Candlesticks and graph patterns in cryptocurrencies
Svíčky a grafové vzorce v kryptoměnách
bachelor thesis (DEFENDED)

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http://hdl.handle.net/20.500.11956/197060Identifiers
Study Information System: 249380
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- Kvalifikační práce [18349]
Author
Advisor
Referee
Trubelík, Ivan
Faculty / Institute
Faculty of Social Sciences
Discipline
Economics and Finance
Department
Institute of Economic Studies
Date of defense
4. 2. 2025
Publisher
Univerzita Karlova, Fakulta sociálních vědLanguage
English
Grade
Excellent
Keywords (Czech)
Technická analýza, Svíčkové grafy, Vzory svíčkových grafů, Grafové vzory, Kryptoměny, Bitcoin, Jednoduchý klouzavý průměr, Caginalp-Laurent strategie výstupu, Test t s úpravou na šikmost, Kladivo, Na krku, Stoupající okno, Padající hvězdaKeywords (English)
Technical analysis, Candlesticks, Candlestick patterns, Chart patterns, Cryptocurrencies, Bitcoin, Simple moving average, Caginalp-Laurent exit strategy, Skewness adjusted t-test, Hammer, On Neck, Rising Window, Shooting StarThis thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...
This thesis investigates the effectiveness of technical analysis, focusing on can- dlestick patterns, in cryptocurrency markets characterized by high volatility and continuous trading. Using statistical methods, including skewness-adjusted t-test and binomial test, the study evaluates 41 bullish and bearish patterns across five datasets: four datasets covering cryptocurrencies in general (excluding stable- coins) and one specific to stablecoins. Gap-dependent patterns were rare due to the continuous trading nature of cryptocurrency markets. Eight patterns demon- strated predictive potential in the non-stablecoin datasets, though two produced returns contrary to their bearish classification. The most compelling patterns are Hammer Bullish, Rising Window Bullish, On Neek Bearish, and Shooting Star Bearish, which produced returns contrary to its bearish classification, as they ap- pear in three datasets. In contrast, the stablecoin dataset showed Doji Star Bullish and Doji Star Bearish as significant; however, these likely reflect price-stabilization mechanisms rather than intrinsic predictive properties. By leveraging large, di- verse datasets and employing modern trend-definition methodology, the study highlights the limited applicability of traditional candlestick patterns and ques- tions the...