Ultra-short term trading using a neural-network based ensemble of financial technical indicators in a closed world market

Abstract

The proposed paper presents the analysis, design, implementation and evaluation of an ultra-short-term frequency trading system for the foreign exchange market (FOREX), which features all stages of the trading process (Pre-Trade Analysis, Trend Forecasting, Trade Execution). The system uses artificial intelligence techniques in an environment that is constantly changing according to the decisions of the participating trading simulators. Our goal is to simulate the judgment and decision-making of the human expert (technical analyst or broker) in a closed world trading system that constantly adjusts exchange rates. We examine the system in terms of its contribution to how exchange rates are affected in an environment which solely consists of traders, without any exogenous factors, and show that our system can outperform all conventional technical indicators, thereby indicating that it could also play the role of facilitating the stabilization of a market by squeezing out non-intelligent traders. We designed and implemented a self-adjusting trading environment whose exchange rate price is only affected by trading within the environment (closed world assumption). We based our work on a modified series of technical indicator simulators, which are fed to an artificial neural network architecture, to eventually generate the trend forecasting signal and a series of customizable ultra-short term automated trading machines, which perform real-time virtual transactions based on the generated forecasting signals. A comparative analysis of the results is carried out to confirm that the proposed architecture outperforms traders based only on the conventional technical indicators while we also document the behavior of the system towards facilitating the attainment of an equilibrium.