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Backtesting Frameworks

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sickn33·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 sickn33/agentic-awesome-skills --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

sickn33

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Install target

Live repository

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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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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.

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  • No credential access: passed

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  • No pipe-to-shell installers: passed

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  • Pinned to a commit: passed

    Scanned sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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Commit
5a4cdd9df8
Manifest
skills/backtesting-frameworks/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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About this skill

The backtesting-frameworks skill provides AI agents with access to a curated collection of tools for simulating trading strategies and analyzing historical market data. It enables agents to test algorithmic ideas against past performance before deploying capital in live markets.

When to use it

  • Validating new quantitative trading hypotheses without risking real funds.
  • Stress-testing portfolio strategies against various historical market conditions.
  • Comparing the performance metrics of different algorithmic approaches side-by-side.
  • Optimizing parameters for existing trading bots based on simulated outcomes.

Key capabilities

  • Access to multiple established backtesting libraries and environments.
  • Simulation of trade execution logic using historical price data.
  • Analysis of strategy profitability, drawdowns, and risk-adjusted returns.

Example prompts

  • "Run a backtest on a moving average crossover strategy using the last five years of S&P 500 data."
  • "Compare the Sharpe ratio and maximum drawdown between a momentum-based bot and a mean-reversion bot."
  • "Simulate how this specific trading algorithm would have performed during the 2008 financial crisis."

Tips & gotchas

Ensure you define clear entry and exit rules for your strategy, as vague instructions will result in inaccurate simulation results. Remember that past performance does not guarantee future results, so treat backtesting outputs as probabilistic estimates rather than certainties.