| //+------------------------------------------------------------------+
|
| //| Trajectory.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"
|
| //+------------------------------------------------------------------+
|
| //| Rewards structure |
|
| //| 0 - Delta Balance |
|
| //| 1 - Delta Equity ( "-" Drawdown / "+" Profit) |
|
| //| 2 - Penalty for no open positions |
|
| //| 3 - NNM |
|
| //| 4 - Latent NNM |
|
| //+------------------------------------------------------------------+
|
| #include "..\NeuroNet_DNG\NeuroNet.mqh"
|
| //---
|
| #define HistoryBars 20 //Depth of history
|
| #define BarDescr 9 //Elements for 1 bar description
|
| #define AccountDescr 12 //Account description
|
| #define NActions 6 //Number of possible Actions
|
| #define NRewards 5 //Number of rewards
|
| #define EmbeddingSize 16
|
| #define Buffer_Size 6500
|
| #define DiscFactor 0.99f
|
| #define FileName "DWSL"
|
| #define LatentLayer 9
|
| #define LatentCount 1024
|
| #define SamplLatentStates 32
|
| #define MaxSL 1000
|
| #define MaxTP 1000
|
| #define MaxReplayBuffer 500
|
| #define StartTargetIteration 50000
|
| #define fCAGrad_C 0.5f
|
| #define iCAGrad_Iters 15
|
| #define Quant 0.001
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| struct STarget
|
| {
|
| vector<float> rewards;
|
| vector<float> actions;
|
| //---
|
| // Implements STarget.
|
| STarget(void)
|
| {
|
| rewards=vector<float>::Zeros(NRewards);
|
| actions=vector<float>::Zeros(NActions);
|
| }
|
| };
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| struct SState
|
| {
|
| float state[HistoryBars * BarDescr];
|
| float account[AccountDescr - 4];
|
| float action[NActions];
|
| float rewards[NRewards];
|
| //---
|
| SState(void);
|
| //---
|
| bool Save(int file_handle);
|
| bool Load(int file_handle);
|
| //--- overloading
|
| void operator=(const SState &obj)
|
| {
|
| ArrayCopy(state, obj.state);
|
| ArrayCopy(account, obj.account);
|
| ArrayCopy(action, obj.action);
|
| ArrayCopy(rewards, obj.rewards);
|
| }
|
| };
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements SState. |
|
| //+------------------------------------------------------------------+
|
| SState::SState(void)
|
| {
|
| ArrayInitialize(state, 0);
|
| ArrayInitialize(account, 0);
|
| ArrayInitialize(action, 0);
|
| ArrayInitialize(rewards, 0);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Saves . |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Loads . |
|
| //+------------------------------------------------------------------+
|
| 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);
|
| //---
|
| bool Save(int file_handle);
|
| bool Load(int file_handle);
|
| };
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements STrajectory. |
|
| //+------------------------------------------------------------------+
|
| STrajectory::STrajectory(void) : Total(0),
|
| // Implements DiscountFactor.
|
| DiscountFactor(DiscFactor),
|
| // Creates and manages object lifecycle for CumCounted.
|
| CumCounted(false)
|
| {
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Saves . |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Loads . |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates and manages object lifecycle for CumRevards. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements Add. |
|
| //+------------------------------------------------------------------+
|
| bool STrajectory::Add(SState &state)
|
| {
|
| if(Total + 1 >= ArraySize(States))
|
| return false;
|
| States[Total] = state;
|
| Total++;
|
| //---
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Saves TotalBase. |
|
| //+------------------------------------------------------------------+
|
| bool SaveTotalBase(void)
|
| {
|
| int total = ArraySize(Buffer);
|
| if(total < 0)
|
| return true;
|
| int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
|
| if(handle < 0)
|
| return false;
|
| int start = MathMax(total - MaxReplayBuffer, 0);
|
| if(FileWriteInteger(handle, total - start) < INT_VALUE)
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| for(int i = start; i < total; i++)
|
| if(!Buffer[i].Save(handle))
|
| {
|
| FileClose(handle);
|
| return false;
|
| }
|
| FileFlush(handle);
|
| FileClose(handle);
|
| //---
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Loads TotalBase. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates or initializes Descriptions. |
|
| //+------------------------------------------------------------------+
|
| bool CreateDescriptions(CArrayObj *actor, CArrayObj *critic, CArrayObj *convolution)
|
| {
|
| //---
|
| CLayerDescription *descr;
|
| //---
|
| if(!actor)
|
| {
|
| actor = new CArrayObj();
|
| if(!actor)
|
| return false;
|
| }
|
| if(!critic)
|
| {
|
| critic = new CArrayObj();
|
| if(!critic)
|
| return false;
|
| }
|
| if(!convolution)
|
| {
|
| convolution = new CArrayObj();
|
| if(!convolution)
|
| return false;
|
| }
|
| //--- Actor
|
| actor.Clear();
|
| //--- Input layer
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| uint prev_count = descr.count = (HistoryBars * BarDescr);
|
| descr.activation = None;
|
| descr.optimization = ADAM;
|
| if(!actor.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(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 2
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronConvOCL;
|
| prev_count = descr.count = HistoryBars;
|
| descr.window = BarDescr;
|
| descr.step = BarDescr;
|
| uint prev_wout = descr.window_out = BarDescr / 2;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 3
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronSoftMaxOCL;
|
| descr.count = prev_count;
|
| descr.step = prev_wout;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 4
|
| 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 = 8;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 5
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronSoftMaxOCL;
|
| descr.count = prev_count;
|
| descr.step = prev_wout;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 6
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = LatentCount;
|
| descr.optimization = ADAM;
|
| descr.activation = LReLU;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 7
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| prev_count = descr.count = LatentCount;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 8
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronConcatenate;
|
| descr.count = 2 * LatentCount;
|
| descr.window = prev_count;
|
| descr.step = AccountDescr;
|
| descr.optimization = ADAM;
|
| descr.activation = SIGMOID;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 9
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronVAEOCL;
|
| descr.count = LatentCount;
|
| descr.optimization = ADAM;
|
| if(!actor.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(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 11
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = LatentCount;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 12
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = 2 * NActions;
|
| descr.activation = SIGMOID;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 13
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronVAEOCL;
|
| descr.count = NActions;
|
| descr.optimization = ADAM;
|
| if(!actor.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- Critic
|
| critic.Clear();
|
| //--- Input layer
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| prev_count = descr.count = LatentCount;
|
| descr.activation = None;
|
| descr.optimization = ADAM;
|
| if(!critic.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 1
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronConcatenate;
|
| descr.count = LatentCount;
|
| descr.window = prev_count;
|
| descr.step = NActions;
|
| descr.optimization = ADAM;
|
| descr.activation = LReLU;
|
| if(!critic.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 2
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = LatentCount;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!critic.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 3
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = LatentCount;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!critic.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 4
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = NRewards;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!critic.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- Convolution
|
| convolution.Clear();
|
| //--- Input layer
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| prev_count = descr.count = (HistoryBars * BarDescr) + AccountDescr;
|
| descr.activation = None;
|
| descr.optimization = ADAM;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 1
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = HistoryBars * BarDescr;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 2
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronSoftMaxOCL;
|
| descr.count = HistoryBars;
|
| descr.step = BarDescr;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 3
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronConvOCL;
|
| prev_count = descr.count = HistoryBars;
|
| descr.window = BarDescr;
|
| descr.step = BarDescr;
|
| prev_wout = descr.window_out = BarDescr / 2;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 4
|
| 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.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!convolution.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 = prev_wout;
|
| descr.step = prev_wout;
|
| prev_wout = descr.window_out = 2;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 6
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronSoftMaxOCL;
|
| descr.count = prev_count * prev_wout;
|
| descr.optimization = ADAM;
|
| descr.activation = None;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //--- layer 7
|
| if(!(descr = new CLayerDescription()))
|
| return false;
|
| descr.type = defNeuronBaseOCL;
|
| descr.count = EmbeddingSize;
|
| descr.activation = LReLU;
|
| descr.optimization = ADAM;
|
| if(!convolution.Add(descr))
|
| {
|
| delete descr;
|
| return false;
|
| }
|
| //---
|
| return true;
|
| }
|
| #ifndef Study
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Checks NewBar. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates and manages object lifecycle for CloseByDirection. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements TrailPosition. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates and manages object lifecycle for ClosePartial. |
|
| //+------------------------------------------------------------------+
|
| 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
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements ForecastAccount. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements EntropyLatentState. |
|
| //+------------------------------------------------------------------+
|
| float EntropyLatentState(CNet &net)
|
| {
|
| //--- random values
|
| double random[];
|
| MathRandomNormal(0, 1, LatentCount * SamplLatentStates, random);
|
| matrix<float> states;
|
| states.Assign(random);
|
| states.Reshape(SamplLatentStates, LatentCount);
|
| //--- get means and std
|
| vector<float> temp;
|
| matrix<float> stats = matrix<float>::Zeros(SamplLatentStates, 2 * LatentCount);
|
| net.GetLayerOutput(LatentLayer - 1, temp);
|
| stats.Row(temp, 0);
|
| stats = stats.CumSum(0);
|
| matrix<float> split[];
|
| stats.Vsplit(2, split);
|
| //--- calculate latent values
|
| states = states * split[1] + split[0];
|
| //--- add current latent value
|
| net.GetLayerOutput(LatentLayer, temp);
|
| states.Resize(SamplLatentStates + 1, LatentCount);
|
| states.Row(temp, SamplLatentStates);
|
| //--- calculate entropy
|
| states.SVD(split[0], split[1], temp);
|
| float result = temp.Sum() / (MathSqrt(MathPow(states, 2.0f).Sum() * MathMax(SamplLatentStates + 1, LatentCount)));
|
| //---
|
| return result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Gets ProbTrajectories. |
|
| //+------------------------------------------------------------------+
|
| vector<float> GetProbTrajectories(STrajectory &buffer[], double lambda)
|
| {
|
| ulong total = buffer.Size();
|
| vector<float> result = vector<float>::Zeros(total);
|
| vector<float> temp;
|
| for(ulong i = 0; i < total; i++)
|
| {
|
| temp.Assign(buffer[i].States[0].rewards);
|
| result[i] = temp.Sum();
|
| if(!MathIsValidNumber(result[i]))
|
| result[i] = -FLT_MAX;
|
| }
|
| float max_reward = result.Max();
|
| //---
|
| vector<float> sorted = result;
|
| bool sort = true;
|
| int iter = 0;
|
| while(sort)
|
| {
|
| sort = false;
|
| for(ulong i = 0; i < sorted.Size() - 1; i++)
|
| if(sorted[i] > sorted[i + 1])
|
| {
|
| float temp = sorted[i];
|
| sorted[i] = sorted[i + 1];
|
| sorted[i + 1] = temp;
|
| sort = true;
|
| }
|
| iter++;
|
| }
|
| //---
|
| float min = result.Min() - 0.1f * MathAbs(max_reward);
|
| if(max_reward > min)
|
| {
|
| float k = sorted.Percentile(80) - max_reward;
|
| vector<float> multipl = MathExp(MathAbs(result - max_reward) / (k == 0 ? -1 : k));
|
| result = (result - min) / (max_reward - min);
|
| result = result / (result + lambda) * multipl;
|
| result.ReplaceNan(0);
|
| }
|
| else
|
| result.Fill(1);
|
| result = result / result.Sum();
|
| result = result.CumSum();
|
| //---
|
| return result;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Implements SampleTrajectory. |
|
| //+------------------------------------------------------------------+
|
| int SampleTrajectory(vector<float> &probability)
|
| {
|
| //--- check
|
| ulong total = probability.Size();
|
| if(total <= 0)
|
| return -1;
|
| //--- randomize
|
| float rnd = float(MathRand() / 32767.0);
|
| //--- search
|
| if(rnd <= probability[0] || total == 1)
|
| return 0;
|
| if(rnd > probability[total - 2])
|
| return int(total - 1);
|
| int result = int(rnd * total);
|
| if(probability[result] < rnd)
|
| while(probability[result] < rnd)
|
| result++;
|
| else
|
| while(probability[result - 1] >= rnd)
|
| result--;
|
| //--- return result
|
| return result;
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
