For quants or data scientists who are intrigued by the stock market, this repo contains simple working examples of several popular machine learning and neural network approaches for predicting stock prices. The repo also contains sample stock data so the code is ready launch with no extra steps.
ML Methods include:
* Gradient Boost
* K-means clustering
* Logistic Regression
* Random Forest
* Support Vector Machines
NN examples are all Feedforward Neural Network (FFNN) for several popular libraries:
* PyTorch
* PyTorch Lightning
* Keras
* Tensorflow
At the very least these examples can be starting points that get the boilerplate out of the way and allow you to develop more sophisticated approaches.
ML Methods include: * Gradient Boost * K-means clustering * Logistic Regression * Random Forest * Support Vector Machines
NN examples are all Feedforward Neural Network (FFNN) for several popular libraries: * PyTorch * PyTorch Lightning * Keras * Tensorflow
At the very least these examples can be starting points that get the boilerplate out of the way and allow you to develop more sophisticated approaches.
I'd really love to hear what you make of this!