Imported from kasyap1234/delta-go (.agent/skills/hft-quant-expert/SKILL.md). Install upstream with npx skills add kasyap1234/delta-go --skill hft-quant-expert. Copyright stays with the author.

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

Common Pitfalls

• Lookahead bias - Using future data
• Survivorship bias - Only existing assets
• Overfitting - Too many parameters
• Ignoring costs - Gas + slippage
• Wrong annualization - 252 daily, 365*24 hourly