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Warrior_EA/references/MQL5/Experts/CWBC/Trajectory.mqh

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fix(cl): stabilize network training with tighter limits, weight decay, and sign-agreement gate - Tighten MAX_WEIGHT from 1.0e6 to 100.0 to prevent unbounded weight growth. - Add MIN_ACTIVATION_DERIVATIVE (1e-4) to avoid zero gradients for saturated units. - Introduce WEIGHT_DECAY (0.01) to decouple decay from Adam updates. - Add MAX_WEIGHT_DELTA (0.1) to clamp per-step Adam updates and prevent overshoot. - Apply sign-agreement gate on weight updates in Adam kernel to only apply steps aligned with current gradient direction. - Fix tanh/sigmoid derivative calculations to use the new floor instead of hard-coded edge-case values.
2026-07-15 21:47:37 -04:00
<EFBFBD><EFBFBD>//+------------------------------------------------------------------+
//| Trajectory.mqh |
//| Copyright DNG<EFBFBD> |
//| https://www.mql5.com/en/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG<00>"
#property link "https://www.mql5.com/en/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| Rewards structure |
//| 0 - Delta Balance |
//| 1 - Delta Equity ( "-" Drawdown / "+" Profit) |
//| 2 - Penalty for no opened positions |
//+------------------------------------------------------------------+
#include "..\RL\FQF.mqh"
//---
#define HistoryBars 100 //Depth of history
#define ValueBars 10 //Depth of history for Value function
#define BarDescr 9 //Elements for 1 bar description
#define NBarInPattern 1 //Bars for 1 pattern description
#define AccountDescr 8 //Account description
#define NActions 6 //Number of possible Actions
#define NRewards 3 //Number of rewards
#define TimeDescription 4
#define LatentCount 1024
#define LatentLayer 2
#define EmbeddingSize 32
#define Buffer_Size 6500
#define DiscFactor 0.99f
#define FileName "CWBC"
#define MaxReplayBuffer 500
#define MaxSL 1000
#define MaxTP 1000
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct STrajectory;
extern STrajectory Buffer[];
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct SState
{
float state[BarDescr * NBarInPattern];
float account[AccountDescr];
float action[NActions];
float rewards[NRewards];
//---
SState(void);
//---
bool Save(int file_handle);
bool Load(int file_handle);
//---
void Clear(void)
{
ArrayInitialize(state, 0);
ArrayInitialize(account, 0);
ArrayInitialize(action, 0);
ArrayInitialize(rewards, 0);
}
//--- overloading
void operator=(const SState &obj)
{
ArrayCopy(state, obj.state);
ArrayCopy(account, obj.account);
ArrayCopy(action, obj.action);
ArrayCopy(rewards, obj.rewards);
}
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
SState::SState(void)
{
ArrayInitialize(state, 0);
ArrayInitialize(account, 0);
ArrayInitialize(action, 0);
ArrayInitialize(rewards, 0);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool SState::Save(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
int total = ArraySize(state);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, state[i]) < sizeof(float))
return false;
//---
total = ArraySize(account);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, account[i]) < sizeof(float))
return false;
//---
total = ArraySize(action);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, action[i]) < sizeof(float))
return false;
//---
total = ArraySize(rewards);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, rewards[i]) < sizeof(float))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool SState::Load(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
if(FileIsEnding(file_handle))
return false;
//---
int total = FileReadInteger(file_handle);
if(total != ArraySize(state))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
state[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(account))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
account[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(action))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
action[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(rewards))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
rewards[i] = FileReadFloat(file_handle);
}
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct STrajectory
{
SState States[Buffer_Size];
int Total;
float DiscountFactor;
bool CumCounted;
//---
STrajectory(void);
//---
bool Add(SState &state);
void CumRevards(void);
void ClearFirstN(const int n);
//---
bool Save(int file_handle);
bool Load(int file_handle);
//--- overloading
void operator=(const STrajectory &obj)
{
Total = obj.Total;
DiscountFactor = obj.DiscountFactor;
CumCounted = obj.CumCounted;
for(int i = 0; i < Buffer_Size; i++)
States[i] = obj.States[i];
}
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory::STrajectory(void) : Total(0),
DiscountFactor(DiscFactor),
CumCounted(false)
{
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Save(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
if(!CumCounted)
CumRevards();
if(FileWriteInteger(file_handle, Total) < sizeof(int))
return false;
if(FileWriteFloat(file_handle, DiscountFactor) < sizeof(float))
return false;
for(int i = 0; i < Total; i++)
if(!States[i].Save(file_handle))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Load(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
Total = FileReadInteger(file_handle);
if(FileIsEnding(file_handle) || Total >= ArraySize(States))
return false;
DiscountFactor = FileReadFloat(file_handle);
CumCounted = true;
//---
for(int i = 0; i < Total; i++)
if(!States[i].Load(file_handle))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
void STrajectory::CumRevards(void)
{
if(CumCounted)
return;
//---
for(int i = Total - 2; i >= 0; i--)
for(int r = 0; r < NRewards; r++)
States[i].rewards[r] += States[i + 1].rewards[r] * DiscountFactor;
CumCounted = true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Add(SState &state)
{
if(Total + 1 >= ArraySize(States))
return false;
States[Total] = state;
Total++;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
void STrajectory::ClearFirstN(const int n)
{
for(int i = 0; i < Buffer_Size - n; i++)
States[i] = States[i + n];
Total = MathMax(0, Buffer_Size - n);
for(int i = Total; i < Buffer_Size; i++)
States[i].Clear();
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
int SaveTotalBase(int min_bars)
{
int total = ArraySize(Buffer);
if(total < 0)
return 0;
int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
if(handle < 0)
return 0;
int indexes[MaxReplayBuffer];
int count = 0;
for(int i = total - 1; i >= 0; i--)
{
if(Buffer[i].Total < min_bars)
continue;
indexes[count] = i;
count++;
}
if(FileWriteInteger(handle, count) < INT_VALUE)
{
FileClose(handle);
return 0;
}
for(int i = count - 1; i >= 0; i--)
if(!Buffer[indexes[i]].Save(handle))
{
FileClose(handle);
return (count - (i + 1));
}
FileFlush(handle);
FileClose(handle);
//---
return count;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool LoadTotalBase(void)
{
int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
if(handle < 0)
return false;
int total = FileReadInteger(handle);
if(total <= 0)
{
FileClose(handle);
return false;
}
if(ArrayResize(Buffer, total) < total)
{
FileClose(handle);
return false;
}
for(int i = 0; i < total; i++)
if(!Buffer[i].Load(handle))
{
FileClose(handle);
return false;
}
FileClose(handle);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CreateDescriptions(CArrayObj *agent)
{
//---
CLayerDescription *descr;
//---
if(!agent)
{
agent = new CArrayObj();
if(!agent)
return false;
}
//--- Agent
agent.Clear();
//--- Input layer
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
int prev_count = descr.count = (BarDescr * NBarInPattern + AccountDescr + TimeDescription + NActions + NRewards);
descr.activation = None;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 1
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBatchNormOCL;
descr.count = prev_count;
descr.batch = 1000;
descr.activation = None;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 2
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronEmbeddingOCL;
prev_count = descr.count = HistoryBars;
{
int temp[] = {BarDescr * NBarInPattern, AccountDescr, TimeDescription, NActions, NRewards};
ArrayCopy(descr.windows, temp);
}
int prev_wout = descr.window_out = EmbeddingSize;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 3
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = EmbeddingSize;
descr.step = prev_count * 5;
descr.activation = None;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 4
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronMLMHAttentionOCL;
prev_count = descr.count = prev_count * 5;
descr.window = EmbeddingSize;
descr.step = 8;
descr.window_out = 32;
descr.layers = 4;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 5
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = EmbeddingSize;
descr.step = EmbeddingSize;
prev_wout = descr.window_out = EmbeddingSize;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 6
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = prev_wout;
descr.step = prev_wout;
prev_wout = descr.window_out = prev_wout / 2;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 7
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count;
descr.step = prev_wout;
descr.activation = None;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 8
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 9
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
prev_count = descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 10
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//--- layer 11
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = NActions;
descr.activation = SIGMOID;
descr.optimization = ADAM;
if(!agent.Add(descr))
{
delete descr;
return false;
}
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CreateRTGDescriptions(CArrayObj *rtg)
{
//---
CLayerDescription *descr;
//---
if(!rtg)
{
rtg = new CArrayObj();
if(!rtg)
return false;
}
//--- RTG
rtg.Clear();
//--- Input layer
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
int prev_count = descr.count = ValueBars * BarDescr;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 1
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBatchNormOCL;
descr.count = prev_count;
descr.batch = 1000;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 2
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = (prev_count + BarDescr - 1) / BarDescr;
descr.window = BarDescr;
descr.step = BarDescr;
int prev_wout = descr.window_out = EmbeddingSize;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 3
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count;
descr.step = EmbeddingSize;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 4
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronMLMHAttentionOCL;
descr.count = prev_count;
descr.window = EmbeddingSize;
descr.step = 8;
descr.window_out = 32;
descr.layers = 4;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 5
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = EmbeddingSize;
descr.step = EmbeddingSize;
prev_wout = descr.window_out = EmbeddingSize;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 6
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = prev_wout;
descr.step = prev_wout;
prev_wout = descr.window_out = prev_wout / 2;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 7
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count;
descr.step = prev_wout;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 8
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 8
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
prev_count = descr.count = LatentCount;
descr.activation = TANH;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 9
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = 2 * NRewards;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//--- layer 10
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronVAEOCL;
descr.count = NRewards;
descr.activation = None;
descr.optimization = ADAM;
if(!rtg.Add(descr))
{
delete descr;
return false;
}
//---
return true;
}
#ifndef Study
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool IsNewBar(void)
{
static datetime last_bar = 0;
if(last_bar >= iTime(Symb.Name(), TimeFrame, 0))
return false;
//---
last_bar = iTime(Symb.Name(), TimeFrame, 0);
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CloseByDirection(ENUM_POSITION_TYPE type)
{
int total = PositionsTotal();
bool result = true;
for(int i = total - 1; i >= 0; i--)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
result = (Trade.PositionClose(PositionGetInteger(POSITION_TICKET)) && result);
}
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool TrailPosition(ENUM_POSITION_TYPE type, double sl, double tp)
{
int total = PositionsTotal();
bool result = true;
//---
for(int i = 0; i < total; i++)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
bool modify = false;
double psl = PositionGetDouble(POSITION_SL);
double ptp = PositionGetDouble(POSITION_TP);
switch(type)
{
case POSITION_TYPE_BUY:
if((sl - psl) >= Symb.Point())
{
psl = sl;
modify = true;
}
if(MathAbs(tp - ptp) >= Symb.Point())
{
ptp = tp;
modify = true;
}
break;
case POSITION_TYPE_SELL:
if((psl - sl) >= Symb.Point())
{
psl = sl;
modify = true;
}
if(MathAbs(tp - ptp) >= Symb.Point())
{
ptp = tp;
modify = true;
}
break;
}
if(modify)
result = (Trade.PositionModify(PositionGetInteger(POSITION_TICKET), psl, ptp) && result);
}
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool ClosePartial(ENUM_POSITION_TYPE type, double value)
{
if(value <= 0)
return true;
//---
for(int i = 0; (i < PositionsTotal() && value > 0); i++)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
double pvalue = PositionGetDouble(POSITION_VOLUME);
if(pvalue <= value)
{
if(Trade.PositionClose(PositionGetInteger(POSITION_TICKET)))
{
value -= pvalue;
i--;
}
}
else
{
if(Trade.PositionClosePartial(PositionGetInteger(POSITION_TICKET), value))
value = 0;
}
}
//---
return (value <= 0);
}
#endif
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> ForecastAccount(float &prev_account[], vector<float> &actions, double prof_1l, float time_label)
{
vector<float> account;
double min_lot = SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
double step_lot = SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_STEP);
double stops = MathMax(SymbolInfoInteger(_Symbol, SYMBOL_TRADE_STOPS_LEVEL), 1) * Point();
double margin_buy, margin_sell;
if(!OrderCalcMargin(ORDER_TYPE_BUY, _Symbol, 1.0, SymbolInfoDouble(_Symbol, SYMBOL_ASK), margin_buy) ||
!OrderCalcMargin(ORDER_TYPE_SELL, _Symbol, 1.0, SymbolInfoDouble(_Symbol, SYMBOL_BID), margin_sell))
return vector<float>::Zeros(prev_account.Size());
//---
account.Assign(prev_account);
//---
if(actions[0] >= actions[3])
{
actions[0] -= actions[3];
actions[3] = 0;
if(actions[0]*margin_buy >= MathMin(account[0], account[1]))
actions[0] = 0;
}
else
{
actions[3] -= actions[0];
actions[0] = 0;
if(actions[3]*margin_sell >= MathMin(account[0], account[1]))
actions[3] = 0;
}
//--- buy control
if(actions[0] < min_lot || (actions[1] * MaxTP * Point()) <= stops || (actions[2] * MaxSL * Point()) <= stops)
{
account[0] += account[4];
account[2] = 0;
account[4] = 0;
}
else
{
double buy_lot = min_lot + MathRound((double)(actions[0] - min_lot) / step_lot) * step_lot;
if(account[2] > buy_lot)
{
float koef = (float)buy_lot / account[2];
account[0] += account[4] * (1 - koef);
account[4] *= koef;
}
account[2] = (float)buy_lot;
account[4] += float(buy_lot * prof_1l);
}
//--- sell control
if(actions[3] < min_lot || (actions[4] * MaxTP * Point()) <= stops || (actions[5] * MaxSL * Point()) <= stops)
{
account[0] += account[5];
account[3] = 0;
account[5] = 0;
}
else
{
double sell_lot = min_lot + MathRound((double)(actions[3] - min_lot) / step_lot) * step_lot;
if(account[3] > sell_lot)
{
float koef = float(sell_lot / account[3]);
account[0] += account[5] * (1 - koef);
account[5] *= koef;
}
account[3] = float(sell_lot);
account[5] -= float(sell_lot * prof_1l);
}
account[6] = account[4] + account[5];
account[1] = account[0] + account[6];
//---
vector<float> result = vector<float>::Zeros(AccountDescr);
result[0] = (account[0] - prev_account[0]) / prev_account[0];
result[1] = account[1] / prev_account[0];
result[2] = (account[1] - prev_account[1]) / prev_account[1];
result[3] = account[2];
result[4] = account[3];
result[5] = account[4] / prev_account[0];
result[6] = account[5] / prev_account[0];
result[7] = account[6] / prev_account[0];
double x = (double)time_label / (double)(D'2024.01.01' - D'2023.01.01');
result[8] = (float)MathSin(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_MN1);
result[9] = (float)MathCos(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_W1);
result[10] = (float)MathSin(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_D1);
result[11] = (float)MathSin(2.0 * M_PI * x);
//--- return result
return result;
}
//+------------------------------------------------------------------+
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