dc.contributor.advisor | Krištoufek, Ladislav | |
dc.creator | Kuna, Václav | |
dc.date.accessioned | 2025-02-25T10:03:03Z | |
dc.date.available | 2025-02-25T10:03:03Z | |
dc.date.issued | 2025 | |
dc.identifier.uri | http://hdl.handle.net/20.500.11956/197060 | |
dc.description.abstract | 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... | cs_CZ |
dc.description.abstract | 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... | en_US |
dc.language | English | cs_CZ |
dc.language.iso | en_US | |
dc.publisher | Univerzita Karlova, Fakulta sociálních věd | cs_CZ |
dc.subject | Technická analýza | cs_CZ |
dc.subject | Svíčkové grafy | cs_CZ |
dc.subject | Vzory svíčkových grafů | cs_CZ |
dc.subject | Grafové vzory | cs_CZ |
dc.subject | Kryptoměny | cs_CZ |
dc.subject | Bitcoin | cs_CZ |
dc.subject | Jednoduchý klouzavý průměr | cs_CZ |
dc.subject | Caginalp-Laurent strategie výstupu | cs_CZ |
dc.subject | Test t s úpravou na šikmost | cs_CZ |
dc.subject | Kladivo | cs_CZ |
dc.subject | Na krku | cs_CZ |
dc.subject | Stoupající okno | cs_CZ |
dc.subject | Padající hvězda | cs_CZ |
dc.subject | Technical analysis | en_US |
dc.subject | Candlesticks | en_US |
dc.subject | Candlestick patterns | en_US |
dc.subject | Chart patterns | en_US |
dc.subject | Cryptocurrencies | en_US |
dc.subject | Bitcoin | en_US |
dc.subject | Simple moving average | en_US |
dc.subject | Caginalp-Laurent exit strategy | en_US |
dc.subject | Skewness adjusted t-test | en_US |
dc.subject | Hammer | en_US |
dc.subject | On Neck | en_US |
dc.subject | Rising Window | en_US |
dc.subject | Shooting Star | en_US |
dc.title | Candlesticks and graph patterns in cryptocurrencies | en_US |
dc.type | bakalářská práce | cs_CZ |
dcterms.created | 2025 | |
dcterms.dateAccepted | 2025-02-04 | |
dc.description.department | Institute of Economic Studies | en_US |
dc.description.department | Institut ekonomických studií | cs_CZ |
dc.description.faculty | Faculty of Social Sciences | en_US |
dc.description.faculty | Fakulta sociálních věd | cs_CZ |
dc.identifier.repId | 249380 | |
dc.title.translated | Svíčky a grafové vzorce v kryptoměnách | cs_CZ |
dc.contributor.referee | Trubelík, Ivan | |
thesis.degree.name | Bc. | |
thesis.degree.level | bakalářské | cs_CZ |
thesis.degree.discipline | Economics and Finance | en_US |
thesis.degree.discipline | Ekonomie a finance | cs_CZ |
thesis.degree.program | Economics and Finance | en_US |
thesis.degree.program | Ekonomie a finance | cs_CZ |
uk.thesis.type | bakalářská práce | cs_CZ |
uk.taxonomy.organization-cs | Fakulta sociálních věd::Institut ekonomických studií | cs_CZ |
uk.taxonomy.organization-en | Faculty of Social Sciences::Institute of Economic Studies | en_US |
uk.faculty-name.cs | Fakulta sociálních věd | cs_CZ |
uk.faculty-name.en | Faculty of Social Sciences | en_US |
uk.faculty-abbr.cs | FSV | cs_CZ |
uk.degree-discipline.cs | Ekonomie a finance | cs_CZ |
uk.degree-discipline.en | Economics and Finance | en_US |
uk.degree-program.cs | Ekonomie a finance | cs_CZ |
uk.degree-program.en | Economics and Finance | en_US |
thesis.grade.cs | Výborně | cs_CZ |
thesis.grade.en | Excellent | en_US |
uk.abstract.cs | 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... | cs_CZ |
uk.abstract.en | 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... | en_US |
uk.file-availability | V | |
uk.grantor | Univerzita Karlova, Fakulta sociálních věd, Institut ekonomických studií | cs_CZ |
thesis.grade.code | B | |
uk.publication-place | Praha | cs_CZ |
uk.thesis.defenceStatus | O | |