This paper explores the integration of Long Short-term Memory (LSTM) models into traditional trading strategies to enhance their performance in the stock market. The study aims to determine whether LSTM models can outperform traditional methods in forecasting closing prices and improving trading decisions. Traditional trading strategies rely on analyzing current closing prices and technical indicators, while LSTM models are designed to forecast future prices more accurately. The research compares the performance of hybrid strategies that incorporate LSTM with traditional strategies using backtesting on historical data. The study uses LSTM neural networks, which are particularly effective in processing time series data and recognizing long-term dependencies. Traditional trading strategies, such as MACD, TEMA, MOM, and P-MA, are tested on major stock indices and individual stocks. The results show that hybrid strategies incorporating LSTM models outperform traditional strategies, with LSTM models providing more accurate predictions and better trading performance. The LSTM model's ability to capture complex patterns and long-term dependencies is highlighted as a key advantage. The paper also discusses the challenges of traditional trading strategies, such as their lag in response to new market information and the need for combining multiple tools and strategies. The integration of LSTM models into these strategies is shown to address these challenges and improve decision-making. The study concludes that LSTM models have significant potential in enhancing trading performance, but traders should remain aware of the model's limitations and the risks associated with predictive-based trading. Keywords: stock market; traditional strategies; LSTM; hybrid strategy; machine learning; trading; decision makingThis paper explores the integration of Long Short-term Memory (LSTM) models into traditional trading strategies to enhance their performance in the stock market. The study aims to determine whether LSTM models can outperform traditional methods in forecasting closing prices and improving trading decisions. Traditional trading strategies rely on analyzing current closing prices and technical indicators, while LSTM models are designed to forecast future prices more accurately. The research compares the performance of hybrid strategies that incorporate LSTM with traditional strategies using backtesting on historical data. The study uses LSTM neural networks, which are particularly effective in processing time series data and recognizing long-term dependencies. Traditional trading strategies, such as MACD, TEMA, MOM, and P-MA, are tested on major stock indices and individual stocks. The results show that hybrid strategies incorporating LSTM models outperform traditional strategies, with LSTM models providing more accurate predictions and better trading performance. The LSTM model's ability to capture complex patterns and long-term dependencies is highlighted as a key advantage. The paper also discusses the challenges of traditional trading strategies, such as their lag in response to new market information and the need for combining multiple tools and strategies. The integration of LSTM models into these strategies is shown to address these challenges and improve decision-making. The study concludes that LSTM models have significant potential in enhancing trading performance, but traders should remain aware of the model's limitations and the risks associated with predictive-based trading. Keywords: stock market; traditional strategies; LSTM; hybrid strategy; machine learning; trading; decision making