Automated Trading with Genetic-Algorithm Neural-Network Risk Cybernetics: An Application on FX Markets

Finamatrix Journal, February 2012

28 Pages Posted: 5 Oct 2010 Last revised: 8 Aug 2015

Lanz Chan

Finamatrix Investments Pte. Ltd.; Beijing Institute of Technology - Zhuhai Campus

Wing-Keung Wong

Asia University, Department of Finance

Date Written: February 20, 2012

Abstract

Recent years have witnessed the advancement of automated algorithmic trading systems as institutional solutions in the form of autobots, black box or expert advisors. However, little research has been done in this area with sufficient evidence to show the efficiency of these systems. This paper builds an automated trading system which implements an optimized genetic-algorithm neural-network (GANN) model with cybernetic concepts and evaluates the success using a modified value-at-risk (MVaR) framework. The cybernetic engine includes a circular causal feedback control feature and a developed golden-ratio estimator, which can be applied to any form of market data in the development of risk-pricing models. The paper applies the Euro and Yen forex rates as data inputs. It is shown that the technique is useful as a trading and volatility control system for institutions including central bank monetary policy as a risk-minimizing strategy. Furthermore, the results are achieved within a 30-second timeframe for an intra-week trading strategy, offering relatively low latency performance. The results show that risk exposures are reduced by four to five times with a maximum possible success rate of 96%, providing evidence for further research and development in this area.

Keywords: Automation, Autobot, Genetic-Algorithm Neural-Network, Risk-Pricing, Risk Cybernetics, Expert Advisor

Suggested Citation

Chan, Lanz and Wong, Wing-Keung, Automated Trading with Genetic-Algorithm Neural-Network Risk Cybernetics: An Application on FX Markets (February 20, 2012). Finamatrix Journal, February 2012 . Available at SSRN: https://ssrn.com/abstract=1687763

Lanz Chan (Contact Author)

Finamatrix Investments Pte. Ltd. ( email )

Singapore

HOME PAGE: http://www.finamatrix.com

Beijing Institute of Technology - Zhuhai Campus ( email )

Tangjia Bay
Zhuhai, Guangdong
China

Wing-Keung Wong

Asia University, Department of Finance ( email )

Taiwan
Taiwan

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