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Backtesting Py Oracle

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terrylica·vlatest·data·MIT

Backtesting Py Oracle allows you to simulate trading strategies using historical data, optimizing performance and validating your investment ideas.

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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 terrylica/cc-skills --skill backtesting-py-oracle -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

terrylica

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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 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

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  • No credential access: passed

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  • Pinned to a commit: passed

    Scanned terrylica/cc-skills at 3a1dc29965.

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Commit
3a1dc29965
Manifest
plugins/quant-research/skills/backtesting-py-oracle/SKILL.md
Scanned
2 files, 331 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.
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About this skill

The backtesting-py-oracle skill enables AI agents to execute quantitative trading strategies using the Python library Backtrader. It connects these agents directly to Oracle databases, allowing for the storage and retrieval of historical market data required for rigorous strategy validation.

When to use it

  • Validate new algorithmic trading hypotheses against decades of historical price data before risking capital.
  • Optimize trade parameters such as entry/exit thresholds or position sizing by analyzing past performance metrics.
  • Perform stress testing on financial models to understand how strategies behave during specific market regimes.
  • Automate the generation of performance reports including equity curves, drawdown analysis, and Sharpe ratios.

Key capabilities

  • Executes Python code within a secure sandboxed environment specifically configured for backtesting.
  • Leverages the Backtrader framework to manage strategy logic, data feeds, and execution engines.
  • Interfaces with Oracle databases to fetch structured financial time-series data efficiently.
  • Generates standard performance statistics essential for evaluating trading system viability.

Example prompts

  • "Run a mean-reversion strategy on S&P 500 components using daily data from the last five years."
  • "Backtest a momentum-based breakout algorithm and provide a summary of its maximum drawdown and total return."
  • "Compare the performance of two different moving average crossovers against historical forex pairs data stored in Oracle."

Tips & gotchas

Ensure your Oracle database contains clean, normalized time-series data to avoid backtesting artifacts like look-ahead bias. This skill is strictly for simulation; it does not execute live trades or connect to real-time market feeds.