Welcome to the Quantitative Finance 101: Backtesting and Optimization project. This project aims to demonstrate how to perform backtesting and optimization in the quantitative finance way.
To get started, please check out the main.py script or the demo.ipynb notebook for instructions on how to use the code.
Ensure you have the necessary libraries installed. You can install the required packages using: pip install -r requirements.txt
The project is organized in a clean and efficient manner, following a facade design pattern. Here's an overview of the structure:
- facade/: Contains the Backtester class and other core components.
- util/: Contains utility functions that support the main functionality.
- main/: Contains the primary scripts and notebooks for running the backtests and optimizations.
- result/: Stores the backtest and optimization results, including Sharpe ratio heatmaps and time series data for certain parameters.
Running Backtests and Optimizations: You can run the main.py script to perform backtesting and optimization. Alternatively, use the demo.ipynb notebook to explore the functionalities interactively.
Viewing Results: The results are stored in the result folder. You can find Sharpe ratio heatmaps and time series data for various parameters within this folder.