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. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "hft-quant-expert" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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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