Why Deep Reinforcement Learning is the Future of Trading
- Published 28th Feb 2019
Last edited 28th Feb 2019
Imagine a situation where you end up in an entirely new environment and has to solve a certain problem. You do not have any relevant experience or any valuable information at your disposal. All you have is your perceptions about the things that surround you. What you will most probably do in this case is to try getting feedback from the environment by testing different scenarios. The more information is collected, the better the outcome gets – just like when one learns to ride a bike.
Deep reinforcement learning – the process of taking the best decisions and making the most out of a situation based on the existing circumstances.
The best example of a working DRL solution is DeepMind’s Alpha Go which managed to defeat the reigning world Go champion at a game with more possible moves than the atoms in the universe.
Deep Reinforcement Learning in Trading
Due to its close resemblance to (although still far from) the way we, as humans, learn and behave, DRL is starting to become a widely popular framework for trying to solve the mystery of financial markets.
- The self-learning process is perfect for a structure like financial markets
The unique thing about deep reinforcement learning is the fact that the agent is capable of learning via interacting with the surrounding environment. Financial markets, in their core, are chaotic structures. This means that the most efficient trading algorithms are those which are capable of self-improvement by analyzing the feedback and the consequences they suffer from their actions. Aside from that, it is worth noting that chaotic structures are very hard to predict. The only way to do that is to feed the algorithm with information about real-time events that are relevant to the current situation and which will help making the best choice.
- Historical data is slowly proving itself irrelevant
Ever since analysts started forecasting financial markets performance, the main basis and source of information is historical data. However, due to their chaotic nature and ever-increasing complexity, financial markets are proving very hard to predict. With the course of time, patterns found in historical data are getting less relevant to the current state of the market and most of the time are short-lived. That is one of the main reasons why AI-driven hedge funds are underperforming. DRL is capable of solving this problem by ensuring higher efficiency and more accurate predictions. How is that? In its core, DRL requires the agent to be rewarded for its good behavior. Or in other words – if the algorithm makes a good trading decision, based on the current market situation, it gets encouraged (and vice-versa). That way, the agent learns what actions to take in similar future situations based on real-time market information and not historical data that may very much be irrelevant to the current circumstances.
- Human traders are still excelling when compared to computers. DRL can change that
The main difference between automated and human traders, as of now, is our ability to adapt to the situation. Market dynamics nowadays are very turbulent. Volatility levels are jumping, liquidity is draining, flash crashes often take place and the good trader is capable of adjusting his style to the particular situation. Computers, up until this stage, were unable to take real-time decisions, based on the specifics of the momentum. Deep reinforcement learning can change that thanks to the way it works – an interactive process in which the agent responds to the changing environment and learns from the immediate result from its actions.
Up till now, DRL has shown great potential at handling complex and sequential decision-making problems. Although deep reinforcement learning may not be capable of entirely solving the mystery of financial markets at this stage, it surely is the way trading will be done in the future. Add to that the fact DRL frameworks have proved to be great starting points in the process of developing more powerful and efficient algorithms, and voila – you have a solid foundation for shaping the trading process of tomorrow.