NeuroBook/Scripts/initial_data/create_initial_data.mq5

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

//|                                          Create_Initial_Data.mq5 |

//|                                  Copyright 2021, MetaQuotes Ltd. |

//|                                https://www.mql5.com/en/users/dng |

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

//| Script creates training and testing datasets                     |

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

#property copyright "Copyright 2021, MetaQuotes Ltd."

#property link      "https://www.mql5.com/en/users/dng"

#property version   "1.00"

#property script_show_inputs

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

//| External parameters for script operation                         |

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

input datetime Start = D'2023.01.01 00:00:00';  // Start of the population period

input datetime End = D'2024.12.31 23:59:00';    // End of the population period

input ENUM_TIMEFRAMES TimeFrame = PERIOD_M5;    // Timeframe for loading data

input int      BarsToLine = 40;                 // Number of historical bars in one pattern

input string   StudyFileName = "study_data.csv";// File name to write the training dataset

input string   TestFileName  = "test_data.csv"; // File name to write the testing dataset

input bool     NormalizeData = true;            // Data normalization flag

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

//| Script program start                                             |

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

void OnStart(void)

  {

//--- Connect indicators to the chart

   int h_ZZ = iCustom(_Symbol, TimeFrame, "Examples\\ZigZag.ex5", 48, 1, 47);

   int h_RSI = iRSI(_Symbol, TimeFrame, 12, PRICE_TYPICAL);

   int h_MACD = iMACD(_Symbol, TimeFrame, 12, 48, 12, PRICE_TYPICAL);

   double close[];

   if(CopyClose(_Symbol, TimeFrame, Start, End, close) <= 0)

      return;

//--- Load indicator data into dynamic arrays

   double zz[], macd_main[], macd_signal[], rsi[];

   datetime end_zz = End + PeriodSeconds(TimeFrame) * 500;

   if(h_ZZ == INVALID_HANDLE || CopyBuffer(h_ZZ, 0, Start, end_zz, zz) <= 0)

     {

      PrintFormat("Error loading indicator %s data", "ZigZag");

      return;

     }

   if(h_RSI == INVALID_HANDLE || CopyBuffer(h_RSI, 0, Start, End, rsi) <= 0)

     {

      PrintFormat("Error loading indicator %s data", "RSI");

      return;

     }

   if(h_MACD == INVALID_HANDLE || CopyBuffer(h_MACD, MAIN_LINE, Start, End, macd_main) <= 0 ||

      CopyBuffer(h_MACD, SIGNAL_LINE, Start, End, macd_signal) <= 0)

     {

      PrintFormat("Error loading indicator %s data", "MACD");

      return;

     }

   int total = ArraySize(close);

   double target1[], target2[], macd_delta[], test[];

   if(ArrayResize(target1, total) <= 0 || ArrayResize(target2, total) <= 0 ||

      ArrayResize(test, total) <= 0 || ArrayResize(macd_delta, total) <= 0)

      return;

//--- Calculate targets: direction and distance to the nearest extremum

   ArrayInitialize(test, 0);

   double extremum = -1;

   for(int i = ArraySize(zz) - 2; i >= 0; i--)

     {

      if(zz[i + 1] > 0 && zz[i + 1] != EMPTY_VALUE)

         extremum = zz[i + 1];

      if(i >= total)

         continue;

      target2[i] = extremum - close[i];

      target1[i] = (target2[i] >= 0 ? 1 : -1);

      macd_delta[i] = macd_main[i] - macd_signal[i];

     }

//--- Data normalization

   if(NormalizeData)

     {

      double main_norm = MathMax(MathAbs(macd_main[ArrayMinimum(macd_main)]),

                                 macd_main[ArrayMaximum(macd_main)]);

      double sign_norm = MathMax(MathAbs(macd_signal[ArrayMinimum(macd_signal)]),

                                 macd_signal[ArrayMaximum(macd_signal)]);

      double delt_norm = MathMax(MathAbs(macd_delta[ArrayMinimum(macd_delta)]),

                                 macd_delta[ArrayMaximum(macd_delta)]);

      for(int i = 0; i < total; i++)

        {

         rsi[i] = (rsi[i] - 50.0) / 50.0;

         macd_main[i] /= main_norm;

         macd_signal[i] /= sign_norm;

         macd_delta[i] /= delt_norm;

        }

     }

//--- Randomly generate data indexes for the test dataset

   int for_test = (int)((total - BarsToLine) * 0.2);

   for(int i = 0; i < for_test; i++)

     {