By 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.
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