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feat: Backtesting Engine + Risk Management + Results DB
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% ✅
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TPTBusiness
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"""Predix Backtesting Package"""
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from .backtest_engine import BacktestMetrics, FactorBacktester
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from .results_db import ResultsDatabase
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from .risk_management import CorrelationAnalyzer, PortfolioOptimizer, AdvancedRiskManager
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__all__ = ['BacktestMetrics', 'FactorBacktester', 'ResultsDatabase',
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'CorrelationAnalyzer', 'PortfolioOptimizer', 'AdvancedRiskManager']
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"""
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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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"""
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Predix Results Database - SQLite für Backtest-Ergebnisse
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"""
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import sqlite3
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, Optional
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class ResultsDatabase:
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def __init__(self, db_path: Optional[str] = None):
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if db_path is None:
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db_path = Path(__file__).parent.parent.parent / "results" / "db" / "backtest_results.db"
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self.db_path = db_path
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Path(db_path).parent.mkdir(parents=True, exist_ok=True)
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self.conn = sqlite3.connect(db_path)
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self._create_tables()
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def _create_tables(self):
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c = self.conn.cursor()
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c.execute("""CREATE TABLE IF NOT EXISTS factors (
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id INTEGER PRIMARY KEY, factor_name TEXT UNIQUE, factor_type TEXT, created_at TIMESTAMP)""")
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c.execute("""CREATE TABLE IF NOT EXISTS backtest_runs (
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id INTEGER PRIMARY KEY, factor_id INTEGER, run_name TEXT, run_date TIMESTAMP,
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ic REAL, sharpe REAL, annual_return REAL, max_drawdown REAL, win_rate REAL)""")
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c.execute("""CREATE TABLE IF NOT EXISTS loop_results (
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id INTEGER PRIMARY KEY, loop_index INTEGER, factors_success INTEGER,
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factors_fail INTEGER, success_rate REAL, best_ic REAL, status TEXT)""")
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self.conn.commit()
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def add_factor(self, name: str, type: str = "unknown") -> int:
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c = self.conn.cursor()
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c.execute("INSERT OR IGNORE INTO factors (factor_name, factor_type, created_at) VALUES (?, ?, ?)",
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(name, type, datetime.now()))
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c.execute("SELECT id FROM factors WHERE factor_name = ?", (name,))
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self.conn.commit()
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result = c.fetchone()
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return result[0] if result else -1
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def add_backtest(self, factor_name: str, metrics: Dict) -> int:
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factor_id = self.add_factor(factor_name)
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c = self.conn.cursor()
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c.execute("""INSERT INTO backtest_runs
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(factor_id, run_name, run_date, ic, sharpe, annual_return, max_drawdown, win_rate)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)""",
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(factor_id, f"{factor_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
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datetime.now(), metrics.get('ic'), metrics.get('sharpe_ratio'),
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metrics.get('annualized_return'), metrics.get('max_drawdown'), metrics.get('win_rate')))
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self.conn.commit()
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return c.lastrowid
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def add_loop(self, loop_idx: int, success: int, fail: int, best_ic: float = None, status: str = "completed") -> int:
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c = self.conn.cursor()
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rate = success / (success + fail) if (success + fail) > 0 else 0
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c.execute("""INSERT INTO loop_results (loop_index, factors_success, factors_fail, success_rate, best_ic, status)
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VALUES (?, ?, ?, ?, ?, ?)""", (loop_idx, success, fail, rate, best_ic, status))
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self.conn.commit()
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return c.lastrowid
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def get_top_factors(self, metric: str = 'sharpe', limit: int = 20) -> pd.DataFrame:
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return pd.read_sql_query(f"""SELECT factor_name, {metric}, ic, annual_return, max_drawdown
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FROM backtest_runs JOIN factors ON factor_id = factors.id
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WHERE {metric} IS NOT NULL ORDER BY {metric} DESC LIMIT ?""",
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self.conn, params=[limit])
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def get_aggregate_stats(self) -> Dict:
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c = self.conn.cursor()
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c.execute("""SELECT COUNT(DISTINCT factor_name), AVG(ic), MAX(sharpe), AVG(annual_return)
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FROM backtest_runs JOIN factors ON factor_id = factors.id""")
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r = c.fetchone()
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return {'total_factors': r[0], 'avg_ic': r[1], 'max_sharpe': r[2], 'avg_return': r[3]}
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def close(self):
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self.conn.close()
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if __name__ == "__main__":
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print("=== DB Test ===")
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db = ResultsDatabase()
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db.add_factor("TestFactor", "Momentum")
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db.add_backtest("TestFactor", {'ic': 0.05, 'sharpe_ratio': 1.5, 'annualized_return': 0.15, 'max_drawdown': -0.08, 'win_rate': 0.55})
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db.add_loop(1, 4, 6, 0.05, "completed")
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print("Top Faktoren:")
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print(db.get_top_factors())
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print("\nAggregate Stats:")
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print(db.get_aggregate_stats())
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db.close()
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print("✅ Test bestanden!")
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"""
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Predix Risk Management - Korrelation, Portfolio-Optimierung
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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, List, Optional
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from datetime import datetime
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import json
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class CorrelationAnalyzer:
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def __init__(self, lookback: int = 60):
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self.lookback = lookback
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def calculate_matrix(self, returns: pd.DataFrame) -> pd.DataFrame:
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return returns.dropna().corr()
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def find_uncorrelated(self, corr: pd.DataFrame, threshold: float = 0.3) -> List[str]:
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result = []
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for f in corr.columns:
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others = [x for x in corr.columns if x != f]
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if corr.loc[f, others].abs().mean() < threshold:
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result.append(f)
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return result
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class PortfolioOptimizer:
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def mean_variance(self, exp_ret: pd.Series, cov: pd.DataFrame) -> np.ndarray:
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try:
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w = np.linalg.inv(cov.values) @ exp_ret.values
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return w / np.sum(w)
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except:
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return np.ones(len(exp_ret)) / len(exp_ret)
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def risk_parity(self, cov: pd.DataFrame, max_iter: int = 100) -> np.ndarray:
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n = cov.shape[0]
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w = np.ones(n) / n
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for _ in range(max_iter):
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marginal = cov.values @ w
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vol = np.sqrt(w @ cov.values @ w)
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if vol == 0: break
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risk_contrib = w * marginal / vol
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scale = np.sum(risk_contrib) / (n * risk_contrib + 1e-10)
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new_w = w * scale
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new_w = new_w / np.sum(new_w)
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if np.max(np.abs(new_w - w)) < 1e-6: break
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w = new_w
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return w
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class AdvancedRiskManager:
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def __init__(self, max_pos: float = 0.2, max_lev: float = 5.0, max_dd: float = 0.20):
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self.max_pos = max_pos
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self.max_lev = max_lev
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self.max_dd = max_dd
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self.corr_analyzer = CorrelationAnalyzer()
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self.optimizer = PortfolioOptimizer()
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def check_limits(self, weights: np.ndarray, vol: float, dd: float) -> Dict[str, bool]:
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return {
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'position_limit': np.max(np.abs(weights)) <= self.max_pos,
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'leverage_limit': np.sum(np.abs(weights)) <= self.max_lev,
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'drawdown_limit': abs(dd) <= self.max_dd,
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}
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if __name__ == "__main__":
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print("=== Risk Test ===")
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np.random.seed(42)
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n, names = 252, ['Mom', 'MeanRev', 'Vol', 'Volu', 'ML']
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ret = pd.DataFrame(np.random.randn(n, 5), columns=names)
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corr = CorrelationAnalyzer().calculate_matrix(ret)
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print("Korrelationsmatrix:")
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print(corr.round(2))
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opt = PortfolioOptimizer()
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exp_ret = pd.Series([0.1, 0.08, 0.06, 0.07, 0.12], index=names)
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cov = ret.cov() * 252
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mv = opt.mean_variance(exp_ret, cov)
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print("\nMean-Variance:")
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for n, w in zip(names, mv): print(f" {n}: {w:.2%}")
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rp = opt.risk_parity(cov)
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print("\nRisk Parity:")
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for n, w in zip(names, rp): print(f" {n}: {w:.2%}")
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rm = AdvancedRiskManager()
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checks = rm.check_limits(mv, 0.15, -0.08)
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print(f"\nLimits OK: {all(checks.values())}")
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print("✅ Test bestanden!")
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