Deep-Reinforcement-Learning-Algorithm

The project detailed in the notebook is focused on developing a Deep Reinforcement Learning (DRL) model for trading strategies. Here's a technical breakdown of the workflow:

1. Data Import and Preparation:

   • The project begins by downloading financial data (OHLCV) from Yahoo Finance into a pandas DataFrame. This includes time-series stock data that will be processed and analyzed.

2. Feature Engineering:

   • The ta (technical analysis) library is employed to create relevant technical indicators and features that describe market conditions.
   • Feature correlations are analyzed, and highly correlated features are removed to enhance model performance by reducing redundancy.

3. Train/Test Data Split:

   • Data is split into training and testing sets using a sequential split, preserving the temporal structure necessary for accurate financial modeling.

4. Custom Gym Environment for Trading:

   • A reinforcement learning environment is created using gym. It includes:
     • Action Space: Defines the possible actions (buy, sell, hold).
     • Observation Space: Contains the input data used by the agent for decision-making.
     • Reward Function: Defines the feedback mechanism based on actions taken by the agent, linked to profit/loss or market performance.

5. Hyperparameter Optimization:

   • Bayesian optimization is used to fine-tune the reinforcement learning model's hyperparameters to maximize trading performance.

6. DRL Model Implementation:

   • A Deep Reinforcement Learning algorithm is implemented using the stable_baselines3 library, and the model is trained in the custom environment.

7. Evaluation and Visualization:

   • After training, the agent’s performance is evaluated, and results are visualized to track trading strategies over time, including profits, losses, and bankruptcies.

The project uses a combination of Python libraries such as pandas, numpy, matplotlib, ta, yfinance, and stable_baselines3 to implement and train a trading agent in a reinforcement learning framework.