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.
Best use case
hft-quant-expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using hft-quant-expert should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hft-quant-expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hft-quant-expert Compares
| Feature / Agent | hft-quant-expert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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