Hft Quant Expert

A practical quant toolkit for crypto and DeFi trading that keeps you from shooting yourself in the foot. It walks you through the standard workflow: defining entry signals with z-scores, sizing positions with Kelly Criterion at 0.25x for safety, backtesting without the usual biases, and actually accounting for gas and slippage in your P&L. The quick reference formulas are handy for Sharpe ratios, mean reversion half-life, and common calculations you'd otherwise have to look up. It's built for someone who knows enough to be dangerous but needs guardrails against lookahead bias, overfitting, and the other classic mistakes that make backtests look amazing until you trade them live.

skills.sh installs: 248
GitHub stars: 1
last updated: Jun 3, 2026

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Install

$ npx -y skills add kasyap1234/delta-go --skill hft-quant-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

HFT Quant Expert

Quantitative trading expertise for DeFi and crypto derivatives.

When to Use

Building trading strategies and signals
Implementing risk management
Calculating position sizes
Backtesting strategies
Analyzing volatility and correlations

Workflow

Step 1: Define Signal

Calculate z-score or other entry signal.

Step 2: Size Position

Use Kelly Criterion (0.25x) for position sizing.

Step 3: Validate Backtest

Check for lookahead bias, survivorship bias, overfitting.

Step 4: Account for Costs

Include gas + slippage in profit calculations.

Quick Formulas

# Z-score
zscore = (value - rolling_mean) / rolling_std

# Sharpe (annualized)
sharpe = np.sqrt(252) * returns.mean() / returns.std()

# Kelly fraction (use 0.25x)
kelly = (win_prob * win_loss_ratio - (1 - win_prob)) / win_loss_ratio

# Half-life of mean reversion
half_life = -np.log(2) / lambda_coef