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The Symmetric and Asymmetric Algorithmic Trading Strategies for the Stablecoins

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Abstract

The symmetric trading algorithm (STA) and asymmetric trading algorithm (ATA) are proposed, and a step-by-step pseudo code of proposed algorithms are presented. Utilizing a real dataset, we examine the profitability of these trading algorithms on stablecoin markets. In the STA, the buying and selling prices are chosen equidistant from the expected asset price, while in the ATA, the choice of the selection of buying and selling prices is flexible. The profitability of the algorithms is computed and it is demonstrated that as the volatility of the considered market is increasing, the average and maximum profits of the both algorithms are raising in general. It is also shown that although the profitability of both algorithms is closer for relatively low volatile price series, the ATA outperforms the STA when the considered market is undervalued (or overvalued), whereas the STA is more profitable when the price series is oscillating near the expected price level. Furthermore, it is observed that although it is critical to determine the optimal profit margin (OPM) to maximize the profit of the trading algorithms, there is not an obvious relation between the volatility of the price series and the OPM values. This problem is solved by machine learning methods, and the naive Bayes classifier is used to classify the OPM values as small, medium or large. It is noted that proposed trading algorithms can be applied to all practical stock and cryptocurrency exchange markets, as they only need two assets with an expected price ratio.

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Data Availability

The real dataset used in this study is publicly available on Binance cryptocurrency exchange market [https://www.binance.com]. Matlab code of the STA and ATA with associated sample dataset is available in a GitHub repository [https://github.com/bagcim/TradingAlgorithm].

Abbreviations

STA:

Symmetric trading algorithm

ATA:

Asymmetric trading algorithm

OPM:

Optimal profit margin

AT:

Algorithmic trading

ML:

Machine learning

HF:

High-frequency

SVM:

Support vector machine

LR:

Logistic Regression

KNN:

K-nearest-neighbor

DT:

Decision tree

RF:

Random forest

NB:

Naive Bayes

PM:

Profit margin

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  1. Mahmut Bağcı, Pınar Kaya Soylu, and Selçuk Kıran have contributed equally to this work.

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  1. Department of Management Information Systems, Marmara University, Kadikoy, 34722, Istanbul, Turkey

    Mahmut Bağcı, Pınar Kaya Soylu & Selçuk Kıran

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MB carried out the methodology and analysis of the research, and drafted the manuscript. PKS prepared literature review and contributed to writing and organizing the manuscript. SK helped draft the manuscript and participated in the design of the research. All authors read and approved the final manuscript.

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Bağcı, M., Kaya Soylu, P. & Kıran, S. The Symmetric and Asymmetric Algorithmic Trading Strategies for the Stablecoins. Comput Econ (2024). https://doi.org/10.1007/s10614-023-10532-x

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