Skip to content
TrustedSkillsbeta
Back to all skills

Backtest Expert

Checked
tradermonty·vlatest·data·MIT

The Backtest Expert analyzes trading strategies using historical data to assess their performance and identify potential improvements for informed 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 tradermonty/claude-trading-skills --skill backtest-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

tradermonty

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 tradermonty/claude-trading-skills at fac23bed61.

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

Commit
fac23bed61
Manifest
skills/backtest-expert/SKILL.md
Scanned
7 files, 2,370 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 Expert skill provides a systematic methodology for analyzing trading strategies using historical data. It focuses on identifying robust strategies that perform consistently under stressful conditions, rather than simply maximizing paper profits. The tool helps users evaluate strategy viability, troubleshoot misleading backtests, and avoid common pitfalls like curve-fitting and look-ahead bias by incorporating friction and stress testing assumptions.

When to use it

  • Developing or validating systematic trading strategies.
  • Evaluating whether a trading idea is robust enough for live implementation.
  • Troubleshooting why a backtest might be misleading.
  • Learning proper backtesting methodology.
  • Assessing parameter sensitivity and regime dependence.

Key capabilities

  • Hypothesis Definition: Requires clear articulation of the trading edge being tested.
  • Rule-Based Codification: Enforces strict, unambiguous rules for entry, exit, position sizing, filters, and universe selection.
  • Stress Testing: Includes parameter sensitivity analysis (stop loss, profit target, timing), execution friction simulation (slippage, order rejection), and time robustness checks (year-by-year performance).
  • Realistic Cost Modeling: Incorporates commissions and slippage estimates into backtest calculations.
  • Sample Size Requirements: Provides guidelines for minimum trade counts to ensure reliable results.

Example prompts

  • "Analyze this trading strategy: Stocks that gap up >3% on earnings and pull back to the previous day's close within the first hour provide a mean-reversion opportunity."
  • "Stress test my strategy by varying the stop loss at 50%, 75%, 100%, 125%, and 150% of its baseline value."
  • “Evaluate this backtest for potential look-ahead bias.”

Tips & gotchas

  • Prerequisites: Requires Python 3.9 or higher to run the evaluation script.
  • User-Provided Data: The skill requires users to provide historical data; it does not have any external data dependencies.
  • Focus on Robustness: The primary goal is to find strategies that survive stressful conditions, not necessarily those with the highest potential profit.