Stock Trading Project with Reinforcement Learning
Beginners, halt! If you skipped ahead: this project assumes you have completed the core curriculum through temporal difference learning and approximation methods (e.g. Volume 2 and Volume 3 or equivalent). You should understand Q-learning, state and action spaces, and at least linear function approximation. If you have not done that yet, start with the Learning path and Course outline. Stock Trading Project Section Introduction This project walks you through building a simplified RL-based stock trading agent: you define an environment (state = market/position info, actions = buy/sell/hold), a reward (e.g. profit or risk-adjusted return), and train an agent using Q-learning with function approximation. The goal is to understand how to go from theory (Q-learning, FA) to a concrete design and code. ...