Using Machine Learning to Predict Stock Prices

Machine learning and deep learning have found their place in the financial institutions for their power in predicting time series data with high degrees of accuracy and the research is still going on to make the models better. This post is the advanced continuation of my introductory template project on using machine learning to predict stock prices. Find the link below:

Introduction to Neural Networks for Finance

It is based on my project AIAlpha, which is a stacked neural network architecture that predicts the stock prices of various companies. This project is also one of the finalists at iNTUtion 2018, a hackathon for undergraduates here in Singapore.

Workflow

The workflow for this project is essentially in these steps:

1. Acquire stock price data
2. Denoise data using Wavelet Transform
3. Extract features using Stacked Autoencoders
4. Train LSTM using features
5. Test model for predictive accuracy