QFSDK Neural Network Toolkit for MT4 Financial Forecasting

qfsdk.mqh

The function usage is in the Readme.md. The qffc.mq4 file is just a demo example that imitates the program in the DemoFann.pdf. Of course, you can use your own data preprocessing methods.

Note that only the maxnet.dll file should be imported in the qfsdk.mqh. The other DLL files are required but won't be used during the MQL programming. If you have Visual Studio on your computer, it is fine to delete the other DLL files except for the maxnet.dll. Happy programming, thanks!

API

build_net()

bool build_net(int& nn_layer, int& feature_num, int& nn_output, const double &inputVector[], const double &outputVector[], const int &neuron_num[], const int & active[]);

nn_layer is the number of the hidden layers, feature_num is the feature number of the training sample. nn_output is the dimension of the label.

inputVector: training sample

outputVector: label

neuron_num: an int type array, stores the neuron number of each hidden layer

activation: the array that indicates which activation function to be used. (0: ReLu ; 1: Sigmoid ; 2: Tanh ; 3: Lee_oscillator )

return build the network successfully or not.

int iter = 5; //training iteration for each batch 

double learn_rate = 0.01;  

double targetMSE = 0.002; 

int feature_num = 3; 

int opt = 0; //optmizer : 1 for Adagrad, else gradient descent 

int neuron_num[] = {10,3}; //hidden_layer neuron num  //0:ReLu ; 1:Sigmoid ; 2:Tanh ; 3:Lee_oscillator  

int activation[] = {0,0};  

bool s = build_net(hidden_layer, feature_num, nn_output, inpt, oupt, neuron_num, activation);

Predict()

void Predict(int feature_num, int nn_output, const double &inputVector[], double & y_hat[]);

y_hat: an array that stores the prediction, it should be initialized before the function is called.

double p_hat[] = {0}; //output_num, initialize 

Predict(feature_num, nn_output, inputVector, p_hat);

train()

double train(int feature_num, int nn_output, const double & inputVector[], const double & outputVector[], int iteration, const double& lr, const int& opt);

iteration: for each sample, we need to update the weights few time, this parameter controls the number of update.

lr: the learning rate.

opt: the choice for the optimizer, recommend to use the gradient descent, Adaptive gradient descent is not yet mature.

return: the mean square error.

release_net()

void release_net();

release the memory of the network, it should be called if the network wouldn't be used anymore. Also we need this when we want to reconstruct the network.

Parameter settings

This is an example for the parameter setting.

int nn_layer = 4; // Number of layers (4 including input and output layers)

int nn_output = 1; // number of outputs 

double trainingData[][4]; // IMPORTANT! size = nn_input + nn_output 

int maxTraining = 1000; 

int hidden_layer = nn_layer - 2;  

int iter = 5; //training iteration 

double learn_rate = 0.01; 

double targetMSE = 0.002;

int feature_num = 3;

int opt = 0; //optmizer 

int neuron_num[] = {10,3};//hidden_layer neuron num  

//0:ReLu ; 1:Sigmoid ; 2:Tanh ; 3:Lee_oscillator  

int activation[] = {0,0};

For the complete code of the SDK please refer to WaimenMak · GitHub

Download Toolkit: /Public/Uploadfiles/20210508/20210508065336_10816.rar