Quantitative Trading Strategy Builder is a finance claude skill built by sickn33. Best for: Quant analysts and traders use this to develop data-driven trading strategies, validate them against historical data with realistic constraints, and analyze risk-adjusted performance before live deployment..

What it does
Build, backtest, and optimize algorithmic trading strategies with risk metrics, portfolio models, and statistical arbitrage techniques.
Category
finance
Created by
sickn33
Last updated
Claude Skillfinance GitHub-backed CuratedadvancedClaude Code

Quantitative Trading Strategy Builder

Build, backtest, and optimize algorithmic trading strategies with risk metrics, portfolio models, and statistical arbitrage techniques.

Skill instructions


name: quant-analyst description: Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. risk: safe source: community date_added: '2026-02-27'

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.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Use this skill

Most skills are portable instruction packages. Claude Code supports SKILL.md directly. Other agents can use adapted files like AGENTS.md, .cursorrules, and GEMINI.md.

Claude Code

Save SKILL.md into your Claude Skills folder, then restart Claude Code.

mkdir -p ~/.claude/skills/quantitative-trading-strategy-builder && curl -L "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/HEAD/skills/quant-analyst/SKILL.md" -o ~/.claude/skills/quantitative-trading-strategy-builder/SKILL.md

Installs to ~/.claude/skills/quantitative-trading-strategy-builder/SKILL.md.

Use cases

Quant analysts and traders use this to develop data-driven trading strategies, validate them against historical data with realistic constraints, and analyze risk-adjusted performance before live deployment.

Reviews

No reviews yet. Be the first to review this skill.

No signup required

Stats

Installs0
GitHub Stars35.4k
Forks5820
LicenseMIT License
UpdatedMar 25, 2026