Artificial Intelligence in Quantitative Finance and Trading
Artificial Intelligence’s primary goal is to achieve superhuman intelligence with zero human input in challenging real-world scenarios. Natural Language Processing and Image processing have become instrumental in achieving this intelligence in many applications. Moreover, Deep Reinforcement Learning has started to yield promising results in the Gaming industry where AI agents are performing better than humans. Could Deep Reinforcement Learning do the same in Finance and Trading Mechanisms? Yes!! This article is an attempt to give a glimpse of how Deep Reinforcement Learning can revolutionize Quantitative Finance and Trading.
While Reinforcement Learning is a methodology of building agents whose long-term goal is to optimize the rewards which they receive based on their learning, Deep Learning is the methodology of developing systems inspired by human cognitive science and neuroscience. These two put together, Deep Reinforcement Learning makes agents which can learn and apply their intelligence in complex environments like finance markets.
Deep Reinforcement Learning Algorithms in Finance Markets
Algorithms applied in the gaming industry, like Deep Q Networks (DQN), Double Q learning, and Duel networks which are based on discounted future rewards can be useful in Financial environments also. These networks are trained on the experience gained by the Reinforcement Learning agents and are quite promising in yielding profits.
But as the data collected in financial markets is mainly of long sequences collected over a period of time, the above neural networks tend to forget what they learnt from distant past. Incorporating Deep Recurrent Q Networks (DRQN) is a good solution to solve this problem. Apart from increasing the chances of making networks remember their distant past, it increases the overall profits made by the agent. DRQN can increase the accuracy and speed with which agent learns from financial environments also significantly. Read our next blog post on how you can reduce the execution time of training DRQN.
Why Not Supervised Learning Algorithms in Finance Markets?
Finance markets are very complex environments where people buy and sell with the primary goal of making profits. These markets are unstable with many hidden states which are very hard to comprehend using conventional methods. Hence present day trading strategies have become significantly data-driven and analytical. It is very tempting to think why Supervised Learning algorithms cannot be used if it is data-driven. As stated earlier, Finance markets have many hidden states which make them Partially Observable Markov Decision Processes in Reinforcement Learning Terms. Supervised Learning is not sufficient to learn about these environments. Hence they are being replaced by Reinforcement learning algorithms.
