Building a Stock Trading Agent
Beating the stock market game using Reinforcement learning
Reinforcement learning, a breakthrough in artificial intelligence, empowers agents to learn from experience and optimize decisions. In this article, we'll explore why we can liken the stock market to a game. Just like a strategic board game, the stock market presents players with choices, outcomes, and a dynamic environment. We'll dive into how reinforcement learning can be applied to create trading agents aiming to outperform the market. Let's unravel the potential of training algorithms for smarter trading and financial success.
We use the OpenAI Gym library, known for its versatility and extensive environment offerings, making it an ideal choice for simulating stock market scenarios.
You can access the complete code and experiment with RL-powered stock trading through our GitHub repository, providing a practical foundation for enhancing your investment strategies.
Training our agent on Nifty-50 stocks
We have built our model using the Gym Trading Environment and powered by the A2C (Advantage Actor-Critic) algorithm. The architecture of our model is designed to maximize rewards and utilizes the MlpPolicy. We have trained our model on Nifty 50 stocks. Our training dataset spanned five years, ensuring that our model is well-prepared to navigate the intricacies of this dynamic market
Measuring performance
To gauge the effectiveness of our model, we conducted tests on five different stocks over one year, each with an initial investment of 100,000 Rs. The primary performance metric we used was the percentage return for each stock. Here are the results we obtained:
| Stock Name | Symbol | Max return | Model Return |
| Inter Globe Aviation Ltd | INDIGO | ~61% | +21.00% |
| Narayana Hrudayalaya | NH | ~62% | +7.59%, |
| JK Lakshmi Cement | JKLAKSHMI | ~ 64% | +0.69%, |
| Britannia Industries | BRITANNIA | ~ 45% | +23.00%, |
| Asian Paints | ASIANPAINT | ~34% | +29.93%, |
In this context, "Max Return" signifies the maximum percentage return achieved over a year, while "Model Return" represents the percentage return generated by our model based on the initial investment.
It's evident from the results that our model has delivered respectable returns for certain stocks, yet there remains room for improvement.
Charting a Path Forward
In our ongoing pursuit of refining and expanding our trading model, we are actively exploring advanced capabilities. Our roadmap includes the integration of options trading, portfolio optimization strategies, AI-generated insights, and the incorporation of user sentiments as vital components of our model.
Furthermore, we're committed to enhancing our evaluation metrics to ensure a more comprehensive assessment of stock performance. Stay tuned for the exciting developments and innovations on the horizon.