stockpredictionai
A complete AI-driven process using GANs with LSTM and CNN to predict stock price movements, incorporating diverse data sources and hyperparameter optimization.
What is stockpredictionai?
StockPredictionAI is an open-source notebook that demonstrates a complete AI pipeline for predicting stock price movements using a Generative Adversarial Network (GAN) with LSTM and CNN architectures. It integrates multiple data sources, including technical indicators, sentiment analysis, and correlated assets, to capture complex market patterns. The project addresses the challenge of forecasting financial time series by leveraging advanced machine learning techniques and hyperparameter optimization.
Data scientists, quantitative analysts, and machine learning engineers interested in applying deep learning to financial forecasting. It's suitable for those with experience in time-series analysis, neural networks, and stock market data.
Developers choose this project for its comprehensive approach, combining cutting-edge AI methods like GANs, reinforcement learning, and diverse feature engineering in a single pipeline. It offers a practical, code-driven example for experimenting with stock prediction models beyond traditional techniques.
Overview
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
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