This repository contains a comprehensive collection of outputs and results related to my dissertation on Optimal Trading Strategies in Stock Markets. The codes employed in this study are sourced from two GitHub repositories, while the theoretical framework and analysis are thoroughly documented in my dissertation.
| Directory Name | Description |
|---|---|
results/ |
Contains generated results, performance metrics, and visualizations |
README.md |
Project documentation and guidelines |
To utilize this repository effectively, follow the steps outlined below:
To obtain a local copy of the repository, execute the following command:
git clone https://github.com/chloecortis/single-asset-and-multi-asset-trading-strategies-in-stock-markets.git
cd single-asset-and-multi-asset-trading-strategies-in-stock-markets- This study leverages two distinct Python scripts, each corresponding to the implementation of trading strategies for single-asset and multi-asset portfolios. These scripts are sourced from separate GitHub repositories. Please access the relevant codes using the links provided below:
- An Application of Deep Reinforcement Learning to Algorithmic Trading: GitHub Repository
- Asset Allocation: From Markowitz to Deep Reinforcement Learning: GitHub Repository
- The dataset utilized in this study can be accessed through the following link: Download Dataset. Please ensure that the dataset is downloaded to your local machine before executing the scripts.
- To run a specific trading strategy, follow the instructions provided in the respective code repositories.
- All generated results, including backtesting performance and statistical metrics, are stored in the
results/directory.
This repository is intended solely for experimental and educational purposes. The author does not provide financial, investment, or trading advice. Any strategies or models implemented herein are purely theoretical. The author assumes no responsibility for financial losses, damages, or other consequences resulting from the use of this material. Users are strongly encouraged to conduct independent research and seek guidance from a qualified financial professional before making any trading decisions.
For inquiries or potential collaboration opportunities, please feel free to reach out.