This paper explores the integration of Long Short-term Memory (LSTM) models into traditional stock market trading strategies to enhance performance. The study compares traditional strategies with those enhanced by LSTM, demonstrating that LSTM-based strategies outperform traditional ones. Traditional strategies rely on technical indicators and historical data, while LSTM models excel at capturing long-term dependencies in financial time series, leading to more accurate price forecasts and better trading decisions. The research tested several traditional strategies—MACD, TEMA, MOM, and P-MA—alongside their LSTM-enhanced counterparts, showing improved profitability. The study also compared these strategies with a "buy and hold" approach, highlighting the advantages of LSTM integration. The results indicate that LSTM models can significantly improve trading performance, particularly for individual stocks, while acknowledging the need for context-specific strategies due to varying market conditions. The paper emphasizes the importance of combining machine learning with traditional methods to adapt to the dynamic nature of financial markets. The findings contribute to the understanding of how LSTM can enhance traditional trading strategies, offering a path toward more effective decision-making in the stock market.This paper explores the integration of Long Short-term Memory (LSTM) models into traditional stock market trading strategies to enhance performance. The study compares traditional strategies with those enhanced by LSTM, demonstrating that LSTM-based strategies outperform traditional ones. Traditional strategies rely on technical indicators and historical data, while LSTM models excel at capturing long-term dependencies in financial time series, leading to more accurate price forecasts and better trading decisions. The research tested several traditional strategies—MACD, TEMA, MOM, and P-MA—alongside their LSTM-enhanced counterparts, showing improved profitability. The study also compared these strategies with a "buy and hold" approach, highlighting the advantages of LSTM integration. The results indicate that LSTM models can significantly improve trading performance, particularly for individual stocks, while acknowledging the need for context-specific strategies due to varying market conditions. The paper emphasizes the importance of combining machine learning with traditional methods to adapt to the dynamic nature of financial markets. The findings contribute to the understanding of how LSTM can enhance traditional trading strategies, offering a path toward more effective decision-making in the stock market.