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 al

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 the same row. Prepare a JSON specification following [references/input contract.md](references/input contract.md). Supply actual period returns. Do not substitute CAGR, Sharpe, cumulative P&L, or other summary metrics. Workflow 1. Define the question before inspecting results State the claimed independent edge in one sentence. Select a primary baseline that is a plausible simple copy of the strategy, then select at least one alternate baseline model. Record these declarations in the config: baseline selection: predeclared strategy return basis and baseline return basis : both gross or both net analysis scope : out of sample , live , or in sample universe data : point in time , current constituents , or not applicable Every declaration is mandatory for a decision grade verdict. Omitting one is treated as undeclared, not as benign, and drops the report to REVIEW REQUIRED . not applicable exists so that a baseline with no universe membership can be declared explicitly rather than left blank. Do not choose a baseline because it gives the preferred residual result. 2. Validate the return series contract Require: unique ISO dates; finite numeric returns greater than 100%; identical frequency and cost basis across strategy and baselines; point in time membership for same universe equal weight or momentum baselines; regime labels defined independently of the loss periods being explained. Stop if the input lacks a dated strategy return series. Report summary only input as insufficient rather than inventing observations. 3. Run the analyzer The script runs the predeclared primary model and all sensitivity models in one execution. It uses an intercept OLS model and HAC/Newey West standard errors. It reports the residual edge ratio as annualized alpha divided by annualized residual volatility; do not calculate a Sharpe ratio from raw OLS residual mean because an intercept makes that mean zero. 4. Interpret the evidence Use the four statuses as diagnostic labels: RESIDUAL EDGE : alpha, residual edge ratio, and rolling stability clear configured thresholds. BASELINE EXPLAINED : baseline R squared is high while residual evidence is weak. RESIDUAL FRAGILE : results fail one or more robustness gates or change across declared baseline models. Also use this status when rolling analysis is disabled, unavailable, incomplete, or no sensitivity model was supplied. INSUFFICIENT EVIDENCE : the sample is below the configured minimum. Read decision eligibility separately. A statistically interesting result remains REVIEW REQUIRED when critical provenance, cost basis, sample, or multicollinearity warnings exist, when rolling evidence is unavailable, or when no alternate baseline was tested. Inspect: 1. primary and sensitivity model status; 2. annualized alpha and HAC t stat; 3. residual edge ratio and residual autocorrelation; 4. rolling alpha stability; 5. VIF for multi factor models; 6. active return breakdown across predeclared regimes. 5. Hand off findings Send baseline choice, OOS, and stability findings back to backtest expert . Send recurring residual failure regimes to signal postmortem . Pass only evidence and operating constraints to trade performance coach . Never change position size, exposure, or orders automatically. Boundaries Do not call this holdings based contribution analysis. Brinson allocation, selection, and interaction effects require historical holdings, benchmark weights, and constituent returns. Do not claim stock selection alpha from a market index only baseline. Do not build equal weight baselines from current constituents and label them point in time. Do not interpret in sample residual edge as confirmed alpha. Do not mine many regime definitions after seeing losses. Predeclare a small set and confirm findings out of sample. Do not assume high R squared makes a strategy worthless; capacity, tail behavior, costs, and implementation value require separate evidence. Resources scripts/analyze residual edge.py — deterministic CSV to JSON/Markdown analyzer. references/input contract.md — CSV/config contract and runnable example. references/methodology.md — statistical definitions, interpretation, and limitations.

residual-edge-analyzer