| //+------------------------------------------------------------------+
|
| //| Study.mq5 |
|
| //| Copyright 2026, DNG |
|
| //| https://www.mql5.com/ru/users/dng |
|
| //+------------------------------------------------------------------+
|
| #property copyright "Copyright 2026, DNG"
|
| #property link "https://www.mql5.com/ru/users/dng"
|
| #property version "1.00"
|
| #property description "Харнесс обучения политик ORION: формирование и калибровка пары актор-критик."
|
| #property description "Проходит стадии жизненного цикла ORION и обучает актор, критиков Q1 и Q2 на исторических барах."
|
| #property description "Только для тестов; ордера не выставляет."
|
|
|
| #define Study
|
| #include "Trajectory.mqh"
|
| input datetime Start = D'2024.01.01';
|
| input datetime End = D'2026.01.01';
|
| input int Iterations = 1000000;
|
| input int EpisodeBars = 2 * StackSize;
|
| input double MinBalance = 50.0;
|
| input int UpdatePolicy = 5;
|
| CNet Actor;
|
| CNet Q1;
|
| CNet Q2;
|
|
|
| CBufferFloat State;
|
| CBufferFloat TimeState;
|
| CBufferFloat Account;
|
| CBufferFloat NextAccount;
|
| CBufferFloat CurrentAction;
|
| CBufferFloat TeacherAction;
|
| CBufferFloat RandomAction;
|
| CBufferFloat CriticInput;
|
| CBufferFloat ScalarTarget;
|
| CBufferFloat ActorObjective;
|
| ulong TeacherTransitions = 0;
|
| ulong TeacherCriticTransitions = 0;
|
| ulong RandomCriticTransitions = 0;
|
| double TeacherRewardSum = 0;
|
| ulong PolicyTransitions = 0;
|
| ulong PolicySkippedInvalid = 0;
|
| //+------------------------------------------------------------------+
|
| //| Loads OrCreateORIONPolicies. |
|
| //+------------------------------------------------------------------+
|
| bool LoadOrCreateORIONPolicies(void)
|
| {
|
| return ORIONLoadOrCreatePolicySet(Actor, Q1, Q2, true);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Saves ORIONPolicies. |
|
| //+------------------------------------------------------------------+
|
| bool SaveORIONPolicies(void)
|
| {
|
| //--- Forecast artifacts are immutable throughout Actor-Critic study. This
|
| //--- bounded lifecycle check avoids hashing three large files every batch.
|
| return ORIONSavePolicySet(Actor, Q1, Q2);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Implements PrepareHistory. |
|
| //+------------------------------------------------------------------+
|
| bool PrepareHistory(int &first_position, int &last_position)
|
| {
|
| int start = iBarShift(Symb.Name(), TimeFrame, Start);
|
| int end = iBarShift(Symb.Name(), TimeFrame, End);
|
| int bars = CopyRates(Symb.Name(), TimeFrame, 0, start, Rates);
|
| if(bars <= 0 || !RSI.BufferResize(bars) || !CCI.BufferResize(bars) ||
|
| !ATR.BufferResize(bars) || !MACD.BufferResize(bars))
|
| ReturnFalse;
|
| int wait = -1;
|
| bool calculated = false;
|
| do
|
| {
|
| calculated = (RSI.BarsCalculated() >= bars && CCI.BarsCalculated() >= bars &&
|
| ATR.BarsCalculated() >= bars && MACD.BarsCalculated() >= bars);
|
| Sleep(100);
|
| wait++;
|
| }
|
| while(!calculated && wait < 100);
|
| if(!calculated)
|
| ReturnFalse;
|
| RSI.Refresh();
|
| CCI.Refresh();
|
| ATR.Refresh();
|
| MACD.Refresh();
|
| if(!ArraySetAsSeries(Rates, true))
|
| ReturnFalse;
|
| first_position = end + 1;
|
| //--- CreateBuffers(position,...,forecast) forms its state from
|
| //--- position+NForecast and needs HistoryBars bars behind it. position is
|
| //--- therefore the end of the realized future window, not its first bar.
|
| last_position = start - HistoryBars - NForecast;
|
| return (last_position > first_position);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Implements PolicyBackward. |
|
| //+------------------------------------------------------------------+
|
| bool PolicyBackward(void)
|
| {
|
| //--- PolicyBackward builds Q+1 in this buffer. ScalarTarget must remain the
|
| //--- immutable TD target consumed by both Critic backward passes.
|
| return PolicyBackward(Actor, Q1, GetPointer(ActorObjective), GetPointer(ORIONMarket), -1);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Implements TrainTransition. |
|
| //+------------------------------------------------------------------+
|
| bool TrainTransition(const int position, const int iteration, bool &terminal)
|
| {
|
| terminal = false;
|
| const bool trace_td = (iteration < 8 || iteration % 10000 == 0);
|
| if(!ORIONForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
|
| { Print("TrainTransition stage=current_forecast"); ReturnFalse; }
|
| if(!Actor.feedForward(GetPointer(Account), 1, false, GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=actor_forward"); ReturnFalse; }
|
| if(!ReadAction(Actor, GetPointer(CurrentAction)))
|
| { Print("TrainTransition stage=read_action"); ReturnFalse; }
|
| const double balance = MathMax(0.0, double(Account[0]) * EtalonBalance);
|
| const double buy_lot = MathMax(0.0, double(CurrentAction[0] - CurrentAction[3]));
|
| const double sell_lot = MathMax(0.0, double(CurrentAction[3] - CurrentAction[0]));
|
| if(trace_td)
|
| PrintFormat("ORION action iteration=%d balance=%.2f buy_lot=%.8f buy_tp=%.8f buy_sl=%.8f sell_lot=%.8f sell_tp=%.8f sell_sl=%.8f",
|
| iteration, balance, buy_lot, double(CurrentAction[1]), double(CurrentAction[2]),
|
| sell_lot, double(CurrentAction[4]), double(CurrentAction[5]));
|
| double reward = 0;
|
| if(!AdvanceAccount(GetPointer(Account), GetPointer(CurrentAction), position, MinBalance,
|
| //--- Gets Pointer.
|
| GetPointer(NextAccount), reward, terminal))
|
| { Print("TrainTransition stage=advance_account"); ReturnFalse; }
|
| const double target_value = reward;
|
| if(trace_td)
|
| PrintFormat("ORION return iteration=%d reward=%.8f target=%.8f balance=%.2f equity=%.2f "
|
| "floating_buy=%.8f floating_sell=%.8f next_buy=%.8f next_sell=%.8f terminal=%s",
|
| iteration, reward, target_value, double(NextAccount[0])*EtalonBalance,
|
| double(NextAccount[2])*balance, double(NextAccount[6])*MathMax(double(NextAccount[0])*EtalonBalance, 1.0),
|
| double(NextAccount[7])*MathMax(double(NextAccount[0])*EtalonBalance, 1.0),
|
| double(NextAccount[4]), double(NextAccount[5]), (terminal ? "true" : "false"));
|
| if(!MathIsValidNumber(target_value) || !ScalarTarget.BufferInit(1, 0) ||
|
| !ScalarTarget.Update(0, float(target_value)))
|
| { Print("TrainTransition stage=scalar_target"); ReturnFalse; }
|
| CNeuronBaseOCL *context_layer = Actor.Layer(0);
|
| CNeuronBaseOCL *actor_layer = Actor.Layer(3);
|
| if(!context_layer || !actor_layer)
|
| { Print("TrainTransition stage=actor_layer"); ReturnFalse; }
|
| //--- Use the live device activation, not the CPU account object.
|
| if(!BuildCriticInput(context_layer.getOutput(), actor_layer.getOutput(), GetPointer(CriticInput)))
|
| { Print("TrainTransition stage=critic_input"); ReturnFalse; }
|
| if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=q1_forward"); ReturnFalse; }
|
| if(!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=q2_forward"); ReturnFalse; }
|
| //--- Function if.
|
| if(trace_td)
|
| {
|
| CNeuronBaseOCL *q1_layer = Q1.Layer(3);
|
| CNeuronBaseOCL *q2_layer = Q2.Layer(3);
|
| CBufferFloat *q1_output = (q1_layer ? q1_layer.getOutput() : NULL);
|
| CBufferFloat *q2_output = (q2_layer ? q2_layer.getOutput() : NULL);
|
| if(!q1_output || !q2_output || q1_output.GetIndex() < 0 || q2_output.GetIndex() < 0 ||
|
| q1_output.Total() != 1 || q2_output.Total() != 1 ||
|
| !q1_output.BufferRead() || !q2_output.BufferRead())
|
| { Print("TrainTransition stage=current_q_read"); ReturnFalse; }
|
| PrintFormat("ORION Q iteration=%d q1=%.8f q2=%.8f td_q1=%.8f td_q2=%.8f",
|
| iteration, double(q1_output[0]), double(q2_output[0]),
|
| target_value - double(q1_output[0]), target_value - double(q2_output[0]));
|
| }
|
| //--- A critic gradient has no valid direction on the discontinuous no-trade
|
| //--- plateau. Keep learning Q there, but let only the realized-future teacher
|
| //--- move Actor back into the executable action manifold.
|
| const bool executable_action = (IsExecutableOrder(buy_lot, CurrentAction[1], CurrentAction[2]) ||
|
| IsExecutableOrder(sell_lot, CurrentAction[4], CurrentAction[5]));
|
| //--- Function if.
|
| if(iteration > 0 && UpdatePolicy > 0 && iteration % UpdatePolicy == 0)
|
| {
|
| //--- Function if.
|
| if(executable_action)
|
| {
|
| if(!PolicyBackward())
|
| { Print("TrainTransition stage=policy_backward"); ReturnFalse; }
|
| PolicyTransitions++;
|
| }
|
| else
|
| PolicySkippedInvalid++;
|
| }
|
| if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1) ||
|
| !Q2.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=critic_backward"); ReturnFalse; }
|
| //--- The teacher uses realized future bars, not one selected Forecast scenario.
|
| //--- It is deliberately a second Critic-only sample: Q1/Q2 are stateless,
|
| //--- whereas Actor has already performed its only forward for this state.
|
| double teacher_reward = 0;
|
| if(!BuildTeacherAction(position, GetPointer(Account), GetPointer(TeacherAction), teacher_reward))
|
| { Print("TrainTransition stage=teacher_action"); ReturnFalse; }
|
| //--- Function if.
|
| if(teacher_reward > 0)
|
| {
|
| if(trace_td)
|
| PrintFormat("ORION teacher iteration=%d reward=%.8f buy_lot=%.8f sell_lot=%.8f",
|
| iteration, teacher_reward,
|
| MathMax(0.0, double(TeacherAction[0] - TeacherAction[3])),
|
| MathMax(0.0, double(TeacherAction[3] - TeacherAction[0])));
|
| //--- Supervised Actor update reuses the current activation; it never advances
|
| //--- the history stack or invokes a second Actor forward.
|
| if(!Actor.backProp(GetPointer(TeacherAction), GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=teacher_actor_backward"); ReturnFalse; }
|
| TeacherTransitions++;
|
| TeacherRewardSum += teacher_reward;
|
| }
|
| //--- Critic receives every oracle action, including a losing one. Only Actor
|
| //--- is restricted to profitable oracle supervision above.
|
| if(!ScalarTarget.BufferInit(1, 0) || !ScalarTarget.Update(0, float(teacher_reward)) ||
|
| !BuildCriticInput(context_layer.getOutput(), GetPointer(TeacherAction), GetPointer(CriticInput)))
|
| { Print("TrainTransition stage=teacher_critic_input"); ReturnFalse; }
|
| if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1) ||
|
| !Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=teacher_critic_forward"); ReturnFalse; }
|
| if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1) ||
|
| !Q2.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=teacher_critic_backward"); ReturnFalse; }
|
| TeacherCriticTransitions++;
|
| //--- A second critic-only sample is uniformly random over executable lots and
|
| //--- stop distances. It never runs Actor backward or advances policy history.
|
| double random_reward = 0;
|
| if(!BuildRandomAction(position, GetPointer(Account), GetPointer(RandomAction), random_reward))
|
| { Print("TrainTransition stage=random_action"); ReturnFalse; }
|
| //--- Function if.
|
| if(random_reward > 0)
|
| {
|
| if(!Actor.backProp(GetPointer(RandomAction), GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=random_actor_backward"); ReturnFalse; }
|
| }
|
| if(!ScalarTarget.BufferInit(1, 0) || !ScalarTarget.Update(0, float(random_reward)) ||
|
| !BuildCriticInput(context_layer.getOutput(), GetPointer(RandomAction), GetPointer(CriticInput)))
|
| { Print("TrainTransition stage=random_critic_input"); ReturnFalse; }
|
| if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1) ||
|
| !Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=random_critic_forward"); ReturnFalse; }
|
| if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1) ||
|
| !Q2.backProp(GetPointer(ScalarTarget), GetPointer(ORIONMarket), -1))
|
| { Print("TrainTransition stage=random_critic_backward"); ReturnFalse; }
|
| RandomCriticTransitions++;
|
| //--- Only the three declared Forecast trainable buffers are read here. The
|
| //--- complete persistent Codebook is checked at checkpoint/lifecycle bounds.
|
| if(!ORIONVerifyFrozenWeightsExact())
|
| { Print("TrainTransition stage=frozen_forecast_check"); ReturnFalse; }
|
| return true;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Implements TrainORIONActorCritic. |
|
| //+------------------------------------------------------------------+
|
| void TrainORIONActorCritic(void)
|
| {
|
| int first = 0, last = 0;
|
| if(!PrepareHistory(first, last))
|
| { PrintFormat("%s -> %d history unavailable", __FUNCTION__, __LINE__); return; }
|
| uint shown = GetTickCount();
|
| int completed_iterations = 0;
|
| int failed_iteration = -1;
|
| int failed_position = -1;
|
| int position = last;
|
| int episode = 0;
|
| TeacherTransitions = 0;
|
| TeacherCriticTransitions = 0;
|
| RandomCriticTransitions = 0;
|
| TeacherRewardSum = 0;
|
| PolicyTransitions = 0;
|
| PolicySkippedInvalid = 0;
|
| for(int iteration = 0; iteration < Iterations && !IsStopped(); iteration++)
|
| {
|
| //--- Function if.
|
| if(episode == 0)
|
| {
|
| if(!ORIONForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
|
| break;
|
| const vector<float> sampled = SampleAccount(GetPointer(State), Rates[position].time, EtalonBalance, MinBalance);
|
| if(sampled.Size() != AccountDescr || !Account.AssignArray(sampled) ||
|
| (Account.GetIndex() >= 0 && !Account.BufferWrite()) || !Actor.Clear())
|
| break;
|
| }
|
| bool terminal = false;
|
| //--- Function if.
|
| if(!TrainTransition(position, iteration, terminal))
|
| {
|
| failed_iteration = iteration;
|
| failed_position = position;
|
| break;
|
| }
|
| completed_iterations++;
|
| if((NextAccount.GetIndex() >= 0 && !NextAccount.BufferRead()) || !Account.AssignArray(GetPointer(NextAccount)) ||
|
| (Account.GetIndex() >= 0 && !Account.BufferWrite()))
|
| { failed_iteration = iteration; failed_position = position; break; }
|
| position--;
|
| episode++;
|
| //--- Function if.
|
| if(position < first || terminal || episode >= MathMax(1, EpisodeBars))
|
| {
|
| if(position < first)
|
| position = last;
|
| episode = 0;
|
| }
|
| //--- Function if.
|
| if(GetTickCount() - shown > 500)
|
| {
|
| const double q1_rmse = MathSqrt(MathMax(0.0, double(Q1.getRecentAverageError()))) / MathMax(EtalonBalance, 1.0);
|
| const double q2_rmse = MathSqrt(MathMax(0.0, double(Q2.getRecentAverageError()))) / MathMax(EtalonBalance, 1.0);
|
| const double teacher_mean = (TeacherTransitions > 0 ? TeacherRewardSum / double(TeacherTransitions) : 0.0);
|
| Comment(StringFormat("ORION AC %6.2f%% Q1 rRMSE %.8f Q2 rRMSE %.8f teacherA %I64u teacherQ %I64u randomQ %I64u R %.2f policy %I64u skip %I64u",
|
| 100.0 * iteration / MathMax(Iterations, 1), q1_rmse, q2_rmse,
|
| TeacherTransitions, TeacherCriticTransitions, RandomCriticTransitions,
|
| teacher_mean, PolicyTransitions, PolicySkippedInvalid));
|
| shown = GetTickCount();
|
| }
|
| }
|
| Comment("");
|
| //--- Function if.
|
| if(completed_iterations != Iterations)
|
| {
|
| if(failed_iteration >= 0)
|
| PrintFormat("%s -> %d publication aborted: TrainTransition failed iteration=%d position=%d completed=%d/%d",
|
| __FUNCTION__, __LINE__, failed_iteration, failed_position, completed_iterations, Iterations);
|
| else
|
| PrintFormat("%s -> %d publication aborted: stop requested completed=%d/%d",
|
| __FUNCTION__, __LINE__, completed_iterations, Iterations);
|
| ExpertRemove();
|
| return;
|
| }
|
| if(!SaveORIONPolicies())
|
| PrintFormat("%s -> %d save failed", __FUNCTION__, __LINE__);
|
| ExpertRemove();
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Function OnInit. |
|
| //+------------------------------------------------------------------+
|
| int OnInit()
|
| {
|
| ResetLastError();
|
| if(!ORIONInitIndicators() || !ORIONLoadForecastInference() || !LoadOrCreateORIONPolicies() ||
|
| //--- Function ORIONVerifyFrozenForecastExact.
|
| !ORIONVerifyFrozenForecastExact())
|
| {
|
| PrintFormat("ORION Actor-Critic initialization failed at line %d error=%d", __LINE__, GetLastError());
|
| return INIT_FAILED;
|
| }
|
| return (EventChartCustom(ChartID(), 1, 0, 0, "Init") ? INIT_SUCCEEDED : INIT_FAILED);
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Function OnDeinit. |
|
| //+------------------------------------------------------------------+
|
| void OnDeinit(const int reason)
|
| {
|
| //--- Only a completed explicit training loop persists all six artifacts and
|
| //--- writes the hash manifest last. Deinit must not create a mixed generation.
|
| if(ORIONFrozenBaselineReady && ORIONForecast != NULL && !ORIONVerifyFrozenForecastExact())
|
| PrintFormat("%s -> %d forecast mutation", __FUNCTION__, __LINE__);
|
| ORIONForecast = NULL;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| Chart event callback. |
|
| //+------------------------------------------------------------------+
|
| void OnChartEvent(const int id, const long &lparam, const double &dparam, const string &sparam)
|
| {
|
| if(id == 1001)
|
| TrainORIONActorCritic();
|
| }
|
| //+------------------------------------------------------------------+ |
