Design and development of a framework for predicting short price jumps in cryptocurrency market

Abstract

The cryptocurrency market's volatility offers significant profit opportunities despite its risks. This thesis aims to capitalize on these opportunities by designing and developing a framework to predict whether a coin will experience growth in the next trading candle. To achieve this, we constructed a robust framework that incorporated various input features. We conducted comprehensive analyses by leveraging six machine learning models. Our methodology involves training these models on historical daily data from the Binance Exchange. Subsequently, we evaluate their performance using diverse testing datasets from January 2022 to December 2023. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. Regarding the backward testing results, the XGBoost model combined with the trading strategy made a profit in all three test datasets of Oct 2023, Jul to Sep 2023, and LSK/USDT achieved 24%, 47%, and 98% profits, respectively. This thesis underscores the potential for leveraging well-designed machine learning models to earn significant profits, even in bearish market conditions.