ConUHacksVII : Arbitrading

This project is our submission for the 2023 ConuHacks Hackathon at Concordia University.

Authors

• Lina Ismail
• Kareem El Assad
• Abhinav Batra
• Alois Clerc

Project Overview

The Backend

The backend is written in python using the Flask framework. It is responsible for the following tasks:

• Top 10 Symbols by Tag: Cancelled
• Top 10 Symbols by Tag: Trade
• Top 10 Symbols by Tag per Second: Cancelled
• Top 10 Symbols by Tag per Second: Trade
• Total Trades Over Time
• Total Cancelled Over Time

A MongoDB Atlas cluster was utilized to store JSON files as collections. The data was then queried using the pandas library.

The Frontend

The frontend is written in React. It is responsible for the following tasks:

• Integrates with axios to make requests to the backend API.
• Displays the data in a horizontal bar graph using chart.js.
• Automatically updates the data in near-real-time using React's state management infrastructure.
• Visualizes the data served by the backend in a user-friendly manner.

Notable Anomalies

A fair amount of anomalies were detected throughout the project.

• Some trades were confirmed/completed without a prior request being made.
• The market is occassionally flooded with requests prior to market open at 9:30am. This is likely to secure a lower price.
• Initial attempts at a purchase tend to start with a significantly low price that is gradualy increased.

Setup Instructions

Backend Setup

1. Cd to the backend folder using cd Backend
2. Create a virtual environment using python -m venv venv
3. Install project requirements using python -m pip install -r requirements.txt
4. Run the backend using flask run

Frontend Setup

1. Cd to the frontend folder using cd Frontend
2. Install project dependencies using npm install
3. Start the project using npm start

Images

License

This project is licensed under the MIT License.

Acknowledgments

We would like to thank the 2023 ConuHacks team for hosting an amazing event.