Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting
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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.
Keywordsd-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.
- Appel, G. (1985). The moving average convergence-divergence trading method. Toronto: Traders Press.Google Scholar
- Aspray, T. (1989). Individual stocks and MACD. Technical Analysis of Stocks & Commodities, 7(2), 56–61.Google Scholar
- Caporale, G., Gil-Alana, L., & Plastun, A. (2016). Searching for inefficiencies in exchange rate dynamics. Computational Economics, 1–28Google Scholar
- 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
- 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
- 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
- FOREX Market. (2016) Retrieved February 28, 2016 from https://en.wikipedia.org/wiki/Foreign_exchange_market.
- 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
- Kaufman, P. (2013). Trading systems and methods (5th ed.). Hoboken, NJ: Wiley.Google Scholar
- Kirkpatrick, Ch., & Dahlquist, J. (2016). Technical analysis: The complete resource for financial market technicians (3rd ed.). Upper Saddle River, NJ: Pearson Education.Google Scholar
- 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
- Tucnik, P. (2010a). Automatic trading system design. In V. Godara (Ed.), Pervasive computing for business: Trends and applications. Sydney: IGI Global.Google Scholar
- 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
- 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