examples/ema_strategy_example.py

"""
EMA 50/200 Crossover Strategy - Usage Examples

Demonstrates the Golden Cross / Death Cross trading strategy.
"""

import pandas as pd
import yfinance as yf
from src.core.trading.ema_crossover_strategy import EMACrossoverStrategy


def example_1_basic_usage():
    """Example 1: Basic EMA crossover analysis."""
    print("\n=== Example 1: Basic EMA Crossover ===\n")

    # Initialize strategy
    strategy = EMACrossoverStrategy(
        fast_ema=50,
        slow_ema=200,
        use_volume_filter=True,
        use_adx_filter=True
    )

    # Download data
    ticker = "AAPL"
    data = yf.download(ticker, period="2y", progress=False)

    # Generate signals
    signals = strategy.generate_signals(data, ticker=ticker)

    # Display recent signals
    print(f"Analysis for {ticker}:")
    print(f"  Current Price: ${signals['Close'].iloc[-1]:.2f}")
    print(f"  EMA 50: ${signals['EMA_Fast'].iloc[-1]:.2f}")
    print(f"  EMA 200: ${signals['EMA_Slow'].iloc[-1]:.2f}")
    print(f"  Position: {strategy.get_current_position(signals)}")

    # Find recent crossovers
    golden_crosses = signals[signals['Golden_Cross']].tail(3)
    death_crosses = signals[signals['Death_Cross']].tail(3)

    if not golden_crosses.empty:
        print(f"\nRecent Golden Crosses:")
        for date, row in golden_crosses.iterrows():
            print(f"  {date.date()}: ${row['Close']:.2f}")

    if not death_crosses.empty:
        print(f"\nRecent Death Crosses:")
        for date, row in death_crosses.iterrows():
            print(f"  {date.date()}: ${row['Close']:.2f}")


def example_2_market_scan():
    """Example 2: Scan market for signals."""
    print("\n=== Example 2: Market Scan ===\n")

    # Initialize strategy
    strategy = EMACrossoverStrategy()

    # Define tickers to scan
    tickers = [
        "AAPL", "MSFT", "GOOGL", "AMZN", "NVDA",
        "TSLA", "META", "AMD", "NFLX", "DIS"
    ]

    # Data loader function
    def load_data(ticker, period):
        return yf.download(ticker, period=period, progress=False)

    # Scan market
    print("Scanning market for EMA crossover signals...\n")
    results = strategy.scan_market(tickers, load_data, period="1y")

    if not results.empty:
        print(f"\nFound {len(results)} signals:\n")
        print(results.to_string(index=False))
    else:
        print("\nNo signals found.")


def example_3_custom_parameters():
    """Example 3: Custom EMA parameters."""
    print("\n=== Example 3: Custom EMA 20/50 ===\n")

    # Faster crossover (20/50 instead of 50/200)
    strategy = EMACrossoverStrategy(
        fast_ema=20,
        slow_ema=50,
        atr_multiplier_sl=1.5,  # Tighter stop
        atr_multiplier_tp=3.0,   # Closer target
        adx_threshold=25,        # Stronger trend required
        use_volume_filter=True,
        use_adx_filter=True
    )

    print(strategy.get_strategy_description())

    # Test on volatile stock
    ticker = "NVDA"
    data = yf.download(ticker, period="6mo", progress=False)
    signals = strategy.generate_signals(data, ticker=ticker)

    print(f"\nAnalysis for {ticker}:")
    print(f"  Current Position: {strategy.get_current_position(signals)}")
    print(f"  ADX: {signals['ADX'].iloc[-1]:.1f}")
    print(f"  Volume Ratio: {signals['Volume_Ratio'].iloc[-1]:.2f}x")


def example_4_signal_details():
    """Example 4: Detailed signal analysis."""
    print("\n=== Example 4: Signal Details ===\n")

    strategy = EMACrossoverStrategy()

    ticker = "TSLA"
    data = yf.download(ticker, period="1y", progress=False)
    signals = strategy.generate_signals(data, ticker=ticker)

    # Find most recent signal
    recent_long = signals[signals['Signal_Long']].tail(1)
    recent_short = signals[signals['Signal_Short']].tail(1)

    if not recent_long.empty:
        row = recent_long.iloc[0]
        print(f"Most Recent LONG Signal for {ticker}:")
        print(f"  Date: {recent_long.index[0].date()}")
        print(f"  Price: ${row['Close']:.2f}")
        print(f"  Stop Loss: ${row['Stop_Loss_Long']:.2f} ({((row['Stop_Loss_Long']/row['Close']-1)*100):.1f}%)")
        print(f"  Take Profit: ${row['Take_Profit_Long']:.2f} ({((row['Take_Profit_Long']/row['Close']-1)*100):.1f}%)")
        print(f"  R:R Ratio: {row['RR_Ratio']:.1f}:1")
        print(f"  ADX: {row['ADX']:.1f}")
        print(f"  Volume: {row['Volume_Ratio']:.2f}x average")

    if not recent_short.empty:
        row = recent_short.iloc[0]
        print(f"\nMost Recent SHORT Signal for {ticker}:")
        print(f"  Date: {recent_short.index[0].date()}")
        print(f"  Price: ${row['Close']:.2f}")
        print(f"  Stop Loss: ${row['Stop_Loss_Short']:.2f}")
        print(f"  Take Profit: ${row['Take_Profit_Short']:.2f}")
        print(f"  R:R Ratio: {row['RR_Ratio']:.1f}:1")


def example_5_backtest_simulation():
    """Example 5: Simple backtest simulation."""
    print("\n=== Example 5: Backtest Simulation ===\n")

    strategy = EMACrossoverStrategy()

    ticker = "SPY"
    data = yf.download(ticker, period="5y", progress=False)
    signals = strategy.generate_signals(data, ticker=ticker)

    # Extract trades
    long_signals = signals[signals['Signal_Long']]
    short_signals = signals[signals['Signal_Short']]

    print(f"Backtest Results for {ticker} (5 years):")
    print(f"  Total LONG signals: {len(long_signals)}")
    print(f"  Total SHORT signals: {len(short_signals)}")

    # Calculate simple stats
    if len(long_signals) > 0:
        avg_adx = long_signals['ADX'].mean()
        avg_vol_ratio = long_signals['Volume_Ratio'].mean()

        print(f"\nLONG Signal Statistics:")
        print(f"  Average ADX: {avg_adx:.1f}")
        print(f"  Average Volume Ratio: {avg_vol_ratio:.2f}x")

    # Show signal distribution by year
    all_signals = pd.concat([long_signals, short_signals])
    if not all_signals.empty:
        signals_by_year = all_signals.groupby(all_signals.index.year).size()
        print(f"\nSignals by Year:")
        for year, count in signals_by_year.items():
            print(f"  {year}: {count} signals")


def example_6_no_filters():
    """Example 6: Pure EMA crossover (no filters)."""
    print("\n=== Example 6: Pure EMA Crossover (No Filters) ===\n")

    # Disable all filters
    strategy = EMACrossoverStrategy(
        use_volume_filter=False,
        use_adx_filter=False
    )

    ticker = "AAPL"
    data = yf.download(ticker, period="2y", progress=False)
    signals = strategy.generate_signals(data, ticker=ticker)

    # Count signals
    total_long = signals['Golden_Cross'].sum()
    total_short = signals['Death_Cross'].sum()

    print(f"Pure EMA Crossover for {ticker}:")
    print(f"  Golden Crosses: {total_long}")
    print(f"  Death Crosses: {total_short}")
    print(f"  Current Position: {strategy.get_current_position(signals)}")


def main():
    """Run all examples."""
    examples = [
        example_1_basic_usage,
        example_2_market_scan,
        example_3_custom_parameters,
        example_4_signal_details,
        example_5_backtest_simulation,
        example_6_no_filters,
    ]

    for example in examples:
        try:
            example()
        except Exception as e:
            print(f"\n✗ Example failed: {e}")
            import traceback
            traceback.print_exc()

        print("\n" + "=" * 70)


if __name__ == "__main__":
    # Run single example
    # example_1_basic_usage()

    # Or run all examples
    main()