An automated cryptocurrency trading system based on the detection of unusual price movements with a Time-Series Clustering-Based approach

DOI: 10.1016/j.eswa.2022.117017

ABSTRACT: The cryptocurrency market, which has a rapidly growing market size, attracts the increasing attention of individual and institutional investors. While this highly volatile market offers great profit opportunities to investors, it also brings risks due to its sensitivity to speculative and the unpredictable behaviour of major investors that can cause unsual price movements. In this paper, we argue that rapid and high price fluctuations or unusual patterns that occur in this way may negatively affect the functionality of technical signals that constitute a basis for feature extraction in a (ML)-based system and this may cause the generalization of the model to deteriorate. To address this problem, we propose an end-to-end ML-based trading system including a time series outlier detection module that detects the periods in which unusual price formations are observed. The training of the classification algorithms for the price direction task was performed on the remaining data. We present the results related to the accuracy of the classification as well as the simulation results obtained using the proposed system for real time trading on the historical data. The findings showed that the outlier detection step significantly increases return on investment for the machine learning-based trading strategies. Besides, the results showed that during the highly volatile periods the trading system becomes more profitable compared to the baseline model and &hold strategy.

– Outlier detection significantly increases return on investment for trading strategies.
– Trading system is more profitable during highly volatile periods.

– Outlier detection significantly increases return on investment for trading strategies.
– Trading system is more profitable during highly volatile periods.

– Outlier detection significantly increases return on investment for trading strategies.
– Trading system is more profitable during highly volatile periods.

– Unusual price movements can negatively affect the functionality of technical signals.
– The generalization of the model may deteriorate due to unusual price formations.

Methods used: – Unusual price movements can negatively affect the functionality of technical signals.
– The generalization of the model may deteriorate due to unusual price formations.

– The proposed system increases return on investment for machine learning-based trading strategies.
– The trading system becomes more profitable during highly volatile periods.

– Outlier detection significantly increases return on investment for trading strategies.
– Trading system is more profitable during highly volatile periods compared to baseline model.

– ML-based trading system with outlier detection improves profitability
– Unusual price movements negatively affect technical signals

– The paper proposes an ML-based trading system for cryptocurrency markets.
– It includes a time series outlier detection module to improve profitability.

In this paper , an end-to-end ML-based trading system including a time series outlier detection module was proposed to detect the periods in which unusual price formations are observed.

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