Q-Learning: an intelligent stochastic control approach for financial trading

Abstract

The objective is to implement a financial trading system, using MATLAB® software, to solve a stochastic control problem, which is the management of a capital. It is an automated model free machine learning based on Reinforcement Learning method, in particular Q-Learning one. This approach is developed by an algorithm which optimizes its behavior in real time based on the reactions it gets from the environment in which it operates. This project is based on a new emerging theory regarding the market efficiency, called Adaptive Market Hypothesis (AMH). I present an algorithm which might to perform in an operative applications using not complex information, which are the current and the four last returns. It operates on a single stock history prices time series selecting three possible actions: buy, sell and stay out from the market. My several simulations, with different parameters values set and on different stocks, show satisfactory operative performances, which are net of transaction costs.