I’ve only recently started learning about reinforcement learning in detail and I was fascinated after understanding how feats in the field were achieved by projects like DeepMind’s AlphaGo and AlphaZero. This made me eager to understand deep RL with all its complexities and so I decided to dissect its components and using them for an interesting application.

Building a consistently profitable trading bot is no easy task, probably impossible to some degree. A perfect model would basically know the state of the market with all its variables along with some variables we don’t know even exist. With a project like AlphaZero, I was amused by the fact that the algorithm was able to develop strategies in the game of Go that were still undiscovered by high level Go players. Based on this notion, I’m interested in exploring the strategies an RL agent could develop in a complex trading environment, and by complex, I just mean across multiple markets.

Most of the articles I’ve read on this topic stick to one market, for example a “Bitcoin trading bot”. My reasoning for adding this ‘complexity’ factor is that first, we are giving our agent more data from different markets reducing its probability to generalize, and second is there could be meaningful patterns in observing multiple markets under the same space which will be the digital currency space for this project.

#machine-learning #python