'].std()
+
+    # Annualize the daily volatility to match Yahoo Finance's convention
+    annualized_volatility = daily_volatility * np.sqrt(252)
 
-    # Prepare the investment advice based on the stock data and trends
+    # Prepare the investment advice based on stock data and trends
     investment_advice = f"""
-    Investment Strategy for {stock_symbol}:
+    Investment Strategy for {selected_stock}:
     - Latest Closing Price: ${latest_close:.2f}
     """
     
-    # Add the 50-day moving average only if it's not None
+    # Add the 50-day moving average only if it's available
     if moving_avg_50 is not None:
-        investment_advice += f"- 50-Day Moving Average: {moving_avg_50:.2f}\n"
+        investment_advice += f"- 50-Day Moving Average: ${moving_avg_50:.2f}\n"
     else:
         investment_advice += "- 50-Day Moving Average: Not available (insufficient data)\n"
 
     investment_advice += f"""
-    - Stock Volatility (1-Month): {volatility:.2%}
-    Based on recent stock data, {stock_symbol} is currently trading at ${latest_close:.2f}. The stock has shown a recent uptrend and is supported by strong fundamentals, making it a solid investment for long-term growth. However, the volatility (standard deviation of returns) indicates some price fluctuations in the short term, so investors should be cautious of potential short-term risks.
+    - Stock Volatility (Annualized 1-Month): {annualized_volatility:.2%}
+    Based on recent stock data, {selected_stock} is currently trading at ${latest_close:.2f}. The stock has shown a recent uptrend and is supported by strong fundamentals, making it a solid investment for long-term growth. However, the volatility indicates some price fluctuations in the short term, so investors should be cautious of potential short-term risks.
     ### Suggested Actions:
     1. **Consider Buying** if the stock is trading below its 50-day moving average, as this may indicate an undervalued opportunity for long-term investors.
     2. **Watch for Volatility**: If the stock experiences significant price swings, it may indicate market uncertainty or external factors affecting the stock.
-    3. **Monitor Earnings Reports**: {stock_symbol}'s quarterly earnings report will provide important insights into its revenue streams and future prospects.
-    4. **Long-term Investment**: Due to its strong market position and potential growth, {stock_symbol} remains a good candidate for long-term portfolios.
+    3. **Monitor Earnings Reports**: {selected_stock}'s quarterly earnings report will provide important insights into its revenue streams and future prospects.
+    4. **Long-term Investment**: Due to its strong market position and potential growth, {selected_stock} remains a good candidate for long-term portfolios.
     ### Risks:
     - **Market Volatility**: As indicated by the volatility of returns, there could be some short-term price swings.
     - **Competition**: The competitive landscape in the industry is constantly evolving, which could impact future performance.
     ### Expected Benefits:
-    - **Growth Potential**: {stock_symbol}'s growth in its core business or new ventures should continue to drive long-term stock price appreciation.
+    - **Growth Potential**: {selected_stock}'s growth in its core business or new ventures should continue to drive long-term stock price appreciation.
     - **Strong Cash Flow**: The company’s significant cash reserves may allow for innovation, stock buybacks, and dividend payments to shareholders.
-    In conclusion, {stock_symbol} represents a good long-term investment, but investors should be mindful of its short-term volatility.
+    In conclusion, {selected_stock} is a strong stock with long-term growth potential, but investors should monitor market trends and the stock's volatility to time their investments effectively.
     """
 
-    # Display the investment strategy in a readable format
+    # Display the generated investment strategy
     st.write(investment_advice)