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Backtesting Trading Strategies

Listed
jeremylongshore·vlatest·data·MIT

This skill automatically tests your trading strategy on historical data to assess its performance and identify potential improvements for better results.

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 jeremylongshore/tons-of-skills-marketplace --skill backtesting-trading-strategies -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

jeremylongshore

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.coingecko.com.

    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.

    Hosts contacted

    api.coingecko.comundeclared

    What it found

    • plugins/crypto/trading-strategy-backtester/skills/backtesting-trading-strategies/scripts/fetch_data.py:86contacts api.coingecko.com
      url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart"

  • 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 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 jeremylongshore/claude-code-plugins-plus-skills at a7be181f58.

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

Commit
a7be181f58
Manifest
plugins/crypto/trading-strategy-backtester/skills/backtesting-trading-strategies/SKILL.md
Scanned
15 files, 2,932 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

This skill allows AI agents to simulate the performance of trading strategies using historical market data. It enables users to test how a strategy would have performed in past market conditions, helping identify strengths and weaknesses before deploying real money.

When to use it

  • You want to evaluate the effectiveness of a new trading strategy without risking capital.
  • You are refining an existing strategy and need to compare its performance across different timeframes or assets.
  • You're preparing for live trading and want to validate your approach using historical data.

Key capabilities

  • Simulate trading strategies with historical financial data
  • Calculate metrics like win rate, risk-to-reward ratio, and drawdowns
  • Compare multiple strategies side by side

Example prompts

  • "Backtest a moving average crossover strategy on the S&P 500 from 2010 to 2020."
  • "Compare the performance of a mean reversion strategy versus a trend-following strategy using Bitcoin data."
  • "Run a backtest for a high-frequency trading algorithm with 1-minute candlestick data."

Tips & gotchas

  • Ensure historical data is accurate and representative of real market conditions.
  • Be mindful of overfitting—strategies that perform well on past data may not work in live markets.