Reinforcement Learning for Stock Market Trading: A Case Study on Nvidia Stock

This project applies a Q-learning agent to develop a trading strategy that maximizes profit through stock trading. The environment is based on historical stock prices of Nvidia over the past two years, containing 504 entries from 02/01/2021 to 01/31/2023.

Key Points

• Objective: Use Q-learning to learn stock price trends and execute profitable trades.
• Actions: Buy, sell, or hold the stock.
• Initial Capital: $100,000.
• Performance Metric: Percentage return on investment (ROI).
• Dataset Features: Includes open price, intraday high and low, close price, adjusted close price, and trading volume.
• Output: Save the Q-table as a pickle file and attach it to your assignment submission.

Steps

1. Implement Q-learning: Adapt your Q-learning agent to the stock trading environment.
2. Train the Agent: Use the historical stock price data to train the agent.
3. Execute Trades: The agent will decide whether to buy, sell, or hold based on the learned strategy.
4. Evaluate Performance: Measure the agent's performance in terms of ROI.
5. Save Q-table: Save the trained Q-table as a pickle file for submission.

The goal is to leverage Q-learning to devise a optimize trading strategy that optimizes profit by effectively learning and acting on stock price trends.