Learning to Trade with Q-RL and DQNs
Price changes in financial products are largely random, representing an efficient market, but are often supplemented by salient features that provide additional structure which can be exploited for trading profits. Experienced traders are skilled at identifying such features and deploying profitable exploits. We present some methods for learning such exploits using Q- function based reinforcement learning and DQNs that are trained on simulation models for markets which progress through levels of realism with data provided by generative models that mimic both the randomness and salient features of the actual markets.
David Samuel is a veteran of the financial markets, working in trading and quantitative research for a number of investment banks and proprietary trading firms. He has a PhD in theoretical physics and published research in a number of top academic journals. David has been a lecturer for the MSc in Mathematical Finance at Oxford University and collaborated on the application of machine learning to trading with researchers at Cambridge University. His recent interest has been on the application of reinforcement learning and deep learning methods to trading. David is co-founder of Prediction Machines which develops algorithms for predicting and trading in commercial transactions markets.