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"""
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()
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