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Backtest

Checked
marketcalls·vlatest·data

Analyzes past trading data to evaluate strategy performance & identify potential improvements for future investments.

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 marketcalls/vectorbt-backtesting-skills --skill backtest -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

marketcalls

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 marketcalls/vectorbt-backtesting-skills at 05d9e8b12f.

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

Commit
05d9e8b12f
Manifest
.claude/skills/backtest/SKILL.md
Scanned
1 file, 77 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 backtest skill allows users to simulate the performance of trading strategies using historical financial data. It leverages vectorized backtesting libraries for efficient and accurate analysis, enabling users to evaluate how a strategy would have performed in past market conditions.

When to use it

  • To test the effectiveness of a new trading algorithm before deploying it live.
  • When analyzing historical price patterns to identify profitable opportunities.
  • For comparing multiple strategies side by side using real-world data.
  • During portfolio optimization to refine entry and exit rules based on past performance.

Key capabilities

  • Simulates trading strategy performance with historical market data
  • Supports vectorized backtesting for speed and efficiency
  • Provides detailed performance metrics like returns, drawdowns, and risk ratios

Example prompts

  • "Backtest a moving average crossover strategy using S&P 500 data from 2010 to 2023."
  • "Simulate the performance of a long-only portfolio with monthly rebalancing over the last decade."
  • "Compare the returns of two different risk management models on NASDAQ historical prices."

Tips & gotchas

  • Ensure you have access to high-quality, clean historical market data for accurate results.
  • Be mindful of overfitting—test strategies on out-of-sample data to avoid misleading performance metrics.