Skip to content
TrustedSkillsbeta
Back to all skills

Backtesting Frameworks

Checked
wshobson·vlatest·data·MIT

This framework enables rapid testing of trading strategies against historical data, optimizing performance and informing investment decisions.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add wshobson/agents --skill backtesting-frameworks -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

wshobson

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned wshobson/agents at 46891e7e60.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
46891e7e60
Manifest
plugins/quantitative-trading/skills/backtesting-frameworks/SKILL.md
Scanned
2 files, 650 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The backtesting-frameworks skill provides tools and libraries that allow users to simulate trading strategies using historical market data. It enables analysis of how a strategy would have performed in the past, helping identify strengths, weaknesses, and potential profitability before real-world deployment.

When to use it

  • You want to evaluate the performance of a new trading algorithm using historical financial data.
  • You are developing an automated trading system and need to validate its logic with simulated market conditions.
  • You aim to compare multiple strategies side-by-side based on their historical returns and risk metrics.

Key capabilities

  • Simulate trades using historical price data
  • Calculate performance metrics like Sharpe ratio, drawdowns, and win rates
  • Support for multiple financial instruments and timeframes

Example prompts

  • "Backtest a moving average crossover strategy on S&P 500 data from 2010 to 2023."
  • "Compare the returns of a long-only portfolio versus a mean-reversion strategy using NASDAQ historical prices."
  • "Generate performance reports for a set of trading strategies over different market conditions."

Tips & gotchas

  • Ensure your historical data is clean and properly formatted before running simulations.
  • Be aware that past performance does not guarantee future results, so backtesting should be used as one tool among others in strategy development.