FANN2MQL 神经网络教学

首先：

请安装 fann2MQL 库，需要用它来测试该示例。 可以从此处下载。

简介

截至目前，只有一个演示如何使用 Fann2MQL 库的示例，它允许交易者在其 MQL 代码中使用开源的神经网络库“FANN”。

但是，该示例由 Fann2MQL 库的开发者编写，不容易理解。 它并不是为新手准备的。

所以，我编写了另一个示例，在理念上更为简单且进行了充分注释。

它跟交易并不直接相关，也没使用任何金融数据。 它是一个简单的静态应用程序示例。

在该示例中，我们将教一个简单的神经网络以识别一个简单模式：

我们要教的形态由 3 个数字组成：a、b 和 c。

if a < b && b < c then expected output = 1
if a < b && b > c then expected output = 0
if a > b && b > c then expected output = 0
if a > b && b < c then expected output = 1

你可以将这些数字想象为矢量坐标（向上或向下的矢量）或市场方向等。 无论如何，可以将该形态理解为：

UP UP = UP

UP DOWN = DOWN

DOWN DOWN = DOWN

DOWN UP = UP

首先，我们要创建一个神经网络。

然后我们要给它展示一些模式的示例，让它学习并推导规则。

最后，我们给这个神经网络展示它从未见过的模式，并让它给出自己的结论。 如果它理解了规则，就能够识别这些模式。

经过注释的代码如下：

// We include the Fann2MQl library
#include <Fann2MQL.mqh>

#property copyright "Copyright © 2009, Julien Loutre"
#property link      "http://www.thetradingtheory.com"

#property  indicator_separate_window
#property  indicator_buffers 0

// the total number of layers. here, there is one input layer,
// 2 hidden layers, and one output layer = 4 layers.
int nn_layer = 4;
int nn_input = 3; // Number of input neurones. Our pattern is made of 3 numbers, 
                  // so that means 3 input neurones.
int nn_hidden1 = 8; // Number of neurones on the first hidden layer
int nn_hidden2 = 5; // number on the second hidden layer
int nn_output = 1; // number of outputs

// trainingData[][] will contain the examples 
// we're gonna use to teach the rules to the neurones.
double      trainingData[][4];  // IMPORTANT! size = nn_input + nn_output


int maxTraining = 500;  // maximum number of time we will train 
                        // the neurones with some examples
double targetMSE = 0.002; // the Mean-Square Error of the neurones we should 
                          // get at most (you will understand this lower in the code)

int ann; // This var will be the identifier of the neuronal network.

// When the indicator is removed, we delete all of the neurnal networks 
// from the memory of the computer.
int deinit() {
   f2M_destroy_all_anns();
   return(0);
}

int init() {
   int i;
   double MSE;
   
   Print("=================================== START EXECUTION ================================");
   
   IndicatorBuffers(0);
   IndicatorDigits(6);
   
   // We resize the trainingData array, so we can use it.
   // We're gonna change its size one size at a time.
   ArrayResize(trainingData,1);
   
   Print("##### INIT #####");
   
   // We create a new neuronal networks
   ann = f2M_create_standard(nn_layer, nn_input, nn_hidden1, nn_hidden2, nn_output);
   
   // we check if it was created successfully. 0 = OK, -1 = error
   debug("f2M_create_standard()",ann);
   
   // We set the activation function. Don't worry about that. Just do it.
        f2M_set_act_function_hidden (ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
        f2M_set_act_function_output (ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
        
        // Some studies show that statistically, the best results are reached using this range; 
     // but you can try to change and see is it gets better or worst
        f2M_randomize_weights (ann, -0.77, 0.77);
        
        // I just print to the console the number of input and output neurones. 
      // Just to check. Just for debug purpose.
   debug("f2M_get_num_input(ann)",f2M_get_num_input(ann));
   debug("f2M_get_num_output(ann)",f2M_get_num_output(ann));
        
   
   Print("##### REGISTER DATA #####");
   
   // Now we prepare some data examples (with expected output) 
   // and we add them to the training set.
   // Once we have add all the examples we want, we're gonna send 
   // this training data set to the neurones, so they can learn.
   // prepareData() has a few arguments:
   // - Action to do (train or compute)
   // - the data (here, 3 data per set)
   // - the last argument is the expected output.
   // Here, this function takes the example data and the expected output, 
   // and add them to the learning set.
   // Check the comment associated with this function to get more details.
   //
   // here is the pattern we're going to teach:
   // There is 3 numbers. Let's call them a, b and c.
   // You can think of those numbers as being vector coordinates 
  // for example (vector going up or down)
   // if a < b && b < c then output = 1
   // if a < b && b > c then output = 0
   // if a > b && b > c then output = 0
   // if a > b && b < c then output = 1
   
   
   // UP UP = UP / if a < b && b < c then output = 1
   prepareData("train",1,2,3,1);
   prepareData("train",8,12,20,1);
   prepareData("train",4,6,8,1);
   prepareData("train",0,5,11,1);

   // UP DOWN = DOWN / if a < b && b > c then output = 0
   prepareData("train",1,2,1,0);
   prepareData("train",8,10,7,0);
   prepareData("train",7,10,7,0);
   prepareData("train",2,3,1,0);

   // DOWN DOWN = DOWN / if a > b && b > c then output = 0
   prepareData("train",8,7,6,0);
   prepareData("train",20,10,1,0);
   prepareData("train",3,2,1,0);
   prepareData("train",9,4,3,0);
   prepareData("train",7,6,5,0);

   // DOWN UP = UP / if a > b && b < c then output = 1
   prepareData("train",5,4,5,1);
   prepareData("train",2,1,6,1);
   prepareData("train",20,12,18,1);
   prepareData("train",8,2,10,1);
   
   // Now we print the full training set to the console, to check how it looks like.
   // this is just for debug purpose.
   printDataArray();
   
   
   Print("##### TRAINING #####");
   
   // We need to train the neurones many time in order 
   // for them to be good at what we ask them to do.
   // Here I will train them with the same data (our examples) over and over again, 
   // until they fully understand the rules we are trying to teach them, or until 
   // the training has been repeated 'maxTraining' number of time  
   // (in this case maxTraining = 500)
   // The better they understand the rule, the lower their mean-Square Error will be.
   // the teach() function returns this mean-Square Error (or MSE)
   // 0.1 or lower is a sufficient number for simple rules
   // 0.02 or lower is better for complex rules like the one 
   // we are trying to teach them (it's a patttern recognition. not so easy.)
   for (i=0;i<maxTraining;i++) {
      MSE = teach(); // everytime the loop run, the teach() function is activated. 
                     // Check the comments associated to this function to understand more.
      if (MSE < targetMSE) { // if the MSE is lower than what we defined (here targetMSE = 0.02)
         debug("training finished. Trainings ",i+1); // then we print in the console 
                                                     // how many training 
                                                     // it took them to understand
         i = maxTraining; // and we go out of this loop
      }
   }
   
   // we print to the console the MSE value once the training is completed
   debug("MSE",f2M_get_MSE(ann));
   
   
   Print("##### RUNNING #####");
   // And now we can ask the neurone to analyse new data that they never saw.
   // Will they recognize the patterns correctly?
   // You can see that I used the same prepareData() function here, 
   // with the first argument set to "compute".
   // The last argument which was dedicated to the expected output 
   // when we used this function for registering examples earlier,
   // is now useless, so we leave it to zero.
   // if you prefer, you can call directly the compute() function.
   // In this case, the structure is compute(inputVector[]);
   // So instead of prepareData("compute",1,3,1,0); you would do something like:
   //    double inputVector[]; // declare a new array
   //    ArrayResize(inputVector,f2M_get_num_input(ann)); 
   // resize the array to the number of neuronal input
   //    inputVector[0] = 1; // add in the array the data
   //    inputVector[1] = 3;
   //    inputVector[2] = 1;
   //    result = compute(inputVector); // call the compute() function, with the input array.
   // the prepareData() function call the compute() function, 
   // which print the result to the console, 
   // so we can check if the neurones were right or not.
   debug("1,3,1 = UP DOWN = DOWN. Should output 0.","");
   prepareData("compute",1,3,1,0);
   
   debug("1,2,3 = UP UP = UP. Should output 1.","");
   prepareData("compute",1,2,3,0);
   
   debug("3,2,1 = DOWN DOWN = DOWN. Should output 0.","");
   prepareData("compute",3,2,1,0);
   
   debug("45,2,89 = DOWN UP = UP. Should output 1.","");
   prepareData("compute",45,2,89,0);
   
   debug("1,3,23 = UP UP = UP. Should output 1.","");
   prepareData("compute",1,3,23,0);
   
   debug("7,5,6 = DOWN UP = UP. Should output 1.","");
   prepareData("compute",7,5,6,0);
   
   debug("2,8,9 = UP UP = UP. Should output 1.","");
   prepareData("compute",2,8,9,0);
   
   Print("=================================== END EXECUTION ================================");
   return(0);
}

int start() {
   return(0);
}

/*************************
** printDataArray()
** Print the datas used for training the neurones
** This is useless. Just created for debug purpose.
*************************/
void printDataArray() {
   int i,j;
   int bufferSize = ArraySize(trainingData)/(f2M_get_num_input(ann)+f2M_get_num_output(ann))-1;
   string lineBuffer = "";
   for (i=0;i<bufferSize;i++) {
      for (j=0;j<(f2M_get_num_input(ann)+f2M_get_num_output(ann));j++) {
         lineBuffer = StringConcatenate(lineBuffer, trainingData[i][j], ",");
      }
      debug("DataArray["+i+"]", lineBuffer);
      lineBuffer = "";
   }
}


/*************************
** prepareData()
** Prepare the data for either training or computing.
** It takes the data, put them in an array, 
** and send them to the training or running function
** Update according to the number of input/output your code needs.
*************************/
void prepareData(string action, double a, double b, double c, double output) {
   double inputVector[];
   double outputVector[];
   // we resize the arrays to the right size
   ArrayResize(inputVector,f2M_get_num_input(ann));
   ArrayResize(outputVector,f2M_get_num_output(ann));
   
   inputVector[0] = a;
   inputVector[1] = b;
   inputVector[2] = c;
   outputVector[0] = output;
   if (action == "train") {
      addTrainingData(inputVector,outputVector);
   }
   if (action == "compute") {
      compute(inputVector);
   }
   // if you have more input than 3, just change the structure of this function.
}


/*************************
** addTrainingData()
** Add a single set of training data 
**(data example + expected output) to the global training set
*************************/
void addTrainingData(double inputArray[], double outputArray[]) {
   int j;
   int bufferSize = ArraySize(trainingData)/(f2M_get_num_input(ann)+f2M_get_num_output(ann))-1;
   
   //register the input data to the main array
   for (j=0;j<f2M_get_num_input(ann);j++) {
      trainingData[bufferSize][j] = inputArray[j];
   }
   for (j=0;j<f2M_get_num_output(ann);j++) {
      trainingData[bufferSize][f2M_get_num_input(ann)+j] = outputArray[j];
   }
   
   ArrayResize(trainingData,bufferSize+2);
}


/*************************
** teach()
** Get all the trainign data and use them to train the neurones one time.
** In order to properly train the neurones, you need to run ,
** this function many time until the Mean-Square Error get low enough.
*************************/
double teach() {
   int i,j;
   double MSE;
   double inputVector[];
   double outputVector[];
   ArrayResize(inputVector,f2M_get_num_input(ann));
   ArrayResize(outputVector,f2M_get_num_output(ann));
   int call;
   int bufferSize = ArraySize(trainingData)/(f2M_get_num_input(ann)+f2M_get_num_output(ann))-1;
   for (i=0;i<bufferSize;i++) {
      for (j=0;j<f2M_get_num_input(ann);j++) {
         inputVector[j] = trainingData[i][j];
      }
      outputVector[0] = trainingData[i][3];
      //f2M_train() is showing the neurones only one example at a time.
      call = f2M_train(ann, inputVector, outputVector);
   }
   // Once we have show them an example, 
   // we check if how good they are by checking their MSE. 
   // If it's low, they learn good!
   MSE = f2M_get_MSE(ann);
   return(MSE);
}


/*************************
** compute()
** Compute a set of data and returns the computed result
*************************/
double compute(double inputVector[]) {
   int j;
   int out;
   double output;
   ArrayResize(inputVector,f2M_get_num_input(ann));
   
   // We sent new data to the neurones.
   out = f2M_run(ann, inputVector);
   // and check what they say about it using f2M_get_output().
   output = f2M_get_output(ann, 0);
   debug("Computing()",MathRound(output));
   return(output);
}


/*************************
** debug()
** Print data to the console
*************************/
void debug(string a, string b) {
   Print(a+" ==> "+b);
}

输出结果

神经网络在控制台的输出结果。

总结

你也可以阅读 Mariusz Woloszyn 写的“在 MetaTrader 中使用神经网络”一文，他是 Fann2MQL 库的开发者。

通过分析这里和 google 上为数不多的可用文档，我花了 4 天时间才理解了如何在 MetaTrader 中使用 Fann。

希望本例能够对你有用，避免浪费过多的试验时间。 后续几周将会跟进更多的文章。

如果你有问题就直接问，我会进行回答。