Quant Analyst

Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use...

Security Scan

The skill's description and runtime instructions are internally consistent for a quantitative-analyst guidance skill; it is instruction-only and does not request unexpected credentials or installs.

Purpose & Capability

Name, description, and SKILL.md focus on quantitative finance tasks (strategy development, backtesting, risk metrics). The guidance to use pandas/numpy/scipy is consistent with that purpose.

Instruction Scope

The instructions remain within the quant-analyst domain and do not ask for unrelated system credentials or data exfiltration. They do instruct the agent to 'open resources/implementation-playbook.md', but that file is not included in the bundle — this is a minor inconsistency that may lead the agent to look for local files. The SKILL.md also names libraries but provides no installation steps (expected for instruction-only skills).

Install Mechanism

No install spec or code files are included (instruction-only), which is low-risk. There is no external URL download or executable install behavior in the package.

Credentials

The skill declares no required environment variables, credentials, or config paths. This is proportionate for a guidance-only skill. Note: practical use for live data or exchange integration would require external API keys that are not requested here (not inherently a problem for an instruction-only skill).

Persistence & Privilege

The skill does not request 'always' or other elevated persistence privileges. It is user-invocable and allows model invocation (platform default).

Assessment

This is an instruction-only guidance skill for quantitative analysis and appears coherent. Before using it in any real trading context: ensure your runtime has Python and the libraries it references (pandas, numpy, scipy), verify where market data and exchange API keys will come from (the skill does not request them), and be aware the SKILL.md references a resources/implementation-playbook.md file that is not included — ask the author for that resource if you need detailed examples. As with any trading guidance, test thoroughly in a sandbox or paper-trading environment before using real capital.

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.