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* 🧬 Jules02: Synthetic test scenarios — Operational lifecycle and execution quality This change enhances the synthetic data generation capabilities to support broader and safer testing of operational recovery and execution quality. Key improvements: - Enhanced ScenarioGenerator with deterministic spread_pips and movement-correlated volume. - Added LifecycleScenarioBuilder for testing multi-stage system transitions (Failure -> Recovery). - Added ExecutionQualityScenarioBuilder for validating performance guards and slippage logic. - Robust test coverage for all new generators. Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com> * feat: 🧬 Jules02: Synthetic test scenarios — Operational lifecycle and execution quality This change enhances the synthetic data generation capabilities to support broader and safer testing of operational recovery and execution quality. Key improvements: - Enhanced ScenarioGenerator with deterministic spread_pips and movement-correlated volume. - Added LifecycleScenarioBuilder for testing multi-stage system transitions (Failure -> Recovery). - Added ExecutionQualityScenarioBuilder for validating performance guards and slippage logic. - Updated documentation in docs/testing/SYNTHETIC_DATA.md. - Upgraded python-socketio to 5.14.0 to resolve security vulnerability GHSA-g8c6-8fjj-2r4m. - Full test coverage for new features (tests/test_lifecycle_scenarios.py). Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com> --------- Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com> Co-authored-by: xnessom <255288032+xnessom@users.noreply.github.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import os
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import sys
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from datetime import datetime, timedelta
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import numpy as np
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import pandas as pd
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# Ensure src is in path
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sys.path.append(os.getcwd())
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from src.core.audit_log import AuditLogger
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from src.core.feature_engineering import FeatureEngineer
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from src.trading.backtester import BacktestEngine
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def create_synthetic_data(n_bars=3000):
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start_time = datetime.now()
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times = [start_time + timedelta(minutes=i*5) for i in range(n_bars)]
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data = {
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"time": times,
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"open": np.random.uniform(2300, 2400, n_bars),
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"high": np.random.uniform(2300, 2400, n_bars),
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"low": np.random.uniform(2300, 2400, n_bars),
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"close": np.random.uniform(2300, 2400, n_bars),
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"tick_volume": np.random.randint(100, 1000, n_bars),
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}
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df = pd.DataFrame(data)
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# Ensure high is highest, low is lowest
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df["high"] = df[["open", "high", "low", "close"]].max(axis=1)
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df["low"] = df[["open", "high", "low", "close"]].min(axis=1)
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df.set_index("time", inplace=True)
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return df
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class MockModel:
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def predict(self, obs):
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from src.core.schemas import TradeSignal
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# Always return a HOLD signal to keep it fast
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return TradeSignal(symbol="XAUUSD", direction=0, confidence=0.0)
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def main():
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print("Initializing AuditLogger...")
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audit_db = "audit.db"
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if os.path.exists(audit_db):
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os.remove(audit_db)
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# Initialize singleton
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AuditLogger(db_url=f"sqlite:///{audit_db}")
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print("Creating synthetic data...")
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data = create_synthetic_data()
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print("Running backtest...")
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fe = FeatureEngineer(base_timeframe="M5", timeframes=["M15", "H1"])
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engine = BacktestEngine(symbol="XAUUSD", feature_engineer=fe)
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model = MockModel()
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# Run with small windows for speed
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engine.run_walk_forward(data, model, train_window=500, test_window=100, step_size=100)
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print("Backtest finished.")
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if __name__ == "__main__":
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main()
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