Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning

This paper examines two transactional strategies based on the classifier which opens positions using some rules and closes them using different rules. A rule set contains time-varying parameters that when matched allow making an investment decision. Researches contain the study of variability of these parameters and the relationship between learning period and testing (using the learned parameters). The strategies are evaluated based on the time series of cumulative profit achieved in the test periods. The study was conducted on the most popular currency pair EURUSD (Euro-Dollar) sampled with interval of 1 hour. An important contribution to the theory of algotrading resulting from presented research is specification of the parameter space (quite large, consisting of 11 parameters) that achieves very good results using cross validation. 1. Introduction The aim of this work is to verify the hypothesis of patterns extraction possibility from time series, which could be classified as providing better statistic and more accurate prognosis. Another important objective is confirmation of assumption that financial markets time series have a “memory” of pattern efficiency in a time period following the time series that was used in learning period. This approach is consistent with the classic aim of machine learning shown by Murphy [1], especially to financial markets described by Satchwell [2]. Research intention was also to follow reproducibility principle of other researchers’ studies, as well as by themselves, in other data environments, to make sense of the use of computational intelligence in its reasonable reproducibility [3, 4], in extracting of the regularity from chaos [5, 6]. An investment strategy with a relatively high complexity (measured by the number of factors included in the model) was built, derived from a strategies group called strategy of simple rules. In the literature those strategies are considered to be mainly strategies based on moving averages—their intersections and derivatives shown, for example, by Brock et al. [7], Cai et al. [8], and many other authors [9–11]. Of course, the world of algorithms as well as prediction methods using a completely different nature, such as regression [12], multiple regression [13, 14], Fourier and wavelet transforms, and many others [15, 16] is plenteous. These methods are used as a basis for comparison; however the main focus is on mentioned simple rules. This paper proposes strategy, which differs by suggesting different behaviors than the ones proposed when using Bollinger’s Band, which has its