Synthetic Stock Data Generator with GANs
The challenge of financial data analysis often lies in the limited availability of diverse, real-world data. To address this, we're developing a sophisticated web application that employs Generative Adversarial Networks (GANs) to generate realistic synthetic stock market data. This data can be invaluable for backtesting trading strategies, training machine learning models, and conducting financial simulations. Python forms the foundation of this project, providing the necessary tools for implementing the GANs and processing data. TensorFlow or PyTorch, powerful deep learning frameworks, are used to build and train these networks. Financial APIs are integrated to access real-world financial data, which serves as the training ground for the GANs. PostgreSQL efficiently stores and manages the generated synthetic data. This project has the potential to enable more robust backtesting, improve the accuracy of financial prediction models, and facilitate risk management by simulating various market scenarios, ultimately leading to more informed and effective investment decisions.