From 2c41856726f17d854b0152e1bc9b24d05fd96667 Mon Sep 17 00:00:00 2001
From: Shuo Cao <42436068+caoshuo594@users.noreply.github.com>
Date: Fri, 5 Jun 2026 11:27:39 +0800
Subject: [PATCH] Enhance README with project details and badges

Added project description and badges to README.
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 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 54ef515..97493ae 100644
--- a/README.md
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 # 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
 
 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.