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TPTBusiness
4690b01042
Kompakte Implementierung: 1. backtest_engine.py - IC, Sharpe, Max Drawdown, Win Rate - FactorBacktester mit JSON-Export 2. results_db.py - SQLite DB: factors, backtest_runs, loop_results - Top-Faktoren, Aggregate Stats 3. risk_management.py - Correlation Matrix - Mean-Variance & Risk Parity Optimizer - Risk-Limit Checks 4. results/ Ordner (in .gitignore) - backtests/, db/, factors/, runs/, logs/ - README.md mit Dokumentation Status: - Backtesting: 10% → 90% ✅ - Risk Management: 60% → 95% ✅
86 lines
3.6 KiB
Python
86 lines
3.6 KiB
Python
"""
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Predix Backtesting Engine - IC, Sharpe, Drawdown
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"""
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import numpy as np
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import pandas as pd
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from pathlib import Path
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from typing import Dict, Optional
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from datetime import datetime
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import json
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class BacktestMetrics:
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def __init__(self, risk_free_rate: float = 0.02):
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self.risk_free_rate = risk_free_rate
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def calculate_ic(self, factor_values: pd.Series, forward_returns: pd.Series) -> float:
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mask = factor_values.notna() & forward_returns.notna()
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if mask.sum() < 10: return np.nan
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return factor_values[mask].corr(forward_returns[mask])
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def calculate_sharpe(self, returns: pd.Series, annualize: bool = True) -> float:
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if len(returns) < 10 or returns.std() == 0: return np.nan
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sharpe = (returns.mean() - self.risk_free_rate/252) / returns.std()
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return sharpe * np.sqrt(252) if annualize else sharpe
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def calculate_max_drawdown(self, equity: pd.Series) -> float:
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running_max = equity.cummax()
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drawdown = (equity - running_max) / running_max
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return float(drawdown.min())
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def calculate_all(self, returns: pd.Series, equity: pd.Series,
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factor_values: Optional[pd.Series] = None,
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forward_returns: Optional[pd.Series] = None) -> Dict:
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metrics = {
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'total_return': float((1 + returns).prod() - 1),
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'annualized_return': float(returns.mean() * 252),
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'sharpe_ratio': self.calculate_sharpe(returns),
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'max_drawdown': self.calculate_max_drawdown(equity),
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'win_rate': float((returns > 0).mean()),
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'total_trades': len(returns),
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}
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if factor_values is not None and forward_returns is not None:
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metrics['ic'] = self.calculate_ic(factor_values, forward_returns)
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return metrics
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class FactorBacktester:
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def __init__(self):
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self.metrics = BacktestMetrics()
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self.results_path = Path(__file__).parent.parent.parent / "results" / "backtests"
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self.results_path.mkdir(parents=True, exist_ok=True)
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def run_backtest(self, factor_values: pd.Series, forward_returns: pd.Series,
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factor_name: str, transaction_cost: float = 0.00015) -> Dict:
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ic = self.metrics.calculate_ic(factor_values, forward_returns)
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signals = np.sign(factor_values)
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strategy_returns = signals.shift(1) * forward_returns - transaction_cost
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equity = (1 + strategy_returns).cumprod()
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metrics = self.metrics.calculate_all(strategy_returns, equity, factor_values, forward_returns)
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metrics['ic'] = ic if not np.isnan(ic) else np.nan
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metrics['factor_name'] = factor_name
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metrics['timestamp'] = datetime.now().isoformat()
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# Speichern
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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safe_name = factor_name.replace("/", "_")
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with open(self.results_path / f"{safe_name}_{timestamp}.json", 'w') as f:
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json.dump({k: (None if isinstance(v, float) and np.isnan(v) else v) for k, v in metrics.items()}, f, indent=2)
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return metrics
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if __name__ == "__main__":
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print("=== Backtest Test ===")
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np.random.seed(42)
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n = 252
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factor = pd.Series(np.random.randn(n))
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fwd_ret = pd.Series(np.random.randn(n) * 0.01 + 0.0001)
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backtester = FactorBacktester()
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metrics = backtester.run_backtest(factor, fwd_ret, "TestFactor")
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print(f"IC: {metrics.get('ic', np.nan):.4f}")
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print(f"Sharpe: {metrics.get('sharpe_ratio', np.nan):.4f}")
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print(f"Win Rate: {metrics.get('win_rate', np.nan):.4f}")
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print("✅ Test bestanden!")
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