Individual skill
residual-edge-analyzer
Separate a strategy return series into declared baseline exposure and residual edge with returns-based OLS attribution, HAC inference, rolling stability, alternate-baseline sensitivity, and regime breakdowns. Use when evaluating whether backtest, out-of-sample, or live returns contain independent alpha beyond market, equal-weight, momentum, sector, or user-supplied factor returns; when explaining whether a drawdown came from baseline exposure or strategy-specific behavior; or when a strategy needs an attribution quality gate after backtesting. Do not use for holdings-based Brinson attribution, feature-level Shapley explanations, or analysis from summary metrics without a dated return series.
skills/residual-edge-analyzer/SKILL.md
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SKILL.mdcore · 5,965 bytesSKILL.mdcore · 5,965 bytesagents/openai.yamlasset · 270 bytesreferences/input-contract.mddoc · 4,360 bytesreferences/methodology.mddoc · 3,403 bytesrequirements.txtdoc · 70 bytesscripts/analyze_residual_edge.pyruns · 50,655 bytesscripts/tests/conftest.pyruns · 554 bytesscripts/tests/test_analyze_residual_edge.pyruns · 17,381 bytes
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# Residual Edge Analyzer
## Overview
Test whether a strategy's apparent performance survives explicit comparison with
predeclared baseline return series. Produce an auditable JSON artifact and a concise
Markdown report without fetching data or changing trading exposure.
Treat this as a falsification gate after `backtest-expert`, not as trade authorization.
## Prerequisites
- Use Python 3.9+.
- Prepare one CSV containing an ISO date, strategy return, and every baseline return on