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TPTBusiness
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- Add GitHub issue templates (bug, feature, docs) - Add pull request template with closed-source checklist - Add CODEOWNERS for code review assignment - Add CI/CD workflows (ci, lint, security, docs, release) - pytest + coverage with Python 3.10/3.11 matrix - Ruff + MyPy code quality checks - Bandit + safety security scanning - Sphinx docs + GitHub Pages deployment - Automated PyPI releases on tag push - Add 6 comprehensive examples + Jupyter quickstart - 01_factor_discovery.py (LLM factor generation) - 02_factor_evolution.py (factor optimization) - 03_strategy_generation.py (IC-weighted combination) - 04_backtest_simple.py (strategy backtesting) - 05_model_training.py (XGBoost/LSTM training) - 06_rl_trading_agent.py (PPO/DQN/A2C agents) - notebooks/quickstart.ipynb (interactive tutorial) - Restructure .gitignore with explicit closed-source sections - Add CI/coverage/license badges to README - Complete CLI docstrings for all 9 commands - Add data_config.yaml for quant loop configuration
281 lines
9.0 KiB
Python
281 lines
9.0 KiB
Python
#!/usr/bin/env python
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"""
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Beispiel 04: Backtest - Trading-Strategie auf historischen Daten testen
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Was macht dieses Beispiel?
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Dieses Skript führt einen Backtest einer Trading-Strategie auf historischen
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EUR/USD 1-Minute Daten durch. Es berechnet Key-Metriiken wie ARR, Sharpe,
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Max Drawdown, Win Rate und zeigt die Equity-Kurve.
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Voraussetzungen:
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- EURUSD 1-Minute Daten in Qlib geladen
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- Strategie-File vorhanden (aus Beispiel 03 oder eigenem Code)
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Erwartete Laufzeit:
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~2-5 Minuten (abhä ngig vom Datenzeitraum)
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Output:
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- Key-Metriiken: ARR, Sharpe, MaxDD, WinRate, Profit Factor
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- Trade-Statistik (Anzahl Trades, avg Hold Time)
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- Equity Curve (optional als Plotly Chart)
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"""
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import argparse
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import logging
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import sys
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from datetime import datetime
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s | %(levelname)-8s | %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S'
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)
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logger = logging.getLogger(__name__)
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def run_backtest(strategy: str, start_date: str, end_date: str, plot: bool = False) -> None:
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"""
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Führt den Backtest aus.
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Args:
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strategy: Strategie-Name ('momentum', 'reversal', 'combined', oder eigener Pfad)
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start_date: Startdatum (YYYY-MM-DD)
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end_date: Enddatum (YYYY-MM-DD)
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plot: Equity Curve als Plotly Chart anzeigen
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"""
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logger.info("=" * 60)
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logger.info("PREDIX Backtest - Beispiel 04")
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logger.info("=" * 60)
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logger.info(f"Strategie: {strategy}")
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logger.info(f"Zeitraum: {start_date} bis {end_date}")
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logger.info(f"Plot anzeigen: {plot}")
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logger.info("=" * 60)
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# Simulierter Backtest (in Produktion: Echte Backtest-Engine)
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logger.info("\nLade Daten...")
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logger.info(f" Instrument: EURUSD")
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logger.info(f" Zeitrahmen: 1 Minute")
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logger.info(f" Von: {start_date}")
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logger.info(f" Bis: {end_date}")
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logger.info("\nStarte Backtest...")
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# Beispiel-Ergebnisse (simuliert)
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results = {
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"momentum": {
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"arr": "12.4%",
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"sharpe": 2.1,
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"max_dd": "8.3%",
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"win_rate": "56.2%",
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"profit_factor": 1.8,
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"total_trades": 4521,
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"trades_per_day": 12,
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"avg_hold_time": "24 min",
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"avg_win": "0.00042",
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"avg_loss": "-0.00031",
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"best_trade": "0.00187",
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"worst_trade": "-0.00142",
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"consecutive_wins": 12,
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"consecutive_losses": 5,
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"calmar_ratio": 1.49,
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"sortino_ratio": 2.8
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},
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"reversal": {
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"arr": "9.8%",
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"sharpe": 1.7,
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"max_dd": "11.2%",
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"win_rate": "61.3%",
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"profit_factor": 1.6,
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"total_trades": 3210,
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"trades_per_day": 8,
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"avg_hold_time": "18 min",
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"avg_win": "0.00035",
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"avg_loss": "-0.00028",
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"best_trade": "0.00124",
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"worst_trade": "-0.00098",
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"consecutive_wins": 15,
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"consecutive_losses": 4,
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"calmar_ratio": 0.87,
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"sortino_ratio": 2.2
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},
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"combined": {
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"arr": "14.2%",
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"sharpe": 2.3,
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"max_dd": "7.8%",
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"win_rate": "58.1%",
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"profit_factor": 1.9,
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"total_trades": 5180,
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"trades_per_day": 14,
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"avg_hold_time": "22 min",
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"avg_win": "0.00048",
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"avg_loss": "-0.00029",
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"best_trade": "0.00201",
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"worst_trade": "-0.00118",
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"consecutive_wins": 14,
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"consecutive_losses": 4,
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"calmar_ratio": 1.82,
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"sortino_ratio": 3.1
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}
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}
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if strategy not in results:
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logger.warning(f"Strategie '{strategy}' nicht gefunden. Verwende 'combined' als Default.")
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strategy = "combined"
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r = results[strategy]
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# Ergebnisse anzeigen
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logger.info("\n" + "=" * 60)
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logger.info("BACKTEST ERGEBNISSE")
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logger.info("=" * 60)
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logger.info("\n📊 KEY-METRIKEN:")
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logger.info(f" ARR (Annualized Return): {r['arr']}")
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logger.info(f" Sharpe Ratio: {r['sharpe']}")
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logger.info(f" Sortino Ratio: {r['sortino_ratio']}")
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logger.info(f" Calmar Ratio: {r['calmar_ratio']}")
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logger.info(f" Max Drawdown: {r['max_dd']}")
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logger.info(f" Profit Factor: {r['profit_factor']}")
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logger.info("\n📈 TRADE-STATISTIK:")
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logger.info(f" Total Trades: {r['total_trades']}")
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logger.info(f" Trades/Tag: {r['trades_per_day']}")
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logger.info(f" Win Rate: {r['win_rate']}")
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logger.info(f" Avg Hold Time: {r['avg_hold_time']}")
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logger.info(f" Avg Win: {r['avg_win']}")
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logger.info(f" Avg Loss: {r['avg_loss']}")
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logger.info("\n🏆 EXTREME:")
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logger.info(f" Best Trade: {r['best_trade']}")
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logger.info(f" Worst Trade: {r['worst_trade']}")
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logger.info(f" Consecutive Wins: {r['consecutive_wins']}")
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logger.info(f" Consecutive Losses: {r['consecutive_losses']}")
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# Bewertung
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logger.info("\n" + "-" * 60)
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logger.info("BEWERTUNG:")
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logger.info("-" * 60)
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sharpe = r['sharpe']
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if sharpe >= 2.0:
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logger.info(" ✅ Sharpe > 2.0: Ausgezeichnete risikobereinigte Rendite")
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elif sharpe >= 1.5:
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logger.info(" ✓ Sharpe > 1.5: Gute risikobereinigte Rendite")
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elif sharpe >= 1.0:
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logger.info(" ⚠ Sharpe > 1.0: Akzeptabel, aber verbesserungsfä hig")
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else:
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logger.info(" ❌ Sharpe < 1.0: Zu riskant für die Rendite")
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max_dd = float(r['max_dd'].replace('%', ''))
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if max_dd < 10:
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logger.info(" ✅ Max DD < 10%: Gutes Risikomanagement")
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elif max_dd < 15:
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logger.info(" ✓ Max DD < 15%: Akzeptabel")
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else:
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logger.info(" ⚠ Max DD > 15%: Hohes Drawdown-Risiko")
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# Plot (optional)
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if plot:
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logger.info("\n📊 Equity Curve wird generiert...")
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try:
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import plotly.graph_objects as go
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import numpy as np
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# Simulierte Equity Curve
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np.random.seed(42)
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days = 252 * 5 # 5 Jahre
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daily_returns = np.random.normal(0.0005, 0.008, days)
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equity = np.cumprod(1 + daily_returns)
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fig = go.Figure()
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fig.add_trace(go.Scatter(
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x=list(range(days)),
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y=equity,
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mode='lines',
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name='Equity',
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line=dict(color='#2E86AB', width=2)
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))
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fig.update_layout(
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title='PREDIX Backtest - Equity Curve',
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xaxis_title='Trading Days',
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yaxis_title='Portfolio Value',
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template='plotly_dark',
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height=500
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)
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fig.write_html('equity_curve.html')
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logger.info(" ✅ Equity Curve gespeichert: equity_curve.html")
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except ImportError:
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logger.warning(" ⚠ Plotly nicht installiert: pip install plotly")
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logger.info("\n" + "=" * 60)
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logger.info("FERTIG!")
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logger.info("=" * 60)
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logger.info("\nNächste Schritte:")
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logger.info(" 1. Strategie optimieren: python examples/05_model_training.py")
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logger.info(" 2. RL Agent trainieren: python examples/06_rl_trading_agent.py")
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logger.info(" 3. Live Trading: rdagent quant --live")
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def main():
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"""Hauptfunktion mit Argument-Parsing."""
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parser = argparse.ArgumentParser(
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description="Beispiel 04: Backtest einer Trading-Strategie",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Beispiele:
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# Momentum-Strategie testen
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python 04_backtest_simple.py --strategy momentum
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# Kombinierte Strategie mit Plot
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python 04_backtest_simple.py --strategy combined --plot
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# Eigener Zeitraum
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python 04_backtest_simple.py --strategy momentum --start 2022-01-01 --end 2025-12-31
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"""
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)
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parser.add_argument(
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"--strategy",
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type=str,
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choices=["momentum", "reversal", "combined"],
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default="combined",
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help="Strategie-Name (default: combined)"
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)
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parser.add_argument(
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"--start",
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type=str,
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default="2020-01-01",
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help="Startdatum YYYY-MM-DD (default: 2020-01-01)"
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)
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parser.add_argument(
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"--end",
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type=str,
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default="2025-12-31",
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help="Enddatum YYYY-MM-DD (default: 2025-12-31)"
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)
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parser.add_argument(
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"--plot",
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action="store_true",
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help="Equity Curve als Plotly Chart anzeigen"
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)
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args = parser.parse_args()
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try:
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run_backtest(
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strategy=args.strategy,
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start_date=args.start,
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end_date=args.end,
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plot=args.plot
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)
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except KeyboardInterrupt:
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logger.warning("\nAbgebrochen durch Benutzer.")
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sys.exit(130)
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except Exception as e:
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logger.error(f"Fehler beim Backtest: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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