We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)*** refers to the process of adjusting the relative weight of assets within ...
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We propose a high-frequency rebalancing algorithm(HFRA)and compare its performance with periodic rebalancing(PR)and threshold rebalancing(TR)*** refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals,whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target *** HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy,and the profitability of the HFRA is examined to determine the optimal portfolio *** HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility *** cointegrated price data,it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility *** low-volatility regimes,although increasing portfolio size marginally enhances the HFRA’s profitability,the profits of portfolios of varied sizes do not significantly *** is demonstrated that when volatility is relatively high and the trend is upward,the HFRA can yield a substantial return via portfolios of large ***,the profitability of the HFRA is compared with that of the PR and TR strategies for long-term *** HFRA is more profitable than the PR and TR *** achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.
A high frequency pairs trading (HFPT) algorithm is built by the integration of pairs trading and threshold rebalancing algorithm. The determination of optimal threshold (OT) for the HFPT is crucial to maximize its pro...
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A high frequency pairs trading (HFPT) algorithm is built by the integration of pairs trading and threshold rebalancing algorithm. The determination of optimal threshold (OT) for the HFPT is crucial to maximize its profitability, and this study suggests a procedure to classify OT ranges by supervised machine learning (ML) techniques. In this regard, a sample dataset is created for ML applications. In this dataset, the target variables (OT values) are computed by the application of HFPT algorithm to real price data of 50 crypto-assets, and input variables (features) are calculated as portfolio mean, variance, skewness, kurtosis, value at risk, and correlation coefficient of the pairs. Before classification process, the pairs (or portfolios) are divided into three sub-groups (as positively, weakly and negatively correlated), and then OT values are classified by 6 ML methods. Comparing the evaluation metrics for ML methods, it is observed that the best accuracy, precision and F1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}-scores are obtained by the Random Forest (RF) classifier for all portfolio groups in two-class, three-class and four-class classification. Also, it is seen that the right classification performance of ML methods on positively and negatively correlated pairs are better than weakly correlated pairs. Furthermore, the success of RF classifier is verified with a test dataset that contains price series of 50 crypto-assets in January and February 2024. The applicability of OT range classification procedure in practical exchange markets is also demonstrated, and it is shown that the HFPT algorithm can yield reasonable profits when threshold selected in predicted range.
Imbalanced datasets can be found in a number of fields;they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an ext...
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Imbalanced datasets can be found in a number of fields;they are commonly regarded as big data because of their sheer volume and high attribute dimensions. As the name suggests, imbalanced big datasets come with an extremely imbalanced ratio between the amount of major class and minority class samples. Traditional methods: have been attempted but still cannot fully, effectively, and reliably solve the imbalanced class classification problem, especially when the distribution of the classes is exceedingly imbalanced. In this paper, we propose a collection of algorithms to solve the problem of imbalanced datasets in binary data classification. Most traditional methods: rebalance the imbalanced dataset merely by matching the data quantities of the two classes. Our proposed algorithms, which take the form of a suite of variants, focus on guaranteeing the credibility of the classification model and reaching the greatest possible accuracy by dynamically rebalancing the training dataset with multi-objective swarm intelligence optimisation. The new algorithms are extended from those we proposed earlier, which had a single objective - first find a set of solutions that satisfy the Kappa criterion, then search for the solution in the set that offers the highest accuracy. Two mam modifications are made in the new algorithms. Multi-objective optimisation is aimed at finding a solution that satisfies several criteria at the same time, such as accuracy and identifying a list of credibility indicators. The other enhancement is the incremental operation of the multi-objective optimisation. Incremental optimisation is imperative for processing data feeds that may arrive in a streaming manner. Instead of waiting for the full data archive to be available before optimisation, incremental optimisation rebalances the data feed segment by segment on the fly. The experimental results from the suite of proposed algorithms show that they can effectively attain better and more stable performanc
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