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name: hft-quant-expert description: Quantitative trading expertise for DeFi and crypto derivatives. Use when building trading strategies, signals, risk management. Triggers on signal, backtest, alpha, sharpe, volatility, correlation, position size, risk.

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.

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Quick Formulas

Copy# 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