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Vectorbt Expert

Checked
marketcalls·vlatest·data

Vectorbt Expert analyzes trading signals from vector-based bots to identify profitable opportunities and optimize your strategy for better returns.

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 vectorbt-expert -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

    Only contacts known or declared hosts: 127.0.0.1.

    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

    127.0.0.1local

  • 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/vectorbt-expert/SKILL.md
Scanned
34 files, 6,443 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 vectorbt-expert skill enables AI agents to perform advanced backtesting of trading strategies using the VectorBT library. It allows for efficient simulation and analysis of financial strategies on historical data, including metrics like returns, risk-adjusted performance, and drawdowns.

When to use it

  • To evaluate the effectiveness of a new trading strategy before deploying it live
  • When analyzing historical market data to optimize entry/exit conditions
  • For comparing multiple strategies side-by-side using standardized performance metrics

Key capabilities

  • Backtesting of custom trading strategies
  • Performance analysis with metrics like Sharpe ratio and maximum drawdown
  • Integration with financial datasets for simulation accuracy

Example prompts

  • "Backtest a moving average crossover strategy on the S&P 500 from 2010 to 2023"
  • "Compare the performance of two portfolio allocation strategies using historical stock data"
  • "Generate a risk-adjusted return analysis for a high-frequency trading algorithm"

Tips & gotchas

  • Ensure your input data is properly formatted and aligned with VectorBT’s requirements.
  • Be mindful of overfitting when tuning strategy parameters based on backtest results.