quant-analyst
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
Best use case
quant-analyst 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. Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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 "quant-analyst" skill to help with this workflow task. Context: Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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/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. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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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