#property copyright "Your Name" #property link "http://www.yourwebsite.com" #property version "1.00" #property strict #include <Trade\Trade.mqh> #include <Arrays\ArrayObj.mqh> // 外部参数 input int InputDimension = 10; // 输入维度 input int HiddenNeurons = 20; // 隐藏层神经元数量 input int OutputDimension = 1; // 输出维度 input double LearningRate = 0.01; // 学习率 input int BatchSize = 32; // 批量大小 input double DropoutRate = 0.5; // Dropout比率 input double RiskPercent = 1; // 每笔交易风险百分比 input string ModelFileName = "NNModel.bin"; // 模型文件名 // 全局变量 CTrade trade; CNeuralNetwork *nn; int logHandle; // 神经网络类 class CNeuralNetwork : public CObject { private: CArrayObj m_layers; double m_learningRate; public: CNeuralNetwork(double learningRate) : m_learningRate(learningRate) {} void AddLayer(CBaseLayer *layer) { m_layers.Add(layer); } void Forward(double &input[], double &output[]) { double *current = input; for(int i = 0; i < m_layers.Total(); i++) { CBaseLayer *layer = m_layers.At(i); layer.Forward(current, output); current = output; } } void Backward(double &target[]) { for(int i = m_layers.Total() - 1; i >= 0; i--) { CBaseLayer *layer = m_layers.At(i); layer.Backward(target, m_learningRate); } } void Train(double &input[], double &target[], int epochs) { for(int epoch = 0; epoch < epochs; epoch++) { double output[]; Forward(input, output); Backward(target); } } bool Save(string fileName) { int fileHandle = FileOpen(fileName, FILE_WRITE|FILE_BIN); if(fileHandle == INVALID_HANDLE) return false; for(int i = 0; i < m_layers.Total(); i++) { CBaseLayer *layer = m_layers.At(i); if(!layer.Save(fileHandle)) return false; } FileClose(fileHandle); return true; } bool Load(string fileName) { int fileHandle = FileOpen(fileName, FILE_READ|FILE_BIN); if(fileHandle == INVALID_HANDLE) return false; for(int i = 0; i < m_layers.Total(); i++) { CBaseLayer *layer = m_layers.At(i); if(!layer.Load(fileHandle)) return false; } FileClose(fileHandle); return true; } }; // 基础层类 class CBaseLayer : public CObject { public: virtual void Forward(double &input[], double &output[]) = 0; virtual void Backward(double &error[], double learningRate) = 0; virtual bool Save(int fileHandle) = 0; virtual bool Load(int fileHandle) = 0; }; // 全连接层类 class CFullyConnectedLayer : public CBaseLayer { private: int m_inputDim; int m_outputDim; double m_weights[]; double m_biases[]; double m_lastInput[]; double m_lastOutput[]; public: CFullyConnectedLayer(int inputDim, int outputDim) { m_inputDim = inputDim; m_outputDim = outputDim; ArrayResize(m_weights, inputDim * outputDim); ArrayResize(m_biases, outputDim); InitializeWeights(); } void Forward(double &input[], double &output[]) override { ArrayCopy(m_lastInput, input); ArrayResize(output, m_outputDim); ArrayResize(m_lastOutput, m_outputDim); for(int i = 0; i < m_outputDim; i++) { output[i] = m_biases[i]; for(int j = 0; j < m_inputDim; j++) { output[i] += input[j] * m_weights[i * m_inputDim + j]; } m_lastOutput[i] = output[i]; } } void Backward(double &error[], double learningRate) override { double inputError[]; ArrayResize(inputError, m_inputDim); for(int i = 0; i < m_outputDim; i++) { for(int j = 0; j < m_inputDim; j++) { double grad = error[i] * m_lastInput[j]; m_weights[i * m_inputDim + j] -= learningRate * grad; inputError[j] += error[i] * m_weights[i * m_inputDim + j]; } m_biases[i] -= learningRate * error[i]; } ArrayCopy(error, inputError); } bool Save(int fileHandle) override { if(FileWriteArray(fileHandle, m_weights) != ArraySize(m_weights)) return false; if(FileWriteArray(fileHandle, m_biases) != ArraySize(m_biases)) return false; return true; } bool Load(int fileHandle) override { if(FileReadArray(fileHandle, m_weights) != ArraySize(m_weights)) return false; if(FileReadArray(fileHandle, m_biases) != ArraySize(m_biases)) return false; return true; } private: void

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