This project investigates the feasibility of forecasting short-term cryptocurrency price movements using machine learning models trained on historical OHLCV data. We evaluate the predictive power of several modeling approaches and assess whether these forecasts can be converted into consistently outperforming trading strategies relative to standard market baselines.