rl-execution

What problem does it solve?

This Skill tackles the challenge of minimizing costs associated with executing large trades in financial markets by using advanced algorithms, including reinforcement learning.

Core Features & Use Cases

• Optimal Trade Splitting: Determines the best way to break down a large order into smaller trades over time to reduce market impact.
• Adaptive Execution: Adjusts trading strategy in real-time based on market conditions, unlike fixed schedules like TWAP or VWAP.
• Reinforcement Learning: Leverages RL to learn complex, non-linear execution policies from simulated market data.
• Use Case: An institutional trader needs to sell 1 million shares of a stock without significantly moving the price. This Skill can calculate an optimal execution schedule that minimizes both temporary and permanent market impact.

Quick Start

Run the python script scripts/execution_simulator.py to compare different execution strategies in a simulated market environment.