Portfolio Management Using Deep Reinforcement Learning
Prediction using supervised learning algorithms for financial time series modelling is hard and converting predictions into actions requires additional naive layer of logic. In this talk I present a financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem. A deep neural network is used for generating signals based on historical price data, these signals are fed to a portfolio memory layer that optimizes transaction costs, the deep reinforcement learning agent trains itself using an exhaustive reward function based on long term and short term market performance. This problem is implemented on the cryptocurrency markets and compared with other well known portfolio management frameworks like trend following, mean reversion and equal weighting scheme. I show that the model outperforms the other frameworks but performance is very much dependent on the overall market trend in the long only setup.
Sonam Srivastava is a quantitative trading professional more than 8 years of professional experience in systematic portfolio management and quantitative trading. She is a IIT Kanpur graduate with a Masters in Financial Engineering from Worldquant University. She has worked as a Portfolio Manager at Qplum, applying machine learning & artificial intelligence to automate investment decision making. She has also worked at HSBC as a Quant Researcher and Edelweiss as an Algorithmic Trader. She is an avid researcher in the field of Quantitative Finance and a Registered Investment Advisor.