Stock Features

Repository for my undergraduate research program titled "Application of Deep Learning in Stock Market Index Prediction"

Repository contains mainly codes for:

• Data Visualization
• Technical Analysis
• Feature Engineering
• Dimensionality Reduction

Final Submitted paper can be found here

Getting Started Guide

Getting Started for macOS and Unix-like

To run the server for this project, we will do the following:

1. Install pip3
2. Install virtualenv and activate it
3. Install all python dependencies for this project
4. Run the python script

First, we will install pip by following command:

$ sudo apt-get install python3-pip

Next, we will install virtualenv using pip3, create a virtual environment, and activate the environment.

$ sudo pip3 install virtualenv
$ virtualenv venv
$ source venv/bin/activate

Next, we will install all requirements/dependencies for this project using pip3.

$ pip3 install -r requirements.txt

Finally, we can view how to use each script by running the python module with the -h flag:

# Make sure you are in <project-root> folder before running the script
$ python3 visualize.py
usage: visualize.py [-h] [--pca] [--ica] [--tsne]

Process and visualization of dataset using Dimensionality Reduction Techniques
like PCA, ICA and t-SNE.

optional arguments:
  -h, --help  show this help message and exit
  --pca       Using PCA to reduce dimensions
  --ica       Using ICA to reduce dimensions
  --tsne      Using t-SNE to reduce dimensions

$ python visualize.py --pca

At the end of our development, we call deactivate in command line to deactivate virtualenv.

We don't install these dependecies everytime when we want to develop for this project. A normal workflow would be:

$ source venv/bin/activate

$ python visualize.py --pca

# When you are done
$ deactivate

Contributors

Done by Liu Zhemin for academic year 2017-2018