gavindiaz/LSTM-Quantitative-Trading-Educational-Project
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Enhance README with project details and badges

Browse Source 
Added project description and badges to README.
This commit is contained in:
Shuo Cao
2026-06-05 11:27:39 +08:00
committed by GitHub
parent 0cf122f4ef
commit 2c41856726
1 changed files with 8 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+8
View File
@@ -1,5 +1,13 @@
# LSTM Quantitative Trading Educational Project - EURUSD H1 Strategy # 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.
![Python](https://img.shields.io/badge/Python-3.11-blue)
![PyTorch](https://img.shields.io/badge/PyTorch-2.5-red)
![ONNX](https://img.shields.io/badge/ONNX-Supported-orange)
![MT5](https://img.shields.io/badge/MetaTrader5-Compatible-green)
![License](https://img.shields.io/badge/License-MIT-brightgreen)
## Overview ## 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. 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.