Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) designed for sequential data, effectively capturing long-term dependencies and addressing the vanishing gradient problem. This method enhances the performance of trend-following strategies in market analysis by predicting future trends. The approach involves acquiring data from MetaTrader 5, training the LSTM model in Python, and then integrating the model into MQL5 for backtesting.

LSTM's ability to model temporal relationships makes it ideal for forecasting applications. The article illustrates using regression techniques to predict future ADX values based on selected features. It details data preparation, model training, and the seamless integration of Python with MQL5.

Implementing LSTM in trading strategies filters out non-profitable trades by predicting market trendiness and thus improves t...
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