forked from mnbvc188199/Warrior_EA
418 lines
33 KiB
MQL5
418 lines
33 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/en/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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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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//+------------------------------------------------------------------+
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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 = 10000;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet ADAPTEncoder;
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CNet ADAPTEndpoints;
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CNet ADAPTProbability;
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CNet StateEncoder;
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CNet EndpointEncoder;
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CNet Actor;
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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 bAccount;
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CBufferFloat bGradient;
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CBufferFloat bProbs;
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CBufferFloat *Result;
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vector<float> check;
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vector<float> STD_Actor;
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vector<float> STD_Goal;
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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(!ADAPTEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!ADAPTEndpoints.Load(FileName + "Endp.nnw", temp, temp, temp, dtStudied, true) ||
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!ADAPTProbability.Load(FileName + "Prob.nnw", temp, temp, temp, dtStudied, true)
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)
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{
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CArrayObj *encoder = new CArrayObj();
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CArrayObj *endpoint = new CArrayObj();
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CArrayObj *prob = new CArrayObj();
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if(!CreateTrajNetDescriptions(encoder, endpoint, prob))
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{
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delete endpoint;
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delete prob;
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delete encoder;
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return INIT_FAILED;
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}
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if(!ADAPTEncoder.Create(encoder) ||
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!ADAPTEndpoints.Create(endpoint) ||
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!ADAPTProbability.Create(prob))
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{
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delete endpoint;
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delete prob;
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delete encoder;
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return INIT_FAILED;
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}
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delete endpoint;
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delete prob;
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delete encoder;
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}
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//---
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if(!StateEncoder.Load(FileName + "StEnc.nnw", temp, temp, temp, dtStudied, true) ||
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!EndpointEncoder.Load(FileName + "EndEnc.nnw", temp, temp, temp, dtStudied, true) ||
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!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true))
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{
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CArrayObj *actor = new CArrayObj();
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CArrayObj *endpoint = new CArrayObj();
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CArrayObj *encoder = new CArrayObj();
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if(!CreateDescriptions(actor, endpoint, encoder))
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{
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delete actor;
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delete endpoint;
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delete encoder;
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return INIT_FAILED;
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}
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if(!Actor.Create(actor) || !StateEncoder.Create(encoder) || !EndpointEncoder.Create(endpoint))
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{
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delete actor;
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delete endpoint;
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delete encoder;
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return INIT_FAILED;
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}
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delete actor;
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delete endpoint;
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delete encoder;
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//---
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}
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//---
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OpenCL = Actor.GetOpenCL();
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StateEncoder.SetOpenCL(OpenCL);
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EndpointEncoder.SetOpenCL(OpenCL);
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ADAPTEncoder.SetOpenCL(OpenCL);
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ADAPTEndpoints.SetOpenCL(OpenCL);
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ADAPTProbability.SetOpenCL(OpenCL);
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//---
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Actor.getResults(Result);
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if(Result.Total() != NActions)
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{
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PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
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return INIT_FAILED;
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}
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//---
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ADAPTEndpoints.getResults(Result);
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if(Result.Total() != 3 * NForecast)
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{
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PrintFormat("The scope of the Endpoints does not match forecat endpoints (%d <> %d)", 3 * NForecast, Result.Total());
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return INIT_FAILED;
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}
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//---
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ADAPTEncoder.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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if(!bGradient.BufferInit(MathMax(AccountDescr, NForecast), 0) ||
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!bGradient.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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Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
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StateEncoder.Save(FileName + "StEnc.nnw", 0, 0, 0, TimeCurrent(), true);
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EndpointEncoder.Save(FileName + "EndEnc.nnw", 0, 0, 0, TimeCurrent(), true);
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ADAPTEncoder.Save(FileName + "Enc.nnw", 0, 0, 0, TimeCurrent(), true);
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ADAPTEndpoints.Save(FileName + "Endp.nnw", 0, 0, 0, TimeCurrent(), true);
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ADAPTProbability.Save(FileName + "Prob.nnw", 0, 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;
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matrix<float> targets, temp_m;
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bool Stop = false;
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//---
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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 batch = GPTBars + 48;
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int state = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2 - 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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ADAPTEncoder.Clear();
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int 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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bState.AssignArray(Buffer[tr].States[i].state);
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//--- Trajectory
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if(!ADAPTEncoder.feedForward((CBufferFloat*)GetPointer(bState), 1, false, (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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if(!ADAPTEndpoints.feedForward((CNet*)GetPointer(ADAPTEncoder), -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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if(!ADAPTProbability.feedForward((CNet*)GetPointer(ADAPTEncoder), -1, (CNet*)GetPointer(ADAPTEndpoints)))
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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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targets = matrix<float>::Zeros(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 + 1 + t].state);
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if(target.Size() > BarDescr)
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{
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matrix<float> temp(1, target.Size());
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temp.Row(target, 0);
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temp.Reshape(target.Size() / BarDescr, BarDescr);
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temp.Resize(temp.Rows(), 3);
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target = temp.Row(temp.Rows() - 1);
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}
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targets.Row(target, t);
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}
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target = targets.Col(0).CumSum();
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targets.Col(target, 0);
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targets.Col(target + targets.Col(1), 1);
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targets.Col(target + targets.Col(2), 2);
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int extr = 1;
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if(target[0] == 0)
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target[0] = target[1];
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int direct = (target[0] > 0 ? 1 : -1);
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for(int i = 1; i < PrecoderBars; i++)
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{
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if((target[i]*direct) < 0)
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break;
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extr++;
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}
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targets.Resize(extr, 3);
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if(direct >= 0)
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{
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target = targets.Max(AXIS_HORZ);
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target[2] = targets.Col(2).Min();
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}
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else
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{
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target = targets.Min(AXIS_HORZ);
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target[1] = targets.Col(1).Max();
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}
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//---
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ADAPTEndpoints.getResults(result);
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targets.Reshape(1, result.Size());
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targets.Row(result, 0);
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targets.Reshape(NForecast, 3);
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temp_m = targets;
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for(int i = 0; i < 3; i++)
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temp_m.Col(temp_m.Col(i) - target[i], i);
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temp_m = MathPow(temp_m, 2.0f);
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ulong pos = temp_m.Sum(AXIS_VERT).ArgMin();
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targets.Row(target, pos);
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Result.AssignArray(targets);
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//---
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if(!ADAPTEndpoints.backProp(Result, (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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if(!ADAPTEncoder.backPropGradient((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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bProbs.AssignArray(vector<float>::Zeros(NForecast));
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bProbs.Update((int)pos, 1);
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bProbs.BufferWrite();
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if(!ADAPTProbability.backProp(GetPointer(bProbs), GetPointer(ADAPTEndpoints)))
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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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//--- Policy
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float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
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bAccount.Clear();
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bAccount.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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bAccount.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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bAccount.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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bAccount.Add(Buffer[tr].States[i].account[2]);
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bAccount.Add(Buffer[tr].States[i].account[3]);
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bAccount.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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bAccount.Add(Buffer[tr].States[i].account[5] / PrevBalance);
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bAccount.Add(Buffer[tr].States[i].account[6] / PrevBalance);
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double time = (double)Buffer[tr].States[i].account[7];
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double x = time / (double)(D'2024.01.01' - D'2023.01.01');
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bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_MN1);
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bAccount.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_W1);
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bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = time / (double)PeriodSeconds(PERIOD_D1);
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bAccount.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(bAccount.GetIndex() >= 0)
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bAccount.BufferWrite();
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//--- State embedding
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if(!StateEncoder.feedForward((CNet *)GetPointer(ADAPTEncoder), -1, (CBufferFloat*)GetPointer(bAccount)))
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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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//--- Endpoint embedding
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if(!EndpointEncoder.feedForward(Result, -1, false, (CBufferFloat*)GetPointer(bProbs)))
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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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//--- Actor
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if(!Actor.feedForward((CNet *)GetPointer(StateEncoder), -1, (CNet*)GetPointer(EndpointEncoder)))
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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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result = vector<float>::Zeros(NActions);
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double value = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_LOSS);
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double risk = AccountInfoDouble(ACCOUNT_EQUITY) * 0.01;
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if(direct > 0)
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{
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if(Buffer[tr].States[i].state[4] > 50 &&
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Buffer[tr].States[i].state[5] > 0 &&
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Buffer[tr].States[i].state[8] > Buffer[tr].States[i].state[7]
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)
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{
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float tp = float(target[1] / _Point / MaxTP);
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result[1] = tp;
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int sl = int(MathMax(MathMax(target[1] / 3, -target[2]) / _Point, MaxSL / 10));
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result[2] = float(sl) / MaxSL;
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result[0] = float(MathMax(risk / (value * sl), 0.01)) + FLT_EPSILON;
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}
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}
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else
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{
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if(Buffer[tr].States[i].state[4] < 50 &&
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Buffer[tr].States[i].state[5] < 0 &&
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Buffer[tr].States[i].state[8] < Buffer[tr].States[i].state[7]
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)
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{
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float tp = float((-target[2]) / _Point / MaxTP);
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result[4] = tp;
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int sl = int(MathMax(MathMax((-target[2]) / 3, target[1]) / _Point, MaxSL / 10));
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result[5] = float(sl) / MaxSL;
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result[3] = float(MathMax(risk / (value * sl), 0.01)) + FLT_EPSILON;
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}
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}
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Result.AssignArray(result);
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if(!Actor.backProp(Result, (CNet *)GetPointer(EndpointEncoder)) ||
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!StateEncoder.backPropGradient(GetPointer(bAccount), (CBufferFloat *)GetPointer(bGradient)) ||
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!EndpointEncoder.backPropGradient(GetPointer(bProbs), (CBufferFloat *)GetPointer(bGradient))
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)
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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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if(!ADAPTEncoder.backPropGradient((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(GetTickCount() - ticks > 500)
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{
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double percent = (double(i - state) / ((end - state)) + iter) * 100.0 / (Iterations);
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string str = StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Actor", percent, Actor.getRecentAverageError());
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str += StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Endpoints", percent, ADAPTEndpoints.getRecentAverageError());
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str += StringFormat("%-14s %6.2f%% -> Error %15.8f\n", "Probability", percent, ADAPTProbability.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 -> %-15s %10.7f", __FUNCTION__, __LINE__, "Actor", Actor.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Endpoints", ADAPTEndpoints.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Probability", ADAPTProbability.getRecentAverageError());
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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