Building an AI Agent for Value Investing

# Install required libraries (run this once)
# pip install yfinance pandas matplotlib

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

# Define the ticker symbol and time period
ticker_symbol = "AAPL"  # Apple Inc.
end_date = datetime.now()
start_date = end_date - timedelta(days=365)  # 1 year of data

# Download stock data
stock_data = yf.download(ticker_symbol, start=start_date, end=end_date)

# Display the first few rows of data
print(f"Stock data for {ticker_symbol}:")
print(stock_data.head())

# Get company information
company_info = yf.Ticker(ticker_symbol)

# Get key financial metrics
print("\nKey Financial Metrics:")
print(f"Market Cap: ${company_info.info.get('marketCap', 'N/A'):,}")
print(f"P/E Ratio: {company_info.info.get('trailingPE', 'N/A')}")
print(f"Forward P/E: {company_info.info.get('forwardPE', 'N/A')}")
print(f"Price-to-Book: {company_info.info.get('priceToBook', 'N/A')}")
print(f"Dividend Yield: {company_info.info.get('dividendYield', 'N/A') * 100 if company_info.info.get('dividendYield') else 'N/A'}%")
print(f"Return on Equity: {company_info.info.get('returnOnEquity', 'N/A') * 100 if company_info.info.get('returnOnEquity') else 'N/A'}%")
print(f"Debt-to-Equity: {company_info.info.get('debtToEquity', 'N/A')}")

# Get income statement data
income_stmt = company_info.income_stmt
print("\nRecent Annual Revenue:")
if not income_stmt.empty:
    print(income_stmt.loc['Total Revenue'].iloc[::-1])

# Get balance sheet data
balance_sheet = company_info.balance_sheet
print("\nRecent Annual Total Assets:")
if not balance_sheet.empty:
    print(balance_sheet.loc['Total Assets'].iloc[::-1])

# Store data in a structured format (CSV in this example)
stock_data.to_csv(f"{ticker_symbol}_stock_data.csv")
print(f"\nStock data saved to {ticker_symbol}_stock_data.csv")

# Plot the stock price
plt.figure(figsize=(12, 6))
plt.plot(stock_data['Close'])
plt.title(f"{ticker_symbol} Stock Price - Past Year")
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.grid(True)
plt.tight_layout()
plt.savefig(f"{ticker_symbol}_stock_chart.png")
print(f"Stock chart saved to {ticker_symbol}_stock_chart.png")

# Install required libraries (run this once)
# pip install yfinance alpha_vantage pandas

import yfinance as yf
from alpha_vantage.fundamentaldata import FundamentalData
import pandas as pd
import os

# Set up Alpha Vantage API (you would need to get your own API key)
# Sign up at: https://www.alphavantage.co/support/#api-key
alpha_vantage_api_key = "YOUR_API_KEY"  # Replace with your actual API key
fd = FundamentalData(key=alpha_vantage_api_key)

# Define the ticker symbol
ticker_symbol = "MSFT"  # Microsoft Corporation

# Create a directory to store the data
os.makedirs("financial_data", exist_ok=True)

# 1. Collect data from Yahoo Finance
print(f"Collecting data for {ticker_symbol} from Yahoo Finance...")
yf_ticker = yf.Ticker(ticker_symbol)

# Get company profile
company_profile = {
    "Name": yf_ticker.info.get('longName', 'N/A'),
    "Industry": yf_ticker.info.get('industry', 'N/A'),
    "Sector": yf_ticker.info.get('sector', 'N/A'),
    "Country": yf_ticker.info.get('country', 'N/A'),
    "Website": yf_ticker.info.get('website', 'N/A'),
    "Summary": yf_ticker.info.get('longBusinessSummary', 'N/A')
}

# Save company profile
pd.DataFrame([company_profile]).to_csv(f"financial_data/{ticker_symbol}_profile.csv", index=False)
print(f"Company profile saved to financial_data/{ticker_symbol}_profile.csv")

# Get financial ratios
financial_ratios = {
    "P/E Ratio": yf_ticker.info.get('trailingPE', 'N/A'),
    "Forward P/E": yf_ticker.info.get('forwardPE', 'N/A'),
    "Price-to-Book": yf_ticker.info.get('priceToBook', 'N/A'),
    "Price-to-Sales": yf_ticker.info.get('priceToSalesTrailing12Months', 'N/A'),
    "Dividend Yield (%)": yf_ticker.info.get('dividendYield', 'N/A') * 100 if yf_ticker.info.get('dividendYield') else 'N/A',
    "ROE (%)": yf_ticker.info.get('returnOnEquity', 'N/A') * 100 if yf_ticker.info.get('returnOnEquity') else 'N/A',
    "ROA (%)": yf_ticker.info.get('returnOnAssets', 'N/A') * 100 if yf_ticker.info.get('returnOnAssets') else 'N/A',
    "Debt-to-Equity": yf_ticker.info.get('debtToEquity', 'N/A')
}

# Save financial ratios
pd.DataFrame([financial_ratios]).to_csv(f"financial_data/{ticker_symbol}_ratios_yf.csv", index=False)
print(f"Financial ratios saved to financial_data/{ticker_symbol}_ratios_yf.csv")

# 2. Collect data from Alpha Vantage (if API key is provided)
if alpha_vantage_api_key != "YOUR_API_KEY":
    print(f"\nCollecting data for {ticker_symbol} from Alpha Vantage...")
    
    try:
        # Get income statement
        income_statement, _ = fd.get_income_statement_annual(ticker_symbol)
        income_statement.to_csv(f"financial_data/{ticker_symbol}_income_statement_av.csv")
        print(f"Income statement saved to financial_data/{ticker_symbol}_income_statement_av.csv")
        
        # Get balance sheet
        balance_sheet, _ = fd.get_balance_sheet_annual(ticker_symbol)
        balance_sheet.to_csv(f"financial_data/{ticker_symbol}_balance_sheet_av.csv")
        print(f"Balance sheet saved to financial_data/{ticker_symbol}_balance_sheet_av.csv")
        
        # Get cash flow statement
        cash_flow, _ = fd.get_cash_flow_annual(ticker_symbol)
        cash_flow.to_csv(f"financial_data/{ticker_symbol}_cash_flow_av.csv")
        print(f"Cash flow statement saved to financial_data/{ticker_symbol}_cash_flow_av.csv")
        
        # Get company overview (contains many financial metrics)
        overview, _ = fd.get_company_overview(ticker_symbol)
        overview.to_csv(f"financial_data/{ticker_symbol}_overview_av.csv")
        print(f"Company overview saved to financial_data/{ticker_symbol}_overview_av.csv")
    
    except Exception as e:
        print(f"Error collecting data from Alpha Vantage: {e}")
        print("Note: Alpha Vantage has rate limits for free API keys.")
else:
    print("\nSkipping Alpha Vantage data collection. Please provide a valid API key.")

print("\nData collection complete!")