Schnick [Support Vector Machine Learning Tool Tester - DEMO] - expert pour MetaTrader 5

This script was written to form part of the "Machine Learning: How Support Vector Machines can be used in Trading" article posted on the MQL5 website.

Imagine this hypothetical scenario, you are a researcher investigating a rare animal only found in the depths of the Arctic call Shnicks. Given the remoteness of these animals, only a small handful have ever been found (lets say around 5000). As a researcher, you are stuck with the question... how can I identify a Schnick?

All you have at your disposal are the research papers previously published by the handful of researchers that have seen one. In these research papers, the authors describe certain characteristics about the Schnicks they found, i.e. height, weight, number of legs etc. but all of these characteristics vary between the research papers with no discernible pattern….

How can we use this data to identify a new animal as a schnick?

One possible solution to our problem is to use a support vector machine to identify the patterns in the data and create a framework that can be used to classify animals as either a schnick or not a schnick. The first step is to create a set of data that can be used to train your support vector machine to identify schnicks. The training data is a set of inputs and matching outputs for the support vector machine to analyze and extract a pattern from.

This script attempts to demonstrate the power of using support vector machines in solving classification style problems by using the Support Vector Machine Learning Tool available in the MQL5 Market. A full description of this hypothetical problem and script can be found in the article "Machine Learning: How Support Vector Machines can be used in Trading". The article includes a walkthrough of how to use the script and how this problem can give insights into using machine learning for assessing market trends.

Code:

//+------------------------------------------------------------------+
//|                                                 Schnick_Demo.mq5 |
//|                        Copyright 2011, MetaQuotes Software Corp. |
//|                                              http://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2011, MetaQuotes Software Corp."
#property link      "http://www.mql5.com"
#property version   "1.00"

//+------------------------------------------------------------------+
//| This script demonstrates the capabilities of the Support Vector
//|                     Machine Learning Tool
//+------------------------------------------------------------------+

//+------------------------------------------------------------------+
//| The following statement imports all of the functions included in
//| the Support Vector Machine Tool 'svMachineTool.ex5'
//+------------------------------------------------------------------+
#import "svMachineTool_demo.ex5"
enum ENUM_TRADE {BUY,SELL};
enum ENUM_OPTION {OP_MEMORY,OP_MAXCYCLES,OP_TOLERANCE};
int  initSVMachine(void);
void setIndicatorHandles(int handle,int &indicatorHandles[],int offset,int N);
void setParameter(int handle,ENUM_OPTION option,double value);
bool genOutputs(int handle,ENUM_TRADE trade,int StopLoss,int TakeProfit,double duration);
bool genInputs(int handle);
bool setInputs(int handle,double &Inputs[],int nInputs);
bool setOutputs(int handle,bool &Outputs[]);
bool training(int handle);
bool classify(int handle);
bool classify(int handle,int offset);
bool classify(int handle,double &iput[]);
void deinitSVMachine(void);
#import
//--- The number of inputs we will be using for the svm
int N_Inputs=7;
//+------------------------------------------------------------------+
//| Expert initialization function                                   |
//+------------------------------------------------------------------+
int OnInit()
  {
   double inputs[];           //empty double array to be used for creating training inputs
   bool   outputs[];          //empty bool array to be used for creating training inputs
   int N_TrainingPoints=5000; //defines the number of training samples to be generated
   int N_TestPoints=5000;     //defines the number of samples to used when testing

   genTrainingData(inputs,outputs,N_TrainingPoints); //Generates the inputs and outputs to be used for training the svm

   int handle1=initSVMachine();             //initializes a new support vector machine and returns a handle
   setInputs(handle1,inputs,7);             //passes the inputs (without errors) to the support vector machine
   setOutputs(handle1,outputs);             //passes the outputs (without errors) to the support vector machine
   setParameter(handle1,OP_TOLERANCE,0.01); //sets the error tolerance parameter to <5%
   training(handle1);                       //trains the support vector machine using the inputs/outputs passed

   insertRandomErrors(inputs,outputs,500);  //takes the original inputs/outputs generated and adds random errors to the data

   int handle2=initSVMachine();             //initializes a new support vector machine and returns a handle
   setInputs(handle2,inputs,7);             //passes the inputs (with errors) to the support vector machine
   setOutputs(handle2,outputs);             //passes the outputs (with errors) to the support vector machine
   setParameter(handle2,OP_TOLERANCE,0.01); //sets the error tolerance parameter to <5%
   training(handle2);                       //trains the support vector machine using the inputs/outputs passed

   double t1=testSVM(handle1,N_TestPoints); //tests the accuracy of the trained support vector machine and saves it to t1
   double t2=testSVM(handle2,N_TestPoints); //tests the accuracy of the trained support vector machine and saves it to t2

   Print("The SVM accuracy is ",NormalizeDouble(t1,2),"% (using training inputs/outputs without errors)");
   Print("The SVM accuracy is ",NormalizeDouble(t2,2),"% (using training inputs/outputs with errors)");
   deinitSVMachine(); //Cleans up all of the memory used in generating the SVM to avoid memory leakage
   return(0);
  }
//+------------------------------------------------------------------+
//| Expert deinitialization function                                 |
//+------------------------------------------------------------------+
void OnDeinit(const int reason)
  {
//--- No functions executed in OnDeinit()
  }
//+------------------------------------------------------------------+
//| Expert tick function                                             |
//+------------------------------------------------------------------+
void OnTick()
  {
//--- No functions executed in OnTick()   
  }
//+------------------------------------------------------------------+
//| This function takes the observation properties of the observed 
//| animal and based on the critera we have chosen, returns
//| true/false whether it is a schnick
//+------------------------------------------------------------------+
bool isItASchnick(double height,double weight,double N_legs,double N_eyes,double L_arm,double av_speed,double f_call)
  {
   if(height   < 1000  || height   > 1100)  return(false); //If the height is outside the parameters > return(false)
   if(weight   < 40    || weight   > 50)    return(false); //If the weight is outside the parameters > return(false)
   if(N_legs   < 8     || N_legs   > 10)    return(false); //If the N_Legs is outside the parameters > return(false)
   if(N_eyes   < 3     || N_eyes   > 4)     return(false); //If the N_eyes is outside the parameters > return(false)
   if(L_arm    < 400   || L_arm    > 450)   return(false); //If the L_arm  is outside the parameters > return(false)
   if(av_speed < 2     || av_speed > 2.5)   return(false); //If the av_speed is outside the parameters > return(false)
   if(f_call   < 11000 || f_call   > 15000) return(false); //If the f_call is outside the parameters > return(false)
   return(true);                                           //Otherwise > return(true)
  }
//+------------------------------------------------------------------+
//| This function takes an empty double array and empty boolean array
//| and generates the inputs/outputs to be used for training the SVM
//+------------------------------------------------------------------+ 
void genTrainingData(double &inputs[],bool &outputs[],int N)
  {
   double in[];                    //creates an empty double array to be used
                                   //for temporarily storing the inputs generated
   ArrayResize(in,N_Inputs);       //resize the in[] array to N_Inputs
   ArrayResize(inputs,N*N_Inputs); //resize the inputs[] array to have a size of N*N_Inputs 
   ArrayResize(outputs,N);         //resize the outputs[] array to have a size of N 
   for(int i=0;i<N;i++)
     {
      in[0]=    randBetween(980,1120);    //Random input generated for height
      in[1]=    randBetween(38,52);       //Random input generated for weight
      in[2]=    randBetween(7,11);        //Random input generated for N_legs
      in[3]=    randBetween(3,4.2);       //Random input generated for N_eyes
      in[4]=    randBetween(380,450);     //Random input generated for L_arms
      in[5]=    randBetween(2,2.6);       //Random input generated for av_speed
      in[6]=    randBetween(10500,15500); //Random input generated for f_call

      //--- copy the new random inputs generated into the training input array
      ArrayCopy(inputs,in,i*N_Inputs,0,N_Inputs);
      //--- assess the random inputs and determine if it is a schnick
      outputs[i]=isItASchnick(in[0],in[1],in[2],in[3],in[4],in[5],in[6]);
     }
  }
//+------------------------------------------------------------------+
//| This function takes the handle for the trained SVM and tests how
//| successful it is at classifying new random inputs
//+------------------------------------------------------------------+ 
double testSVM(int handle,int N)
  {
   double in[];
   int atrue=0;
   int afalse=0;
   int N_correct=0;
   bool Predicted_Output;
   bool Actual_Output;
   ArrayResize(in,N_Inputs);
   for(int i=0;i<N;i++)
     {
      in[0]=    randBetween(980,1120);    //Random input generated for height
      in[1]=    randBetween(38,52);       //Random input generated for weight
      in[2]=    randBetween(7,11);        //Random input generated for N_legs
      in[3]=    randBetween(3,4.2);       //Random input generated for N_eyes
      in[4]=    randBetween(380,450);     //Random input generated for L_arms
      in[5]=    randBetween(2,2.6);       //Random input generated for av_speed
      in[6]=    randBetween(10500,15500); //Random input generated for f_call

      //--- uses the isItASchnick fcn to determine the actual desired output
      Actual_Output=isItASchnick(in[0],in[1],in[2],in[3],in[4],in[5],in[6]);
      //--- uses the trained SVM to return the prediced output.
      Predicted_Output=classify(handle,in);
      if(Actual_Output==Predicted_Output)
        {
         N_correct++; //This statement keeps count of the number of times the predicted output is correct.
        }
     }
//--- returns the accuracy of the trained SVM as a percentage
   return(100*((double)N_correct/(double)N));
  }
//+------------------------------------------------------------------+
//| This function takes the correct training inputs and outputs 
//| generated and inserts N random errors into the data
//+------------------------------------------------------------------+ 
void insertRandomErrors(double &inputs[],bool &outputs[],int N)
  {
   int nTrainingPoints=ArraySize(outputs); //calculates the number of training points
   int index;                              //creates new integer 'index'
   bool randomOutput;                      //creates new bool 'randomOutput'
   double in[];                            //creates an empty double array to be used
                                           //for temporarily storing the inputs generated
   ArrayResize(in,N_Inputs);               //resize the in[] array to N_Inputs
   for(int i=0;i<N;i++)
     {
      in[0]=    randBetween(980,1120);    //Random input generated for height
      in[1]=    randBetween(38,52);       //Random input generated for weight
      in[2]=    randBetween(7,11);        //Random input generated for N_legs
      in[3]=    randBetween(3,4.2);       //Random input generated for N_eyes
      in[4]=    randBetween(380,450);     //Random input generated for L_arms
      in[5]=    randBetween(2,2.6);       //Random input generated for av_speed
      in[6]=    randBetween(10500,15500); //Random input generated for f_call

      //--- randomly chooses one of the training inputs to insert an error
      index=(int)MathRound(randBetween(0,nTrainingPoints-1));
      //--- generates a random boolean output to be used to create error
      if(randBetween(0,1)>0.5) randomOutput=true;
      else                     randomOutput=false;

      //--- copy the new random inputs generated into the training input array
      ArrayCopy(inputs,in,index*N_Inputs,0,N_Inputs);
      //--- copy the new random output generated into the training output array
      outputs[index]=randomOutput;
     }
  }
//+------------------------------------------------------------------+
//| This function is used to create a random value between t1 and t2
//+------------------------------------------------------------------+ 
double randBetween(double t1,double t2)
  {
   return((t2-t1)*((double)MathRand()/(double)32767)+t1);
  }
//+------------------------------------------------------------------+