{"repo":"caoshuo594/LSTM-Quantitative-Trading-Educational-Project","free":true,"listed":false,"github":"https://github.com/caoshuo594/LSTM-Quantitative-Trading-Educational-Project","clone":"git clone https://github.com/caoshuo594/LSTM-Quantitative-Trading-Educational-Project.git","description":"An educational open-source project demonstrating how LSTM neural networks can be trained on real EURUSD market data and deployed to MetaTrader 5 through ONNX for quantitative trading research and learning.","language":"Python","stars":10,"topics":["algorithmic-trading","deep-learning","forex","forex-market","lstm","machine-learning","mt5","mt5-ea","onnx","pytorch"],"license":"MIT","category":"machine-learning","readme_excerpt":"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 Expert Advisors (EA) Backtesting and strategy evaluation Key Features Deep Learning Based Forecasting LSTM neural network for time-series prediction Automatic feature extraction from OHLCV market data Support for CUDA GPU acceleration Validation-based model selection Early stopping to reduce overfitting Production Deployment ONNX model export for platform-independent deployment Native integration with ","default_branch":null,"files":null,"tree":[],"storefront":"/r/caoshuo594","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/caoshuo594/LSTM-Quantitative-Trading-Educational-Project/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}