Translate README to English and enhance content
Updated README to provide English translations and improve clarity.
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Shuo Cao
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# LSTM量化交易教学项目 - EURUSD H1策略
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# LSTM Quantitative Trading Educational Project - EURUSD H1 Strategy
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## 项目简介
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## Overview
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基于深度学习LSTM网络的外汇量化交易完整解决方案,使用真实EURUSD历史数据训练模型,通过ONNX格式在MT5平台实现实盘交易。本项目涵盖从数据获取、模型训练、导出到回测的完整工作流,是学习AI量化交易的理想教学案例。
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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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## 技术栈
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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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- **深度学习框架**: PyTorch 2.5+ (支持CUDA GPU加速)
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The project covers the entire pipeline:
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- **模型架构**: LSTM (Long Short-Term Memory)
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- **模型格式**: ONNX (跨平台部署)
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* Historical data acquisition from MetaTrader 5
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- **交易平台**: MetaTrader 5
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* Data preprocessing and normalization
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- **编程语言**: Python 3.11 + MQL5
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* LSTM model training with GPU acceleration
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- **数据源**: MT5实时行情数据
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* Early stopping and validation monitoring
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* ONNX model export
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## LSTM在量化交易中的优势
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* Deployment inside MetaTrader 5 Expert Advisors (EA)
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* Backtesting and strategy evaluation
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**LSTM (长短期记忆网络)** 是循环神经网络(RNN)的变体,特别适合时间序列预测:
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## Key Features
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1. **长期依赖捕捉**: 通过门控机制(遗忘门、输入门、输出门)记住长期价格趋势
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2. **自动特征提取**: 无需手动构造技术指标,直接从原始OHLCV数据学习
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### Deep Learning Based Forecasting
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3. **非线性建模**: 可拟合复杂的市场动态,优于传统线性模型
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4. **梯度稳定性**: 解决传统RNN的梯度消失问题,支持深层网络训练
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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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## 项目文件结构
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* Support for CUDA GPU acceleration
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* Validation-based model selection
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```
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* Early stopping to reduce overfitting
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LSTM/
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├── requirements.txt # Python依赖包清单
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### Production Deployment
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├── train_lstm.py # 训练脚本(支持GPU加速)
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├── lstm_model.onnx # 训练生成的ONNX模型(~11KB)
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* ONNX model export for platform-independent deployment
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├── scaler_params.npy # MinMax归一化参数
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* Native integration with MetaTrader 5
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├── training_loss.png # 训练曲线图(自动生成)
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* Real-time inference inside MQL5 Expert Advisors
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├── LSTM_EA.mq5 # MQL5交易EA代码
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* Lightweight model size (~10 KB)
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├── prompt_simple.txt # 简单版提示词模板
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├── prompt_professional.txt # 专业版提示词模板
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### Educational Focus
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└── README.md # 项目文档
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```
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* Clear and well-documented code
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* Complete training-to-deployment workflow
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## 快速开始
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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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### 环境准备
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## Technology Stack
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**硬件要求**:
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- CPU: 任意现代处理器
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| Component | Technology |
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- GPU: NVIDIA显卡(可选,推荐RTX系列)
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| ----------------------- | ---------------------------- |
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- 内存: 8GB+
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| Deep Learning Framework | PyTorch 2.5+ |
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- 硬盘: 1GB可用空间
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| Model Architecture | LSTM |
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| Model Format | ONNX |
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**软件要求**:
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| Trading Platform | MetaTrader 5 |
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- Python 3.11+
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| Languages | Python 3.11, MQL5 |
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- MetaTrader 5 (已登录账户)
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| Data Source | MetaTrader 5 Historical Data |
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- CUDA 12.1+ (GPU训练需要)
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## Why LSTM for Quantitative Trading?
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### 1. 安装依赖
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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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```bash
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pip install -r requirements.txt
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Advantages include:
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```
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* Capturing long-term market dependencies
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**依赖包说明**:
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* Learning nonlinear price dynamics
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- `torch`: PyTorch深度学习框架
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* Automatic extraction of temporal patterns
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- `onnx`: ONNX模型格式支持
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* Improved gradient stability compared with traditional RNNs
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- `MetaTrader5`: MT5 Python API
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* Strong performance on financial forecasting tasks
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- `pandas`: 数据处理
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- `scikit-learn`: 数据归一化
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## Model Architecture
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- `numpy`: 数值计算
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Input Shape:
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**安装GPU版PyTorch** (如有NVIDIA显卡):
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(10 timesteps × 5 features)
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```bash
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pip uninstall torch torchvision torchaudio -y
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Features:
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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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```
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* Open
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* High
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### 2. 训练模型
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* Low
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* Close
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```bash
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* Volume
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python train_lstm.py
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```
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Network Structure:
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**训练流程**:
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Input Layer
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1. 自动检测CUDA并使用GPU加速(如可用)
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→ LSTM Layer (hidden_size=20)
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2. 连接MT5终端获取EURUSD历史数据
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→ Fully Connected Layer
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3. 使用MinMaxScaler归一化OHLCV数据
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→ Next-Candle Close Price Prediction
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4. **数据集划分**: 80%训练集 + 20%验证集
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5. **智能训练**: 最多500个epoch，支持早停机制
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Model Statistics:
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6. **验证监控**: 实时追踪训练Loss和验证Loss
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7. **最佳模型保存**: 自动保存验证Loss最低的模型
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* Parameters: ~2,040
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8. **可视化**: 生成训练曲线图(training_loss.png)
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* ONNX Size: ~10 KB
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9. 导出ONNX格式模型
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* Inference Latency: <1 ms
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10. 保存归一化参数供推理使用
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## Training Pipeline
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**训练输出示例**:
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```
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1. Retrieve EURUSD H1 historical data from MT5
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使用设备: cuda
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2. Normalize OHLCV features using MinMaxScaler
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GPU型号: NVIDIA GeForce RTX 4090
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3. Split data into:
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获取到 31,053 条EURUSD H1数据
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* 80% Training Set
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训练集形状: torch.Size([24833, 10, 5])
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* 20% Validation Set
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验证集形状: torch.Size([6209, 10, 5])
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4. Train LSTM model using Adam optimizer
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5. Monitor validation loss
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开始训练...
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6. Apply Early Stopping
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Epoch 10 | 训练Loss: 0.307397 | 验证Loss: 0.301387
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7. Save best-performing model
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Epoch 20 | 训练Loss: 0.198500 | 验证Loss: 0.189706
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8. Export ONNX model
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...
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9. Generate training loss visualization
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Epoch 164 | 训练Loss: 0.000424 | 验证Loss: 0.000152 ← 最佳模型
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## Trading Logic
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早停触发! 验证损失已经20个epoch没有改善
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已恢复最佳模型 (Epoch 164)
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For each newly completed H1 candle:
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训练总结:
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1. Retrieve the latest 10 historical candles
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总训练轮数: 184 epochs
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2. Normalize input features
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最佳模型: Epoch 164
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3. Run ONNX inference
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最佳验证Loss: 0.000152
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4. Convert prediction back to real price values
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✓ 模型状态: 良好，没有明显的过拟合
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5. Generate trading signals:
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训练曲线已保存: training_loss.png
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Buy:
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ONNX模型已保存: lstm_model.onnx (10.27 KB)
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Predicted Price > Current Price × 1.0001
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```
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Sell:
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### 3. 部署到MT5
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Predicted Price < Current Price × 0.9999
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#### 步骤A: 复制ONNX模型
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Otherwise:
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```
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No Trade
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将 lstm_model.onnx 复制到:
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MT5安装目录/MQL5/Files/lstm_model.onnx
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## Educational Purpose
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```
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This repository is intended for:
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#### 步骤B: 验证归一化参数
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训练脚本会输出归一化参数,确保与 `LSTM_EA.mq5` 第20-21行一致:
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* Machine Learning students
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```cpp
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* Quantitative Trading beginners
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double data_min[5] = {0.95383, 0.95586, 0.95351, 0.95383, 9.00000};
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* Forex traders interested in AI
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double data_max[5] = {1.23394, 1.23485, 1.23327, 1.23396, 24004.00000};
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* MetaTrader 5 developers
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```
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* Researchers studying financial forecasting
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#### 步骤C: 编译EA
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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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1. 将 `LSTM_EA.mq5` 复制到 `MT5/MQL5/Experts/`
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2. 在MetaEditor中打开并编译(F7)
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## Disclaimer
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#### 步骤D: 策略回测
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This project is provided strictly for educational and research purposes.
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1. 打开策略测试器(Ctrl+R)
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2. 配置参数:
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It does not constitute financial advice, investment recommendations, or guarantees of future performance.
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- EA: LSTM_EA
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- 品种: EURUSD
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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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- 周期: H1
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- 模式: 每笔成交
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## License
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- 日期: 选择样本外数据(如最近3个月)
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- 初始资金: 10,000 USD
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MIT License
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3. 点击"开始"运行回测
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Free to use, modify, and distribute.
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### 4. EA参数说明
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| 参数 | 默认值 | 说明 |
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|------|--------|------|
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| LotSize | 0.01 | 每笔交易手数 |
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| StopLoss | 100 | 止损点数 |
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| TakeProfit | 150 | 止盈点数 |
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## 模型架构详解
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### 网络结构
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```
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输入层: [batch_size, 10, 5]
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├─ 10: 时间步长(10根历史K线)
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└─ 5: 特征维度(OHLCV)
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↓
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LSTM层: input_size=5, hidden_size=20, num_layers=1
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├─ 遗忘门: 决定丢弃哪些历史信息
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├─ 输入门: 决定存储哪些新信息
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└─ 输出门: 决定输出哪些信息
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↓
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全连接层: Linear(20 → 1)
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↓
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输出层: [batch_size, 1]
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└─ 预测下一根K线收盘价(归一化值)
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```
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**模型参数**:
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- 总参数量: ~2,040个
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- ONNX文件大小: 10.27 KB
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- 推理速度: <1ms/次 (GPU)
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### 数据预处理
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**归一化公式**:
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```python
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normalized = (value - min) / (max - min) # 映射到[0,1]
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```
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**反归一化公式**:
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```python
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real_value = normalized * (max - min) + min
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```
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### 训练参数
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| 参数 | 值 | 说明 |
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|------|-----|------|
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| 数据划分 | 80%/20% | 训练集/验证集 |
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| 优化器 | Adam | 自适应学习率 |
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| 学习率 | 0.001 | 初始学习率 |
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| 损失函数 | MSE | 均方误差 |
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| 批大小 | 全量 | 使用所有训练数据 |
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| 最大轮数 | 500 | 最多训练500个epoch |
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| 早停耐心 | 20 | 验证Loss 20轮不降则停止 |
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| 最佳模型 | 自动保存 | 保存验证Loss最低的模型 |
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## 交易逻辑
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```python
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每小时新K线形成时:
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1. 获取最近10根H1 K线 [Open, High, Low, Close, Volume]
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2. 使用训练时的参数归一化数据
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3. 输入LSTM模型进行推理
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4. 反归一化得到预测收盘价
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5. 交易决策:
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- 预测价 > 当前价 * 1.0001 → 买入
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- 预测价 < 当前价 * 0.9999 → 卖出
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- 其他情况 → 不交易
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6. 设置止损止盈并提交订单
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```
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## 性能优化
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### 提升模型效果
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1. **增加模型容量**:
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```python
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self.lstm = nn.LSTM(input_size=5, hidden_size=50, num_layers=2)
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```
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2. **扩展时间窗口**:
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```python
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lookback = 20 # 使用20根K线预测
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```
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3. **调整训练参数**:
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```python
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patience = 30 # 增加早停耐心值
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optimizer = torch.optim.Adam(model.parameters(), lr=0.0005) # 降低学习率
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```
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4. **添加技术指标**:
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```python
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features = ['open', 'high', 'low', 'close', 'volume', 'rsi', 'macd']
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```
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5. **数据增强**:
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- 添加噪声
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- 时间窗口滑动
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- 多时间周期融合
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### 防止过拟合
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✅ **已实现的功能**:
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1. **训练集/验证集分割**:
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```python
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train_size = int(len(X) * 0.8) # 80%训练，20%验证
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X_train = X[:train_size]
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X_val = X[train_size:]
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```
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2. **早停法(Early Stopping)**:
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```python
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patience = 20 # 验证Loss 20轮不降则停止
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if val_loss < best_val_loss:
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best_model_state = model.state_dict() # 保存最佳模型
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```
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3. **验证Loss监控**:
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```python
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# 每10轮显示训练和验证Loss对比
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print(f"训练Loss: {train_loss:.6f} | 验证Loss: {val_loss:.6f}")
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```
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4. **训练曲线可视化**:
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- 自动生成 `training_loss.png`
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- 双曲线对比(训练vs验证)
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- 标记最佳模型位置
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⚙️ **可选的进阶方法**:
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||||||
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5. **添加Dropout**:
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```python
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self.dropout = nn.Dropout(0.2)
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|
||||||
```
|
|
||||||
|
|
||||||
6. **L2正则化**:
|
|
||||||
```python
|
|
||||||
optimizer = Adam(model.parameters(), lr=0.001, weight_decay=1e-5)
|
|
||||||
```
|
|
||||||
|
|
||||||
7. **学习率衰减**:
|
|
||||||
```python
|
|
||||||
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5)
|
|
||||||
```
|
|
||||||
|
|
||||||
## 常见问题排查
|
|
||||||
|
|
||||||
### 训练相关
|
|
||||||
|
|
||||||
**Q: 训练时CUDA不可用?**
|
|
||||||
```bash
|
|
||||||
# 检查PyTorch版本
|
|
||||||
python -c "import torch; print(torch.__version__)"
|
|
||||||
|
|
||||||
# 应显示 2.5.1+cu121 (带+cu121后缀)
|
|
||||||
# 如果是 2.5.1+cpu,需重装GPU版本:
|
|
||||||
pip install torch --index-url https://download.pytorch.org/whl/cu121
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: MT5连接失败?**
|
|
||||||
```
|
|
||||||
解决方案:
|
|
||||||
1. 确保MT5已启动并登录
|
|
||||||
2. 检查工具→选项→EA交易→允许DLL导入
|
|
||||||
3. 尝试手动在MT5图表上加载EURUSD H1
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: 训练Loss不下降?**
|
|
||||||
```
|
|
||||||
可能原因:
|
|
||||||
1. 学习率过大 → 降低到0.0001
|
|
||||||
2. 数据未归一化 → 检查scaler
|
|
||||||
3. 数据质量差 → 检查MT5历史数据完整性
|
|
||||||
4. 模型过于简单 → 增加hidden_size或num_layers
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: 验证Loss远高于训练Loss?**
|
|
||||||
```
|
|
||||||
原因: 模型过拟合训练数据
|
|
||||||
解决方案:
|
|
||||||
1. 查看训练曲线图(training_loss.png)确认
|
|
||||||
2. 减少训练轮数或降低模型复杂度
|
|
||||||
3. 增加训练数据量
|
|
||||||
4. 添加Dropout层或L2正则化
|
|
||||||
5. 使用早停机制(已自动启用)
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: 如何判断模型训练效果好坏?**
|
|
||||||
```
|
|
||||||
关键指标:
|
|
||||||
1. ✅ 验证Loss越小越好 (如<0.001)
|
|
||||||
2. ✅ 训练Loss和验证Loss接近 (差异<10%)
|
|
||||||
3. ✅ Loss曲线平稳收敛不震荡
|
|
||||||
4. ✅ 早停在合理位置触发
|
|
||||||
|
|
||||||
查看方法:
|
|
||||||
- 打开 training_loss.png 查看曲线
|
|
||||||
- 阅读终端输出的"训练总结"
|
|
||||||
- 关注"模型状态"的诊断结果
|
|
||||||
```
|
|
||||||
|
|
||||||
### 回测相关
|
|
||||||
|
|
||||||
**Q: ONNX推理失败 "wrong dimension"?**
|
|
||||||
```cpp
|
|
||||||
// 确保在OnInit()中设置输入输出形状
|
|
||||||
ulong input_shape[] = {1, 10, 5};
|
|
||||||
OnnxSetInputShape(model_handle, 0, input_shape);
|
|
||||||
|
|
||||||
ulong output_shape[] = {1, 1};
|
|
||||||
OnnxSetOutputShape(model_handle, 0, output_shape);
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: 回测没有交易信号?**
|
|
||||||
```
|
|
||||||
检查清单:
|
|
||||||
1. 归一化参数是否一致
|
|
||||||
2. ONNX模型是否正确加载
|
|
||||||
3. 时间周期是否为H1
|
|
||||||
4. 查看Expert日志的预测值
|
|
||||||
```
|
|
||||||
|
|
||||||
**Q: 回测收益为负?**
|
|
||||||
```
|
|
||||||
这是正常的,原因:
|
|
||||||
1. LSTM难以预测随机市场
|
|
||||||
2. 模型过拟合训练数据
|
|
||||||
3. 交易成本(点差/滑点)未考虑
|
|
||||||
4. 需要优化止损止盈参数
|
|
||||||
|
|
||||||
建议:
|
|
||||||
- 仅用于学习,不要实盘
|
|
||||||
- 尝试不同市场和时间周期
|
|
||||||
- 结合其他技术指标
|
|
||||||
```
|
|
||||||
|
|
||||||
## 进阶扩展方向
|
|
||||||
|
|
||||||
### 1. 模型改进
|
|
||||||
- **双向LSTM**: 同时考虑过去和未来信息
|
|
||||||
- **GRU**: 更简单的门控单元,训练更快
|
|
||||||
- **Transformer**: 注意力机制,捕捉长期依赖
|
|
||||||
- **CNN-LSTM**: 卷积提取局部特征+LSTM捕捉时序
|
|
||||||
|
|
||||||
### 2. 特征工程
|
|
||||||
- **技术指标**: RSI, MACD, 布林带, KDJ
|
|
||||||
- **市场情绪**: VIX指数, 恐慌指数
|
|
||||||
- **基本面**: 经济数据, 新闻情绪分析
|
|
||||||
- **多周期**: M5/M15/H4数据融合
|
|
||||||
|
|
||||||
### 3. 策略优化
|
|
||||||
- **强化学习**: PPO/DQN优化交易决策
|
|
||||||
- **仓位管理**: Kelly公式动态调整手数
|
|
||||||
- **风控系统**: 最大回撤限制, 连续亏损止损
|
|
||||||
- **集成学习**: 多模型投票/加权平均
|
|
||||||
|
|
||||||
### 4. 工程化部署
|
|
||||||
- **实时推理服务**: FastAPI + Docker
|
|
||||||
- **模型监控**: MLflow跟踪模型性能
|
|
||||||
- **在线学习**: 定期用新数据微调模型
|
|
||||||
- **多品种交易**: 扩展到黄金/原油/股指
|
|
||||||
|
|
||||||
## 性能基准
|
|
||||||
|
|
||||||
### 训练性能 (RTX 4090)
|
|
||||||
|
|
||||||
| 指标 | 数值 |
|
|
||||||
|------|------|
|
|
||||||
| 数据量 | 31,053条 |
|
|
||||||
| 训练集 | 24,833条 (80%) |
|
|
||||||
| 验证集 | 6,209条 (20%) |
|
|
||||||
| 训练时间 | ~30秒 (含早停) |
|
|
||||||
| 实际轮数 | 164-184 (早停触发) |
|
|
||||||
| 最佳验证Loss | ~0.0001-0.0002 |
|
|
||||||
| 显存占用 | ~500MB |
|
|
||||||
|
|
||||||
### 推理性能 (MT5)
|
|
||||||
|
|
||||||
| 指标 | 数值 |
|
|
||||||
|------|------|
|
|
||||||
| 单次推理 | <1ms |
|
|
||||||
| H1周期延迟 | 可忽略 |
|
|
||||||
| CPU占用 | <1% |
|
|
||||||
|
|
||||||
## 免责声明
|
|
||||||
|
|
||||||
**重要提示**:
|
|
||||||
|
|
||||||
1. 本项目仅用于教学和研究目的,不构成任何投资建议
|
|
||||||
2. 金融市场存在不可预测性,过去表现不代表未来收益
|
|
||||||
3. LSTM模型可能过拟合历史数据,实盘表现可能与回测不符
|
|
||||||
4. 实盘交易前务必在模拟账户充分测试
|
|
||||||
5. 交易有风险,投资需谨慎,请勿使用无法承受损失的资金
|
|
||||||
|
|
||||||
## 参考资料
|
|
||||||
|
|
||||||
- [PyTorch官方文档](https://pytorch.org/docs/stable/index.html)
|
|
||||||
- [ONNX格式规范](https://onnx.ai/)
|
|
||||||
- [MQL5语言参考](https://www.mql5.com/zh/docs)
|
|
||||||
- [LSTM原理论文](https://www.bioinf.jku.at/publications/older/2604.pdf)
|
|
||||||
- [量化交易策略](https://www.quantstart.com/)
|
|
||||||
|
|
||||||
## 许可证
|
|
||||||
|
|
||||||
本项目采用 MIT 许可证开源,允许自由使用、修改和分发。
|
|
||||||
|
|
||||||
## 作者与贡献
|
|
||||||
|
|
||||||
本项目由AI辅助生成,用于教学演示。欢迎提出改进建议和Bug反馈。
|
|
||||||
|
|
||||||
---
|
|
||||||
|
|
||||||
**最后更新**: 2025-10-27
|
|
||||||
**版本**: 2.0.0
|
|
||||||
**更新内容**:
|
|
||||||
- ✅ 新增训练集/验证集划分 (80%/20%)
|
|
||||||
- ✅ 新增早停机制 (Early Stopping)
|
|
||||||
- ✅ 新增最佳模型自动保存
|
|
||||||
- ✅ 新增训练曲线可视化
|
|
||||||
- ✅ 新增训练状态智能诊断
|
|
||||||
- ✅ 优化训练流程和文档说明
|
|
||||||
|
|
||||||
**适用人群**: 量化交易初学者、深度学习实践者、MQL5开发者
|
|
||||||
|
|||||||
Reference in New Issue
Block a user