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The newly established algorithms were used to solve many difficult problems from various fields of science and to produce solutions facilitating many areas of life. Therefore, the application of such methods to improve the process of strategy adjustment seemed to be a natural choice.

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The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of a strategy quality.

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The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. Considered algorithms — the Extended Hill Climbing, Grid Method and Differential Evolution Method are based on the well-known machine learning methods the algorithm of a weak strategist without indicators intuitive ideas based on observation of previous steps in order to improve the next ones. The machine learning methods, discussed in this paper were designed to select the strategy parameters in order to maximize strategy performance, measured by the specified optimization criterion.

The methods operated on the in-sample data, containing 16 years of daily prices, and their results were verified on 4 years of out-of-sample data.

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The major hypothesis verified in this paper is that results of the machine learning methods are the same or only slightly worse than the ones near the highest evaluation criterion, obtained by the Exhaustive Search brute force approachbut the time required for their execution is significantly lower than computation time of checking all the points from the solution space. The additional research question is that the strategies obtained by the machine learning methods are associated with a lower risk of overfitting than the strategies resulted from the Exhaustive Search procedure.

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The distributions of optimization criteria and the computation time of executions of different methods were compared and presented along with the Exhaustive Search results.

The adjustment quality was assessed on in-sample data and additional out of sample data in order to test the overfitting tendency.

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Let us emphasise that the purpose of this paper is not to design the most profitable strategy, but to compare the efficiency of different machine learning methods and the Exhaustive Search brute force.

Tests in the out-of-sample period were performed to assess the overfitting problem.

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The simulations for different sets of assets was executed in the same framework implemented for the purpose of this research. The basic machine learning methods have serious disadvantages.

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For instance, the well-known Hill Climbing returns the local extremum, without guarantee of reaching the global one. That algorithm is inadequate for the global search problem, but it could be used as a main component of more complex and efficient methods of global optimization.

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Since the machine learning methods proved their value, by solving plenty of complicated problems, hence, it was reasonable to expect the satisfying results of such methods used for the strategy optimization.

The initial intuition was that the machine learning methods would return the results a bit worse than the optimal one, but in disproportionately shorter time, than checking all the possibilities in order to get the best ones the Exhaustive Search. Moreover, it was expected that the machine learning methods were less likely to overfit strategy than the Exhaustive Search.

The discussed methods were based on an assumption that conditional expected value of the optimization criterion is usually higher for the points surrounded by those with high value of this criterion.

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There was no reason to assume even a moderate level of the space regularity, so the machine learning methods probably could not find the optimal points, if they were not in the high-valued neighbourhood. That property could lead to reducing overfitting risk, because usually, the parameter vector surrounded by those with similar strategy performance have the algorithm of a weak strategist without indicators bigger chance to be profitable in the future, than those from a less stable place.

The structure of this paper is composed as follows.

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The second chapter contains the literature review. In the third part, machine learning methods used in this paper are explained, as well as the trading assumptions and basic make quick and big money. The fourth chapter is devoted to data description, when the fifth contains efficiency tests of considered machine learning methods, with special focus on the optimization criterion and computation time distributions.

The summary of results and conclusions are included in the last part.

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Nevertheless, the increased interest in that field was observed in recent years due to the technical possibility to apply the artificial intelligence in the various fields of science and life. The phenomenon of learning from the computational viewpoint was discussed by Valiant This approach is followed by plenty of the modern machine learning methods and it is close to the general ideas of the classic statistical modelling, where including new dataset leads to changes in the model properties.

Mark the strong signals and weak signals. Once that is done you can take an average of the number of bars needed. Both for the strong and for the weak signals to move into the money. If you are using a chart of hourly prices and your signal takes an average of 3.

The traditional statistical and econometric models usually assume that data is produced by the stochastic process from the specified class. The fitting procedure is aimed at finding the process accurate to actual data when the machine learning methods are often based on the iterated improvements without specified model form.

Finrally com review day trade without indicators

The differences between these two approaches called data models and algorithmic models respectively, are widely discussed in Breiman The field of machine learning contains plenty of various algorithms and methods, used to solve a wide range of problems. Some methods have strong mathematical foundations, for instance, methods based on Markov Chain Monte Carlo Neal,when others, such as the Hill Climbing or evolutionary methods, are based on heuristic approach Juels and Wattenbergy, The commonly used methods and algorithms with application in scientific problems are discussed by Hastie et al.

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The algorithmic strategies are widely used in the financial markets, but most of them are not discussed in papers, due to exclusive character. Nevertheless, some types of the quantitative strategies are widely known, and therefore, discussed in books and papers.

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The strategy based on the technical analysis indicators, such as the simple moving average crossover method considered in this paper is analysed for specified cases in Gunasekarage and Power Since machine learning methods have started to gain popularity, as a tool to solve problems in various fields, numerous attempts to use it for trading strategies occurred.