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