examples/advanced_macd_trading_example.py

"""
Advanced MACD Trading Strategy - Complete Example
Demonstrates all features: backtesting, market scanning, risk management
"""

import pandas as pd
import yfinance as yf
import warnings

from src.core.trading import AdvancedMACDStrategy, VectorizedBacktest, RiskEngine

warnings.filterwarnings('ignore')


def load_data(ticker: str, period: str = '2y') -> pd.DataFrame:
    """Load historical data using yfinance"""
    return yf.Ticker(ticker).history(period=period)


def main():
    print("=" * 70)
    print("📊 ADVANCED MACD STRATEGY - COMPLETE EXAMPLE")
    print("=" * 70)

    strategy = AdvancedMACDStrategy(
        ema_period=200,
        macd_fast=12,
        macd_slow=26,
        macd_signal=9,
        atr_period=14,
        atr_multiplier_sl=1.5,
        atr_multiplier_tp=3.0,
        adx_period=14,
        adx_threshold=25,
        volume_period=20,
        rsi_period=14,
        use_divergences=False,
        cooldown_candles=5
    )

    risk_engine = RiskEngine(
        max_risk_per_trade=0.02,
        max_portfolio_heat=0.06,
        max_drawdown=0.15,
        kelly_fraction=0.25
    )

    print("\n🔧 STRATEGY SETTINGS:")
    print(f"   • EMA Period: {strategy.ema_period}")
    print(f"   • MACD: {strategy.macd_fast}/{strategy.macd_slow}/{strategy.macd_signal}")
    print(f"   • ATR Stop-Loss: {strategy.atr_multiplier_sl}x")
    print(f"   • ATR Take-Profit: {strategy.atr_multiplier_tp}x")
    print(f"   • ADX Threshold: {strategy.adx_threshold}")

    print("\n🛡️  RISK MANAGEMENT:")
    print(f"   • Risk per Trade: {risk_engine.max_risk_per_trade*100}%")
    print(f"   • Max Portfolio Heat: {risk_engine.max_portfolio_heat*100}%")
    print(f"   • Max Drawdown: {risk_engine.max_drawdown*100}%")

    backtest_ticker = 'AAPL'
    print(f"\n{'=' * 70}")
    print(f"🧪 BACKTESTING: {backtest_ticker}")
    print(f"{'=' * 70}\n")

    data = load_data(backtest_ticker, period='2y')

    if len(data) >= strategy.ema_period:
        backtest = VectorizedBacktest(
            strategy=strategy,
            risk_engine=risk_engine,
            initial_capital=100000,
            commission=0.001
        )

        metrics = backtest.run(data, backtest_ticker)
        backtest.print_report()

        trades_df = backtest.get_trades_df()
        if not trades_df.empty:
            print(f"\n📋 LAST 10 TRADES:")
            cols = ['Type', 'Entry_Date', 'Exit_Date', 'Entry_Price',
                    'Exit_Price', 'PnL', 'PnL_Pct', 'Hit']
            print(trades_df[cols].tail(10).to_string(index=False))
    else:
        print(f"❌ Not enough data for {backtest_ticker}")

    print(f"\n{'=' * 70}")
    print("🔍 MARKET SCANNER")
    print(f"{'=' * 70}\n")

    tickers = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA',
               'NVDA', 'META', 'JPM', 'V', 'WMT']

    print(f"🔎 Scanning {len(tickers)} tickers...\n")

    signals = strategy.scan_market(tickers, load_data, period='6mo')

    if not signals.empty:
        print("\n🎯 FOUND SIGNALS:\n")
        print(signals.to_string(index=False))

        print(f"\n📊 STATISTICS:")
        print(f"   • Total Signals: {len(signals)}")
        print(f"   • LONG: {len(signals[signals['Signal'] == 'LONG'])}")
        print(f"   • SHORT: {len(signals[signals['Signal'] == 'SHORT'])}")
        print(f"   • STRONG: {len(signals[signals['Strength'] == 'STRONG'])}")
        print(f"   • Avg ADX: {signals['ADX'].mean():.1f}")
        print(f"   • Avg RSI: {signals['RSI'].mean():.1f}")
        print(f"   • Avg RR: {signals['RR_Ratio'].mean():.2f}")

        if len(signals) > 0:
            ticker = signals.iloc[0]['Ticker']
            print(f"\n{'=' * 70}")
            print(f"🔬 DETAILED ANALYSIS: {ticker}")
            print(f"{'=' * 70}\n")

            data = load_data(ticker, period='6mo')
            df = strategy.generate_signals(data, ticker=ticker)

            key_cols = ['Close', 'EMA_200', 'Impulse_MACD', 'ZeroLag_MACD',
                       'ADX', 'RSI', 'ATR_Pct', 'Signal_Long', 'Signal_Short']

            print(df[key_cols].tail(10).to_string())

            last = df.iloc[-1]
            signal_type = 'LONG' if last['Signal_Long'] else 'SHORT'

            print(f"\n💼 TRADE PARAMETERS:")
            print(f"   Type:          {signal_type}")
            print(f"   Entry:         ${last['Close']:.2f}")
            print(f"   Stop-Loss:     ${last['Stop_Loss_Long' if signal_type == 'LONG' else 'Stop_Loss_Short']:.2f}")
            print(f"   Take-Profit:   ${last['Take_Profit_Long' if signal_type == 'LONG' else 'Take_Profit_Short']:.2f}")
            print(f"   Risk/Reward:   {last['RR_Ratio_Long']:.2f}")

            print(f"\n📊 INDICATORS:")
            print(f"   ADX:          {last['ADX']:.1f} ({'STRONG ✅' if last['ADX'] > 25 else 'WEAK ⚠️'})")
            print(f"   RSI:          {last['RSI']:.1f}")
            print(f"   ATR%:         {last['ATR_Pct']:.2f}%")
            print(f"   Volume Ratio: {last['Volume'] / last['Avg_Volume']:.2f}x")
            print(f"   Volatility:   {'HIGH ✅' if last['High_Volatility'] else 'LOW ⚠️'}")

            account_value = 100000
            entry_price = last['Close']
            stop_loss = last['Stop_Loss_Long' if signal_type == 'LONG' else 'Stop_Loss_Short']

            position_size = risk_engine.calculate_position_size(
                account_value=account_value,
                entry_price=entry_price,
                stop_loss=stop_loss
            )

            risk_amount = position_size * abs(entry_price - stop_loss)
            risk_pct = (risk_amount / account_value) * 100

            print(f"\n💰 RISK ENGINE (for ${account_value:,.0f}):")
            print(f"   Position Size: {position_size} shares")
            print(f"   Investment:    ${position_size * entry_price:,.2f}")
            print(f"   Risk:          ${risk_amount:,.2f} ({risk_pct:.2f}%)")
            tp = last['Take_Profit_Long' if signal_type == 'LONG' else 'Take_Profit_Short']
            print(f"   Potential P&L: ${position_size * abs(entry_price - tp):,.2f}")
    else:
        print("\n❌ No signals found")

    print(f"\n{'=' * 70}")
    print("✅ EXAMPLE COMPLETED")
    print(f"{'=' * 70}")

    print("""
📚 KEY FEATURES:

1. Zero-Lag MACD - Reduces lag for faster signal generation
2. Impulse MACD - More sensitive, less false signals in sideways markets
3. ATR Volatility Filter - Only trades when volatility is elevated
4. ADX Trend Strength - Confirms trend strength > threshold
5. Volume Filter - Confirms movement with above-average volume
6. RSI & MACD Divergence - Optional: detect price/indicator divergence
7. Risk Management - Position sizing, portfolio heat, drawdown control
8. Kelly Criterion - Optimal position sizing based on strategy stats

📊 INDICATORS USED:

• EMA 200: Trend direction
• MACD: Momentum and reversal points
• ATR: Volatility measurement
• ADX: Trend strength (>20-30 = strong trend)
• RSI: Overbought/oversold
• Volume: Trade confirmation

🎯 SIGNAL GENERATION:

LONG SIGNAL:
  ✓ MACD bullish cross (histogram crosses above 0)
  ✓ Price > EMA 200
  ✓ High volatility (ATR% > mean)
  ✓ Strong trend (ADX > 25)
  ✓ High volume (> 20-day average)

SHORT SIGNAL:
  ✓ MACD bearish cross (histogram crosses below 0)
  ✓ Price < EMA 200
  ✓ High volatility
  ✓ Strong trend
  ✓ High volume

💼 RISK MANAGEMENT:

• Stop-Loss: 1.5x ATR below entry (LONG) / above entry (SHORT)
• Take-Profit: 3.0x ATR above entry (LONG) / below entry (SHORT)
• Risk/Reward Ratio: 1:2 (risking 1 to make 2)
• Fixed Risk: Max 2% risk per trade
• Portfolio Heat: Max 6% total risk
• Max Drawdown: 15% threshold to stop trading

🔧 CUSTOMIZATION:

1. Adjust parameters in AdvancedMACDStrategy:
   - EMA period for trend
   - MACD periods for sensitivity
   - ATR multipliers for risk/reward
   - ADX threshold for trend strength
   - Volume period for filter

2. Enable divergence detection:
   strategy.use_divergences = True

3. Adjust risk parameters in RiskEngine:
   - Max risk per trade
   - Portfolio heat limit
   - Max drawdown
   - Kelly fraction

📈 NEXT STEPS:

1. Run backtest on different timeframes
2. Scan multiple markets for signals
3. Test parameter combinations
4. Implement walk-forward analysis
5. Add position management (trailing stop, etc.)
6. Connect to live market data
7. Paper trade before live trading
    """)


if __name__ == '__main__':
    main()