shadow-account

Trade journal backtesting compares a rule-based shadow strategy against your real results to pinpoint what truly contributed. It derives simple winning rules from profitable roundtrips and generates a differential attribution report explaining delta PnL.

Yes, multi-market shadow backtesting works across A-shares, HK, US, and crypto markets. It computes per-market and combined performance plus delta PnL to help you understand which patterns drive performance across different asset classes.

Generating a profit attribution PDF report involves producing an 8-section HTML document with a signed difference breakdown highlighting noise trades, early exits, and top counterfactual trades. The system includes a PDF fallback mechanism if initial generation fails.

Yes, you must run a prior journal analysis (analyze_trade_journal) before counterfactual trade analysis. The system enforces minimum profitable roundtrips to avoid fabricating rules and requires validated input data to proceed with shadow backtesting.

Differential attribution in trade backtesting breaks down delta PnL into signed categories like noise trades, early or late exits, overtrading, and residual. This difference attribution highlights top counterfactual trades by impact to identify behavioral performance leaks.