381 líneas
30 KiB
MQL5
381 líneas
30 KiB
MQL5
//+------------------------------------------------------------------+
|
|
//| EVD.mqh |
|
|
//| Copyright DNG® |
|
|
//| https://www.mql5.com/ru/users/dng |
|
|
//+------------------------------------------------------------------+
|
|
#property copyright "Copyright DNG®"
|
|
#property link "https://www.mql5.com/ru/users/dng"
|
|
#property version "1.00"
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
#include "ICM.mqh"
|
|
//---
|
|
#define defEVD 0x7794 ///<Neuron Net \details Identified class #CICM
|
|
//+------------------------------------------------------------------+
|
|
//| Exploration via Disagreement |
|
|
//+------------------------------------------------------------------+
|
|
class CEVD : protected CNet
|
|
{
|
|
protected:
|
|
uint iMinBufferSize;
|
|
uint iStateEmbedingLayer;
|
|
double dPrevBalance;
|
|
bool bUseTargetNet;
|
|
bool bTrainMode;
|
|
//---
|
|
CNet cTargetNet;
|
|
CReplayBuffer cReplay;
|
|
CNet cForwardNet;
|
|
|
|
virtual bool AddInputData(CArrayFloat *inputVals);
|
|
|
|
public:
|
|
CEVD();
|
|
CEVD(CArrayObj *Description, CArrayObj *Forward);
|
|
bool Create(CArrayObj *Description, CArrayObj *Forward);
|
|
~CEVD();
|
|
int feedForward(CArrayFloat *inputVals, int window = 1, bool tem = true); ///< Feed Forward method.@param[in] prevLayer Pointer to previos layer. @param[in] window Window of input data. @param[in] tem Use Time Embedding.
|
|
bool backProp(int batch, float discount = 0.999f);
|
|
int getAction(int state_size = 0); ///< Method to get results of feed forward process.@param[out] resultVals Array of result values
|
|
float getRecentAverageError() { return recentAverageError; } ///< Method to check quality of study. @return Average error
|
|
bool Save(string file_name, bool common = true);
|
|
bool Save(string dqn, string forward, bool common = true);
|
|
///< Save method. @param[in] file_name File name to save @param[in] error Average error @param[in] undefine Undefined percent @param[in] Foecast percent @param[in] time Last study time @param[in] common Common flag
|
|
virtual bool Load(string file_name, bool common = true);
|
|
bool Load(string dqn, string forward, uint state_layer, bool common = true);
|
|
///< Load method. @param[in] file_name File name to save @param[out] error Average error @param[out] undefine Undefined percent @param[out] Foecast percent @param[out] time Last study time @param[in] common Common flag
|
|
//---
|
|
virtual int Type(void) const { return defEVD; }///< Identificator of class.@return Type of class
|
|
virtual bool TrainMode(bool flag) { bTrainMode = flag; return (CNet::TrainMode(flag) && cForwardNet.TrainMode(flag)); } ///< Set Training Mode Flag
|
|
virtual bool GetLayerOutput(uint layer, CBufferFloat *&result) ///< Retutn Output data of layer. @param[in] layer Number of layer @param[out] return Buffer with data
|
|
{ return CNet::GetLayerOutput(layer, result); }
|
|
//---
|
|
virtual void SetStateEmbedingLayer(uint layer) { iStateEmbedingLayer = layer; }
|
|
virtual void SetBufferSize(uint min, uint max);
|
|
};
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
CEVD::CEVD() : iMinBufferSize(100),
|
|
bUseTargetNet(false),
|
|
bTrainMode(true)
|
|
{
|
|
Create(NULL, NULL);
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
CEVD::~CEVD()
|
|
{
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::Create(CArrayObj *Description, CArrayObj *Forward)
|
|
{
|
|
if(!CNet::Create(Description))
|
|
return false;
|
|
if(!cForwardNet.Create(Forward))
|
|
return false;
|
|
cTargetNet.Create(Description);
|
|
bUseTargetNet = false;
|
|
bTrainMode = true;
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::backProp(int batch, float discount = 0.999000f)
|
|
{
|
|
//---
|
|
if(cReplay.Total() < (int)iMinBufferSize || !bTrainMode)
|
|
return true;
|
|
//---
|
|
CBufferFloat *state1, *state2, *targetVals = new CBufferFloat();
|
|
vector<float> target, actions, st1, st2, result;
|
|
matrix<float> forward;
|
|
double reward;
|
|
int action;
|
|
//--- цикл обучения в размере batch
|
|
for(int i = 0; i < batch; i++)
|
|
{
|
|
//--- получаем случайное состояние и реплай буфера
|
|
if(!cReplay.GetRendomState(state1, action, reward, state2))
|
|
return false;
|
|
//--- прямой проход обучаемой моделм ("текущее" состояие)
|
|
if(!CNet::feedForward(state1, 1, false))
|
|
return false;
|
|
getResults(target);
|
|
//--- выгружаем эмбединг состояния
|
|
if(!GetLayerOutput(iStateEmbedingLayer, state1))
|
|
return false;
|
|
//--- прямой проход target net
|
|
if(!cTargetNet.feedForward(state2, 1, false))
|
|
return false;
|
|
//--- корректировка вознаграждения
|
|
if(bUseTargetNet)
|
|
{
|
|
cTargetNet.getResults(targetVals);
|
|
reward += discount * targetVals.Maximum();
|
|
}
|
|
target[action] = (float)reward;
|
|
if(!targetVals.AssignArray(target))
|
|
return false;
|
|
//--- обратный проход обучаемой модели
|
|
CNet::backProp(targetVals);
|
|
//--- прямой проход forward net - прогноз следующего состояния
|
|
if(!cForwardNet.feedForward(state1, 1, false))
|
|
return false;
|
|
//--- выгружаем эмбединг "будущего" состояния
|
|
if(!cTargetNet.GetLayerOutput(iStateEmbedingLayer, state2))
|
|
return false;
|
|
//--- подготовка целей forward net
|
|
cForwardNet.getResults(result);
|
|
forward.Init(1, result.Size());
|
|
forward.Row(result, 0);
|
|
forward.Reshape(result.Size() / state2.Total(), state2.Total());
|
|
int ensemble = (int)(forward.Rows() / target.Size());
|
|
//--- копируем целевое состояние в матрицу целей ансамбля
|
|
state2.GetData(st2);
|
|
for(int r = 0; r < ensemble; r++)
|
|
forward.Row(st2, r * target.Size() + action);
|
|
//--- обратный проход foward net
|
|
targetVals.AssignArray(forward);
|
|
if(!cForwardNet.backProp(targetVals))
|
|
return false;
|
|
}
|
|
//---
|
|
delete state1;
|
|
delete state2;
|
|
delete targetVals;
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
int CEVD::getAction(int state_size = 0)
|
|
{
|
|
CBufferFloat *temp;
|
|
//--- получаем результат обучаемой модели.
|
|
CNet::getResults(temp);
|
|
if(!temp)
|
|
return -1;
|
|
//--- в режиме обучения делаем поправку на "любопытство"
|
|
if(bTrainMode && state_size > 0)
|
|
{
|
|
vector<float> model;
|
|
matrix<float> forward;
|
|
cForwardNet.getResults(model);
|
|
forward.Init(1, model.Size());
|
|
forward.Row(model, 0);
|
|
temp.GetData(model);
|
|
//
|
|
cForwardNet.GetLayerOutput(1,temp);
|
|
//---
|
|
int actions = (int)model.Size();
|
|
forward.Reshape(forward.Cols() / state_size, state_size);
|
|
matrix<float> ensemble[];
|
|
if(!forward.Hsplit(forward.Rows() / actions, ensemble))
|
|
return -1;
|
|
matrix<float> means = ensemble[0];
|
|
int total = ArraySize(ensemble);
|
|
for(int i = 1; i < total; i++)
|
|
means += ensemble[i];
|
|
means = means / total;
|
|
for(int i = 0; i < total; i++)
|
|
ensemble[i] -= means;
|
|
means = MathPow(ensemble[0], 2.0);
|
|
for(int i = 1; i < total; i++)
|
|
means += MathPow(ensemble[i], 2.0);
|
|
model += means.Sum(1) / total;
|
|
temp.AssignArray(model);
|
|
}
|
|
//---
|
|
return temp.Argmax();
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::AddInputData(CArrayFloat *inputVals)
|
|
{
|
|
if(!inputVals)
|
|
return false;
|
|
if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_BALANCE)))
|
|
return false;
|
|
if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_EQUITY)))
|
|
return false;
|
|
if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_MARGIN_FREE)))
|
|
return false;
|
|
if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_MARGIN_LEVEL)))
|
|
return false;
|
|
if(!inputVals.Add((float)AccountInfoDouble(ACCOUNT_PROFIT)))
|
|
return false;
|
|
//---
|
|
double buy_value = 0, sell_value = 0, buy_profit = 0, sell_profit = 0;
|
|
int total = PositionsTotal();
|
|
for(int i = 0; i < total; i++)
|
|
{
|
|
if(PositionGetSymbol(i) != _Symbol)
|
|
continue;
|
|
switch((int)PositionGetInteger(POSITION_TYPE))
|
|
{
|
|
case POSITION_TYPE_BUY:
|
|
buy_value += PositionGetDouble(POSITION_VOLUME);
|
|
buy_profit += PositionGetDouble(POSITION_PROFIT);
|
|
break;
|
|
case POSITION_TYPE_SELL:
|
|
sell_value += PositionGetDouble(POSITION_VOLUME);
|
|
sell_profit += PositionGetDouble(POSITION_PROFIT);
|
|
break;
|
|
}
|
|
}
|
|
if(!inputVals.Add((float)buy_value))
|
|
return false;
|
|
if(!inputVals.Add((float)sell_value))
|
|
return false;
|
|
if(!inputVals.Add((float)buy_profit))
|
|
return false;
|
|
if(!inputVals.Add((float)sell_profit))
|
|
return false;
|
|
//---
|
|
return true;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
int CEVD::feedForward(CArrayFloat *inputVals, int window = 1, bool tem = true)
|
|
{
|
|
if(!AddInputData(inputVals))
|
|
return -1;
|
|
//---
|
|
if(!CNet::feedForward(inputVals, window, tem))
|
|
return -1;
|
|
//---
|
|
int action = -1;
|
|
if(bTrainMode)
|
|
{
|
|
CBufferFloat *state;
|
|
//if(!GetLayerOutput(1, state))
|
|
// return -1;
|
|
if(!GetLayerOutput(iStateEmbedingLayer, state))
|
|
return -1;
|
|
if(!cForwardNet.feedForward(state, 1, false))
|
|
{
|
|
delete state;
|
|
return -1;
|
|
}
|
|
double balance = AccountInfoDouble(ACCOUNT_BALANCE);
|
|
double reward = (dPrevBalance == 0 ? 0 : balance - dPrevBalance);
|
|
dPrevBalance = balance;
|
|
action = getAction(state.Total());
|
|
delete state;
|
|
if(action < 0 || action > 3)
|
|
return -1;
|
|
if(!cReplay.AddState(inputVals, action, reward))
|
|
return -1;
|
|
}
|
|
else
|
|
action = getAction();
|
|
//---
|
|
return action;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::Save(string file_name, bool common = true)
|
|
{
|
|
if(file_name == NULL)
|
|
return false;
|
|
//---
|
|
int handle = FileOpen(file_name, (common ? FILE_COMMON : 0) | FILE_BIN | FILE_WRITE);
|
|
if(handle == INVALID_HANDLE)
|
|
return false;
|
|
//---
|
|
if(FileWriteInteger(handle, iMinBufferSize) <= 0 || FileWriteInteger(handle, iStateEmbedingLayer) <= 0)
|
|
{
|
|
FileClose(handle);
|
|
return false;
|
|
}
|
|
bool result = true;
|
|
if(!CNet::Save(handle) || !cForwardNet.Save(handle))
|
|
result = false;
|
|
FileFlush(handle);
|
|
FileClose(handle);
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::Load(string file_name, bool common = true)
|
|
{
|
|
if(file_name == NULL)
|
|
return false;
|
|
//---
|
|
int handle = FileOpen(file_name, (common ? FILE_COMMON : 0) | FILE_BIN | FILE_READ);
|
|
if(handle == INVALID_HANDLE)
|
|
return false;
|
|
//---
|
|
iMinBufferSize = (uint)FileReadInteger(handle);
|
|
iStateEmbedingLayer = (uint)FileReadInteger(handle);
|
|
bool result = true;
|
|
if(!CNet::Load(handle))
|
|
result = false;
|
|
if(!cForwardNet.Load(handle))
|
|
result = false;
|
|
FileFlush(handle);
|
|
FileClose(handle);
|
|
float temp = 0;
|
|
datetime dt = 0;
|
|
if(CNet::Save(TargetNetFile, temp, temp, temp, dt, false))
|
|
bUseTargetNet = cTargetNet.Load(TargetNetFile, temp, temp, temp, dt, false);
|
|
TrainMode(true);
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::Save(string dqn, string forward, bool common = true)
|
|
{
|
|
if(dqn == NULL || forward == NULL)
|
|
return false;
|
|
bool result = true;
|
|
if(!CNet::Save(dqn, getRecentAverageError(), 0, 0, 0, common) ||
|
|
!cForwardNet.Save(forward, cForwardNet.getRecentAverageError(), 0, 0, 0, common))
|
|
result = false;
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
bool CEVD::Load(string dqn, string forward, uint state_layer, bool common = true)
|
|
{
|
|
if(dqn == NULL || forward == NULL)
|
|
return false;
|
|
bool result = true;
|
|
float err, undef, forecast;
|
|
datetime date;
|
|
if(!CNet::Load(dqn, err, undef, forecast, date, common) ||
|
|
!cForwardNet.Load(forward, err, undef, forecast, date, common) ||
|
|
!cTargetNet.Load(dqn, err, undef, forecast, date, common))
|
|
result = false;
|
|
iStateEmbedingLayer = state_layer;
|
|
TrainMode(true);
|
|
bUseTargetNet = true;
|
|
//---
|
|
return result;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| |
|
|
//+------------------------------------------------------------------+
|
|
void CEVD::SetBufferSize(uint min, uint max)
|
|
{
|
|
iMinBufferSize = MathMin(min, max);
|
|
cReplay.SetMaxSize(max);
|
|
}
|
|
//+------------------------------------------------------------------+
|