prosperity3opt 0.2.0

Hyperparameter optimizer for IMC Prosperity 3 algorithms with multi-objective optimization. Based on the original optimizer by jmerle.

IMC Prosperity 3 Optimizer

Hyperparameter optimizer for IMC Prosperity 3 algorithms with multi-objective optimization support.

Note: This project is based on the original imc-prosperity-3-optimizer by Jasper van Merle (jmerle). The original project provided the foundation for this version.

Features

• Multi-objective optimization (default): Uses NSGA-II to optimize PnL, Sharpe ratio, and drawdown simultaneously
• Single-objective mode: Optional PnL-only optimization using TPE
• Post-optimization analysis: Sensitivity analysis, knife-edge detection, and visualizations
• Overfitting protection: Default trial limits (50-80 for multi-objective, 30-50 for single-objective)

Installation

pip install prosperity3opt

Usage

Basic Usage

Hyperparameters that need to be optimized must be annotated in your code like this:

RAINFOREST_RESIN_VALUE = 10_000 # opt: int(10_000 - 3, 10_000 + 3)

You can use any of Optuna's trial.suggest_* methods after the "# opt: " comment:

• # opt: categorical(choices) - suggest a value for a categorical parameter
• # opt: float(low, high, *[, step, log]) - suggest a value for a floating point parameter
• # opt: int(low, high, *[, step, log]) - suggest a value for an integer parameter

Running the Optimizer

Multi-objective mode (default) - Optimizes PnL, Sharpe ratio, and drawdown:

# Optimize on all days from round 1
prosperity3opt algorithm.py 1

# Optimize on specific days
prosperity3opt algorithm.py 1-0 1--1

Single-objective mode (PnL-only) - Optimizes only PnL:

prosperity3opt algorithm.py 1 --pnl-only

Options

• --pnl-only: Use single-objective optimization (TPE) instead of multi-objective (NSGA-II)
• --trials N: Maximum number of trials (default: 65 for multi-objective, 40 for single-objective)
• --out PATH: Path to save optimization results (default: prosperity3opt.log)
• --grid: Use grid search instead of TPE/NSGA-II
• --jobs N: Number of parallel backtests (default: -1, uses all CPU cores)

Run prosperity3opt --help for all available options.

Design Philosophy

Multi-Objective Optimization (Default)

The default mode uses NSGA-II to optimize three objectives simultaneously:

1. PnL: Maximize total profit
2. Sharpe Ratio: Maximize risk-adjusted returns
3. Drawdown: Minimize maximum drawdown

This returns a Pareto front of solutions, allowing you to choose the highest PnL candidate with acceptable Sharpe ratio and drawdown.

Trial Limits

To prevent overfitting on limited backtest data:

• Multi-objective: 50-80 trials (default: 65)
• Single-objective: 30-50 trials (default: 40)

Post-Optimization Analysis

After optimization, the tool provides:

• Pareto front visualization: Shows tradeoffs between objectives
• Parameter importance: Identifies which parameters matter most
• Sensitivity analysis: Flags parameters on "knife edges" (unstable performance)
• Optimization history: Tracks PnL progression over trials

How It Works

The optimizer:

1. Scans your algorithm file for # opt: annotations
2. Runs backtests using the prosperity3bt module with different parameter combinations
3. Optimizes parameters using Optuna (NSGA-II for multi-objective, TPE for single-objective)
4. Returns a Pareto front (multi-objective) or best solution (single-objective)

Note: The optimizer uses the prosperity3bt module to access historical data and run backtests. You don't need to upload day-specific code - the backtester handles data access automatically.

Visualization

After optimization, you can use the Optuna Dashboard to visualize results:

optuna-dashboard prosperity3opt.log

Credits

This project is based on the original imc-prosperity-3-optimizer by Jasper van Merle (jmerle). The original project provided the core optimizer functionality.

License

MIT License