hft-quant-expertlisted

# 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 ```python # 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