| //+------------------------------------------------------------------+
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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 = 1e7;
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| STrajectory Buffer[];
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| CNet Value;
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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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| //+------------------------------------------------------------------+
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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(!Value.Load(FileName + "Val.nnw", temp, temp, temp, dtStudied, true))
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| {
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| CArrayObj *value = new CArrayObj();
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| if(!CreateValueDescriptions(value))
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| {
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| delete value;
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| return INIT_FAILED;
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| }
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| if(!Value.Create(value))
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| {
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| delete value;
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| return INIT_FAILED;
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| }
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| delete value;
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| //---
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| }
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| //---
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| Value.getResults(Result);
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| if(Result.Total() != NRewards)
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| {
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| PrintFormat("The scope of the Value does not match the rewards count (%d <> %d)", NRewards, Result.Total());
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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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| Value.Save(FileName + "Val.nnw", 0, 0, 0, TimeCurrent(), true);
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| delete Result;
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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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| int total_tr = ArraySize(Buffer);
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| uint ticks = GetTickCount();
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| int check = 0;
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| //---
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| bool StopFlag = false;
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| for(int iter = 0; (iter < Iterations && !IsStopped() && !StopFlag); iter ++)
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| {
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| int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
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| int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2 * ValueBars));
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| if(i < 0)
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| {
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| iter--;
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| continue;
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| check++;
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| if(check >= total_tr)
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| break;
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| }
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| check = 0;
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| //--- History data
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| State.AssignArray(Buffer[tr].States[i].state);
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| for(int state = 1; state < ValueBars; state++)
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| State.AddArray(Buffer[tr].States[i + state].state);
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| //--- Study
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| if(!Value.feedForward(GetPointer(State),1,true,(CBufferFloat*)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| StopFlag = true;
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| break;
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| }
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| vector<float> target, result;
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| target.Assign(Buffer[tr].States[i + ValueBars - 1].rewards);
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| result.Assign(Buffer[tr].States[i + 2 * ValueBars - 1].rewards);
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| target = target - result*MathPow(DiscFactor,ValueBars);
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| Value.getResults(result);
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| Result.AssignArray(CAGrad(target - result) + result);
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| if(!Value.backProp(Result, (CBufferFloat *)NULL, (CBufferFloat *)NULL))
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| {
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| PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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| StopFlag = 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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| string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Value", iter * 100.0 / (double)(Iterations), Value.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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| Comment("");
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| //---
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| PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Value", Value.getRecentAverageError());
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| ExpertRemove();
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| //---
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| }
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| //+------------------------------------------------------------------+
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| //| |
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| //+------------------------------------------------------------------+
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| //| | Creates and manages object lifecycle for CAGrad. |
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| //+------------------------------------------------------------------+
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| vector<float> CAGrad(vector<float> &grad)
|
| {
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| matrix<float> GG = grad.Outer(grad);
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| GG.ReplaceNan(0);
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| if(MathAbs(GG).Sum() == 0)
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| return grad;
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| float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
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| GG = GG / MathPow(scale, 2);
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| vector<float> Gg = GG.Mean(1);
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| float gg = Gg.Mean();
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| vector<float> w = vector<float>::Zeros(grad.Size());
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| float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
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| vector<float> w_best = w;
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| float obj_best = FLT_MAX;
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| vector<float> moment = vector<float>::Zeros(w.Size());
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| for(int i = 0; i < iCAGrad_Iters; i++)
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| {
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| vector<float> ww;
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| w.Activation(ww, AF_SOFTMAX);
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| float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
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| if(MathAbs(obj) < obj_best)
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| {
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| obj_best = MathAbs(obj);
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| w_best = w;
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| }
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| if(i < (iCAGrad_Iters - 1))
|
| {
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| float loss = -obj;
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| vector<float> derev = Gg + GG.MatMul(ww) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2) + ww.MatMul(GG) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2);
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| vector<float> delta = derev * loss;
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| ulong size = delta.Size();
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| matrix<float> ident = matrix<float>::Identity(size, size);
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| vector<float> ones = vector<float>::Ones(size);
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| matrix<float> sm_der = ones.Outer(ww);
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| sm_der = sm_der.Transpose() * (ident - sm_der);
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| delta = sm_der.MatMul(delta);
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| if(delta.Ptp() != 0)
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| delta = delta / delta.Ptp();
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| moment = delta * 0.8f + moment * 0.5f;
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| w += moment;
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| if(w.Ptp() != 0)
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| w = w / w.Ptp();
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| }
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| }
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| w_best.Activation(w, AF_SOFTMAX);
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| float gw_norm = MathSqrt(w.MatMul(GG).Dot(w) + 1.0e-4f);
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| float lmbda = c / (gw_norm + 1.0e-4f);
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| vector<float> result = ((w * lmbda + 1.0f / (float)grad.Size()) * grad) / (1 + MathPow(fCAGrad_C, 2));
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| //---
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| return result;
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| }
|
| //+------------------------------------------------------------------+
|
|
|
