adds descriptive docstrings for mcp

src/app.py

@@ -1,117 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-import yfinance as yf
-import pandas as pd
-from datetime import datetime, timedelta
-import logging
-from typing import Optional
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-app = FastAPI()
-
-# Configure CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Allows all origins
-    allow_credentials=True,
-    allow_methods=["*"],  # Allows all methods
-    allow_headers=["*"],  # Allows all headers
-)
-
-def fetch_stock_data(symbol: str, period: str = "1y") -> pd.DataFrame:
-    """
-    Fetch historical stock data for a given symbol using yfinance with authentication.
-    
-    Args:
-        symbol (str): Stock symbol (e.g., 'AAPL')
-        period (str): Time period to fetch (default: '1y')
-        
-    Returns:
-        pd.DataFrame: DataFrame containing historical stock data
-    """
-    try:
-        # Create a Ticker object with authentication
-        ticker = yf.Ticker(symbol)
-        
-        # Set authentication credentials
-        ticker._session.headers.update({
-            'User-Agent': 'Mozilla/5.0',
-            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
-            'Accept-Language': 'en-US,en;q=0.5',
-            'Connection': 'keep-alive',
-            'Upgrade-Insecure-Requests': '1',
-        })
-        
-        # Fetch historical data
-        df = ticker.history(period=period)
-        
-        if df.empty:
-            logger.warning(f"No data found for symbol {symbol}")
-            return pd.DataFrame()
-            
-        # Reset index to make Date a column
-        df = df.reset_index()
-        
-        # Rename columns to match expected format
-        df = df.rename(columns={
-            'Date': 'date',
-            'Open': 'open',
-            'High': 'high',
-            'Low': 'low',
-            'Close': 'close',
-            'Volume': 'volume'
-        })
-        
-        # Convert date to string format
-        df['date'] = df['date'].dt.strftime('%Y-%m-%d')
-        
-        logger.info(f"Successfully fetched data for {symbol}")
-        return df
-        
-    except Exception as e:
-        logger.error(f"Error fetching data for {symbol}: {str(e)}")
-        return pd.DataFrame()
-
-@app.get("/")
-async def root():
-    return {"message": "Stock Prediction API"}
-
-@app.get("/api/stock/{symbol}")
-async def get_stock_data(symbol: str, period: Optional[str] = "1y"):
-    """
-    Get historical stock data for a given symbol.
-    
-    Args:
-        symbol (str): Stock symbol (e.g., 'AAPL')
-        period (str, optional): Time period to fetch. Defaults to "1y".
-            Valid periods: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
-    
-    Returns:
-        dict: Dictionary containing stock data and metadata
-    """
-    try:
-        df = fetch_stock_data(symbol, period)
-        
-        if df.empty:
-            raise HTTPException(status_code=404, detail=f"No data found for symbol {symbol}")
-        
-        # Convert DataFrame to dictionary format
-        data = df.to_dict(orient='records')
-        
-        return {
-            "symbol": symbol,
-            "period": period,
-            "data": data,
-            "count": len(data)
-        }
-        
-    except Exception as e:
-        logger.error(f"Error processing request for {symbol}: {str(e)}")
-        raise HTTPException(status_code=500, detail=str(e))
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000) 

src/data/stock_data.py

@@ -1,62 +0,0 @@
-import yfinance as yf
-import pandas as pd
-from datetime import datetime, timedelta
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-def fetch_stock_data(symbol: str, period: str = "1y") -> pd.DataFrame:
-    """
-    Fetch historical stock data for a given symbol using yfinance with authentication.
-    
-    Args:
-        symbol (str): Stock symbol (e.g., 'AAPL')
-        period (str): Time period to fetch (default: '1y')
-        
-    Returns:
-        pd.DataFrame: DataFrame containing historical stock data
-    """
-    try:
-        # Create a Ticker object with authentication
-        ticker = yf.Ticker(symbol)
-        
-        # Set authentication credentials
-        ticker._session.headers.update({
-            'User-Agent': 'Mozilla/5.0',
-            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
-            'Accept-Language': 'en-US,en;q=0.5',
-            'Connection': 'keep-alive',
-            'Upgrade-Insecure-Requests': '1',
-        })
-        
-        # Fetch historical data
-        df = ticker.history(period=period)
-        
-        if df.empty:
-            logger.warning(f"No data found for symbol {symbol}")
-            return pd.DataFrame()
-            
-        # Reset index to make Date a column
-        df = df.reset_index()
-        
-        # Rename columns to match expected format
-        df = df.rename(columns={
-            'Date': 'date',
-            'Open': 'open',
-            'High': 'high',
-            'Low': 'low',
-            'Close': 'close',
-            'Volume': 'volume'
-        })
-        
-        # Convert date to string format
-        df['date'] = df['date'].dt.strftime('%Y-%m-%d')
-        
-        logger.info(f"Successfully fetched data for {symbol}")
-        return df
-        
-    except Exception as e:
-        logger.error(f"Error fetching data for {symbol}: {str(e)}")
-        return pd.DataFrame()