Shuo Cao
177 lines
5.9 KiB
Markdown
177 lines
5.9 KiB
Markdown
# LSTM Quantitative Trading Educational Project - EURUSD H1 Strategy
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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.
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## Overview
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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.
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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.
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The project covers the entire pipeline:
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* Historical data acquisition from MetaTrader 5
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* Data preprocessing and normalization
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* LSTM model training with GPU acceleration
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* Early stopping and validation monitoring
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* ONNX model export
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* Deployment inside MetaTrader 5 Expert Advisors (EA)
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* Backtesting and strategy evaluation
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## Key Features
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### Deep Learning Based Forecasting
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* LSTM neural network for time-series prediction
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* Automatic feature extraction from OHLCV market data
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* Support for CUDA GPU acceleration
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* Validation-based model selection
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* Early stopping to reduce overfitting
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### Production Deployment
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* ONNX model export for platform-independent deployment
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* Native integration with MetaTrader 5
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* Real-time inference inside MQL5 Expert Advisors
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* Lightweight model size (~10 KB)
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### Educational Focus
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* Clear and well-documented code
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* Complete training-to-deployment workflow
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* Practical example using real EURUSD data
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* Suitable for beginners in AI-powered quantitative trading
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## Technology Stack
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| Component | Technology |
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| ----------------------- | ---------------------------- |
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| Deep Learning Framework | PyTorch 2.5+ |
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| Model Architecture | LSTM |
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| Model Format | ONNX |
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| Trading Platform | MetaTrader 5 |
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| Languages | Python 3.11, MQL5 |
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| Data Source | MetaTrader 5 Historical Data |
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## Why LSTM for Quantitative Trading?
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Long Short-Term Memory (LSTM) networks are specifically designed for sequential data and financial time-series forecasting.
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Advantages include:
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* Capturing long-term market dependencies
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* Learning nonlinear price dynamics
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* Automatic extraction of temporal patterns
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* Improved gradient stability compared with traditional RNNs
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* Strong performance on financial forecasting tasks
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## Model Architecture
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Input Shape:
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(10 timesteps × 5 features)
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Features:
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* Open
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* High
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* Low
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* Close
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* Volume
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Network Structure:
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Input Layer
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→ LSTM Layer (hidden_size=20)
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→ Fully Connected Layer
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→ Next-Candle Close Price Prediction
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Model Statistics:
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* Parameters: ~2,040
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* ONNX Size: ~10 KB
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* Inference Latency: <1 ms
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## Training Pipeline
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1. Retrieve EURUSD H1 historical data from MT5
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2. Normalize OHLCV features using MinMaxScaler
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3. Split data into:
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* 80% Training Set
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* 20% Validation Set
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4. Train LSTM model using Adam optimizer
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5. Monitor validation loss
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6. Apply Early Stopping
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7. Save best-performing model
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8. Export ONNX model
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9. Generate training loss visualization
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## Trading Logic
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For each newly completed H1 candle:
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1. Retrieve the latest 10 historical candles
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2. Normalize input features
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3. Run ONNX inference
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4. Convert prediction back to real price values
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5. Generate trading signals:
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Buy:
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Predicted Price > Current Price × 1.0001
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Sell:
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Predicted Price < Current Price × 0.9999
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Otherwise:
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No Trade
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## Educational Purpose
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This repository is intended for:
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* Machine Learning students
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* Quantitative Trading beginners
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* Forex traders interested in AI
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* MetaTrader 5 developers
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* Researchers studying financial forecasting
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The project demonstrates how modern deep learning models can be integrated into traditional trading platforms and serves as a practical learning resource for AI-powered algorithmic trading.
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## Disclaimer
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This project is provided strictly for educational and research purposes.
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It does not constitute financial advice, investment recommendations, or guarantees of future performance.
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Financial markets are inherently uncertain, and historical performance does not guarantee future results. Any live trading conducted using this code is done entirely at the user's own risk.
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## License
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MIT License
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Copyright (c) 2025 Cao Shuo
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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