A diversified investment strategy using autonomous agents

Abstract

In a previously published article, we presented an architecture for implementing agents with the ability to trade autonomously in the Forex market. At the core of this architecture is an ensemble of classification and regression models that is used to predict the direction of the price of a currency pair. In this paper, we will describe a diversified investment strategy consisting of five agents which were implemented using that architecture. By simulating trades with 18 months of out-of-sample data, we will demonstrate that data mining models can produce profitable predictions, and that the trading risk can be diminished through investment diversification.