Diagnose failed backtests by analyzing artifacts, code, and configurations.
npx skills add https://github.com/GGwujun/SigmX --skill backtest-diagnose-ggwujun
Please help me install this Agent Skill. Skill: backtest-diagnose Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/backtest-diagnose Command: npx skills add https://github.com/GGwujun/SigmX --skill backtest-diagnose-ggwujun
Diagnoses failed or underperforming backtests, locates root causes, and provides actionable fixes to improve reliability and results.
Run the backtest diagnose workflow on your project to identify the root cause of a failure and apply a fix.
High-intent search queries and answers about installing and using this skill.
Diagnose a failing backtest by analyzing artifacts like metrics.csv, equity.csv, and trades.csv alongside signal_engine.py and config.json to identify root causes. The workflow reads these files to classify issues using a defined taxonomy and guide targeted fixes.
Debug an underperforming backtest by inspecting config.json for misconfigurations and signal_engine.py for faulty logic. Reading artifacts like equity.csv helps assess backtest health, enabling you to classify issues and apply remediation steps to the source files.
Backtest failures often stem from faulty logic in signal_engine.py or errors in config.json. By reading artifacts such as trades.csv and metrics.csv, you can classify the issue using a predefined error taxonomy, apply fixes to the code, and re-run the backtest to verify.
Yes, reproducing and fixing a backtest failure requires access to artifacts/metrics.csv, artifacts/equity.csv, trades.csv, code/signal_engine.py, and config.json. You also need the ability to edit these files and re-run the backtest to verify improvements.
Verify a backtest fix by re-running the backtest after editing source files, then inspecting updated metrics to ensure stability and correctness. This confirms that the applied fixes resolved the classified issues and improved the backtest results.
Using a predefined error taxonomy, you can classify issues in backtest configurations and code to identify misconfigurations in config.json or faulty logic in signal_engine.py. This structured classification guides the remediation steps needed to fix the backtest.
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