# Build Crypto Quant Strategy Reviews

Crypto trading strategies often fail from weak signals, biased tests, or unmanaged costs. This skill helps Claude, Codex, and Claude Code review quant workflows before implementation.

## Install

```bash
npx skillstore add barissozen/hft-quant-expert
```

## Metadata

- - Slug: barissozen-hft-quant-expert
- - Version: 1.0.0
- - Author: BarisSozen
- - GitHub username: BarisSozen
- - License: MIT
- - Repository: https://github.com/BarisSozen/claude/tree/main/.claude/skills/hft-quant-expert
- - Ref: main
- - Supported tools: Claude, Codex, Claude Code
- - Risk level: safe
- - Quality score: 80
- - Quality tier: silver
- - Public page: https://skillstore.pages.dev/skills/barissozen-hft-quant-expert
- - Manifest: https://skillstore.pages.dev/api/skills/barissozen-hft-quant-expert/manifest

## Capabilities

- Explains z-score, Sharpe ratio, Kelly sizing, and mean reversion half-life formulas.
- Guides entry signal definition for quantitative trading strategies.
- Reviews backtest design for lookahead bias, survivorship bias, and overfitting.
- Highlights gas, slippage, and transaction costs in DeFi profit calculations.
- Supports risk management discussion for crypto derivatives and DeFi strategies.

## Use Cases

- Review a Mean Reversion Signal: Check whether a z-score strategy has clear entries, exits, and cost assumptions before coding.
- Assess Backtest Quality: Identify common testing flaws such as lookahead bias, survivorship bias, and overfitting in a proposed study.
- Set Risk Controls: Translate win rate, payout ratio, volatility, and drawdown limits into practical position sizing guidance.

## Prompt Templates

### Define a Basic Signal

```
Help me define a simple z-score trading signal for a crypto pair. Include entry rules, exit rules, and required data.
```

### Check Backtest Bias

```
Review my backtest plan for lookahead bias, survivorship bias, overfitting, and missing DeFi transaction costs.
```

### Size a Position

```
Given win probability, payout ratio, volatility, and maximum drawdown, propose a conservative position sizing approach.
```

### Audit a Full Strategy

```
Evaluate this crypto derivatives strategy from signal design through execution costs, risk controls, and performance metrics.
```

## Limitations

- Does not execute trades, connect to exchanges, or manage live capital.
- Does not provide guaranteed investment returns or personalized financial advice.
- Does not include market data feeds, backtest engines, or exchange integrations.
- Requires users to validate assumptions with current market data and compliance rules.

## Best Practices

- Provide asset universe, timeframe, fee model, slippage model, and data source before asking for review.
- Separate signal research, execution assumptions, and risk limits so each part can be tested.
- Validate every result out of sample and include realistic DeFi costs before trusting performance.

## Anti Patterns

- Treating a high Sharpe ratio as reliable without checking sample size and bias.
- Ignoring gas, funding, slippage, liquidation risk, or failed transaction costs.
- Changing many parameters until the backtest looks profitable.

## Security Audit

- - Safe to publish: true
- - Audited at: 2026-06-28T12:51:28.917\+00:00
- - Summary: Static analysis flagged external command execution at SKILL.md:39 and weak cryptography at SKILL.md:3. Both are false positives: the first is a fenced Python formula block, and the second refers to crypto derivatives, not cryptographic algorithms.

## Stats

- - Views: 199
- - Downloads: 14
- - Favorites: 0
- - Popularity score: 0