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Data Analysis · Business / AGENT SKILL
residual-edge-analyzer
0
Separate strategy returns into baseline exposure and residual alpha using statistical attribution.
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.
Understand the trade-offs.
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Original instructions from the publisher’s SKILL.md
# 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 ```bash python3 skills/residual-edge-analyzer/scripts/analyze_residual_edge.py \ --input reports/strategy_returns.csv \ --config reports/residual_edge_config.json \ --output-json reports/residual_edge_report.json \ --output-markdown reports/residual_edge_report.md ``` 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.