Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency

[1]Ardia D., Boudt K., Carl P., Mullen K. M., Peterson B.G. Differential Evolution with DEoptim: An Application to Non-Convex Portfolio Optimization. The R Journal, 2010.ArdiaD.BoudtK.CarlP.MullenK. M.PetersonB.GDifferential Evolution with DEoptim: An Application to Non-Convex Portfolio OptimizationThe R Journal201010.32614/RJ-2011-005Search in Google Scholar

[2]Ardia D., Boudt K., Carl P., Mullem K. M., Peterson B.G. Large-scale portfolio optimization with DEoptim CRAN R, 2011a.ArdiaD.BoudtK.CarlP.MullemK. M.PetersonB.GLarge-scale portfolio optimization with DEoptimCRAN R2011aSearch in Google Scholar

[3]Breiman L. Statistical Modeling: The Two Cultures Statistical Science 2001, Vol. 16, No. 3, Pages 199–231, 2001.BreimanLStatistical Modeling: The Two CulturesStatistical Science2001Vol. 16No3Pages199–231200110.1214/ss/1009213725Search in Google Scholar

[4]Brest J. et alSelf-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems IEEE Transactions on Evolutionary Computation, Volume 10, Issue 6, 2006.BrestJet alSelf-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark ProblemsIEEE Transactions on Evolutionary ComputationVolume 10Issue 6200610.1109/TEVC.2006.872133Search in Google Scholar

[5]Choundhry R., Kumkum G. A Hybrid Machine Learning System for Stock Market Forecasting International Journal of Computer and Information Engineering Vol:2, No:3, 2008.ChoundhryR.KumkumGA Hybrid Machine Learning System for Stock Market ForecastingInternational Journal of Computer and Information EngineeringVol:2No32008Search in Google Scholar

[6]Conceicao E. Differential Evolution Optimization in Pure R CRAN R Project, 2016.ConceicaoEDifferential Evolution Optimization in Pure RCRAN R Project2016Search in Google Scholar

[7]Dunis C.L, Nathani A. Quantitative trading of gold and silver using nonlinear models Neural Network World: International Journal on Neural and Mass – Parallel Computing and Information Systems, 2007.DunisC.LNathaniAQuantitative trading of gold and silver using nonlinear models Neural Network World: International Journal on Neural and Mass – Parallel Computing and Information Systems2007Search in Google Scholar

[8]Gunasekarage A., Power D.M. The profitability of moving average trading rules in South Asian stock markets Emerging Markets Review, Volume 2, Issue 1, Pages 17–33, 2001.GunasekarageA.PowerD.MThe profitability of moving average trading rules in South Asian stock marketsEmerging Markets ReviewVolume 2Issue 1Pages17–33200110.1016/S1566-0141(00)00017-0Search in Google Scholar

[9]Hastie T., Tibshirani R., Friedman J. H. The Elements of Statistical Learning Springer, 2001.HastieT.TibshiraniR.FriedmanJ. HThe Elements of Statistical LearningSpringer200110.1007/978-0-387-21606-5Search in Google Scholar

[10]Hastie T., Tibshirani R., James G., Witten D. An Introduction to Statistical Learning: With Applications in R Springer, 2013.HastieT.TibshiraniR.JamesG.WittenDAn Introduction to Statistical Learning: With Applications in RSpringer201310.1007/978-1-4614-7138-7Search in Google Scholar

[11]Shen S., Jiang H., Zhang T. Stock Market Forecasting Using Machine Learning Algorithms Department of Electrical Engineering, Stanford University, Stanford, CA, 1–5, 2012.ShenS.JiangH.ZhangTStock Market Forecasting Using Machine Learning AlgorithmsDepartment of Electrical Engineering, Stanford UniversityStanford, CA1–52012Search in Google Scholar

[12]Juels A., Wattenbergy M., Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Advances in Neural Information Processing Systems 8, 1995.JuelsA.WattenbergyM.Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic AlgorithmsAdvances in Neural Information Processing Systems81995Search in Google Scholar

[13]Dahlquist J.R., Kirkpatrick C.D. Technical Analysis: The Complete Resource for Financial Market Technicians FT Press, 2011.DahlquistJ.R.KirkpatrickC.DTechnical Analysis: The Complete Resource for Financial Market TechniciansFT Press2011Search in Google Scholar

[14]Patel J., Shah S., Thakkar P., Kotecha K. Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques Expert Systems with Applications Volume 42, Issue 1, Pages 259–268, 2015.PatelJ.ShahS.ThakkarP.KotechaKPredicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniquesExpert Systems with ApplicationsVolume 42Issue 1Pages259–268201510.1016/j.eswa.2014.07.040Search in Google Scholar

[15]Mullen K. et alPackage ‘DEoptim’ – Global Optimization by Differential Evolution CRAN R Project, 2016.MullenKet alPackage ‘DEoptim’ – Global Optimization by Differential EvolutionCRAN R Project2016Search in Google Scholar

[16]Pardo R. The Evaluation and Optimization of Trading Strategies Wiley Trading, 2011.PardoRThe Evaluation and Optimization of Trading StrategiesWiley Trading201110.1002/9781119196969Search in Google Scholar

[17]Radford M.N. Probabilistic Inference Using Markov Chain Monte Carlo Methods Technical Report CRG-TR-93-1, Department of Computer Science University of Toronto, 1993. s Ritter G. Machine Learning for trading New York, 2017.RadfordM.NProbabilistic Inference Using Markov Chain Monte Carlo Methods Technical Report CRG-TR-93-1, Department of Computer Science University of Toronto, 1993. s Ritter G. Machine Learning for tradingNew York2017Search in Google Scholar

[18]Russell S.J., Nowig P. Artificial Intelligence – A Modern Approach, Second Edition. Pearson Education, Inc. 2003.RussellS.J.NowigPArtificial Intelligence – A Modern Approach, Second EditionPearson Education, Inc2003Search in Google Scholar

[19]Samuel A. *Some Studies in Machine Learning Using the Game of Checkers“*. IBM Journal of Research and Development 3(3): pages 210–229, 1959.SamuelA*Some Studies in Machine Learning Using the Game of Checkers“*IBM Journal of Research and Development33pages210–229195910.1007/978-1-4613-8716-9_14Search in Google Scholar

[20]Skiena S. S. The Algorithm Design Manual, Second Edition Springer-Verlag London Limited, 2008.SkienaS. SThe Algorithm Design Manual, Second EditionSpringer-Verlag London Limited200810.1007/978-1-84800-070-4Search in Google Scholar

[21]Smola A., Vishwanathan S.V.N. Introduction to Machine Learning Cambridge University Press, 2008.SmolaA.VishwanathanS.V.NIntroduction to Machine LearningCambridge University Press2008Search in Google Scholar

[22]Stanković J., Marković I., Stojanović M. Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets Procedia Economics and Finance Volume 19, 2015. Pages 51–62.StankovićJ.MarkovićI.StojanovićM.Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging MarketsProcedia Economics and FinanceVolume 192015Pages51–6210.1016/S2212-5671(15)00007-6Search in Google Scholar

[23]Storn, R.M, Price, K.V Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces Journal of Global Optimization, 1997. Pages 341–359.StornR.MPriceK.VDifferential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous SpacesJournal of Global Optimization1997Pages341–35910.1023/A:1008202821328Search in Google Scholar

[24]Storn, R.M., Price, K.V. Lampinen J.A. Differential Evolution – A Practical Approach to Global Optimization. Berlin Heidelberg: Springer-Verlag, 2006.StornR.M.PriceK.VLampinenJ.A.Differential Evolution – A Practical Approach to Global OptimizationBerlin HeidelbergSpringer-Verlag2006Search in Google Scholar

[25]Valiant L. A theory of the learnable CACM, 1984.ValiantLA theory of the learnableCACM198410.1145/800057.808710Search in Google Scholar