Predicting Index Futures Price Movement

This project contains both a back-end (Flask) and a front-end (React/Vite/etc.). Follow the instructions below to run the project on your operating system.

Getting Started

macOS/Linux

1. Clone the repository:

   git clone https://github.com/UpLiftL1f3/CSCE5214_LineChasers.git
   cd CSCE5214_LineChasers.git

2. Move the RFmodel.pkl file into the backend folder:

   • Make sure the RFmodel.pkl file (included in the project zip) is placed in the backend folder.

3. Give execution permissions to the script:

   chmod +x run-mac.sh

4. Run the project:

   ./run-mac.sh

   This script will:

   • Create a Python virtual environment (if one doesn’t exist).
   • Install the necessary Python packages (Flask, Flask-CORS, pandas, numpy, scikit-learn).
   • Start the back-end server (Flask).
   • Install Node.js dependencies and start the front-end server.

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Windows

1. Clone the repository:

   git clone https://github.com/UpLiftL1f3/CSCE5214_LineChasers.git
   cd CSCE5214_LineChasers.git

2. Move the RFmodel.pkl file into the backend folder:

   • Make sure the RFmodel.pkl file (generated from PickleFile.ipynb) is placed in the backend folder.

3. Give execution permissions to the script:

   chmod +x run-mac.sh
   

4. Run the project:

   • Run the script:

     ./run-windows.sh

   • Navigate to the backend folder:

     cd backend

   • Activate the virtual environment:

     source venv/Scripts/activate

   • Go back to the original folder:

     cd ..

   • Run the script again:

     ./run-windows.sh

   This script will:

   • Create a Python virtual environment (if one doesn’t exist).
   • Install the necessary Python packages (Flask, Flask-CORS, pandas, numpy, scikit-learn).
   • Start the back-end server (Flask) in a new command window.
   • Install Node.js dependencies and start the front-end server in a new command window.

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Dataset

The dataset used was obtained from Kaggle:
Jim Zhang (Xiaotian). High Frequency Price Prediction of Index Futures. https://kaggle.com/competitions/caltech-cs155-2020, 2020. Kaggle.

Notes

• Ensure you have Python 3.x, Node.js, and npm installed on your system.
• Make sure ports 5000 (for Flask) and 3000 (for the front-end) are available before running the scripts.