Machine learning algorithms are increasingly being embraced for their easy implementation and effective outcomes, particularly in financial predictions. In financial applications, these algorithms have emerged as potent tools forinformation processing and analysis, particularly in trading strategies,price predictions, and portfolio management. Deep learning algorithms excel in creating high-performance classification models, especially in complex datasets. Decision tree algorithms are suitable for solving classification and regression problems, whereas genetic algorithms have emerged as optimal methods for addressing complex problems, optimization, and modeling systems related to randomness.

We aim to produce high-accuracy buying and selling signals in the cryptomarket, operating continuously 24 ha day. The approach combines MACD parameters, optimized with a genetic algorithm in the cryptocurrency market, machine learning methods, and technical analysis indicators. Machine learning algorithms are essential tools that transform datasets into models. We compared the performances of different machine learning algorithms. 

Furthermore, we examine the results obtained by optimizing the performance of these algorithms using genetic algorithms. The data collection process commenced by obtaining candlestick data for ADAUSDT and LTCUSDT assets through the API of Binance Crypto Exchange.A 1-h candlestick chart was used for the analysis, spanning November 9, 2021, to November 22, 2022. Each candlestick data item includes information such as transaction time, opening, closing, high values, low values, and trading volume. The candlestickdata were separated into optimization and test data. The optimization data were created by adding CMF, RSI, Z-SCORE, ATR, BOP, CMO, and CC INDICATOR signals during the strategy generation phase. Next, thisdataset was dividedinto machine learning training and test data. Long and short trading signals were established based on MACD and Signal line crossovers, and a decision column was added.

During the optimization phase, the parameters of the MACD indicator were optimized using a genetic algorithm. The MACD indicator uses the default parameters of 12, 26, and 9. Employing the NSGA-II genetic algorithm, the optimal parameter values for the MACD indicator were calculated over 25 cycles. Subsequently, optimized MACD parameters were identified for ADAUSDT and LTCUSDT based on the test results. Backtesting was performed using the optimized MACD parameters to obtain gain and profitability ratios. Upon examining the accuracy rates of various machine learning algorithms,deep learning exhibitedthe highest accuracy rate.

The optimized MACD parameters, training data of the most suitable machine learning algorithm, and the test data were consolidated.The test data were evaluated by backtesting, employing optimized MACD parameters and trained machine learning algorithms;this process revealedthe profit rate and profitability. The results indicate a high gain ratio and profitability in the LTCUSDT asset when optimized MACD parameters and a deep learning algorithm were employed. However, for the ADAUSDT asset, the combination failed to generate a profit. This highlights that the success of certain combinations of assets and strategies may vary depending on market conditions. Therefore, for effective implementation of the proposed model, each asset’sstrategy needs careful evaluation.

In backtests focusing on ADAUSDT and LTCUSDT cryptocurrency pairs, long–short trades using optimized MACD parameters yieldedhigher profits thantrades executed with nonoptimized settings. The default MACD parameters typically involve three parameters: a fast period of 12, a slow period of 26, and a correction period of 9. However,the averages obtained from the optimization process deviatednoticeably from these standard values,suggesting that investors engaged in cryptoasset trading maybenefit from an indicator responding rapidly to market dynamics. 

Additionally, owingto the volatile nature of cryptoassets, the applicationof machine learning algorithms poses challenges in accurately determining profits and losses before ensuring a clear data separation. This underscores the risk of exposure to sudden price fluctuations in cryptoassets. In conclusion, this study found that long–short trades using optimized MACD parameters do not always yield optimal results. However, profitability can be enhancedby leveraging machine learning algorithms to filter out data leading to losses. Notably, the developed model demonstrated better compatibility with the LTCUSDT pair than the ADAUSDT, showcasing a profit of 93.55% in the LTCUSDT pair. These results underscorethe potential advantagesof integrating machine learning and technical analysis in the cryptocurrency market.