IMC Prosperity 2.0 - Trading Algorithm Competition

Introduction

IMC Prosperity 2.0 is a project developed for the IMC Prosperity trading algorithm competition organized by IMC Trading. This competition focuses on developing and testing algorithmic trading strategies. Participants create algorithms that can trade financial products efficiently and profitably. The competition provides a backtesting environment, where participants can test their algorithms using historical data before submitting them for evaluation in real trading scenarios.

Competition Overview

The IMC Prosperity competition involves teams from universities and other institutions developing strategies that are evaluated based on their performance. The goal is to optimize trading algorithms to achieve the highest profitability while adhering to trading limits and other constraints set by the competition.

Key Features:

• Backtesting Environment: Test algorithms using historical data.
• Data Provided: Prices and anonymized trade data for various financial products.
• Evaluation: Strategies are evaluated based on performance, focusing on profitability and adherence to trading limits.

For more detailed information, you can refer to the GitHub repository for the competition.

Our Performance

• Global Rank: 1021
• Country (India) Rank: 532

Team Members

• Tanmay Goel (https://github.com/codergoel)
• Harsh Khetan (https://github.com/HK260)

Installation

To set up and run this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/HK260/imc.git

2. Navigate to the Project Directory:

   cd imc

3. Install Dependencies: Ensure you have Python installed. Then, install the required packages:

   pip install -r requirements.txt

Usage

To run the trading algorithm, use the following command:

python main.py

Project Structure

• Round1/: Code for the first round of the competition.
• Round2/: Code for the second round of the competition.
• Tutoria;/: Some first round code (Note: Consider renaming this folder for clarity).
• __pycache__/: Cache files generated by Python.
• data/: Contains the historical data used for backtesting.
• .gitignore: Git ignore file specifying which files to ignore.
• README.md: Project documentation.
• datamodel.py: Some first round code.

Contribution Guidelines

We welcome contributions to improve our trading algorithms and backtesting environment. To contribute, follow these steps:

1. Fork the Repository
2. Create a New Branch:

   git checkout -b feature-branch

3. Make Your Changes
4. Commit Your Changes:

   git commit -m "Describe your changes"

5. Push to Your Branch:

   git push origin feature-branch

6. Create a Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

We thank IMC Trading for organizing the IMC Prosperity competition and providing a platform for participants to enhance their skills in algorithmic trading and financial markets.

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For any queries or further information, please contact Tanmay Goel at official.tanmay1306@gmail.com .