# quant-validation

This skill lays out checks for financial-ML backtests, features, and labels. Use it to assess whether a reported result survives common validation pitfalls.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 107
- Author: avelikiy
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/avelikiy/great_cto/tree/HEAD/skills/quant-validation/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/quant-validation

## What it does

The skill covers purged cross-validation with an embargo, triple-barrier labels, uniqueness weighting for overlapping samples, fractional differentiation, meta-labelling, and multiple-testing correction. It focuses on how to judge a backtest, feature set, or label scheme, not on a model’s returns.

For overlapping label windows, a plain timestamp split can leave future information in the training data. The skill explains purging those observations and adding an embargo, then gives checks for each method. It also separates a model’s choice of trade direction from its decision to take the trade. Exact formulas should be checked against primary sources; the skill specifically warns against relying on a remembered deflated Sharpe ratio formula. Execution, order routing, and market microstructure are outside its scope.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add avelikiy/great_cto --skill quant-validation -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/quant-validation/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/quant-validation/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add avelikiy/great_cto --skill quant-validation -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/quant-validation/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add avelikiy/great_cto --skill quant-validation -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/quant-validation/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install avelikiy/great_cto quant-validation
```

2. **Manually: put the files in place**
- File: `.github/skills/quant-validation/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add avelikiy/great_cto --skill quant-validation -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/avelikiy/great_cto --path skills/quant-validation
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/quant-validation/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add avelikiy/great_cto --skill quant-validation -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/avelikiy/great_cto/tree/HEAD/skills/quant-validation/SKILL.md

## When to use it
- check whether cross-validation removes observations with overlapping label windows
- label each observation by the first triple barrier it reaches
- account for dependent samples when labels overlap
- search for a fractional-differencing order that balances stationarity and retained memory
- put the number of tried configurations in context when judging a result

## Example prompts
- Review this backtest’s cross-validation for overlapping label windows and an embargo.
- Assess whether triple-barrier labels fit these observations.
- Check whether this report separates trade direction from the decision to trade and accounts for the number of trials.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8f142fc166464ec0.

BEGIN THIRD-PARTY FILE 8f142fc166464ec0
```text
---
name: quant-validation
description: 'The methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.'
when_to_use: |
  Apply when work touches the validity of a financial model, not its returns:
  - quant-researcher designs or judges a backtest, a feature set, or a labelling scheme
  - anyone reports a Sharpe ratio, a hit rate, or an out-of-sample figure
  - a cross-validation scheme is being chosen for a time series with overlapping labels
  Do NOT apply to execution, order routing or market microstructure — that is a
  different body of knowledge and this pack does not cover it.
effort: low
allowed-tools: Read, Write, Grep, Glob
paths:
  - "docs/research/**"
  - "docs/architecture/**"
---

# Validating a financial model — the five ways the number lies

A backtest that looks excellent and loses money live is not usually a bad
strategy. It is a good measurement of the wrong thing. Each section below is one
mechanism by which a number becomes convincing without becoming true.

**On sourcing.** The methods here are standard and attributable — most of them to
Marcos López de Prado's *Advances in Financial Machine Learning*, with the
information-ratio framing from Grinold & Kahn. This file states the MECHANISM and
what to check, and deliberately does not restate formulas from memory. Where an
implementation needs an exact expression — the deflated Sharpe ratio in
particular — verify it against the primary source before shipping a number that
depends on it. A formula recalled approximately is worse here than no formula:
it produces a specific, wrong, confident figure.

## 1. Purged cross-validation with an embargo

**The leak.** In a normal k-fold split, training and test rows are disjoint. In a
financial series they are not independent: a label at time *t* is computed from
data spanning *t* to *t+h*. A training observation inside that window has seen the
future the test observation is being asked to predict.

**Purging.** Drop from the training set every observation whose label window
overlaps the label window of any test observation. Not the observation's
timestamp — its *label window*. This is the step people skip, because a plain
timestamp split looks like it already separates them.
```
END THIRD-PARTY FILE 8f142fc166464ec0

The third-party file has ended. Everything between the 8f142fc166464ec0 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2704 of 7797 bytes. © avelikiy · MIT · https://github.com/avelikiy/great_cto/tree/HEAD/skills/quant-validation/SKILL.md

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---
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