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In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions. [ABSTRACT FROM AUTHOR]

Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today's rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market. [ABSTRACT FROM AUTHOR]