Does it include risk management frameworks?

Absolutely. It covers a three-layer risk framework including trade-level stops, strategy-level drawdown limits, and portfolio-level Value at Risk (VaR) monitoring.

What Python libraries are recommended when using this skill?

The skill is optimized for use with pandas, numpy, and established backtesting frameworks like Backtrader, Zipline, and QuantConnect.

How does the Quant Trading skill improve strategy development?

It provides a systematic workflow from hypothesis to live monitoring, ensuring developers include critical steps like out-of-sample testing and realistic transaction cost analysis.

Can this skill identify errors in my backtesting code?

Yes, it specifically monitors for 'Sharp Edges' such as look-ahead bias (using future data) and overfitting, providing immediate corrective actions and proper code patterns.

Quant Trading & Strategy Optimization

Name: Quant Trading & Strategy Optimization
Author: miles990

bymiles990

•

Data Science & ML

Develops, backtests, and optimizes systematic trading strategies with integrated risk management and bias detection.

This skill transforms Claude into a quantitative research assistant, providing end-to-end guidance for algorithmic trading. It facilitates the development of diverse strategy types—from trend following to statistical arbitrage—while enforcing professional standards in backtesting and performance evaluation. The skill includes sophisticated frameworks for calculating risk metrics like the Sharpe ratio and Max Drawdown, and it features specialized 'Sharp Edge' detection to alert developers to common pitfalls such as look-ahead bias, overfitting, and survivorship bias, ensuring that strategies are robust and scientifically sound before moving to live environments.

Key Features

01Factor research and Alpha mining across Value, Momentum, and Quality dimensions

02Rigorous backtesting frameworks with integrated transaction cost and slippage modeling

03Advanced risk management tools including VaR, Kelly Criterion, and position sizing

04Automated 'Sharp Edge' detection for identifying backtesting biases and overfitting

05Comprehensive strategy development for Trend Following, Mean Reversion, and Arbitrage

061 GitHub stars

Use Cases

01Building and validating automated algorithmic trading systems in Python

02Performing quantitative performance audits and risk assessments on existing strategies

03Conducting systematic factor research to identify market anomalies and investment opportunities

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add miles990/claude-domain-skills quant-trading

For use in Claude.ai and ChatGPT

Download Skill