/residual-edge-analyzer

name: residual-edge-analyzer
description: 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.

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 the

[input contract](https://github.com/tradermonty/claude-trading-skills/blob/main/skills/residual-edge-analyzer/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

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.