Automated Stock Trading and Portfolio Optimization Using XCS Trader and Technical Analysis

Financial market is highly dynamic system for which finding underlying price pattern is highly complex. We have extended the previous work done on automatic stock trading using extended classifier system (XCS) by implementing Q (1) and Q (λ) Reinforcement Learning algorithm. We developed 14 XCS agents using different technical indicatorslike Moving averages,RSI,CMF,SAR,ADX etc. We showed that by modeling financial prediction as single step reinforcement learning problem and using the concept of delayed reward for checking correctness of action taken, all the benchmarks strategies like buyand hold, 'keeping money in bank' etc could be beaten. We have also shown that stock price movement is co-related with other day price movement and reformulated the financial forecasting as a multi step process. We introduced the concept of passive set and found that multi step problem formulation gives best results. Q learninggave 18% better performance than single step reward only RL. Finally we build a portfolio managementand optimization system which learns online and does monthly or quarterly rebalancing using the best trader to trade. The results showed that reacting to the market dynamics doesn’t necessarily give us the best result. We showed that such a system give us average performance between the best trader and the worst trader. We also employed different trading strategieslike “using more than 1 best agent” and “ mean reversalstrategy” to do portfolio optimization.