About hft-quant-expert
A skill for quantitative trading in DeFi and crypto derivatives, with guidance on signals, position sizing, backtesting, and risk management. It also covers bias, overfitting, gas, and slippage considerations.
Key features
- 01Trading signal design
- 02Kelly Criterion position sizing
- 03Backtest validation
- 04Volatility and correlation analysis
- 05Gas and slippage accounting
Use cases
- 01Designing quantitative strategies and signals
- 02Calculating position sizes
- 03Checking backtests for bias and overfitting
- 04Assessing trading costs against returns
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/hft-quant-expertin Claude Code to confirm it loaded.
Bundled filesskill-report.json
Details
From the project READMEHFT 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
Comments
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