Computational Economics

, Volume 51, Issue 4, pp 761–807 | Cite as

Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting

The d-BackTest PS method
  • D. Th. VezerisEmail author
  • C. J. Schinas
  • G. Papaschinopoulos


Back testing process is widely used today in forecasting experiments tests. This method is to calculate the profitability of a trading system, applied to specific past period. The data which are used, correspond to that specific past period and are called “historical data” or “training data”. There is a plethora of trading systems, which include technical indicators, trend following indicators, oscillators, control indicators of price level, etc. It is common nowadays for calculations of technical indicator values to be used along with the prices of securities or shares, as training data in fuzzy, hybrid and support vector machine/regression (SVM/SVR) systems. Whether the data are used in fuzzy systems, or for SVM and SVR systems training, the historical data period selection on most occasions is devoid of validation (In this research we designate historical data as training data). We substantiate that such an expert trading system, has a profitability edge—with regard to future transactions—over currently applied trading strategies that merely implement parameters’ optimization. Thus not profitable trading systems can be turned into profitable. To that end, first and foremost, an optimal historical data period must be determined, secondarily a parameters optimization computation must be completed and finally the right conditions of parameters must be applied for optimal parameters’ selection. In this new approach, we develop an integrated dynamic computation algorithm, called the “d-BackTest PS Method”, for selection of optimal historical data period, periodically. In addition, we test conditions of parameters and values via back-testing, using multi agent technology, integrated in an automated trading expert system based on Moving Average Convergence Divergence (MACD) technical indicator. This dynamic computation algorithm can be used in Technical indicators, Fuzzy, SVR and SVM and hybrid forecasting systems. The outcome crystalizes in an autonomous intelligent trading system.


d-BackTest PS method Optimal historical data period selection Back testing optimization algorithms Expert back testing systems MACD optimization Forex 



We thank all partners for their unfailing support and assistance in completing the research and Alexandro Kesidi, Fotio Chartsioudi and Antonio Arvanitidi, in particular. We thank all reviewers for their valuable comments and advice. We thank Savvas Chatzichristofis for his valuable advises.


  1. Anbalagana, T., & Maheswarib, S. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214–221.CrossRefGoogle Scholar
  2. Appel, G. (1985). The moving average convergence-divergence trading method. Toronto: Traders Press.Google Scholar
  3. Aspray, T. (1989). Individual stocks and MACD. Technical Analysis of Stocks & Commodities, 7(2), 56–61.Google Scholar
  4. Cai, B., Cai, C., & Keasey, K. (2005). Market efficiency and returns to simple technical trading rules: Further evidence from U.S., U.K., Asian and Chinese stock markets. Asia-Pacific Financial Markets, 12, 45–60.CrossRefGoogle Scholar
  5. Caporale, G., Gil-Alana, L., & Plastun, A. (2016). Searching for inefficiencies in exchange rate dynamics. Computational Economics, 1–28Google Scholar
  6. Cheng, J., Chen, H., & Lin, Y. (2009). A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5. Expert Systems with Applications, 37(2010), 1814–1820.Google Scholar
  7. Chong, T. T. L., Ng, W. K., & Liew, V. K. S. (2014). Revisiting the performance of MACD and RSI oscillators. Journal of Financial Risk Management, 7, 1–12.CrossRefGoogle Scholar
  8. Chourmouziadis, K., & Chatzoglou, P. (2015). An intelligent short term stock trading fuzzy system for assisting investors in portfolio management. Expert Systems with Applications, 43(2016), 298–311.Google Scholar
  9. Deng, S., Yoshiyama, K., Mitsubuchi, T., & Sakurai, A. (2013). Hybrid method of multiple kernel learning and genetic algorithm for forecasting short-term foreign exchange rates. Computational Economics, 45, 49–89.CrossRefGoogle Scholar
  10. Fama, E. F. (1970). Efficient capital market, a review of theory and empirical work. Journal of Finance, 25, 383–417.CrossRefGoogle Scholar
  11. Fernandez, P., Bodas, D. J., Soltero, F. J., & Hidalgo, J. I. (2008). Technical market indicators optimization using evolutionary algorithms. In GECCO ’08 Proceedings of the 10th annual conference companion on Genetic and evolutionary computation (pp. 1851–1858).Google Scholar
  12. FOREX Market. (2016) Retrieved February 28, 2016 from
  13. Gorgulho, A., Neves, R., & Horta, N. (2011). Applying a GA kernel on optimizing technical analysis rules for stock picking and portfolio composition. Expert Systems with Applications, 38(2011), 14072–14085.Google Scholar
  14. Ibrahim, A. E. M. (2015). Developing profitable trading system, University of Sharjah, Sharjah, UAE. Journal of Stock & Forex Trading, 4(1), 1000145.CrossRefGoogle Scholar
  15. Janowicz, M., Orłowski, A., & Warzyński, F. (2014). Optimization of investment management in Warsaw stock market. Przedsiebiorczosc i Zarzadzanie, 15(2), 143–156.CrossRefGoogle Scholar
  16. Kaufman, P. (2013). Trading systems and methods (5th ed.). Hoboken, NJ: Wiley.Google Scholar
  17. Kiani, K., & Kastens, T. (2008). Testing forecast accuracy of foreign exchange rates: Predictions from feed forward and various recurrent neural network architectures. Computational Economics, 32, 383–406.CrossRefGoogle Scholar
  18. Kirkpatrick, Ch., & Dahlquist, J. (2016). Technical analysis: The complete resource for financial market technicians (3rd ed.). Upper Saddle River, NJ: Pearson Education.Google Scholar
  19. Murphy, J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications (New York Institute of Finance). Englewood Cliffs: Prentice Hall Press.Google Scholar
  20. Rosilloab, R., de la Fuentea, D., & Brugosb, J. (2012). Technical analysis and the Spanish stock exchange: Testing the RSI, MACD, momentum and stochastic rules using Spanish market companies. Applied Economics, 45(12), 1541–1550.CrossRefGoogle Scholar
  21. Tucnik, P. (2010a). Automatic trading system design. In V. Godara (Ed.), Pervasive computing for business: Trends and applications. Sydney: IGI Global.Google Scholar
  22. Tucnik, P. (2010b). Optimization of automated trading system’s interaction with market environment (Vol. 64, pp. 55–61)., Lecture Notes in Business Information Processing Springer: Berlin.Google Scholar
  23. Vanstone, B., & Finnie, G. (2008). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(2009), 6668–6680.Google Scholar
  24. Vasilakis, G., Theofilatos, K., Georgopoulos, E., Karathanasopoulos, A., & Likothanassis, S. (2012). A genetic programming approach for EUR/USD exchange rate forecasting and trading. Computational Economics, 42, 415–431.CrossRefGoogle Scholar
  25. Vasiliou, D., Eriotis, N., & Papathanasiou, S. (2006). How rewarding is technical analysis? Evidence from Athens stock exchange. Operational Research, 6(2), 85–102.CrossRefGoogle Scholar
  26. Wiles, P. S., & Enke, D. (2015). Optimizing MACD parameters via genetic algorithms for soybean futures. Procedia Computer Science, 61, 85–91.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece

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