{"skill":{"slug":"qc-deep-feature-forensics","displayName":"Qc Deep Feature Forensics","summary":"12-dimensional technical feature attribution engine — compares winner vs loser trade entry conditions using RSI, Bollinger, MACD, volume surge, gap, and more...","tags":{"latest":"1.0.0"},"stats":{"comments":0,"downloads":147,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":1},"createdAt":1773757263738,"updatedAt":1773762110313},"latestVersion":{"version":"1.0.0","createdAt":1773757263738,"changelog":"Initial release of qc-deep-feature-forensics — a 12-dimensional technical feature attribution engine for quantitative trading.\n\n- Compares entry conditions of winning vs losing trades using 12 key market features (e.g., RSI, Bollinger, MACD, volume, gap).\n- Produces a report with winner/loser feature comparison, what-if filter analysis, and the statistical profile of ideal winning entries.\n- Supports batch order reconstruction, historical data download with per-ticker caching, and full feature matrix export.\n- Includes robust caching, diagnostic outputs, and best-practice usage notes.\n- Requires Python 3, pip3, and Python packages: pandas, numpy, yfinance.","license":"MIT-0"},"metadata":{"os":null,"systems":null},"owner":{"handle":"tltby12341","userId":"s17b0eq3j5h16t0deanr16r2hx83qchd","displayName":"tltby12341","image":"https://avatars.githubusercontent.com/u/30487767?v=4"},"moderation":null}