[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119631-en":3,"doc-seo-119631-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119631,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Predicting Cryptocurrency Price Movements Using Machine Learning And Technical Indicators - Master’s thesis","Cryptocurrency markets remain highly volatile and forecasting-oriented research faces persistent uncertainty. This thesis examines machine learning approaches for short-term price movement prediction using hourly Binance data for five cryptocurrencies, leveraging technical indicators and candlestick-derived features. It formulates both classification (direction) and regression (future high/low) tasks and applies automated machine learning via PyCaret to compare many algorithms across input windows and prediction horizons. Results show Ridge Classifier often best for direction, with different regressors excelling by horizon, while errors like MAPE may reflect lagged reactive behavior rather than true forward skill, limiting live trading value.","Predicting Cryptocurrency Price Movements Using Machine Learning And Technical Indicators  \nMike Miettinen  \nMaster’s thesis October 2025  \nMaster’s Degree Programme in Artificial Intelligence and Data Analytics  \nMiettinen, Mike  \nPredicting Cryptocurrency Price Movements Using Machine Learning And Technical Indicators  \nJyväskylä: Jamk University of Applied Sciences, October 2025, 79 pages.  \nDegree Programme in Artificial Intelligence and Data Analytics. Master’s thesis.  \nPermission for open access publication: Yes  \nLanguage of publication: English  \nAbstract  \nCryptocurrency markets are highly volatile and difficult to forecast, posing challenges for both investors and researchers. This study investigated the use of machine learning models to predict short-term cryptocurrency price movements, using hourly data from Binance for five cryptocurrencies. A wide range of technical indicators and candlestick features were employed as inputs, and both classification tasks (price direction) and regression tasks (future high and low prices) were performed. Automated machine learning (AutoML) with PyCaret was used to systematically evaluate a broad set of algorithms across different input window sizes and prediction horizons.  \nThe results showed that the Ridge Classifier generally outperformed other models in predicting price direction. For regression tasks at the one-hour horizon, Huber, Orthogonal Matching Pursuit, and Ridge regression achieved the lowest error rates, while Extra Trees gave better results for longer horizons. Incorporating multiple previous hours of data improved model accuracy up to a threshold, after which additional lagged features yielded little benefit. Prediction accuracy decreased substantially when extending the forecasting horizon beyond one hour.  \nA key finding was that low error metrics, particularly mean absolute percentage error (MAPE), did not correspond to genuine predictive power. Instead, the models often exhibited reactive behavior, replicating the most recent price movement and effectively lagging behind by one time step. This limitation highlights the difficulty of achieving forward-looking prediction in cryptocurrency markets.  \nThe study concludes that while machine learning can provide systematic benchmarks and descriptive insights into short-term price dynamics, its practical utility for live trading remains limited without additional predictive signals. Future work should expand datasets, incorporate alternative data sources such as sentiment or blockchain network activity, explore deep learning architectures designed for sequential data, and evaluate models in simulated environments to better assess their real-world applicability.  \nKeywords/tags (subjects)  \nCryptocurrencies, technical analysis, trading, machine learning, supervised learning, classification, regression  \nMiscellaneous (Confidential information)  \n-  \nContents  \nTerms.................................................................................................................................. 3  \n1 Introduction ................................................................................................................ 5  \n2 Ethical Considerations.................................................................................................. 6  \n3 Research Method (CRISP-DM) ...................................................................................... 7  \n4 Technical Analysis........................................................................................................ 9  \n4.1 Moving Averages ............................................................................................................. 10  \n4.2 Trend Indicators .............................................................................................................. 13  \n4.3 Momentum Indicators .................................................................................................... 18  \n4.4 Volatility Indicators .","cbCaibR2NCHYiXsV","https://ap.wps.com/l/cbCaibR2NCHYiXsV","pdf",4494012,1,104,"English","en",105,"# Terms\n# Introduction\n# Ethical Considerations\n# Research Method (CRISP-DM)\n# Technical Analysis\n## Moving Averages\n## Trend Indicators\n## Momentum Indicators\n## Volatility Indicators\n## Volume Indicators\n# Candlestick Patterns\n# Machine Learning\n## Classification\n## Regression\n## PyCaret\n# Prior Studies\n## Technical Indicator Comparisons\n## Machine Learning Model Comparisons\n# Research Implementation\n## Business Understanding\n## Data Understanding & Preparation\n## Feature Calculation\n## Candlestick Patterns\n## Modeling","[{\"question\":\"What data and prediction targets does the thesis use?\",\"answer\":\"The study uses hourly Binance data for five cryptocurrencies and targets both price direction (classification) and future high/low values (regression). It uses technical indicators and candlestick features as inputs.\"},{\"question\":\"Which models performed best for predicting price direction and why?\",\"answer\":\"The Ridge Classifier generally outperformed other models for predicting price direction. The thesis also shows that best regression models vary by forecast horizon.\"},{\"question\":\"Why can low error metrics fail to indicate real predictive power?\",\"answer\":\"The thesis finds models may behave reactively—reproducing the most recent price movement and effectively lagging by one time step—so metrics like MAPE can be misleading regarding true forward-looking skill.\"}]","Predicting Cryptocurrency Price Movements Using Machine Learning And Technical Indicators - Master’s thesis | PDF",1785725388,262,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-cryptocurrency-price-movements-using-machine-learning-and-technical-indicators-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-cryptocurrency-price-movements-using-machine-learning-and-technical-indicators-masters-thesis/119631/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and prediction targets does the thesis use?","Question",{"text":75,"@type":76},"The study uses hourly Binance data for five cryptocurrencies and targets both price direction (classification) and future high/low values (regression). It uses technical indicators and candlestick features as inputs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models performed best for predicting price direction and why?",{"text":80,"@type":76},"The Ridge Classifier generally outperformed other models for predicting price direction. The thesis also shows that best regression models vary by forecast horizon.",{"name":82,"@type":73,"acceptedAnswer":83},"Why can low error metrics fail to indicate real predictive power?",{"text":84,"@type":76},"The thesis finds models may behave reactively—reproducing the most recent price movement and effectively lagging by one time step—so metrics like MAPE can be misleading regarding true forward-looking skill.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]