Backtesting Py Oracle

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.