backtest-review
Skill0 starsChanged 3 months agoMITView on GitHub
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.
2 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- JerryLiu369/awesome-quant-skillsedited, first seen Nov 1, 2026
- shakeebshaan/claude-code-quant-skillstheir own version, first seen Apr 17, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- JerryLiu369/awesome-quant-skills★ 394% same
Wrote their own version · 1
- shakeebshaan/claude-code-quant-skills★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository4 other files it gives its agents.
- data-quality-auditSkill
- paper-reproSkill
- stats-test-pickerSkill
- ticker-briefingSkill
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
- Distinctive: line 16
Check every data access for information that would not exist at decision time:
- Distinctive: line 27
Ask: how many total variants were tried before this one?
≈ “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.
Files
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.