Welcome to our latest exploration of the Support Vector Machine Learning Tool for MetaTrader 5! This handy script is designed to complement our article, "Machine Learning: How Support Vector Machines can be used in Trading", which you can find on the MQL5 website.
Note: This version of the code is tailored for the demo version of the Support Vector Machine Learning Tool, which is available for free from the MQL5 Market.
Picture this: you're a researcher on a quest to identify a rare creature found only in the Arctic, known as the Schnick. With only about 5,000 of these elusive animals recorded, you're faced with the challenge of recognizing one based solely on a handful of research papers that describe their characteristics—like height, weight, and so on. But here's the kicker: these descriptions vary widely, leaving you with no clear pattern to follow.
So, how do we sort through this data to identify a Schnick?
One potential solution is to utilize a Support Vector Machine (SVM) to uncover patterns within the data and establish a framework for classifying animals as either Schnicks or not. The first step involves creating a dataset to train your SVM, which consists of inputs and corresponding outputs for analysis and pattern extraction.
This script showcases the power of Support Vector Machines in tackling classification problems by leveraging the Support Vector Machine Learning Tool available on the MQL5 Market. For a deep dive into this hypothetical scenario and the script itself, check out our article "Machine Learning: How Support Vector Machines can be used in Trading", where we guide you through the script's usage and how machine learning can shed light on market trends.
Here's the 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 criteria 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 predicted 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); } //+------------------------------------------------------------------+