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JerryLiu369JerryLiu369/awesome-quant-skills/skills/backtesting-audit/backtest-review/SKILL.md

backtest-review

Skill3 starsChanged 32 days agoMITView on GitHub

Review any strategy artifact in this order. Do not skip steps; most fatal flaws live in step 2. Identify: universe, rebalance frequency, signal definition, execution assumptions, cost model, in-sample vs out-of-sample split, number of variants tried. If any of these are unstated, ask before analyzing. Check every data access for information that would not exist at decision time: - Full-sample normalization (z-scores, min-max) computed across the whole history - Signals using close prices of day t to trade day t…

2 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check every data access for information that would not exist at decision time:

    line 22
  2. Distinctive:

    Ask: how many total variants were tried before this one?

    line 33

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md
<!-- Imported from: https://github.com/saksham10arora-dotcom/quant-skills Source Path: skills/backtest-review/SKILL.md Curated for awesome-quant-skills -->

name: backtest-review description: Review a trading strategy or backtest for look-ahead bias, overfitting, and statistical validity. Use when the user shares backtest code, equity curves, Sharpe ratios, or asks whether a trading signal is real.

Backtest Review

Review any strategy artifact in this order. Do not skip steps; most fatal flaws live in step 2.

1. Inventory before judging

Identify: universe, rebalance frequency, signal definition, execution assumptions, cost model, in-sample vs out-of-sample split, number of variants tried. If any of these are unstated, ask before analyzing.

2. Look-ahead sweep (kills most strategies)

Check every data access for information that would not exist at decision time:

  • Full-sample normalization (z-scores, min-max) computed across the whole history
  • Signals using close prices of day t to trade day t
  • Fundamentals joined without point-in-time availability dates
  • Survivorship: universe built from today's index membership
  • Targets shifted the wrong direction (df.shift(-1) used as feature instead of label)

Report each finding with file and line references.

3. Multiple testing honesty

Ask: how many total variants were tried before this one? Apply these rules of thumb:

  • Under ~5 trials: judge the raw statistics
  • 5-50 trials: require out-of-sample confirmation on data never touched during iteration
  • 50+: demand explicit deflation analysis (deflated Sharpe ratio, Bailey & Lopez de Prado); a Sharpe under 1 is presumptively noise at this search scale

4. Distribution sanity

Compute and report: skewness, kurtosis, max drawdown, longest drawdown duration, lag-1 autocorrelation of returns.

  • |autocorrelation| significant at sqrt(n) > 2 → suspect smoothed marks or stale fills
  • Kurtosis > 6 → warn that Sharpe understates tail risk
  • Win rate > 65% with positive skew on daily bars → check for hidden short-vol exposure

5. Verdict format

Deliver exactly:

VERDICT: REAL / UNPROVEN / LIKELY ARTIFACT
Fatal issues: [list or none]
Statistics: [Sharpe, PSR-style confidence note, DD]
Next test: [single highest-value next action]

Be direct. A kind lie about a strategy costs more than a harsh truth.

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