432 lines
33 KiB
MQL5
432 lines
33 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/ru/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/ru/users/dng"
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#property version "1.00"
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#define Study
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#include "Trajectory.mqh"
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input int Iterations = 1e4;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet Encoder;
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CNet Decoder;
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CNet Noise;
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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 State;
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CBufferFloat *Result;
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CBufferFloat LastEncoder;
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CBufferFloat Gradient;
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vector<float> STE;
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vector<float> STE_Noise;
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//---
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COpenCLMy *OpenCL;
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//+------------------------------------------------------------------+
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//| Expert initialization function |
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//+------------------------------------------------------------------+
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int OnInit()
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{
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//---
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ResetLastError();
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if(!LoadTotalBase())
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{
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PrintFormat("Error of load study data: %d", GetLastError());
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return INIT_FAILED;
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}
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//--- load models
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float temp;
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if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!Decoder.Load(FileName + "Dec.nnw", temp, temp, temp, dtStudied, true) ||
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!Noise.Load(FileName + "NP.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("Init new models");
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CArrayObj *encoder = new CArrayObj();
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CArrayObj *decoder = new CArrayObj();
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CArrayObj *noise = new CArrayObj();
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if(!CreateTrajNetDescriptions(encoder, decoder, noise))
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{
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delete encoder;
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delete decoder;
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delete noise;
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return INIT_FAILED;
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}
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if(!Encoder.Create(encoder) || !Decoder.Create(decoder) ||
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!Noise.Create(noise))
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{
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delete encoder;
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delete decoder;
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delete noise;
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return INIT_FAILED;
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}
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delete encoder;
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delete decoder;
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delete noise;
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//---
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}
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//---
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OpenCL = Encoder.GetOpenCL();
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Decoder.SetOpenCL(OpenCL);
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Noise.SetOpenCL(OpenCL);
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//---
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Encoder.getResults(Result);
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if(Result.Total() != EmbeddingSize)
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{
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PrintFormat("The scope of the Encoder does not match the embedding size count (%d <> %d)", EmbeddingSize, Result.Total());
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return INIT_FAILED;
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}
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//---
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Encoder.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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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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}
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//---
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Decoder.GetLayerOutput(0, Result);
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if(Result.Total() != EmbeddingSize)
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{
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PrintFormat("Input size of Decoder doesn't match Encoder output (%d <> %d)", Result.Total(), EmbeddingSize);
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return INIT_FAILED;
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}
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//---
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Noise.GetLayerOutput(0, Result);
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if(Result.Total() != EmbeddingSize)
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{
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PrintFormat("Input size of Noise Prediction model doesn't match Encoder output (%d <> %d)", Result.Total(), EmbeddingSize);
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return INIT_FAILED;
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}
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//---
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Noise.getResults(Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Output size of Noise Prediction model doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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return INIT_FAILED;
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}
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//---
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if(!LastEncoder.BufferInit(EmbeddingSize, 0) ||
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!Gradient.BufferInit(EmbeddingSize, 0) ||
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!LastEncoder.BufferCreate(OpenCL) ||
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!Gradient.BufferCreate(OpenCL))
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{
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PrintFormat("Error of create buffers: %d", GetLastError());
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return INIT_FAILED;
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}
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//---
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if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
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{
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PrintFormat("Error of create study event: %d", GetLastError());
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return INIT_FAILED;
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}
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//---
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return(INIT_SUCCEEDED);
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}
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//+------------------------------------------------------------------+
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//| Expert deinitialization function |
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//+------------------------------------------------------------------+
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void OnDeinit(const int reason)
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{
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//---
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if(!(reason == REASON_INITFAILED || reason == REASON_RECOMPILE))
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{
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Encoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
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Decoder.Save(FileName + "Dec.nnw", Decoder.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Noise.Save(FileName + "NP.nnw", Noise.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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}
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delete Result;
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delete OpenCL;
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}
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//+------------------------------------------------------------------+
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//| ChartEvent function |
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//+------------------------------------------------------------------+
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void OnChartEvent(const int id,
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const long &lparam,
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const double &dparam,
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const string &sparam)
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{
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//---
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if(id == 1001)
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Train();
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}
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//+------------------------------------------------------------------+
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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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//---
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vector<float> probability = GetProbTrajectories(Buffer, 0.9);
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//---
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vector<float> result, target, inp;
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matrix<float> targets;
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matrix<float> delta;
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STE = vector<float>::Zeros((HistoryBars + PrecoderBars) * 3);
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STE_Noise = vector<float>::Zeros(HistoryBars * BarDescr);
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int std_count = 0;
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int batch = GPTBars + 50;
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bool Stop = false;
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uint ticks = GetTickCount();
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//---
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for(int iter = 0; (iter < Iterations && !IsStopped() && !Stop); iter ++)
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{
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int tr = SampleTrajectory(probability);
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int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
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if(state < 0)
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{
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iter--;
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continue;
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}
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Encoder.Clear();
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Decoder.Clear();
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Noise.Clear();
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LastEncoder.BufferInit(EmbeddingSize, 0);
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int end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
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delta = matrix<float>::Zeros(end - state - 1, Buffer[tr].States[state].state.Size());
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for(int i = state; i < end; i++)
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{
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inp.Assign(Buffer[tr].States[i].state);
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State.AssignArray(inp);
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int row = i - state;
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if(i < (end - 1))
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delta.Row(inp, row);
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if(row > 0)
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delta.Row(delta.Row(row - 1) - inp, row - 1);
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//---
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if(!LastEncoder.BufferWrite() || !Encoder.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(LastEncoder)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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if(!Decoder.feedForward(GetPointer(Encoder), -1, (CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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if(!Noise.feedForward(GetPointer(Encoder), -1, (CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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target.Assign(Buffer[tr].States[i].state);
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ulong size = target.Size();
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targets = matrix<float>::Zeros(1, size);
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targets.Row(target, 0);
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if(size > BarDescr)
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targets.Reshape(size / BarDescr, BarDescr);
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ulong shift = targets.Rows();
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targets.Resize(shift + PrecoderBars, 3);
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for(int t = 0; t < PrecoderBars; t++)
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{
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target.Assign(Buffer[tr].States[i + t].state);
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if(size > BarDescr)
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{
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matrix<float> temp(1, size);
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temp.Row(target, 0);
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temp.Reshape(size / BarDescr, BarDescr);
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temp.Resize(size / BarDescr, 3);
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target = temp.Row(temp.Rows() - 1);
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}
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targets.Row(target, shift + t);
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}
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targets.Reshape(1, targets.Rows()*targets.Cols());
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target = targets.Row(0);
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Decoder.getResults(result);
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vector<float> error = target - result;
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std_count = MathMin(std_count, 999);
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STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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vector<float> check = MathAbs(error) - STE * STE_Multiplier;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(CAGrad(error) + result);
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if(!Decoder.backProp(Result, (CNet *)NULL) ||
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!Encoder.backPropGradient(GetPointer(LastEncoder), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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target = vector<float>::Zeros(delta.Cols());
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Noise.getResults(result);
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error = (target - result) * STE_Noise_Multiplier;
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STE_Noise = MathSqrt((MathPow(STE_Noise, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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std_count++;
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check = MathAbs(error) - STE_Noise;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(CAGrad(error) + result);
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if(!Noise.backProp(Result, (CNet *)NULL) ||
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!Encoder.backPropGradient(GetPointer(LastEncoder), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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Encoder.getResults(result);
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LastEncoder.AssignArray(result);
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//---
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if(GetTickCount() - ticks > 500)
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{
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double percent = (double(i - state) / (2 * (end - state)) + iter) * 100.0 / (Iterations);
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string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Decoder", percent, Decoder.getRecentAverageError());
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str += StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Noise Prediction", percent, Noise.getRecentAverageError());
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Comment(str);
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ticks = GetTickCount();
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}
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}
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//--- With noise
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vector<float> std_delta = delta.Std(0) * STD_Delta_Multiplier;
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vector<float> mean_delta = delta.Mean(0);
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ulong inp_total = std_delta.Size();
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vector<float> noise = vector<float>::Zeros(inp_total);
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double ar_noise[];
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tr = SampleTrajectory(probability);
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state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 3 - PrecoderBars - batch));
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if(state < 0)
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{
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iter--;
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continue;
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}
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Encoder.Clear();
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Decoder.Clear();
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Noise.Clear();
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LastEncoder.BufferInit(EmbeddingSize, 0);
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end = MathMin(state + batch, Buffer[tr].Total - PrecoderBars);
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for(int i = state; i < end; i++)
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{
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if(!MathRandomNormal(0, 1, (int)inp_total, ar_noise))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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noise.Assign(ar_noise);
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noise = mean_delta + std_delta * noise;
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inp.Assign(Buffer[tr].States[i].state);
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inp = inp + noise;
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State.AssignArray(inp);
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//---
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if(!LastEncoder.BufferWrite() || !Encoder.feedForward((CBufferFloat*)GetPointer(State), 1, false, (CBufferFloat*)GetPointer(LastEncoder)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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if(!Decoder.feedForward(GetPointer(Encoder), -1, (CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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if(!Noise.feedForward(GetPointer(Encoder), -1, (CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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//---
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target.Assign(Buffer[tr].States[i].state);
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ulong size = target.Size();
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targets = matrix<float>::Zeros(1, size);
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targets.Row(target, 0);
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if(size > BarDescr)
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targets.Reshape(size / BarDescr, BarDescr);
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ulong shift = targets.Rows();
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targets.Resize(shift + PrecoderBars, 3);
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for(int t = 0; t < PrecoderBars; t++)
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{
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target.Assign(Buffer[tr].States[i + t].state);
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if(size > BarDescr)
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{
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matrix<float> temp(1, size);
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temp.Row(target, 0);
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temp.Reshape(size / BarDescr, BarDescr);
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temp.Resize(size / BarDescr, 3);
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target = temp.Row(temp.Rows() - 1);
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}
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targets.Row(target, shift + t);
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}
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targets.Reshape(1, targets.Rows()*targets.Cols());
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target = targets.Row(0);
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Decoder.getResults(result);
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vector<float> error = target - result;
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std_count = MathMin(std_count, 999);
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STE = MathSqrt((MathPow(STE, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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vector<float> check = MathAbs(error) - STE * STE_Multiplier;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(CAGrad(error) + result);
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if(!Decoder.backProp(Result, (CNet *)NULL) ||
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!Encoder.backPropGradient(GetPointer(LastEncoder), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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target = noise;
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Noise.getResults(result);
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error = (target - result) * STE_Noise_Multiplier;
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STE_Noise = MathSqrt((MathPow(STE_Noise, 2) * std_count + MathPow(error, 2)) / (std_count + 1));
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std_count++;
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check = MathAbs(error) - STE_Noise;
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if(check.Max() > 0)
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{
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//---
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Result.AssignArray(CAGrad(error) + result);
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if(!Noise.backProp(Result, (CNet *)NULL) ||
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!Encoder.backPropGradient(GetPointer(LastEncoder), GetPointer(Gradient)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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Stop = true;
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break;
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}
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}
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//---
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Encoder.getResults(result);
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LastEncoder.AssignArray(result);
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//---
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if(GetTickCount() - ticks > 500)
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{
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double percent = (double(i - state) / (2 * (end - state)) + iter + 0.5) * 100.0 / (Iterations);
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string str = StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Decoder", percent, Decoder.getRecentAverageError());
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str += StringFormat("%-20s %6.2f%% -> Error %15.8f\n", "Noise Prediction", percent, Noise.getRecentAverageError());
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Comment(str);
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ticks = GetTickCount();
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}
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}
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}
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Comment("");
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//---
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PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Decoder", Decoder.getRecentAverageError());
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PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Noise Prediction", Noise.getRecentAverageError());
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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