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.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. 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.

2. 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.
  • Supply actual period returns. Do not substitute CAGR, Sharpe, cumulative P&L, or other summary metrics.

3. Quick Start 

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

4. 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.

5. Resources

References:

  • skills/residual-edge-analyzer/references/input-contract.md
  • skills/residual-edge-analyzer/references/methodology.md

Scripts:

  • skills/residual-edge-analyzer/scripts/analyze_residual_edge.py