import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta
import pytz

def is_market_open():
    """Check if US market is currently open"""
    now = datetime.now(pytz.timezone('America/New_York'))
    # Market hours are 9:30 AM - 4:00 PM Eastern Time, Monday to Friday
    is_weekday = now.weekday() < 5
    is_market_hours = 9.5 <= now.hour + (now.minute / 60) <= 16

    # Pre-market (4:00 AM - 9:30 AM) and After-hours (4:00 PM - 8:00 PM)
    is_extended_hours = (4 <= now.hour + (now.minute / 60) <= 20)

    return is_weekday and (is_market_hours or is_extended_hours)

def fetch_market_data(symbol, period='1d', interval='15m'):
    """Fetch market data from Yahoo Finance"""
    try:
        # Check if we have a valid symbol
        if not symbol:
            raise Exception("Please enter a valid trading symbol")

        # Always fetch 1 day of data to ensure we have enough history
        ticker = yf.Ticker(symbol)
        df = ticker.history(period='1d', interval='15m', prepost=True)

        if df.empty:
            raise Exception(f"No data available for {symbol}. Please verify the symbol is correct.")

        # Get the market status
        market_status = "Market Open" if is_market_open() else "Market Closed"

        # Trim data based on selected timeframe
        now = datetime.now(pytz.timezone('America/New_York'))
        if period.endswith('m'):
            minutes = int(period[:-1])
            cutoff_time = now - timedelta(minutes=minutes)
        else:
            hours = int(period[:-1])
            cutoff_time = now - timedelta(hours=hours)

        df = df[df.index >= cutoff_time]

        if df.empty:
            raise Exception(f"No recent data available. {market_status}.")

        return df

    except Exception as e:
        if "symbol may be delisted" in str(e).lower():
            raise Exception(f"Symbol {symbol} not found. Please verify the symbol is correct.")
        raise Exception(f"Data fetch error: {str(e)}")

def calculate_performance_metrics(predictions, actual):
    """Calculate prediction performance metrics"""
    if len(predictions) == 0 or len(actual) == 0:
        return {
            'accuracy': 0,
            'success_rate': 0,
            'total_predictions': 0
        }

    correct_predictions = sum(p == a for p, a in zip(predictions, actual))

    return {
        'accuracy': correct_predictions / len(predictions),
        'success_rate': correct_predictions / len(predictions) * 100,
        'total_predictions': len(predictions)
    }