Predicting Price Movement of the BTCUSDT Pair Using LightGBM Classification Modeling for Cryptocurrency Trading

Abstract

This research proposes a machine learning-based system that aims to form an investment strategy that is capable of buying and selling cryptocurrencies. Predicting the price movement of crypto assets has a number of challenges, especially due to the high volatility of trading prices. Traditionally this process is carried out by conducting technical analysis using technical indicators which requires extensive knowledge and experience to get maximum profit in cryptocurrency trading. The CRISP-DM methodology was applied to create a classification model to predict the movement of the BTCUSDT cryptocurrency pair using a tree-based classification algorithm with the Gradient Boosting framework, namely the Light Gradient Boosting Machine (LGBM). The Logistic Regression algorithm was used as a comparison. As input data, the klines dataset of the BTCUSDT cryptocurrency pair is used and feature engineering is carried out in the form of labelling (up/down) as well as a number of technical indicators commonly used in trading activities. The prediction results of the classification model are then used in the further evaluation stage by being tested (used for trading) against the original data of BTCUSDT price movements for one month and the Return on Investment (ROI) obtained is evaluated against the Buy and Hold strategy. Based on the evaluation results, the resulting model has a better performance than Logistic Regression, but the model's performance has not been able to exceed the ROI value of the Buy and Hold strategy consistently and still can't generate profit.