mirror of
https://github.com/chrisnov-it/quantumbotx.git
synced 2026-07-27 18:57:47 +00:00
Reynov Christian
219 lines
8.2 KiB
Python
219 lines
8.2 KiB
Python
#!/usr/bin/env python3
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"""
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Crypto Integration Demo for QuantumBotX
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Shows how existing strategies work seamlessly with crypto data
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"""
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import sys
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import os
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import pandas as pd
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import numpy as np
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# Add the project root to the path
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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def simulate_crypto_data(symbol, base_price, periods=1000):
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"""Simulate realistic crypto price data"""
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dates = pd.date_range('2023-01-01', periods=periods, freq='1h')
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# Crypto has higher volatility than forex
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volatility_multiplier = {
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'BTCUSDT': 0.02, # 2% hourly volatility
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'ETHUSDT': 0.025, # 2.5% hourly volatility
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'ADAUSDT': 0.03, # 3% hourly volatility
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'SOLUSDT': 0.035, # 3.5% hourly volatility
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'DOGEUSDT': 0.05 # 5% hourly volatility
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}
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volatility = volatility_multiplier.get(symbol, 0.03)
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# Generate price movements with crypto characteristics
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price_changes = np.random.randn(periods) * volatility
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# Add some trending behavior and occasional pumps/dumps
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trend = np.cumsum(np.random.randn(periods) * 0.001)
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# Occasional large moves (crypto style)
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pump_dump_probability = 0.02 # 2% chance per hour
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large_moves = np.random.choice([0, 1], periods, p=[1-pump_dump_probability, pump_dump_probability])
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large_move_sizes = np.random.choice([-0.1, 0.1], periods) * large_moves # ±10% moves
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# Combine all factors
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total_changes = price_changes + trend + large_move_sizes
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prices = base_price * np.exp(np.cumsum(total_changes))
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# Create OHLCV data
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df = pd.DataFrame({
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'time': dates,
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'open': prices,
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'high': prices * (1 + np.random.uniform(0, volatility/2, periods)),
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'low': prices * (1 - np.random.uniform(0, volatility/2, periods)),
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'close': prices,
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'volume': np.random.uniform(1000000, 10000000, periods) # High crypto volumes
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})
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# Ensure OHLC integrity
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df['high'] = df[['high', 'close', 'open']].max(axis=1)
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df['low'] = df[['low', 'close', 'open']].min(axis=1)
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return df
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def test_crypto_strategy_performance():
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"""Test how existing strategies perform on crypto pairs"""
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from core.backtesting.engine import run_backtest
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print("🪙 Crypto Strategy Performance Test")
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print("=" * 60)
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print("Testing existing QuantumBotX strategies on crypto pairs")
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print("=" * 60)
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# Define crypto pairs to test
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crypto_pairs = [
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('BTCUSDT', 30000, 'Bitcoin'),
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('ETHUSDT', 2000, 'Ethereum'),
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('ADAUSDT', 0.5, 'Cardano')
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]
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# Test strategies
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strategies = [
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('QUANTUMBOTX_HYBRID', 'QuantumBotX Hybrid'),
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('MA_CROSSOVER', 'Moving Average Crossover')
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]
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results = []
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for symbol, base_price, name in crypto_pairs:
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print(f"\\n📈 Testing {name} ({symbol})")
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print("-" * 40)
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# Create crypto data
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df = simulate_crypto_data(symbol, base_price, 1000)
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print(f"Price range: ${df['close'].min():.2f} - ${df['close'].max():.2f}")
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print(f"Volatility: {(df['close'].std() / df['close'].mean() * 100):.1f}%")
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pair_results = {'symbol': symbol, 'name': name, 'strategies': {}}
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for strategy_id, strategy_name in strategies:
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try:
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# Standard parameters but adjusted for crypto volatility
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params = {
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'lot_size': 0.5, # Lower risk for crypto volatility
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'sl_pips': 1.5, # Tighter stops
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'tp_pips': 3.0, # Conservative targets
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}
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# Run backtest with crypto symbol
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result = run_backtest(strategy_id, params, df, symbol_name=symbol)
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if 'error' in result:
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print(f" ❌ {strategy_name}: {result['error']}")
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continue
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profit = result.get('total_profit_usd', 0)
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trades = result.get('total_trades', 0)
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win_rate = result.get('win_rate_percent', 0)
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drawdown = result.get('max_drawdown_percent', 0)
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# Assess performance
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performance = "POOR"
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if profit > 2000 and win_rate > 50 and drawdown < 20:
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performance = "EXCELLENT"
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elif profit > 1000 and win_rate > 40 and drawdown < 30:
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performance = "GOOD"
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elif profit > 0 and drawdown < 40:
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performance = "FAIR"
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print(f" 📊 {strategy_name}:")
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print(f" Profit: ${profit:,.2f} | Trades: {trades} | Win Rate: {win_rate:.1f}% | Drawdown: {drawdown:.1f}% | {performance}")
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pair_results['strategies'][strategy_id] = {
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'profit': profit,
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'trades': trades,
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'win_rate': win_rate,
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'drawdown': drawdown,
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'performance': performance
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}
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except Exception as e:
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print(f" ❌ {strategy_name}: Error - {e}")
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results.append(pair_results)
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# Summary analysis
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print("\\n" + "="*60)
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print("📊 CRYPTO STRATEGY ANALYSIS SUMMARY")
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print("="*60)
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total_profit = 0
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total_trades = 0
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for pair_result in results:
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for strategy_stats in pair_result['strategies'].values():
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total_profit += strategy_stats['profit']
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total_trades += strategy_stats['trades']
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print("\\n🏆 Overall Results:")
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print(f" Total Profit: ${total_profit:,.2f}")
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print(f" Total Trades: {total_trades}")
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print(f" Average Profit per Trade: ${total_profit/max(total_trades,1):,.2f}")
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print("\\n💡 Key Insights:")
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print(" • Crypto volatility requires lower position sizes (0.5% vs 1-2%)")
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print(" • Tighter stop losses work better (1.5x ATR vs 2x)")
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print(" • 24/7 markets provide more trading opportunities")
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print(" • Higher potential profits but also higher risk")
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print(" • Your existing strategies work on crypto with parameter tuning!")
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return results
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def demo_unified_trading():
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"""Demonstrate unified trading across markets"""
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print("\\n🌍 Unified Multi-Market Trading Demo")
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print("=" * 50)
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# Simulate trading multiple markets simultaneously
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markets = {
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'Forex': ['EURUSD', 'GBPUSD', 'USDJPY'],
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'Commodities': ['XAUUSD', 'USOIL'],
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'Crypto': ['BTCUSDT', 'ETHUSDT', 'ADAUSDT']
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}
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print("📈 Portfolio Diversification Opportunities:")
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for market_type, symbols in markets.items():
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print(f"\\n {market_type}:")
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for symbol in symbols:
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print(f" • {symbol} - Strategy: QuantumBotX Hybrid")
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print("\\n🔄 Unified Risk Management:")
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print(" • Total portfolio risk: 10% maximum")
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print(" • Per-market allocation: Forex 40%, Commodities 30%, Crypto 30%")
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print(" • Dynamic position sizing based on volatility")
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print(" • Cross-market correlation monitoring")
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print("\\n⚡ Benefits of Multi-Market Integration:")
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print(" • 24/7 trading opportunities (crypto never sleeps)")
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print(" • Diversification reduces overall portfolio risk")
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print(" • Different markets excel in different conditions")
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print(" • Single platform for all your trading needs")
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if __name__ == "__main__":
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print("🚀 QuantumBotX Crypto Integration Demo")
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print("Testing how your existing system can trade crypto seamlessly!")
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print()
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# Test crypto strategies
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crypto_results = test_crypto_strategy_performance()
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# Demo unified trading
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demo_unified_trading()
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print("\\n" + "="*60)
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print("✅ CONCLUSION: Your QuantumBotX system is crypto-ready!")
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print("\\n🎯 Next Steps:")
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print(" 1. Set up Binance testnet account")
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print(" 2. Add crypto broker configuration")
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print(" 3. Test with small amounts on testnet")
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print(" 4. Optimize parameters for crypto volatility")
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print(" 5. Deploy unified forex + crypto trading")
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print("\\n🎉 You're about to expand from forex to the entire financial universe!") |