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

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

//|                                        ONNX.Price.Prediction.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"

#resource "Python/model.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)



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

//| Script for demonstration of identical execution of               |

//| Python\PricePrediction.py and                                    |

//| ONNX.Price.Prediction.mq5                                        |

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

int OnStart(void)

  {

//--- create a model from static buffer

//--- model.onnx file from resources is represented as const uchar model[89591]

   long handle=OnnxCreateFromBuffer(ExtModel,ONNX_DEFAULT);

   if(handle==INVALID_HANDLE)

     {

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

      return(-1);

     }



//--- 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(handle,0,input_shape))

     {

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

      OnnxRelease(handle);

      return(-1);

     }



//--- 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(handle,0,output_shape))

     {

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

      OnnxRelease(handle);

      return(-1);

     }



//--- prepare input data

   matrix  rates;                         // matrix to get test sample OHLC