Cryptocurrency Price Forecasting Using Machine Learning: Building Intelligent Financial Prediction Models

Abstract

Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting ‎cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast ‎the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume ‎patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity ‎proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer ‎understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine ‎learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the ‎liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed ‎their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These ‎results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating ‎these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers ‎seeking to create smarter and more risk-aware strategies in the U.S. digital assets market‎.