forked from kelmut/NeuroBook
181 lines
16 KiB
MQL5
181 lines
16 KiB
MQL5
//+------------------------------------------------------------------+
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//| Create_Initial_Data.mq5 |
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//| Copyright 2021, MetaQuotes Ltd. |
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//| https://www.mql5.com/en/users/dng |
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//+------------------------------------------------------------------+
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//| Script creates training and testing datasets |
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//+------------------------------------------------------------------+
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#property copyright "Copyright 2021, MetaQuotes Ltd."
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#property link "https://www.mql5.com/en/users/dng"
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#property version "1.00"
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#property script_show_inputs
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//+------------------------------------------------------------------+
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//| External parameters for script operation |
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//+------------------------------------------------------------------+
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input datetime Start = D'2023.01.01 00:00:00'; // Start of the population period
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input datetime End = D'2024.12.31 23:59:00'; // End of the population period
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input ENUM_TIMEFRAMES TimeFrame = PERIOD_M5; // Timeframe for loading data
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input int BarsToLine = 40; // Number of historical bars in one pattern
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input string StudyFileName = "study_data.csv";// File name to write the training dataset
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input string TestFileName = "test_data.csv"; // File name to write the testing dataset
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input bool NormalizeData = true; // Data normalization flag
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//+------------------------------------------------------------------+
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//| Script program start |
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//+------------------------------------------------------------------+
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void OnStart(void)
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{
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//--- Connect indicators to the chart
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int h_ZZ = iCustom(_Symbol, TimeFrame, "Examples\\ZigZag.ex5", 48, 1, 47);
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int h_RSI = iRSI(_Symbol, TimeFrame, 12, PRICE_TYPICAL);
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int h_MACD = iMACD(_Symbol, TimeFrame, 12, 48, 12, PRICE_TYPICAL);
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double close[];
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if(CopyClose(_Symbol, TimeFrame, Start, End, close) <= 0)
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return;
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//--- Load indicator data into dynamic arrays
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double zz[], macd_main[], macd_signal[], rsi[];
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datetime end_zz = End + PeriodSeconds(TimeFrame) * 500;
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if(h_ZZ == INVALID_HANDLE || CopyBuffer(h_ZZ, 0, Start, end_zz, zz) <= 0)
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{
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PrintFormat("Error loading indicator %s data", "ZigZag");
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return;
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}
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if(h_RSI == INVALID_HANDLE || CopyBuffer(h_RSI, 0, Start, End, rsi) <= 0)
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{
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PrintFormat("Error loading indicator %s data", "RSI");
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return;
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}
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if(h_MACD == INVALID_HANDLE || CopyBuffer(h_MACD, MAIN_LINE, Start, End, macd_main) <= 0 ||
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CopyBuffer(h_MACD, SIGNAL_LINE, Start, End, macd_signal) <= 0)
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{
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PrintFormat("Error loading indicator %s data", "MACD");
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return;
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}
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int total = ArraySize(close);
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double target1[], target2[], macd_delta[], test[];
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if(ArrayResize(target1, total) <= 0 || ArrayResize(target2, total) <= 0 ||
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ArrayResize(test, total) <= 0 || ArrayResize(macd_delta, total) <= 0)
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return;
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//--- Calculate targets: direction and distance to the nearest extremum
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ArrayInitialize(test, 0);
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double extremum = -1;
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for(int i = ArraySize(zz) - 2; i >= 0; i--)
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{
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if(zz[i + 1] > 0 && zz[i + 1] != EMPTY_VALUE)
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extremum = zz[i + 1];
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if(i >= total)
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continue;
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target2[i] = extremum - close[i];
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target1[i] = (target2[i] >= 0 ? 1 : -1);
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macd_delta[i] = macd_main[i] - macd_signal[i];
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}
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//--- Data normalization
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if(NormalizeData)
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{
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double main_norm = MathMax(MathAbs(macd_main[ArrayMinimum(macd_main)]),
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macd_main[ArrayMaximum(macd_main)]);
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double sign_norm = MathMax(MathAbs(macd_signal[ArrayMinimum(macd_signal)]),
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macd_signal[ArrayMaximum(macd_signal)]);
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double delt_norm = MathMax(MathAbs(macd_delta[ArrayMinimum(macd_delta)]),
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macd_delta[ArrayMaximum(macd_delta)]);
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for(int i = 0; i < total; i++)
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{
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rsi[i] = (rsi[i] - 50.0) / 50.0;
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macd_main[i] /= main_norm;
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macd_signal[i] /= sign_norm;
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macd_delta[i] /= delt_norm;
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}
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}
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//--- Randomly generate data indexes for the test dataset
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int for_test = (int)((total - BarsToLine) * 0.2);
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for(int i = 0; i < for_test; i++)
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{
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int t = (int)((double)(MathRand() * MathRand()) / MathPow(32767.0, 2) * (total - 1 - BarsToLine)) + BarsToLine;
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if(test[t] == 1)
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{
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i--;
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continue;
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}
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test[t] = 1;
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}
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//--- Open the training dataset file for writing
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int Study = FileOpen(StudyFileName, FILE_WRITE | FILE_CSV | FILE_ANSI, ",", CP_UTF8);
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if(Study == INVALID_HANDLE)
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{
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PrintFormat("Error opening file %s: %d", StudyFileName, GetLastError());
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return;
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}
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//--- Open the testing dataset file for writing
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int Test = FileOpen(TestFileName, FILE_WRITE | FILE_CSV | FILE_ANSI, ",", CP_UTF8);
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if(Test == INVALID_HANDLE)
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{
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PrintFormat("Error opening file %s: %d", TestFileName, GetLastError());
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return;
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}
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//--- Write datasets to files
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for(int i = BarsToLine - 1; i < total; i++)
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{
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Comment(StringFormat("%.2f%%", i * 100.0 / (double)(total - BarsToLine)));
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if(!WriteData(target1, target2, rsi, macd_main, macd_signal, macd_delta, i, BarsToLine, (test[i] == 1 ? Test : Study)))
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{
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PrintFormat("Error to write data: %d", GetLastError());
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break;
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}
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}
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//--- Close files
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Comment("");
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FileFlush(Study);
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FileClose(Study);
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FileFlush(Test);
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FileClose(Test);
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PrintFormat("Study data saved to file %s\\MQL5\\Files\\%s",
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TerminalInfoString(TERMINAL_DATA_PATH), StudyFileName);
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PrintFormat("Test data saved to file %s\\MQL5\\Files\\%s",
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TerminalInfoString(TERMINAL_DATA_PATH), TestFileName);
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}
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//+------------------------------------------------------------------+
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//| Function for writing pattern to file |
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//+------------------------------------------------------------------+
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bool WriteData(double &target1[], // Buffer 1 of target values
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double &target2[], // Buffer 2 of target values
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double &data1[], // Buffer 1 of historical data
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double &data2[], // Buffer 2 of historical data
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double &data3[], // Buffer 3 of historical data
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double &data4[], // Buffer 4 of historical data
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int cur_bar, // Current pattern end bar
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int bars, // Number of historical bars in one pattern
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int handle) // File handle for writing
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{
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//--- Validate file handle
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if(handle == INVALID_HANDLE)
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{
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Print("Invalid Handle");
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return false;
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}
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//--- Determine the index of the first record of pattern historical data
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int start = cur_bar - bars + 1;
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if(start < 0)
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{
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Print("Too small current bar");
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return false;
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}
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//--- Check the correctness of the data index and write it to a file
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int size1 = ArraySize(data1);
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int size2 = ArraySize(data2);
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int size3 = ArraySize(data3);
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int size4 = ArraySize(data4);
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int sizet1 = ArraySize(target1);
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int sizet2 = ArraySize(target2);
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string pattern = (string)(start < size1 ? data1[start] : 0.0) + "," +
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(string)(start < size2 ? data2[start] : 0.0) + "," +
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(string)(start < size3 ? data3[start] : 0.0) + "," +
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(string)(start < size4 ? data4[start] : 0.0);
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for(int i = start + 1; i <= cur_bar; i++)
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{
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pattern = pattern + "," + (string)(i < size1 ? data1[i] : 0.0) + "," +
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(string)(i < size2 ? data2[i] : 0.0) + "," +
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(string)(i < size3 ? data3[i] : 0.0) + "," +
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(string)(i < size4 ? data4[i] : 0.0);
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}
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return (FileWrite(handle, pattern, (double)(cur_bar < sizet1 ? target1[cur_bar] : 0), (double)(cur_bar < sizet2 ? target2[cur_bar] : 0)) > 0);
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}
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//+------------------------------------------------------------------+
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