Cryptocurrency Market: Choice of Technical Indicators in Trading Strategies of Individual Investors

Lyukevich, Igor; Gorbatenko, Irina; Бессонова, Е. А.

doi:10.1145/3527049.3527089

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…On the other hand, the predictive power using the technical indicators (i.e. the "ohlc_tech" feature dataset) is only slightly better than the "ohlc" predictors, which is consistent with the findings in [38], [39], but it is less noticeable than using the centrality measures. Overall, our findings suggest that Centrality measures serve as valuable indicators to enhance the precision of predicting short-term fluctuations in cryptocurrency prices, which has not been studied in the literature.…”

Section: A Static Price Movement Predictionsupporting

confidence: 82%

Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis

Ho,

Hou,

Georgiades

et al. 2024

IEEE Access

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The emerging cryptocurrency market is one of the largest financial markets in the world, with a market capitalization that is already surpassing the gross domestic product of many developed economies. Cryptocurrencies are increasingly being adopted as a means of transaction and ownership in the digital domain, particularly in areas like decentralized finance and non-fungible tokens. Known for its high volatility, this market offers investors the potential for higher returns than traditional financial markets like stocks, foreign exchange, and commodities. However, it remains underexplored in academic research. In this paper, we propose the use of social network analysis to effectively model and analyze the cryptocurrency market and conduct a comprehensive numerical study to explore its key properties, including correlation structure, topological characteristics, stability, and influence. Furthermore, we propose the use of centrality measures as novel indicators to improve the accuracy of cryptocurrency price movement predictions. Our research introduces a novel method for understanding and navigating the cryptocurrency market, enabling investors to integrate advanced analytical tools into their decision-making processes.INDEX TERMS Centrality measures, cryptocurrency, price movement prediction, and social network analysis.

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Section: A Static Price Movement Predictionsupporting

confidence: 82%

Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis

Ho,

Hou,

Georgiades

et al. 2024

IEEE Access

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show abstract

“…Technical analysis, which is commonly employed in forecasting cryptocurrency market trends, is founded on the premise that historical pricing movements and patterns can be utilized to anticipate future trends. It provides objective and data-driven insights into market trends by using technical indicators, (6) trading rules such as the trading range break-out which is based on support and resistance levels, (7) and chart patterns, (8) traders can make decisions regarding selling, hodling or buying a given cryptocurrency. In technical analysis, technical indicators are calculated from the historical price and other transactions data of a cryptocurrency, or a list of aggregated values over a given time window such as Japanese candlestick (referenced to as OHLC values for Open, High, Low and Close) or Heikin-Ashi candlesticks.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

El Youssefi,

Hessane,

Zeroual

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows

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Cryptocurrency Market: Choice of Technical Indicators in Trading Strategies of Individual Investors

Cited by 2 publications

References 13 publications

Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis

Exploring Key Properties and Predicting Price Movements of Cryptocurrency Market Using Social Network Analysis

Utilizing Machine Learning and Deep Learning for Predicting Crypto-currency Trends

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