Аннотация:
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.
