📊 S&P100 Analysis with Graph Neural Networks 📈

This project focuses on analyzing the S&P100 stocks, which represent 100 leading U.S. stocks, using the power of Graph Neural Networks (GNNs) for advanced tasks.

Overview

The project focuses on leveraging Graph Neural Networks (GNNs) to analyze the S&P100 stocks, representing 100 leading U.S. stocks, providing comprehensive insights into their relationships and behaviors. Through various notebooks, it covers data collection, preprocessing, graph creation, dataset preparation, model implementation, and applications such as forecasting, clustering, trend classification, and portfolio optimization. The aim is to enable users to gain deeper understanding and make informed decisions in the dynamic landscape of financial markets.

Notebooks

For easy understanding and execution, the project is organized into several Jupyter notebooks:

1. Data collection and preprocessing: This notebook provides detailed instructions and code for collecting stock data (sectors, fundamentals, historical prices).

2. Graph creation: Focuses on the creation of graphs to model relationships between S&P100 stocks based on sector and fundamentals correlation.

3. PyTorch Geometric custom dataset: Demonstrates how to use PyTorch Geometric to create datasets suitable for GNN training and evaluation.

4. Temporal Graph Neural Network Models: Implementation of Temporal Graph Convolutional Networks, such as T-GCN and A3T-GCN.

5. Stock prices forecasting: Focuses on using Spatio-Temporal Graph Neural Networks (STGCNNs) for forecasting future stock prices, enabling predictive analysis.

6. Stocks clustering with Deep Graph Clustering: Uses a novel architecture - Temporal Convolutional Graph Autoencoder - to cluster stocks based on the graph structure and their historical prices.

7. S&P100 weights optimization via Deep Reinforcement Learning: Uses Deep Reinforcement Learning to optimize the weights of the 100 stocks in the S&P100 based on historical data and GNN predictions. The goal of the agent is to outperform the equal weights S&P100 index by adjusting its portfolio to the current market.
   Note: I do not have the necessary computing resources to completely train the model in this notebook. However, the code is provided for reference and is fully functional.

8. Stocks trend classification: Uses a Temporal Graph Convolutional Network (T-GCN) to classify the trend of S&P100 stocks (up/down) $n$ weeks ahead based on historical data.

9. Optimal portfolio selection: Illustrates how the previously trained classifier can help optimize a portfolio by selecting stocks with the highest predicted returns. The stocks with the highest probability of going up are selected to form the portfolio, and compared to the market performance.

Dependencies

Ensure you have the following dependencies (of the requirements.txt file) installed to run the notebooks smoothly:

• Gymnasium
• Matplotlib
• NetworkX
• Jupyter Notebook
• NumPy
• Pandas
• scikit-learn
• ta (Technical Analysis library)
• Tensorboard
• PyTorch
• PyTorch Geometric
• tqdm
• Wikipedia
• yFinance

Getting Started

1. Clone this repository to your local machine.
2. Install the necessary dependencies.
3. Open and run the desired notebook(s) in your Jupyter Notebook environment.
4. Follow the instructions within each notebook to execute the code and explore the analyses.

Contributions

Contributions and feedback are welcomed! If you have any suggestions, improvements, or discover any issues, feel free to submit a pull request or open an issue in the GitHub repository.

Licence

This project is licensed under the MIT License.

References

• Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li, Temporal Graph Neural Networks for Traffic Forecasting, 2018, arXiv:1811.05320
• Jiawei Zhu, Yujiao Song, Ling Zhao, Haifeng Li, A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting, 2021, arXiv:2006.11583
• Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, 2017, arXiv:1707.01926
• Pacreau, Grégoire and Lezmi, Edmond and Xu, Jiali, Graph Neural Networks for Asset Management (December 2, 2021). Available at SSRN: https://ssrn.com/abstract=3976168 or http://dx.doi.org/10.2139/ssrn.3976168

Happy analyzing! 📈🔍