Performance Comparison of Genetic and Machine Learning Algorithms in Crypto Markets

The evolution of statistical methodologies for research analysis has notably contributed to the diversification of analytical and predictive techniques. Notably, machine learning, which leverages mathematical and statistical approaches to draw meaningful inferences from data, has made remarkable strides in artificial intelligence,generating predictions based on these inferences.Encompassing a spectrum of algorithms that transform datasets into models, machine learning emerges as a cornerstone in analytical and predictive processes. Herein, weproduce high-accuracy buying and selling signals in the cryptomarket—a market that continuously operates 24 h a day. This is achieved by integrating MACD (Moving Average Convergence Divergence) parameters optimized with a genetic algorithm specific tothe cryptocurrency market, machinelearning methods, and technical analysis indicators. Contextually, we compared the performances of different machine learning algorithms. Using genetic algorithm optimization, we identified the most suitable model.Results underscore the enhanced profitability of trades executed with optimized MACD parameterscompared with those executed using nonoptimizedMACD parameters. Themodel performed optimallyon the LTCUSDT pair. Notably,the deep learning algorithm exhibitedbetter profitability in the LTCUSDT pair.However, its effectiveness in generating profits in the ADAUSDT pair was somewhere limited;this can be attributed to the high volatility, instability, and rapid response of the cryptomarket to current news, whether positive or negative. Therefore,the developed model fits different cryptocurrency pairs to varying degrees.