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Backtesting Trading Strategies

Listed
gracefullight·vlatest·data

Simulates past trades to evaluate strategy performance and risk, helping users refine their plans for future profitability.

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 gracefullight/stock-checker --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

gracefullight

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

    • .agents/skills/backtesting-trading-strategies/scripts/fetch_data.py:81contacts 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 gracefullight/stock-checker at 40a4dcb82e.

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

Commit
40a4dcb82e
Manifest
.agents/skills/backtesting-trading-strategies/SKILL.md
Scanned
10 files, 2,010 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 backtest trading strategies against historical data, simulating past trades to evaluate performance and risk before deploying them with real capital. It provides a complete framework including eight pre-built strategies, comprehensive performance metrics, and parameter optimization capabilities. The tool helps users refine their trading plans by analyzing trade history and identifying optimal strategy settings.

When to use it

  • Strategy Validation: Before risking real money, test the viability of a new or modified trading strategy.
  • Parameter Optimization: Fine-tune existing strategies by finding the best parameter combinations for maximum profitability and acceptable risk.
  • Performance Analysis: Understand how a strategy performed in the past, including key metrics like Sharpe Ratio, Sortino Ratio, and Maximum Drawdown.
  • Risk Assessment: Evaluate potential risks associated with a trading strategy through metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR).

Key capabilities

  • 8 Pre-built Trading Strategies: SMA, EMA, RSI, MACD, Bollinger Bands, Breakout, Mean Reversion, Momentum.
  • Performance Metrics: Sharpe Ratio, Sortino Ratio, Calmar Ratio, VaR, CVaR, Max Drawdown, Total Return, CAGR, Volatility.
  • Trade-by-trade Analysis: Detailed log of individual trades with profit/loss information.
  • Equity Curve Visualization: Graphical representation of portfolio value over time.
  • Parameter Grid Search Optimization: Automated search for optimal strategy parameters.

Example prompts

  • "Run a backtest on the SMA crossover strategy for BTC-USD using the last year of data."
  • "Optimize the RSI reversal strategy for ETH-USD, varying the period and overbought/oversold levels."
  • "Show me the performance metrics (Sharpe Ratio, Max Drawdown) for the Bollinger Bands strategy applied to BTC-USD over a two-year period."

Tips & gotchas

  • Dependencies: Requires pandas, numpy, yfinance, and matplotlib. ta-lib, scipy, and scikit-learn are optional for advanced features. Install these using pip install ....
  • Data Fetching: The skill automatically caches historical data to avoid repeated downloads. Ensure the correct symbol and period are specified when fetching data.
  • Parameter Formatting: When specifying custom parameters, use a valid JSON string format (e.g., '{"period": 14, "overbought": 70}').