Marketplace hft-quant-expert

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.

install

source · Clone the upstream repo

git clone https://github.com/aiskillstore/marketplace

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiskillstore/marketplace "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/barissozen/hft-quant-expert" ~/.claude/skills/aiskillstore-marketplace-hft-quant-expert && rm -rf "$T"

manifest: skills/barissozen/hft-quant-expert/SKILL.md

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