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3 2019

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

100%

Ryś P. , Ślepaczuk R.

2018

tom 5

nr 52

The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. 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 strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013 and was validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).

A comparison of deep convolutional neural networksfor image-based detection of concrete surface cracks

84%

Słoński M.

Computer Assisted Methods in Engineering and Science

2019

tom Vol. 26, no. 2

105--112

The aim of this paper is to compare the performance of four deep convolutional neural networks in theproblem of image-based automated detection of concrete surface cracks in the case of a small dataset. Thiscrack detection problem is treated as a binary classification problem, and it is solved by training a deepconvolutional neural network on the small dataset. In this context, overfitting during training was the mainissue to cope with and various techniques were applied to overcome this issue. The results of the experi-ments suggest that the best approach for this problem is to use the pretrained convolutional base of a largepretrained convolutional neural network as an automatic feature extraction method and adding a new bi-nary classifier on top of the convolutional base. Then, at the training the new classifier and fine-tuningthe last few layers of the pretrained network take place at the same time. The classification accuracy of thebest deep convolutional neural network on the testing set is about 94%.

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

67%

Ślepaczuk R. , Zenkova M.

2018

tom 5

nr 52

This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.