LSTM Quantitative Trading Educational Project - EURUSD H1 Strategy An open-source educational project demonstrating how to build, train, validate, export, and deploy LSTM-based financial forecasting models using real EURUSD market data, PyTorch, ONNX, and MetaTrader 5. Overview This project provides a complete end-to-end quantitative trading workflow based on a Long Short-Term Memory (LSTM) neural network. Using real EURUSD historical market data, the model is trained in PyTorch, exported to ONNX format, and deployed directly in MetaTrader 5 (MT5) for backtesting and live inference. The repository is designed as an educational resource for developers, students, quantitative traders, and machine learning practitioners who want to learn how deep learning can be applied to financial time-series forecasting and algorithmic trading. The project covers the entire pipeline: Historical data acquisition from MetaTrader 5 Data preprocessing and normalization LSTM model training with GPU acceleration Early stopping and validation monitoring ONNX model export Deployment inside MetaTrader 5 Exper

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