Candlesticks and graph patterns in cryptocurrencies
| Thesis title in Czech: | Svíčky a grafové vzorce v kryptoměnách |
|---|---|
| Thesis title in English: | Candlesticks and graph patterns in cryptocurrencies |
| Key words: | 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 hvězda |
| English key words: | 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 Star |
| Academic year of topic announcement: | 2021/2022 |
| Thesis type: | Bachelor's thesis |
| Thesis language: | angličtina |
| Department: | Institute of Economic Studies (23-IES) |
| Supervisor: | prof. PhDr. Ladislav Krištoufek, Ph.D. |
| Author: | hidden - assigned by the advisor |
| Date of registration: | 22.08.2022 |
| Date of assignment: | 22.08.2022 |
| Date and time of defence: | 04.02.2025 09:00 |
| Venue of defence: | Opletalova, O105, místnost č. 105 |
| Date of electronic submission: | 07.01.2025 |
| Date of proceeded defence: | 04.02.2025 |
| Opponents: | Mgr. Ivan Trubelík |
| References |
| Baltagi, Badi H. (Badi Hani) (1954-2021): “Econometric analysis of panel data”
Corbet, Shaen ; Eraslan, Veysel ; Lucey, Brian ; Sensoy, Ahmet (2019): “The effectiveness of technical trading rules in cryptocurrency markets” Finance research letters, 2019-12, Vol.31, p.32-37 Fang, Fan ; Ventre, Carmine ; Basios, Michail ; Kanthan, Leslie ; Martinez-Rego, David ; Wu, Fan ; Li, Lingbo (2022): “Cryptocurrency trading: a comprehensive survey” Financial Innovation, 2022-02-07, Vol.8 (1), p.1-59 Grobys, Klaus ; Ahmed, Shaker ; Sapkota, Niranjan (2020): “Technical trading rules in the cryptocurrency market” Finance research letters, 2020-01, Vol.32, p.101396 Hudson, Robert ; Urquhart, Andrew (2019): “Technical trading and cryptocurrencies” Annals of operations research, 2019-08-30, Vol.297 (1-2), p.191-220 Thomsett, Michael C. (2019): “Understanding momentum in investment technical analysis : making better predictions based on price, trend strength, and speed of change” Trivedi, Smita Roy; Kyal, Ashish H. (2021): “Effective trading in financial markets using technical analysis” Tsinaslanidis, Prodromos E.; Zapranis, Achilleas D. (2016): “Technical Analysis for Algorithmic Pattern Recognition” Wooldridge, Jeffrey M. (2010): “Econometric analysis of cross section and panel data” Wooldridge, Jeffrey M. (2020): “Introductory econometrics : a modern approach” |
| Preliminary scope of work in English |
| Research question and motivation
Technical analysis as it is known today has been in use since the beginning of the 20th century and has its origin back in the 19th century. The markets at that time were distinct from the markets we can observe today. Not only have the traditional markets (e. g. stock and commodity market) evolved, but completely new markets have emerged. The one I will be focusing on throughout my work is the cryptocurrency market. The main focus of my thesis will be the relevance and significance of a specific section of technical analysis performed on cryptocurrencies with a stress on cryptocurrencies with the largest market capitalization. This specific section of technical analysis will cover specific candlesticks, candlestick patterns and chart patterns (hereinafter referred to as simple technical analysis) whose designations contain the words bullish or bearish. There are also two sub-questions that I will try to find answers to. The first regards significance of the simple technical analysis among specific cryptocurrency sub-populations and during specific time periods. The notion behind this question is reasoning that the behavior of the cryptocurrencies with large market capitalization can be different in comparison with the very small unknown cryptocurrencies, thus the simple technical analysis can perform differently. Similar reasoning holds for specific time periods since it is possible that the simple technical analysis performs differently during bear markets and bull markets. The second sub-question is more specific and relates to the connection between the simple technical analysis significance and market capitalization and total age of cryptocurrencies. Candlesticks, candlestick patterns and chart patterns are relatively straightforward and easily comprehensible for ordinary people compared to the more advanced technical analysis indicators. They are even more understandable when they have bullish or bearish right in their names. This can be one of the reasons why it is excessively used by different types of influencers and thus by many of their followers (amateurs in this matter). However, the research in this area is not large and deals with more complex methods. This is one of the main reasons why I see fit to address this topic. Contribution There is a lot of literature covering technical analysis but the majority is related to the classical financial markets. I intend to contribute to the very small subsection of literature that regards cryptocurrencies. The main objective of my thesis is to support the bullish or bearish sentiment of the chosen technical analysis indicators by historical data. Alternatively, rank them according to their ability to predict future price developments. Methodology The most challenging part of my thesis will be the data collection. The four main data sources will be cryptocurrency exchanges Kraken, Coinbase, Bittrex and Bitfinex. I will use python script to obtain open-high-low-close data for individual cryptocurrencies and a set of algorithms to detect the specific candlesticks, candlestick patterns and chart patterns. Once the data are obtained I will start with the data analysis. I will perform two analyses. The first one will be simple. After the occurrence of an indicator, there will be a test period during which the indicator should demonstrate its prognostic value. There will be given certain levels for each indicator. By reaching these levels during the test period, one can label each occurrence with marks how well or how badly the indicator performed. After aggregation of all the occurrences, there will be a certain notion about performance of an indicator whether it regards specific candlesticks, candlestick patterns or chart patterns. In the second one, I will extend the first analysis with an econometric approach. The baseline will be very similar. I will try to find a relationship between indicator occurrences and subsequent price changes of cryptocurrencies during specific time periods. The dependent variable will thus be the price changes of a cryptocurrency during given time intervals and the independent variables will represent if an indicator occurred or not. Before the analysis itself, I will have to verify that all the assumptions needed are met. Outline Abstract Introduction - What is technical analysis - Trading and market efficiency - Research motivation - Organizational structure - Literature review Data description and collection - Indicators description - Data sources - Obtaining data - Methods used for obtaining specific indicators and patterns - Discussion of chosen parameters Methodology - Methods used - Tests used Results - Results of the first approach - Results of the second approach - Comparison of the first and second approach Conclusion - General analysis of the results - Possible different methods and improvements - Suggestion for future research |