Optimal Trade Execution via Reinforcement Learning

This project implements an intelligent trading agent using Reinforcement Learning (PPO) to minimize execution cost during large institutional orders. Instead of static strategies like TWAP/VWAP, our RL agent dynamically adjusts the quantity to trade at each timestep based on live market data.

...

📌 Credits

Developed as part of a quantitative finance research initiative focused on execution optimization using AI/ML techniques.