Quantitative strategy backtestingāimplement, run, and tune trading rules on historical data; performance metrics (CAGR, max drawdown, Sharpe, win rate) and s...
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install strategy-backtestOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install strategy-backtestā ļø Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js ā
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/strategy-backtest/š”Extract and place the folder at the path above, then restart your agent.
Category
šFinance & InvestmentPlatforms
What Strategy Backtest can do for your AI workflow
Quantitative strategy directly from your Claude conversation
Works across Claude, Cursor, OpenClaw ā install once, use everywhere
One-command installation ā no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Strategy Backtest
Help me get started with Strategy Backtest
Explains what Strategy Backtest does, walks through the setup, and runs a quick demo based on your current project
Use Strategy Backtest to quantitative strategy backtestingāimplement, run, and tune trading ...
Invokes Strategy Backtest with the right parameters and returns the result directly in the conversation
What can I do with Strategy Backtest in my finance & investment workflow?
Lists the top use cases for Strategy Backtest, with example commands for each scenario
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Strategy Backtest extends your AI assistant with the ability to quantitative strategy backtestingāimplement, run, and tune trading rules on historical data; performance metrics (CAGR, max drawdown, Sharpe, win rate) and s... Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly ā and it will take care of the task end-to-end, using Strategy Backtest as its underlying capability.
Strategy Backtest works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) ā an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Strategy Backtest once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Strategy Backtest installs like any other MCP skill: drop the folder into `~/.claude/skills/strategy-backtest/` for global access, or `.claude/skills/strategy-backtest/` to keep it scoped to one project. After a quick restart of Claude, you can trigger it explicitly with `/strategy-backtest`, or let the AI decide when it's the right tool for your request.
Strategy Backtest has 359 installs and is part of the growing Finance & Investment skill ecosystem on DiscoverAISkills. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/strategy-backtest/ for personal use (all projects), or .claude/skills/strategy-backtest/ for project-specific use. Restart your AI client, then invoke with /strategy-backtest or let the AI discover it automatically.
Strategy Backtest supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Strategy Backtest is free to install. Check the repository for licensing information.
Quantitative strategy backtestingāimplement, run, and tune trading rules on historical data; performance metrics (CAGR, max drawdown, Sharpe, win rate) and s...
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Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Backtesting Trading Strategies
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity...
Strategy
Automate my finance & investment tasks using Strategy Backtest
Identifies repetitive steps in your workflow and sets up Strategy Backtest to handle them automatically
Strategy Backtest is categorized under Finance & Investment. These skills help AI agents perform specialized tasks in this domain.
Design robust strategies for any domain with proven frameworks, cognitive bias protection, and constraint-aware recommendations.