forked from mnbvc188199/Warrior_EA
1018 lines
62 KiB
MQL5
1018 lines
62 KiB
MQL5
//+------------------------------------------------------------------+
| |
//| Trajectory.mqh |
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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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//| Rewards structure |
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//| 0 - Delta Balance |
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//| 1 - Delta Equity ( "-" Drawdown / "+" Profit) |
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//| 2 - Penalty for no open positions |
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//| 3 - NNM |
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//| 4 - Latent NNM |
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//+------------------------------------------------------------------+
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#include "..\NeuroNet_DNG\NeuroNet.mqh"
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//---
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#define HistoryBars 20 //Depth of history
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#define BarDescr 9 //Elements for 1 bar description
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#define AccountDescr 12 //Account description
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#define NActions 6 //Number of possible Actions
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#define NRewards 5 //Number of rewards
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#define EmbeddingSize 16
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#define Buffer_Size 6500
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#define DiscFactor 0.99f
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#define FileName "ExORL"
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#define LatentLayer 9
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#define LatentCount 1024
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#define SamplLatentStates 32
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#define MaxSL 1000
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#define MaxTP 1000
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#define MaxReplayBuffer 500
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#define StartTargetIteration 50000
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#define fCAGrad_C 0.5f
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#define iCAGrad_Iters 15
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#define Quant 0.001
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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struct STarget
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{
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vector<float> rewards;
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vector<float> actions;
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//---
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STarget(void)
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{
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rewards=vector<float>::Zeros(NRewards);
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actions=vector<float>::Zeros(NActions);
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}
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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struct SState
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{
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float state[HistoryBars * BarDescr];
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float account[AccountDescr - 4];
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float action[NActions];
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float rewards[NRewards];
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//---
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SState(void);
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//---
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bool Save(int file_handle);
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bool Load(int file_handle);
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//--- overloading
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void operator=(const SState &obj)
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{
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ArrayCopy(state, obj.state);
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ArrayCopy(account, obj.account);
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ArrayCopy(action, obj.action);
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ArrayCopy(rewards, obj.rewards);
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}
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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SState::SState(void)
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{
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ArrayInitialize(state, 0);
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ArrayInitialize(account, 0);
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ArrayInitialize(action, 0);
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ArrayInitialize(rewards, 0);
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool SState::Save(int file_handle)
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{
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if(file_handle == INVALID_HANDLE)
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return false;
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//---
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int total = ArraySize(state);
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if(FileWriteInteger(file_handle, total) < sizeof(int))
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return false;
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for(int i = 0; i < total; i++)
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if(FileWriteFloat(file_handle, state[i]) < sizeof(float))
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return false;
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//---
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total = ArraySize(account);
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if(FileWriteInteger(file_handle, total) < sizeof(int))
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return false;
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for(int i = 0; i < total; i++)
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if(FileWriteFloat(file_handle, account[i]) < sizeof(float))
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return false;
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//---
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total = ArraySize(action);
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if(FileWriteInteger(file_handle, total) < sizeof(int))
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return false;
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for(int i = 0; i < total; i++)
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if(FileWriteFloat(file_handle, action[i]) < sizeof(float))
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return false;
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total = ArraySize(rewards);
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if(FileWriteInteger(file_handle, total) < sizeof(int))
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return false;
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for(int i = 0; i < total; i++)
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if(FileWriteFloat(file_handle, rewards[i]) < sizeof(float))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool SState::Load(int file_handle)
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{
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if(file_handle == INVALID_HANDLE)
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return false;
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if(FileIsEnding(file_handle))
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return false;
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//---
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int total = FileReadInteger(file_handle);
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if(total != ArraySize(state))
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return false;
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//---
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for(int i = 0; i < total; i++)
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{
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if(FileIsEnding(file_handle))
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return false;
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state[i] = FileReadFloat(file_handle);
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}
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//---
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total = FileReadInteger(file_handle);
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if(total != ArraySize(account))
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return false;
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//---
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for(int i = 0; i < total; i++)
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{
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if(FileIsEnding(file_handle))
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return false;
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account[i] = FileReadFloat(file_handle);
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}
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//---
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total = FileReadInteger(file_handle);
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if(total != ArraySize(action))
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return false;
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//---
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for(int i = 0; i < total; i++)
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{
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if(FileIsEnding(file_handle))
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return false;
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action[i] = FileReadFloat(file_handle);
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}
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//---
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total = FileReadInteger(file_handle);
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if(total != ArraySize(rewards))
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return false;
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//---
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for(int i = 0; i < total; i++)
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{
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if(FileIsEnding(file_handle))
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return false;
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rewards[i] = FileReadFloat(file_handle);
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}
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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struct STrajectory
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{
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SState States[Buffer_Size];
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int Total;
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float DiscountFactor;
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bool CumCounted;
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//---
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STrajectory(void);
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//---
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bool Add(SState &state);
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void CumRevards(void);
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//---
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bool Save(int file_handle);
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bool Load(int file_handle);
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};
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory::STrajectory(void) : Total(0),
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DiscountFactor(DiscFactor),
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CumCounted(false)
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{
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool STrajectory::Save(int file_handle)
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{
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if(file_handle == INVALID_HANDLE)
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return false;
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//---
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if(!CumCounted)
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CumRevards();
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if(FileWriteInteger(file_handle, Total) < sizeof(int))
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return false;
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if(FileWriteFloat(file_handle, DiscountFactor) < sizeof(float))
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return false;
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for(int i = 0; i < Total; i++)
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if(!States[i].Save(file_handle))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool STrajectory::Load(int file_handle)
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{
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if(file_handle == INVALID_HANDLE)
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return false;
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//---
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Total = FileReadInteger(file_handle);
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if(FileIsEnding(file_handle) || Total >= ArraySize(States))
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return false;
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DiscountFactor = FileReadFloat(file_handle);
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CumCounted = true;
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//---
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for(int i = 0; i < Total; i++)
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if(!States[i].Load(file_handle))
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return false;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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void STrajectory::CumRevards(void)
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{
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if(CumCounted)
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return;
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//---
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for(int i = Total - 2; i >= 0; i--)
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for(int r = 0; r < NRewards; r++)
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States[i].rewards[r] += States[i + 1].rewards[r] * DiscountFactor;
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CumCounted = true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool STrajectory::Add(SState &state)
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{
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if(Total + 1 >= ArraySize(States))
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return false;
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States[Total] = state;
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Total++;
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool SaveTotalBase(void)
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{
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int total = ArraySize(Buffer);
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if(total < 0)
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return true;
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int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
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if(handle < 0)
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return false;
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int start = MathMax(total - MaxReplayBuffer, 0);
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if(FileWriteInteger(handle, total - start) < INT_VALUE)
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{
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FileClose(handle);
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return false;
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}
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for(int i = start; i < total; i++)
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if(!Buffer[i].Save(handle))
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{
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FileClose(handle);
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return false;
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}
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FileFlush(handle);
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FileClose(handle);
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool LoadTotalBase(void)
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{
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int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
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if(handle < 0)
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return false;
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int total = FileReadInteger(handle);
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if(total <= 0)
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{
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FileClose(handle);
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return false;
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}
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if(ArrayResize(Buffer, total) < total)
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{
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FileClose(handle);
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return false;
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}
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for(int i = 0; i < total; i++)
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if(!Buffer[i].Load(handle))
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{
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FileClose(handle);
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return false;
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}
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FileClose(handle);
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//---
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return true;
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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bool CreateDescriptions(CArrayObj *actor, CArrayObj *critic, CArrayObj *convolution)
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{
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//---
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CLayerDescription *descr;
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//---
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if(!actor)
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{
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actor = new CArrayObj();
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if(!actor)
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return false;
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}
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if(!critic)
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{
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critic = new CArrayObj();
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if(!critic)
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return false;
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}
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if(!convolution)
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{
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convolution = new CArrayObj();
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if(!convolution)
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return false;
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}
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//--- Actor
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actor.Clear();
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//--- Input layer
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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int prev_count = descr.count = (HistoryBars * BarDescr);
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descr.activation = None;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 1
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBatchNormOCL;
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descr.count = prev_count;
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descr.batch = 1000;
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descr.activation = None;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 2
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronConvOCL;
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prev_count = descr.count = HistoryBars;
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descr.window = BarDescr;
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descr.step = BarDescr;
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int prev_wout = descr.window_out = BarDescr / 2;
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descr.activation = LReLU;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 3
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronSoftMaxOCL;
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descr.count = prev_count;
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descr.step = prev_wout;
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descr.optimization = ADAM;
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descr.activation = None;
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if(!convolution.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 4
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronConvOCL;
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prev_count = descr.count = prev_count;
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descr.window = prev_wout;
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descr.step = prev_wout;
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prev_wout = descr.window_out = 8;
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descr.activation = LReLU;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 5
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronSoftMaxOCL;
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descr.count = prev_count;
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descr.step = prev_wout;
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descr.optimization = ADAM;
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descr.activation = None;
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if(!convolution.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 6
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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descr.count = LatentCount;
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descr.optimization = ADAM;
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descr.activation = LReLU;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 7
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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prev_count = descr.count = LatentCount;
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descr.activation = LReLU;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 8
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronConcatenate;
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descr.count = 2 * LatentCount;
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descr.window = prev_count;
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descr.step = AccountDescr;
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descr.optimization = ADAM;
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descr.activation = SIGMOID;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 9
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronVAEOCL;
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descr.count = LatentCount;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 10
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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descr.count = LatentCount;
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descr.activation = LReLU;
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descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 11
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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descr.count = LatentCount;
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descr.activation = LReLU;
| |
descr.optimization = ADAM;
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if(!actor.Add(descr))
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{
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delete descr;
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return false;
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}
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//--- layer 12
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if(!(descr = new CLayerDescription()))
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return false;
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descr.type = defNeuronBaseOCL;
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descr.count = 2 * NActions;
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descr.activation = SIGMOID;
| |
descr.optimization = ADAM;
| |
if(!actor.Add(descr))
| |
{
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delete descr;
| |
return false;
| |
}
| |
//--- layer 13
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if(!(descr = new CLayerDescription()))
| |
return false;
| |
descr.type = defNeuronVAEOCL;
| |
descr.count = NActions;
| |
descr.optimization = ADAM;
| |
if(!actor.Add(descr))
| |
{
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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 + NActions;
| |
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
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
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;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
float EntropyLatentState(CNet &net)
| |
{
| |
//--- random values
| |
double random[];
| |
Math::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;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
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;
| |
}
| |
//+------------------------------------------------------------------+
| |
//| |
| |
//+------------------------------------------------------------------+
| |
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;
| |
}
| |
//+------------------------------------------------------------------+
|