| //+------------------------------------------------------------------+
|
| //| Study.mq5 |
|
| //| Copyright DNG® |
|
| //| https://www.mql5.com/ru/users/dng |
|
| //+------------------------------------------------------------------+
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| #property copyright "Copyright DNG®"
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| #property link "https://www.mql5.com/ru/users/dng"
|
| #property version "1.00"
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| #define Study
|
| #include "Trajectory.mqh"
|
| //+------------------------------------------------------------------+
|
| //| Input parameters |
|
| //+------------------------------------------------------------------+
|
| input int Iterations = 100000;
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| //+------------------------------------------------------------------+
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| //| |
|
| //+------------------------------------------------------------------+
|
| STrajectory Buffer[];
|
| CNet Encoder;
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| //---
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| float dError;
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| datetime dtStudied;
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| //---
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| CBufferFloat bState;
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| CBufferFloat *Result;
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| vector<float> check;
|
| //+------------------------------------------------------------------+
|
| //| Expert initialization function |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| //---
|
| ResetLastError();
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| if(!LoadTotalBase())
|
| {
|
| PrintFormat("Error of load study data: %d", GetLastError());
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| return INIT_FAILED;
|
| }
|
| //--- load models
|
| float temp;
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| if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true))
|
| {
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| Print("Create new model");
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| CArrayObj *encoder = new CArrayObj();
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| if(!CreateEncoderDescriptions(encoder))
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| {
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| delete encoder;
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| return INIT_FAILED;
|
| }
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| if(!Encoder.Create(encoder))
|
| {
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| delete encoder;
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| return INIT_FAILED;
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| }
|
| delete encoder;
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| }
|
| //---
|
| Encoder.getResults(Result);
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| if(Result.Total() != NForecast * BarDescr)
|
| {
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| PrintFormat("The scope of the Encoder does not match the forecast state count (%d <> %d)", NForecast * BarDescr, Result.Total());
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| return INIT_FAILED;
|
| }
|
| //---
|
| Encoder.GetLayerOutput(0, Result);
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| if(Result.Total() != (HistoryBars * BarDescr))
|
| {
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| PrintFormat("Input size of Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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| return INIT_FAILED;
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| }
|
| //---
|
| if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
|
| {
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| PrintFormat("Error of create study event: %d", GetLastError());
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| return INIT_FAILED;
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| }
|
| //---
|
| return(INIT_SUCCEEDED);
|
| }
|
| //+------------------------------------------------------------------+
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| //| Expert deinitialization function |
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| //+------------------------------------------------------------------+
|
| void OnDeinit(const int reason)
|
| {
|
| //---
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| if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
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| Encoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
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| delete Result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| ChartEvent function |
|
| //+------------------------------------------------------------------+
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| void OnChartEvent(const int id,
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| const long &lparam,
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| const double &dparam,
|
| const string &sparam)
|
| {
|
| //---
|
| if(id == 1001)
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| Train();
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| }
|
| //+------------------------------------------------------------------+
|
| //| Train function |
|
| //+------------------------------------------------------------------+
|
| //| | T ra in implementation. |
|
| //+------------------------------------------------------------------+
|
| void Train(void)
|
| {
|
| //---
|
| vector<float> probability = GetProbTrajectories(Buffer, 0.9);
|
| //---
|
| vector<float> result, target, state;
|
| bool Stop = false;
|
| //---
|
| uint ticks = GetTickCount();
|
| //---
|
| for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter ++)
|
| {
|
| int tr = SampleTrajectory(probability);
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| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2 - NForecast));
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| if(i <= 0)
|
| {
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| iter --;
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| continue;
|
| }
|
| state.Assign(Buffer[tr].States[i].state);
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| if(MathAbs(state).Sum() == 0)
|
| {
|
| iter --;
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| continue;
|
| }
|
| bState.AssignArray(state);
|
| //--- State Encoder
|
| if(!Encoder.feedForward((CBufferFloat*)GetPointer(bState), 1, false, (CBufferFloat*)NULL))
|
| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| Stop = true;
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| break;
|
| }
|
| //--- Collect target data
|
| if(!Result.AssignArray(Buffer[tr].States[i + NForecast].state))
|
| continue;
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| if(!Result.Resize(BarDescr * NForecast))
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| continue;
|
| if(!Encoder.backProp(Result, (CBufferFloat*)NULL))
|
| {
|
| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| Stop = true;
|
| break;
|
| }
|
| //---
|
| if(GetTickCount() - ticks > 500)
|
| {
|
| double percent = double(iter) * 100.0 / (Iterations);
|
| string str = StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Encoder", percent, Encoder.getRecentAverageError());
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| Comment(str);
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| ticks = GetTickCount();
|
| }
|
| }
|
| Comment("");
|
| //---
|
| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Encoder", Encoder.getRecentAverageError());
|
| ExpertRemove();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
