hft-quant-expert

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