This study presents a comparative analysis of two algorithmic trading bots employing distinct artificial intelligence algorithms, namely Lorentzian classification and K-Nearest Neighbors (KNN), in the context of Bitcoin/USD trading. Over a period spanning from February to April 2024, the bots were deployed on the Pionex exchange with an initial investment of $100 each, with profits systematically reinvested at each trade. Through meticulous examination of the code, strategy comparison, and rigorous evaluation of results, this study aims to discern which algorithm yields superior performance in terms of profitability and efficiency. While both bots demonstrate viability in generating profits, the KNN algorithm-driven bot showcases superior performance across key metrics. Its higher win rate, profit factor, and average profit per trade underscore the robustness and efficacy of the KNN algorithm in capturing profitable trading opportunities. Nonetheless, the Lorentzian classification algorithm-driven bot still exhibits commendable performance, highlighting the versatility of AI algorithms in algorithmic trading contexts. Further research and refinement of algorithmic trading strategies utilizing AI algorithms hold promise for enhancing performance and unlocking new frontiers in algorithmic trading excellence.