Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications

Abstract

This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.