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A derivative-free optimization approach for the autotuning of a Forex trading strategy

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Abstract

A trading strategy simply consists in a procedure which defines conditions for buying or selling a security on a financial market. These decisions rely on the values of some indicators that, in turn, affect the tuning of the strategy parameters. The choice of these parameters significantly affects the performance of the trading strategy. In this work, an optimization procedure is proposed to find the best parameter values of a chosen trading strategy by using the security price values over a given time period; these parameter values are then applied to trade on the next incoming security price sequence. The idea is that the market is sufficiently stable so that a trading strategy that is optimally tuned in a given period still performs well in the successive period. The proposed optimization approach tries to determine the parameter values which maximize the profit in a trading session, therefore the objective function is not defined in closed form but through a procedure that computes the profit obtained in a sequence of transactions. For this reason the proposed optimization procedures are based on a black-box optimization approach. Namely they do not require the assumption that the objective function is continuously differentiable and do not use any first order information. Numerical results obtained in a real case seem to be encouraging.

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References

  1. Abramson, M.A., Audet, C., Chrissis, J.W., Walston, J.G.: Mesh adaptive direct search algorithms for mixed variable optimization. Optim. Lett. 3(1), 35–47 (2009)

    Article  MathSciNet  Google Scholar 

  2. Atsalakis, G., Valavanis, K.: Surveying stock market forecasting techniques, part 2: soft computing methods. Expert Syst. Appl. 36, 5932–5941 (2009)

    Article  Google Scholar 

  3. Audet, C., Dang, K.C., Orban, D.: Optimization of algorithms with OPAL. Math. Progr. Comput. 6(3), 233–254 (2014)

    Article  MathSciNet  Google Scholar 

  4. Audet, C., Dennis Jr., J.E.: Pattern search algorithms for mixed variable programming. SIAM J. Optim. 11(3), 573–594 (2001)

    Article  MathSciNet  Google Scholar 

  5. Audet, C., Le Digabel, S., Tribes, C.: The mesh adaptive direct search algorithm for granular and discrete variables. SIAM J. Optim. 29(2), 1164–1189 (2019)

    Article  MathSciNet  Google Scholar 

  6. Audet, C., Orban, D.: Finding optimal algorithmic parameters using derivative-free optimization. SIAM J. Optim. 17(3), 642–664 (2006)

    Article  MathSciNet  Google Scholar 

  7. Chand, S., Shahid, K., Imran, A.: Modeling and volatility analysis of share prices using arch and garch models. World Appl. Sci. J. 19(1), 77–82 (2012)

    Google Scholar 

  8. Dase, R., Pawar, D.: Application of artificial neural network for stock market predictions: a review of literature. Int. J. Mach. Intel. Eng. Technol. 2(2), 14–17 (2010)

    Article  Google Scholar 

  9. Dase, R., Pawar, D., Daspute, D.: Methodologies for prediction of stock market: an artificial neural network. Int. J. Stat. Math. 1(1), 8–15 (2011)

    Google Scholar 

  10. De Santis, A., Dellepiane, U., Lucidi, S., Renzi, S.: Optimal step-wise parameter optimization of a Forex trading strategy. Technical report, Department of Computer, Control, and Management Engineering Antonio Ruberti (2014)

  11. Di Pillo, G., Lucidi, S., Rinaldi, F.: A derivative-free algorithm for constrained global optimization based on exact penalty functions. J. Optim. Theory Appl. 164(3), 862–882 (2015)

    Article  MathSciNet  Google Scholar 

  12. Fasano, G., Liuzzi, G., Lucidi, S., Rinaldi, F.: A linesearch-based derivative-free approach for nonsmooth constrained optimization. SIAM J. Optim. 24(3), 959–992 (2014)

    Article  MathSciNet  Google Scholar 

  13. Jahn, J.: Introduction to the Theory of Nonlinear Optimization. Springer, Berlin (2007)

    MATH  Google Scholar 

  14. Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the lipschitz constant. J. Optim. Theory Appl. 79(1), 157–181 (1993)

    Article  MathSciNet  Google Scholar 

  15. Kirkpatrtck, S., Gelatf, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 621–680 (1983)

    MathSciNet  Google Scholar 

  16. Larson, J., Menickelly, M., Wild, S.M.: Derivative-free optimization methods. Acta Numer. 28, 287–404 (2019)

    Article  MathSciNet  Google Scholar 

  17. Levinson, M.: The Economist Guide to Financial Markets 6th edn: why they exist and how they work. PublicAffairs, New York (2014)

  18. Liu, C., Yeh, C., Lee, S.: Application of type-2 neuro-fuzzy modeling in stock price prediction. Appl. Soft Comput. 12, 1348–1358 (2012)

    Article  Google Scholar 

  19. Liuzzi, G., Lucidi, S., Piccialli, V.: Exploiting derivative-free local searches in direct-type algorithms for global optimization. Comput. Optim. Appl. 48, 1–27 (2014)

    MATH  Google Scholar 

  20. Liuzzi, G., Lucidi, S., Rinaldi, F.: Derivative-free methods for bound constrained mixed-integer optimization. Comput. Optim. Appl. 53, 505–526 (2011)

    Article  MathSciNet  Google Scholar 

  21. Lucidi, S., Piccioni, M.: Random tunneling by means of acceptance-rejection sampling for global optimization. J. Optim. Theory Appl. 62(2), 255–277 (1989)

    Article  MathSciNet  Google Scholar 

  22. Mitra, S.: Is hurst exponent value useful in forecasting financial time series? Asian Soc. Sci. 8(8), 111–120 (2012)

    Article  Google Scholar 

  23. Müller, J.: Miso: mixed-integer surrogate optimization framework. Optim. Eng. 17(1), 177–203 (2016)

    Article  MathSciNet  Google Scholar 

  24. Myszkowski, P., Bicz, A.: Evolutionary algorithm in forex trade strategy generation. In: Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 81–88 (2010)

  25. Pai, P., Lin, C.: A hybrid arima and support vector machines model in stock price forecasting. Omega Int. J. Manag. Sci. 33, 497–505 (2005)

    Article  Google Scholar 

  26. Paulavičius, R., Sergeyev, Y.D., Kvasov, D.E., Žilinskas, J.: Globally-biased disimpl algorithm for expensive global optimization. J. Global Optim. 59(2), 545–567 (2014)

    Article  MathSciNet  Google Scholar 

  27. Porcelli, M., Toint, P.L.: Bfo, a trainable derivative-free brute force optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables. ACM Trans. Math. Softw. 44(1), 1–25 (2017)

    Article  MathSciNet  Google Scholar 

  28. Razi, M.A., Athappilly, K.: Comparative predictive analysis of neural networks(nns), nonlinear regression and classification and regression tree (cart) models. Expert Syst. Appl. 29, 65–74 (2009)

    Article  Google Scholar 

  29. Solis, F.B., Wets, R.: Minimization by random search techniques. Math. Oper. Res. 6(1), 19–30 (1981)

    Article  MathSciNet  Google Scholar 

  30. Sparks, J., Yurova, Y.: Comparative performance of arima and arch/garch models on time series of daily equity prices for large companies. In: SWDSI Proceedings of 37-th Annual Conference, pp. 563–573 (2006)

  31. Valeriy, G., Supriya, B.: Support vector machine as an efficient framework for stock market volatility forecasting. Comput. Manag. Sci. 3, 147–160 (2006)

    Article  MathSciNet  Google Scholar 

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Correspondence to Stefano Lucidi.

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De Santis, A., Dellepiane, U., Lucidi, S. et al. A derivative-free optimization approach for the autotuning of a Forex trading strategy. Optim Lett 15, 1649–1664 (2021). https://doi.org/10.1007/s11590-020-01546-7

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  • DOI: https://doi.org/10.1007/s11590-020-01546-7

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