A new open-source Python algorithm has been developed to assist in detecting crests and troughs in hourly Bitcoin trades using moving averages. The algorithm simulates borrowing and selling Bitcoin at peak prices and buying back at troughs, aiming to capitalize on market fluctuations. The algorithm requires the installation of pandas and numpy libraries and can be integrated with real-time data using APIs like ccxt. Users are advised to run the algorithm hourly using schedulers such as APScheduler and to conduct thorough backtesting. Caution is advised as trading involves inherent risks and fees, with no guaranteed returns.