Intelligent Dynamic Backlash Agent: A Trading Strategy Based on the Directional Change Framework

The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs).

[1]  Christoph Lattemann,et al.  High-Frequency Trading , 2011 .

[2]  Steffen Bohn,et al.  The slippage paradox , 2011, 1103.2214.

[3]  Bastien Chopard,et al.  Multi-Scale Representation of High Frequency Market Liquidity , 2014, Algorithmic Finance.

[4]  Edward P. K. Tsang,et al.  Backlash Agent: A trading strategy based on Directional Change , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[5]  Shaima M.K. Hussein,et al.  Event-based microscopic analysis of the FX market , 2013 .

[6]  Edward Tsang,et al.  Profiling high-frequency equity price movements in directional changes , 2017 .

[7]  Alexandre Dupuis,et al.  High Frequency Finance: Using Scaling Laws to Build Trading Models , 2012 .

[8]  Marcelo Portes Albuquerque,et al.  Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods , 2009, Expert Syst. Appl..

[9]  Fernando E. B. Otero,et al.  Evolving trading strategies using directional changes , 2017, Expert Syst. Appl..

[10]  Jun Chen,et al.  Regime Change Detection Using Directional Change Indicators in the Foreign Exchange Market to Chart Brexit , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[11]  Jörg Prokop.  Further Evidence on the Role of Ratio Choice in Hedge Fund Performance Evaluation , 2012 .

[12]  Bruce J. Vanstone,et al.  Financial time series forecasting with machine learning techniques: a survey , 2010, ESANN.

[13]  Jack L. Treynor.  How to Rate Management of Investment Funds , 2012 .

[14]  Gavin Finnie,et al.  Developing High-Frequency Foreign Exchange Trading Systems , 2012 .

[15]  M. A. H. Dempster,et al.  High-Performance Computing in Finance: Problems, Methods, and Solutions , 2018 .

[16]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[17]  James B. Glattfelder,et al.  Patterns in high-frequency FX data: discovery of 12 empirical scaling laws , 2008, 0809.1040.

[18]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[19]  Edward P. K. Tsang,et al.  TSFDC: A trading strategy based on forecasting directional change , 2018, Intell. Syst. Account. Finance Manag..

[20]  W. Sharpe.  Asset allocation , 1992 .

[21]  Amer Bakhach,et al.  Forecasting directional changes in the FX markets , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[22]  Fatos Xhafa,et al.  Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation , 2013, Math. Comput. Model..

[23]  Wing Lon Ng,et al.  A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk-Adjusted Performance of AI Trading Algorithms , 2015, Intell. Syst. Account. Finance Manag..

[24]  Dimitrios I. Vortelinos,et al.  Intraday analysis of macroeconomic news surprises and asymmetries in mini-futures markets , 2017 .

[25]  Olivier V. Pictet,et al.  From the bird's eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets , 1997, Finance Stochastics.

[26]  Andrew Ang,et al.  Downside Risk , 2004 .

[27]  Alexandre Dupuis,et al.  The scale of market quakes , 2009 .

[28]  Anton Golub,et al.  The Alpha Engine: Designing an Automated Trading Algorithm , 2017 .

[29]  Frank Schuhmacher,et al.  Robust evidence on the similarity of Sharpe ratio and drawdown-based hedge fund performance rankings , 2013 .

[30]  Nora Alkhamees,et al.  Event detection from time-series streams using directional change and dynamic thresholds , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[31]  Alex Kulesza,et al.  Empirical Limitations on High Frequency Trading Profitability , 2010, 1007.2593.

[32]  M. Aloud.  Directional-Change Event Trading Strategy: Profit-Maximizing Learning Strategy , 2015 .