The IMC Prosperity³ competition is an international quantitative trading challenge hosted by IMC Trading. Teams of students compete in a simulated market environment where they develop algorithmic trading strategies across a range of financial instruments.

• Market Simulation: Participants interact with a live market featuring real-time order books, limit orders, and price volatility.
• Product Diversity: The market includes a variety of assets such as baskets, vouchers, and components, each with unique behaviors and constraints.
• Strategic Focus: Success relies on applying concepts from statistics, game theory, and market microstructure, while responding to dynamic market conditions.
• Rounds-Based: The competition progresses in rounds (trading days), with increasing complexity and new products introduced over time.
• Performance Metric: Teams are ranked based on their final Profit & Loss (PnL) while managing risk and respecting position limits.

I participated as a quant developer and strategist, where I:

• Engineered market-making and mean-reversion strategies using Python.
• Designed signal features from historical price spreads, VWAPs, and basket-component relationships.
• Built automated trading agents with adaptive logic to adjust to real-time market shifts.
• Performed backtesting and parameter tuning on thousands of timestamps.

This repository contains code, notes, and strategy insights developed during the competition.

The competition provides a datamodel.py file that contains essential data structures and classes used for trading:

• TradingState: Contains the current market state (timestamp, order depths, positions, trades, observations)
• OrderDepth: Represents bid and ask orders in the order book
• Order: Structure for creating buy/sell orders
• Trade: Represents executed trades
• Listing, Observation: Additional market data structures
• ProsperityEncoder: JSON encoder for logging

All trading algorithms must import from this file to interact with the market environment.

To test your trading algorithms locally:

Installation:

# Install the latest version of the backtester
pip install -U prosperity3bt

Running the backtester:

# Run the backtester on an algorithm using all data from round 0
prosperity3bt <path to algorithm file> 0

# Example:
prosperity3bt terence_rainforest.py 0

The backtester simulates the competition environment and provides performance metrics, allowing you to iterate on strategies before submission.

🚀 Feel free to explore the code and reach out if you'd like to discuss quantitative trading, algorithmic strategy design, or the IMC competition experience.