A Look at the Project Ahead

• Preprocess the stock dataset by cleaning and adding trading indicators including Exponential moving averages (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), On-Balance Volume (OBV), and Bollinger Bands.

• Define the action space, which represents the actions the agent can take. In this case, the agent can either buy or sell the stock.
• Define the state space, which represents the current state of the stock. In this case, the state space can include the current price and the indicators.
• Define the reward function, which will be used to evaluate the agent’s performance. In this case, the reward function can be based on the profit or loss made by the agent.

• Define the agent, which will take actions based on the current state of the stock and the reward function.
• Choose a reinforcement learning algorithm such as Q-Learning or Deep Q-Network (DQN) to train the agent.

• Train the agent using the stock data and the defined reward function.
• The agent should learn the optimal policy for buying and selling the stock based on the current state of the stock and the reward function.

• Test the agent on a separate dataset to evaluate its performance.

• Refine the model by adjusting the parameters of the reinforcement learning algorithm, the reward function, or the state space.

What You’ll Need