Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and...

Full description

Saved in:
Bibliographic Details
Published in:Computation 2024-07, Vol.12 (7), p.132
Main Authors: Uzun, Illia, Lobachev, Mykhaylo, Kharchenko, Vyacheslav, Schöler, Thorsten, Lobachev, Ivan
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Automation
candlesticks
Community support
Crypto-currencies
cryptocurrencies
Data analysis
Decision-making
Deep learning
Digital currencies
Investor behavior
Libraries
Machine learning
Mathematical functions
Methods
Neural networks
Object recognition (Computers)
Pattern analysis
Pattern recognition
Pattern recognition systems
Prices
Program trading
Python
R&D
recognition
Research & development
Research methodology
rule-based method
Securities markets
Securities trading
Software
Structural reliability
Support vector machines
Time series
Trends
Volatility
Volatility (Finance)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
ISSN:2079-3197
2079-3197
DOI:10.3390/computation12070132