ONNX.Price.Prediction/ONNX.Price.Prediction.Test2.mq5

<EFBFBD><EFBFBD>//+------------------------------------------------------------------+

//|                                   ONNX.Price.Prediction.Test.mq5 |

//|                                  Copyright 2023, MetaQuotes Ltd. |

//|                                             https://www.mql5.com |

//+------------------------------------------------------------------+

#property copyright   "Copyright 2023, MetaQuotes Ltd."

#property link        "https://www.mql5.com"

#property version     "1.00"

#property description "Evaluation of the quality of the next Close price prediction by Python\\model.onnx.\n"

"Start in strategy tester on EURUSD,H1, open prices, from 2023.01.01 to 2023.02.01"



#resource "Python/model2.onnx" as uchar ExtModel[]



#define SAMPLE_SIZE 10  // bars count in sample as defined in Python\PricePredictionTraining.py

// X, y = collect_dataset(df, history_size=10)



long     ExtHandle=INVALID_HANDLE;

double   ExtPredicted=0;

datetime ExtNextBar=0;

long     ExtTests=0;

long     ExtRightDirection=0;

double   ExtSumAbsoluteError=0.0;

//+------------------------------------------------------------------+

//| Expert initialization function                                   |

//+------------------------------------------------------------------+

int OnInit()

  {

   if(Symbol()!="EURUSD" || Period()!=PERIOD_H1)

     {

      Print("model must work with EURUSD,H1");

      return(INIT_FAILED);

     }



//--- create a model from static buffer

   ExtHandle=OnnxCreateFromBuffer(ExtModel,ONNX_DEFAULT);

   if(ExtHandle==INVALID_HANDLE)

     {

      Print("OnnxCreateFromBuffer error ",GetLastError());

      return(INIT_FAILED);

     }



//--- since not all sizes defined in the input tensor we must set them explicitly

//--- first index - batch size, second index - series size, third index - number of series (OHLC)

   const long input_shape[] = {1,SAMPLE_SIZE,4};

   if(!OnnxSetInputShape(ExtHandle,0,input_shape))

     {

      Print("OnnxSetInputShape error ",GetLastError());

      return(INIT_FAILED);

     }



//--- since not all sizes defined in the output tensor we must set them explicitly

//--- first index - batch size, must match the batch size of the input tensor

//--- second index - number of predicted prices (we only predict Close)

   const long output_shape[] = {1,1};

   if(!OnnxSetOutputShape(ExtHandle,0,output_shape))

     {

      Print("OnnxSetOutputShape error ",GetLastError());

      return(INIT_FAILED);

     }

//---

   return(INIT_SUCCEEDED);

  }

//+------------------------------------------------------------------+

//| Expert deinitialization function                                 |

//+------------------------------------------------------------------+

void OnDeinit(const int reason)

  {

   if(ExtHandle!=INVALID_HANDLE)

     {

      OnnxRelease(ExtHandle);

      ExtHandle=INVALID_HANDLE;

     }

  }

//+------------------------------------------------------------------+

//| Expert tick function                                             |

//+------------------------------------------------------------------+

void OnTick()

  {

//--- check new bar

   if(TimeCurrent()<ExtNextBar)

      return;

   ExtTests++;

//--- set next bar time

   ExtNextBar=TimeCurrent();

   ExtNextBar-=ExtNextBar%PeriodSeconds();

   ExtNextBar+=PeriodSeconds();

//--- check predicted price

   CheckPredicted();

//--- predict next price