Hft Quant Expert
- 14 installs
- 82 repo stars
- Updated August 2, 2026
- aaaaqwq/claude-code-skills
hft-quant-expert is a Claude Code skill that provides quantitative-trading expertise for DeFi and crypto derivatives, covering signals, sizing, backtesting and risk.
About
hft-quant-expert is a lightweight skill that supplies quantitative-trading expertise for DeFi and crypto derivatives. It walks the agent through defining an entry signal, sizing positions with a fractional Kelly criterion, validating backtests against common biases, and accounting for gas and slippage costs. It includes quick Python formulas for z-score, Sharpe ratio, Kelly fraction and mean-reversion half-life. A developer uses it as a reference when building trading strategies and risk management.
- Provides quantitative trading guidance for DeFi and crypto derivatives
- Covers signal definition, Kelly-based position sizing, backtest validation and cost accounting
- Ships quick formulas for z-score, Sharpe, Kelly fraction and mean-reversion half-life
Hft Quant Expert by the numbers
- 14 all-time installs (skills.sh)
- Ranked #752 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
- Data as of Aug 3, 2026 (Skillselion catalog sync)
hft-quant-expert capabilities & compatibility
- Capabilities
- trading · risk management · backtesting
- Use cases
- trading · data analysis
- Pricing
- Free
What hft-quant-expert says it does
Quantitative trading expertise for DeFi and crypto derivatives.
Use Kelly Criterion (0.25x) for position sizing.
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| Installs | 14 |
|---|---|
| repo stars | ★ 82 |
| Last updated | August 2, 2026 |
| Repository | aaaaqwq/claude-code-skills ↗ |
What it does
Use it as quantitative-trading guidance when building signals, position sizing, backtests and risk management for crypto.
Who is it for?
Building crypto/DeFi trading strategies with sound signal, sizing, backtest and cost practices.
Skip if: Non-trading domains or teams needing full backtesting engines rather than reference formulas.
When should I use this skill?
The user mentions signal, backtest, alpha, sharpe, volatility, correlation, position size or risk.
What you get
Strategy guidance that sizes positions with fractional Kelly and validates against bias and costs.
- Quant strategy and risk-management guidance
- Reference formulas for z-score, Sharpe, Kelly and half-life
By the numbers
- Kelly Criterion at 0.25x for sizing
- 5 common pitfalls listed
Files
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.
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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_coefCommon 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
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What position sizing does it recommend?
The Kelly Criterion at 0.25x (fractional Kelly).
What backtest pitfalls does it flag?
Lookahead bias, survivorship bias, overfitting, ignoring gas/slippage costs, and wrong annualization.