StockSense

StockSense is a stock price prediction system that uses machine learning to perform automatic stock trading. We aim to explore the possibilities of multi-stock prediction and trading, and evaluate the performance of different machine learning models.

This project is a part of the course "Introduction to Machine Learning" at National Tsing Hua University.

Setup

Server

1. Install Python 3.12
2. Create a virtual environment via python -m venv .venv (make sure to use the correct Python version)
3. Activate the virtual environment via source .venv/bin/activate
4. Install and activate venv via make setup

• Run the server via make run-server
• Run the server in development mode via make dev-server

Client

1. Install Node.js 23.2
2. Go to the client directory via cd client
3. Install the dependencies via npm install

• Build and run the client via make run-client
• Run the client in development mode via make dev-client

Contrubution

• Yan-Fu Chen @AaronChenNMNL - research study, methodology design.
• Yi-Ning Chang @Chang-Yining - result analysis, proposal presentation, evaluation metrics.
• Sheng-You Chien @s111062111 - methodology design, result analysis.
• Bo-Yi Mao @Dogeon188 - data curation, video presentation, project outline.
• Jie-Hung Chen @JieHongChen - methodology design, evaluation metrics.
• Yi-Hsueh Chu @Ethanzzzzz - study research, data curation.