data_loader.py

import yfinance as yf
import pandas as pd
import streamlit as st
from datetime import datetime, timedelta
import io

@st.cache_data(ttl=3600)
def load_nifty50_symbols():
    """Load Nifty 50 symbols"""
    symbols = [
        # IT Sector
        "TCS.NS", "INFY.NS", "HCLTECH.NS", "TECHM.NS", "WIPRO.NS",

        # Banking & Finance
        "HDFCBANK.NS", "ICICIBANK.NS", "SBIN.NS", "AXISBANK.NS", "KOTAKBANK.NS",
        "BAJFINANCE.NS", "BAJAJFINSV.NS", "HDFC.NS", "INDUSINDBK.NS",

        # Energy & Oil
        "RELIANCE.NS", "ONGC.NS", "POWERGRID.NS", "NTPC.NS", "BPCL.NS",

        # Automobile
        "TATAMOTORS.NS", "M&M.NS", "MARUTI.NS", "HEROMOTOCO.NS", "EICHERMOT.NS",

        # Consumer Goods
        "HINDUNILVR.NS", "ITC.NS", "NESTLEIND.NS", "BRITANNIA.NS", "TITAN.NS",

        # Metals & Mining
        "TATASTEEL.NS", "HINDALCO.NS", "JSWSTEEL.NS", "COALINDIA.NS",

        # Pharmaceuticals
        "SUNPHARMA.NS", "DRREDDY.NS", "CIPLA.NS", "DIVISLAB.NS",

        # Infrastructure
        "LT.NS", "ADANIPORTS.NS", "ULTRACEMCO.NS", "SHREECEM.NS", "GRASIM.NS",

        # Telecommunications
        "BHARTIARTL.NS",

        # Others
        "ASIANPAINT.NS", "HDFCLIFE.NS", "SBILIFE.NS", "UPL.NS", "ADANIENT.NS",
        "BAJAJ-AUTO.NS", "APOLLOHOSP.NS", "DMART.NS", "PIDILITIND.NS"
    ]
    return symbols

@st.cache_data(ttl=3600)
def load_market_indices():
    """Load major Indian market indices"""
    indices = {
        "NIFTY 50": "^NSEI",
        "SENSEX": "^BSESN",
        "BANK NIFTY": "^NSEBANK",
        "NIFTY IT": "NIFTYIT.NS",
        "NIFTY PHARMA": "NIFTYPHARMA.NS",
        "NIFTY AUTO": "NIFTYAUTO.NS",
        "NIFTY FMCG": "NIFTYFMCG.NS",
        "NIFTY METAL": "NIFTYMETAL.NS",
        "BSE 500": "^BSEFTY",
        "NSE 500": "NIFTY500.NS"
    }
    return indices

def process_uploaded_file(uploaded_file):
    """Process uploaded CSV/Excel file"""
    try:
        if uploaded_file.name.endswith('.csv'):
            df = pd.read_csv(uploaded_file)
        elif uploaded_file.name.endswith(('.xls', '.xlsx')):
            df = pd.read_excel(uploaded_file)
        else:
            raise ValueError("Unsupported file format")

        # Ensure required columns exist
        required_columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
        if not all(col in df.columns for col in required_columns):
            raise ValueError("Missing required columns")

        # Convert Date column to datetime
        df['Date'] = pd.to_datetime(df['Date'])
        return df
    except Exception as e:
        st.error(f"Error processing file: {str(e)}")
        return None

@st.cache_data(ttl=3600)
def fetch_stock_data(symbol, period='1y'):
    """Fetch stock data from Yahoo Finance"""
    try:
        stock = yf.Ticker(symbol)
        data = stock.history(period=period)
        return data
    except Exception as e:
        st.error(f"Error fetching data for {symbol}: {str(e)}")
        return None

@st.cache_data(ttl=3600)
def get_stock_info(symbol):
    """Get stock information"""
    try:
        stock = yf.Ticker(symbol)
        info = stock.info
        return {
            'name': info.get('longName', symbol),
            'sector': info.get('sector', 'N/A'),
            'market_cap': info.get('marketCap', 0),
            'pe_ratio': info.get('trailingPE', 0),
            'volume': info.get('volume', 0),
            'recommendation': info.get('recommendationKey', 'N/A'),
            'target_price': info.get('targetMeanPrice', 0)
        }
    except Exception as e:
        st.error(f"Error fetching info for {symbol}: {str(e)}")
        return None

def get_market_summary():
    """Get market summary for indices"""
    try:
        indices = load_market_indices()
        summaries = {}

        for name, symbol in indices.items():
            ticker = yf.Ticker(symbol)
            data = ticker.history(period='1d')

            if not data.empty:
                summaries[name] = {
                    'index_value': data['Close'].iloc[-1],
                    'change': data['Close'].iloc[-1] - data['Open'].iloc[0],
                    'change_percent': ((data['Close'].iloc[-1] - data['Open'].iloc[0]) / data['Open'].iloc[0]) * 100
                }

        return summaries
    except Exception as e:
        st.error(f"Error fetching market summary: {str(e)}")
        return None

def get_stock_suggestions(data):
    """Generate stock suggestions based on technical indicators"""
    try:
        # Simple moving averages
        data['SMA20'] = data['Close'].rolling(window=20).mean()
        data['SMA50'] = data['Close'].rolling(window=50).mean()

        # Get latest values
        current_price = data['Close'].iloc[-1]
        sma20 = data['SMA20'].iloc[-1]
        sma50 = data['SMA50'].iloc[-1]

        # Generate suggestion
        if sma20 > sma50:
            trend = "BULLISH"
            suggestion = "Consider buying. Price is above moving averages."
        elif sma20 < sma50:
            trend = "BEARISH"
            suggestion = "Consider selling. Price is below moving averages."
        else:
            trend = "NEUTRAL"
            suggestion = "Market is sideways. Wait for clear trend."

        return {
            'trend': trend,
            'suggestion': suggestion,
            'current_price': current_price,
            'sma20': sma20,
            'sma50': sma50
        }
    except Exception as e:
        st.error(f"Error generating suggestions: {str(e)}")
        return None