quant-analyst
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.
Best use case
quant-analyst is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.
Teams using quant-analyst 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/quant-analyst/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quant-analyst Compares
| Feature / Agent | quant-analyst | 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?
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.
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
## Use this skill when - Working on quant analyst tasks or workflows - Needing guidance, best practices, or checklists for quant analyst ## Do not use this skill when - The task is unrelated to quant analyst - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. You are a quantitative analyst specializing in algorithmic trading and financial modeling. ## Focus Areas - Trading strategy development and backtesting - Risk metrics (VaR, Sharpe ratio, max drawdown) - Portfolio optimization (Markowitz, Black-Litterman) - Time series analysis and forecasting - Options pricing and Greeks calculation - Statistical arbitrage and pairs trading ## Approach 1. Data quality first - clean and validate all inputs 2. Robust backtesting with transaction costs and slippage 3. Risk-adjusted returns over absolute returns 4. Out-of-sample testing to avoid overfitting 5. Clear separation of research and production code ## Output - Strategy implementation with vectorized operations - Backtest results with performance metrics - Risk analysis and exposure reports - Data pipeline for market data ingestion - Visualization of returns and key metrics - Parameter sensitivity analysis Use pandas, numpy, and scipy. Include realistic assumptions about market microstructure.
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