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NN_in_Trading/Experts/AC-SRM/Study.mq5

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MQL5
原始文件永久链接普通视图历史

2026-09-29 00:57:33 +03:00
//+------------------------------------------------------------------+
//| 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 strict
#include "Trajectory.mqh"
//---
input group "---- AC-SRM training ----"
input datetime Start = D'2024.01.01'; //Training period start
input datetime End = D'2026.01.01'; //Training period end
input int Iterations = 200000; //Training iterations
input int EpisodeBars = 2 * StackSize; //Bars per episode
input int InpDealHorizon = 24; //Deal evaluation horizon (bars)
input double MinBalance = 50.0; //Minimum account balance
input int UpdatePolicy = 2; //Actor update interval (PolicyUpdatePeriod)
input int InpSeed = 20260921; //Replay seed base (per-owner)
input int InpUpdateTargets = 20; //Target update interval (manifest metadata)
input float InpTauT = 1.0f; //Target copy tau (1.0 = full copy)
//---
input group "---- SRM critic head ----"
input int InpRiskType = defSRM_MEAN_CVAR; //Risk measure (defSRM_*)
input float InpRiskParameter = 0.1f; //Risk parameter (alpha/lambda/nu)
input float InpMeanWeight = 0.0f; //Mean-CVaR omega
input float InpProbWeight = 1.0f; //Distribution probability weight
input float InpValueWeight = 1.0f; //Distribution value weight
input int InpQuantiles = 32; //Head quantiles (factory-fixed)
input int InpSelector = 0; //Policy SRM gradient source (0=owner 1=Q1 2=Q2)
//---
input group "---- AC-SRM production checkpoint ----"
input ENUM_OMPB_STAGE InpOMPBStage = OMPB_STAGE_BASE_POLICY; //Chain stage: keep 03 (base policy)
//---
int Epochs = 0;
CNet Actor;
CNet Q1;
CNet Q2;
CBufferFloat State;
CBufferFloat TimeState;
CBufferFloat Account;
CBufferFloat NextAccount;
CBufferFloat CurrentAction;
CBufferFloat TeacherAction;
CBufferFloat RandomAction;
CBufferFloat CriticInput;
CBufferFloat SRMValue;
CBufferFloat SRMGrad;
ulong PolicyTransitions = 0;
ulong PolicyEvents = 0;
ulong PolicySkippedInvalid = 0;
ulong CriticTransitions = 0;
ulong CriticQ1Transitions = 0;
ulong CriticQ2Transitions = 0;
ulong TeacherTransitions = 0;
ulong TeacherCriticTransitions = 0;
ulong RandomCriticTransitions = 0;
ulong CriticQ1Total = 0;
ulong CriticQ2Total = 0;
ulong UnresolvedLabels = 0;
ulong TagTP = 0;
ulong TagSL = 0;
ulong TagHorizon = 0;
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double RewardSum = 0;
double CriticQ1Error = 0;
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double CriticQ2Error = 0;
bool Stage03StopRequestedLogged = false;
//+-------------------------------------------------------------------+
//| Logs one user-requested Stage 03 stop without misclassifying it. |
//+-------------------------------------------------------------------+
void LogStage03StopRequested(const string phase)
{
if(Stage03StopRequestedLogged)
return;
PrintFormat("ACSRM_STAGE03_STOP_REQUESTED phase=%s", phase);
Stage03StopRequestedLogged = true;
}
//+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
//| Creates the AC-SRM Actor and distribution-Critic architectures. Actor : Base(13)+Cross(3)+Conv(GELU 48->16)+Conv(SIGMOID 16->6). Critic: Base(19)+Cross(5)+Conv(GELU 80->16)+Base(32,None)+ ACSRM head{count=1,window_out=32,None} (factory-fixed). |
//+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
bool CreateActorCriticDescriptions(CArrayObj *&actor_descr, CArrayObj *&critic_descr)
{
actor_descr = new CArrayObj();
critic_descr = new CArrayObj();
if(!actor_descr || !critic_descr)
{
DeleteObj(actor_descr);
DeleteObjAndFalse(critic_descr);
}
actor_descr.FreeMode(true);
critic_descr.FreeMode(true);
//--- Actor: plain scenario trunk without the D2Skill bank layer.
if(!SkillAddBase(actor_descr, AccountDescr) ||
!SkillAddCross(actor_descr, false) ||
!SkillAddConv(actor_descr, 1, (3 * EmbeddingSize), EmbeddingSize, 1, GELU) ||
!SkillAddConv(actor_descr, 1, EmbeddingSize, NActions / 2, 2, SIGMOID))
{
DeleteObj(actor_descr);
DeleteObjAndFalse(critic_descr);
}
//--- Critic: Base(19) feeds the Cross(5) trunk; the last plain Base(32,None)
//--- presents exactly 32 inputs to the factory-fixed ACSRM head.
if(!SkillAddBase(critic_descr, AccountDescr + NActions) ||
!SkillAddCross(critic_descr, true) ||
!SkillAddConv(critic_descr, 1, (5 * EmbeddingSize), EmbeddingSize, 1, GELU) ||
!SkillAddBase(critic_descr, 32))
{
DeleteObj(actor_descr);
DeleteObjAndFalse(critic_descr);
}
CLayerDescription *head = new CLayerDescription();
if(!head)
{
DeleteObj(actor_descr);
DeleteObjAndFalse(critic_descr);
}
head.type = defNeuronACSRM;
head.count = 1;
head.window_out = InpQuantiles;
head.activation = None;
head.optimization = ADAM;
head.batch = BatchSize;
if(!critic_descr.Add(head))
{
DeleteObjAndFalse(head);
DeleteObj(actor_descr);
DeleteObjAndFalse(critic_descr);
}
return(true);
}
//+------------------------------------------------------------------+
//| Validates the AC-SRM policy shapes (no D2 banks, ACSRM heads). |
//+------------------------------------------------------------------+
bool ValidatePolicyShapeACSRM(CNet &actor, CNet &q1, CNet &q2)
{
CNeuronBaseOCL *a0 = actor.Layer(0);
CNeuronBaseOCL *a1 = actor.Layer(1);
CNeuronBaseOCL *a3 = actor.Layer(3);
if(!a0 || !a1 || !a3 || a0.getOutput().Total() != (int)AccountDescr ||
a1.Type() != defNeuronScenarioCrossAttention ||
a3.getOutput().Total() != (int)NActions)
ReturnFalse;
for(int i = 0; i < 2; i++)
{
CNet *critic = (i == 0 ? GetPointer(q1) : GetPointer(q2));
CNeuronBaseOCL *c0 = critic.Layer(0);
CNeuronBaseOCL *c3 = critic.Layer(3);
CNeuronBaseOCL *c4 = critic.Layer(4);
if(!c0 || !c3 || !c4 ||
c0.getOutput().Total() != (int)(AccountDescr + NActions) ||
c3.getOutput().Total() != 32 ||
c4.Type() != defNeuronACSRM ||
c4.getOutput().Total() != 1)
ReturnFalse;
}
return(true);
}
//+------------------------------------------------------------------+
//| Creates fresh AC-SRM Actor + Q1 + Q2 on the Market context. |
//+------------------------------------------------------------------+
bool CreatePolicySet(void)
{
CArrayObj *actor_descr = NULL, *critic_descr = NULL;
if(!CreateActorCriticDescriptions(actor_descr, critic_descr))
{
DeleteObj(actor_descr);
DeleteObj(critic_descr);
ReturnFalse;
}
bool result = Actor.Create(actor_descr);
if(result)
result = Actor.SetOpenCLChecked(SkillMarket.GetOpenCL());
if(result)
result = Q1.Create(critic_descr);
if(result)
result = Q1.SetOpenCLChecked(SkillMarket.GetOpenCL());
if(result)
result = Q2.Create(critic_descr);
if(result)
result = Q2.SetOpenCLChecked(SkillMarket.GetOpenCL());
DeleteObj(actor_descr);
DeleteObj(critic_descr);
return(result);
}
//+------------------------------------------------------------------+
//| Loads or creates ACSRMActor.nnw / ACSRMQ1.nnw / ACSRMQ2.nnw. |
//+------------------------------------------------------------------+
bool LoadOrCreatePolicies(void)
{
if(!SkillACRecoverTransaction(Skill_ACTOR_FILE, Skill_Q1_FILE, Skill_Q2_FILE,
Skill_AC_MANIFEST_FILE, Skill_ACTOR_NEXT_FILE,
Skill_Q1_NEXT_FILE, Skill_Q2_NEXT_FILE,
Skill_AC_MANIFEST_NEXT_FILE, Skill_ACTOR_PREVIOUS_FILE,
Skill_Q1_PREVIOUS_FILE, Skill_Q2_PREVIOUS_FILE,
Skill_AC_MANIFEST_PREVIOUS_FILE,
Skill_AC_TRANSACTION_FILE))
{
Print("Skill policy recovery=FAIL; incomplete checkpoint requires repair");
ReturnFalse;
}
const bool manifest_exists = FileIsExist(Skill_AC_MANIFEST_FILE, FILE_COMMON);
bool loaded = false;
if(manifest_exists)
{
loaded = (SkillValidateACManifestFile(true, Skill_AC_MANIFEST_FILE, false) &&
SkillLoadPolicyNet(Actor, Skill_ACTOR_FILE) &&
SkillLoadPolicyNet(Q1, Skill_Q1_FILE) &&
SkillLoadPolicyNet(Q2, Skill_Q2_FILE));
if(!loaded)
Print("Skill policy restore=FAIL; recreating incompatible policy set");
}
if(!loaded)
{
if(!CreatePolicySet())
ReturnFalse;
}
if(!SkillBindPolicyOpenCLChecked(Actor, Q1, Q2) ||
!ValidatePolicyShapeACSRM(Actor, Q1, Q2))
ReturnFalse;
Actor.TrainMode(true);
Q1.TrainMode(true);
Q2.TrainMode(true);
return(true);
}
//+------------------------------------------------------------------+
//| Saves the Stage 03 AC-SRM policy checkpoint. |
//+------------------------------------------------------------------+
bool SavePolicies(void)
{
return(SkillSaveStage03StopCheckpoint(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;
//--- The deal outcome must be resolvable INSIDE the training window: the
//--- full horizon is reserved past the End boundary so every label
//--- (TP/SL/HORIZON) completes within [Start, End].
first_position = end + MathMax(1, SkillManifestDealHorizon);
//--- CreateBuffers(position,...,forecast) forms its state from
//--- position+NForecast and needs HistoryBars bars behind it.
last_position = start - HistoryBars - NForecast;
return(last_position > first_position);
}
//+-------------------------------------------------------------------+
//| Applies the distribution target (realized return) to one critic. |
//+-------------------------------------------------------------------+
bool UpdateCriticDistribution(CNet &critic, const double reward, const bool is_q1)
{
CNeuronBaseOCL *base = critic.Layer(3);
CNeuronBaseOCL *head = critic.Layer(4);
if(!base || !head || head.Type() != defNeuronACSRM || !MathIsValidNumber(reward) ||
base.getOutput().Total() != 32)
ReturnFalse;
CNeuronACSRM *acsrm = (CNeuronACSRM*)head;
if(!acsrm.calcDistributionTargetGradients(base, float(reward),
InpProbWeight, InpValueWeight))
ReturnFalse;
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 //--- Real ACSRM critic error: RMSE of the FQF centroids from the realized
//--- outcome. backPropGradient never updates getRecentAverageError(), so
//--- the old dashboard rRMSE stayed 0 forever; track the loss here.
double rmse = -1;
{
CBufferFloat *quantiles = NULL, *probabilities = NULL;
if(acsrm.GetDistributionBuffers(quantiles, probabilities) &&
quantiles != NULL && quantiles.Total() > 0 && quantiles.BufferRead())
{
double sum_sq = 0;
bool valid = true;
for(int i = 0; i < quantiles.Total(); i++)
{
const double z = double(quantiles[i]);
if(!MathIsValidNumber((float)z))
{ valid = false; break; }
sum_sq += (z - reward) * (z - reward);
}
if(valid)
rmse = MathSqrt(sum_sq / double(quantiles.Total()));
}
}
if(rmse >= 0)
{
if(is_q1)
CriticQ1Error += (rmse - CriticQ1Error) * 0.01;
else
CriticQ2Error += (rmse - CriticQ2Error) * 0.01;
}
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 return(critic.backPropGradient(GetPointer(SkillMarket), -1, -1, true));
}
//+------------------------------------------------------------------+
//| Actor policy update through the frozen critic's SRM. |
//+------------------------------------------------------------------+
int SelectPolicyCritic(const int event_index)
{
//--- Exactly ONE Critic is the gradient source per policy-update event.
//--- The default selector is balanced over Actor-update EVENTS (a hash of
//--- the event counter and the replay seed), NOT over the Owner of the
//--- training example: with a sequential position-- sampler consecutive
//--- policy events inside a stretch would share one Owner parity.
switch(InpSelector)
{
case 1: return(0);
case 2: return(1);
default: return(int((ulong(MathMax(event_index, 0)) * ulong(2654435761u) +
ulong(InpSeed)) & 1));
}
}
//+------------------------------------------------------------------+
//| Returns the scalar SRM value of one Critic head distribution. |
//+------------------------------------------------------------------+
bool CriticSRMValue(CNet &critic, float &value)
{
CNeuronBaseOCL *head = critic.Layer(4);
if(!head || head.Type() != defNeuronACSRM)
ReturnFalse;
CNeuronACSRM *acsrm = (CNeuronACSRM*)head;
if(!SRMValue.BufferInit(1, 0) || !SRMValue.BufferCreate(SkillMarket.GetOpenCL()) ||
!SRMValue.BufferWrite())
ReturnFalse;
if(!acsrm.SRMForward(GetPointer(SRMValue), 1, InpRiskType, InpRiskParameter, InpMeanWeight))
ReturnFalse;
if(!SRMValue.BufferRead())
ReturnFalse;
value = float(SRMValue[0]);
return(MathIsValidNumber(value));
}
//+----------------------------------------------------------------------------------------------------------------+
//| Reports whether the last SRM refusal was a distribution-contract rejection rather than an execution failure. |
//+----------------------------------------------------------------------------------------------------------------+
bool CriticLastSRMInvalid(CNet &critic)
{
CNeuronBaseOCL *head = critic.Layer(4);
if(!head || head.Type() != defNeuronACSRM)
return(false);
return(((CNeuronACSRM*)head).LastSRMInvalid());
}
//+-----------------------------------------------------------------------------------+
//| Actor policy update through the frozen critic's SRM (PolicyOutputGradientACSRM). |
//+-----------------------------------------------------------------------------------+
bool PolicyOutputGradientACSRM(CNet &actor, CNet &critic, const float upstream = 1.0f)
{
CNeuronBaseOCL *critic_head = critic.Layer(4);
CNeuronBaseOCL *critic_base = critic.Layer(0);
CNeuronBaseOCL *actor_output = actor.Layer(3);
if(!critic_head || critic_head.Type() != defNeuronACSRM || !critic_base || !actor_output ||
critic_base.getGradient().Total() != (int)(AccountDescr + NActions) ||
actor_output.getGradient().Total() != (int)NActions)
ReturnFalse;
CNeuronACSRM *acsrm = (CNeuronACSRM*)critic_head;
if(!SRMValue.BufferInit(1, 0) || !SRMValue.BufferCreate(SkillMarket.GetOpenCL()) ||
!SRMValue.BufferWrite())
ReturnFalse;
if(!acsrm.SRMForward(GetPointer(SRMValue), 1, InpRiskType, InpRiskParameter, InpMeanWeight))
ReturnFalse;
if(!SRMGrad.BufferInit(1, upstream) || !SRMGrad.BufferCreate(SkillMarket.GetOpenCL()) ||
!SRMGrad.BufferWrite())
ReturnFalse;
if(!acsrm.SRMGradient(GetPointer(SRMGrad), 1, InpRiskType, InpRiskParameter, InpMeanWeight))
ReturnFalse;
//--- Freeze critic weights while propagating the policy gradient.
if(!critic.SetWeightsUpdate(false))
ReturnFalse;
bool result = critic.backPropGradient(GetPointer(SkillMarket), -1, -1, true);
if(!critic.SetWeightsUpdate(true))
result = false;
CBufferFloat account_gradient;
if(!(result &&
account_gradient.BufferInit(AccountDescr, 0) &&
(account_gradient.GetIndex() >= 0 || account_gradient.BufferCreate(SkillMarket.GetOpenCL())) &&
SkillDevice.Split2(GetPointer(account_gradient), actor_output.getGradient(),
critic_base.getGradient(), AccountDescr, NActions) &&
ApplyActorSigmoidDerivative(actor_output)))
result = false;
return(result);
}
//+---------------------------------------------------------------------------------------------------------------------+
//| Accumulates one supervised signal (teacher/random) into the actor output-layer gradient: (t - a) .* a .* (1 - a). |
//+---------------------------------------------------------------------------------------------------------------------+
bool AddActorSupervisedGradient(CNet &actor, CBufferFloat *target)
{
CNeuronBaseOCL *actor_output = actor.Layer(3);
if(!actor_output || !target || target.Total() != (int)NActions ||
actor_output.getOutput().Total() != (int)NActions ||
actor_output.getGradient().Total() != (int)NActions || target.GetIndex() < 0 ||
actor_output.getOutput().GetIndex() < 0 || actor_output.getGradient().GetIndex() < 0)
ReturnFalse;
if(!ActorDerivOneMinus.BufferInit(NActions, 0) ||
!ActorDerivOneMinus.BufferCreate(SkillMarket.GetOpenCL()) ||
!ActorDerivOneMinus.BufferWrite() ||
!ActorGradScratch.BufferInit(NActions, 0) ||
!ActorGradScratch.BufferCreate(SkillMarket.GetOpenCL()) ||
!ActorGradScratch.BufferWrite() ||
!SkillDevice.Bind(SkillMarket.GetOpenCL()))
ReturnFalse;
//--- diff = t - a; scaled = diff * a * (1 - a); accumulate on device.
//--- This reproduces the library's calcOutputGradients for a SIGMOID
//--- output exactly, but pre-update and summed before ONE weight update.
if(!SkillDevice.Subtract(target, actor_output.getOutput(), GetPointer(ActorDerivOneMinus), NActions) ||
!SkillDevice.DeActivationOnDevice(actor_output.getOutput(), GetPointer(ActorGradScratch),
GetPointer(ActorDerivOneMinus), SIGMOID) ||
!SkillDevice.Add(actor_output.getGradient(), GetPointer(ActorGradScratch),
actor_output.getGradient(), NActions))
ReturnFalse;
return(true);
}
//+------------------------------------------------------------------+
//| Implements TrainTransition. |
//+------------------------------------------------------------------+
bool TrainTransition(const int position, const int iteration, bool &terminal)
{
terminal = false;
const bool trace_td = (iteration < 24 || iteration % 10000 == 0 || iteration >= Iterations - 1);
if(!SkillForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
{ Print("TrainTransition stage=current_forecast"); ReturnFalse; }
double reward = 0;
if(!Actor.feedForward(GetPointer(Account), 1, false, GetPointer(SkillMarket), -1) ||
!ReadAction(Actor, GetPointer(CurrentAction)) ||
!AdvanceAccount(GetPointer(Account), GetPointer(CurrentAction), position, MinBalance,
GetPointer(NextAccount), reward, terminal))
{ Print("TrainTransition stage=account_transition"); ReturnFalse; }
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("ACSRM action iteration=%d balance=%.2f buy_lot=%.8f buy_tp=%.8f buy_sl=%.8f " +
"sell_lot=%.8f sell_tp=%.8f sell_sl=%.8f",
iteration, MathMax(0.0, double(Account[0]) * EtalonBalance),
buy_lot, double(CurrentAction[1]), double(CurrentAction[2]),
sell_lot, double(CurrentAction[4]), double(CurrentAction[5]));
if(trace_td)
PrintFormat("ACSRM return iteration=%d reward=%.8f balance=%.2f terminal=%s",
iteration, reward, MathMax(0.0, double(NextAccount[0]) * EtalonBalance),
(terminal ? "true" : "false"));
//--- Critic input is built from the live device activations.
CNeuronBaseOCL *context_layer = Actor.Layer(0);
CNeuronBaseOCL *actor_layer = Actor.Layer(3);
if(!context_layer || !actor_layer ||
!BuildCriticInput(context_layer.getOutput(), actor_layer.getOutput(), GetPointer(CriticInput)))
{ Print("TrainTransition stage=critic_input"); ReturnFalse; }
if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1) ||
!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1))
{ Print("TrainTransition stage=critic_forward"); ReturnFalse; }
//--- Offline target: the COMPLETED result of the specific deal fixed at this
//--- bar over the configured horizon (TP first / SL first / HORIZON outcome),
//--- discounted by elapsed bars. Realized future bars may label the deal but
//--- never enter the decision state (SkillForwardForecast already shifts the
//--- window to the strictly-past bars). It is a finished result and needs no
//--- target bootstrap.
int deal_tag = 0;
const double deal_y = EvaluateAction(GetPointer(CurrentAction),
MathMax(0.0, double(Account[0]) * EtalonBalance),
(uint)position, InpDealHorizon, deal_tag);
if(!MathIsValidNumber(deal_y))
{ Print("TrainTransition stage=deal_label"); ReturnFalse; }
if(trace_td)
PrintFormat("ACSRM offline iteration=%d outcome=%.8f tag=%d advance_reward=%.8f horizon=%d",
iteration, deal_y, deal_tag, reward, InpDealHorizon);
//--- Offline Owner separation: the example id (position,slot,seed) fixes one
//--- stable Owner-Critic; re-reading the same record keeps its label there.
const int owner = SkillObservationOwner(position, 0, InpSeed);
if(deal_tag == 0)
UnresolvedLabels++;
else
{
CNet *owner_critic = (owner == 0 ? GetPointer(Q1) : GetPointer(Q2));
if(!owner_critic.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1) ||
!UpdateCriticDistribution(owner_critic, deal_y, (owner_critic == GetPointer(Q1))))
{
if(IsStopped())
{
LogStage03StopRequested("critic_distribution_backward");
return(false);
}
Print("TrainTransition stage=critic_distribution_backward");
ReturnFalse;
}
CriticTransitions++;
if(owner_critic == GetPointer(Q1))
{
CriticQ1Transitions++;
CriticQ1Total++;
}
else
{
CriticQ2Transitions++;
CriticQ2Total++;
}
RewardSum += deal_y;
if(deal_tag == 1)
TagTP++;
else
if(deal_tag == 2)
TagSL++;
else
TagHorizon++;
}
//--- Actor multi-objective update. Policy, teacher and random signals are
//--- computed on the SAME forward activations and the SAME pre-update
//--- weights and ACCUMULATED into the output-layer gradient; ONE
//--- backPropGradient applies them together. No weight change happens
//--- between the contributions so credit assignment stays consistent.
bool actor_update_needed = false;
bool actor_update_blocked = false;
//--- Gradient hygiene: clear the Actor output gradient once BEFORE the
//--- current observation accumulates its contributions. A deferred policy
//--- event (UpdatePolicy > 1) would otherwise Add teacher/random on top of
//--- the stale gradient the previous combined update consumed, since Split2
//--- only overwrites the gradient on the policy event.
if(actor_layer.getGradient().Total() != (int)NActions ||
!actor_layer.getGradient().Fill(0))
{ Print("TrainTransition stage=actor_gradient_reset"); ReturnFalse; }
//--- Policy gate: a RESOLVABLE deal label (tag != 0). Requiring broker
//--- executability here would give the cold-start Actor no gradient while
//--- its lots are below LotsMin(); with the min-lot floor inside
//--- CheckAction the gradient direction exists from the first iteration.
const bool resolvable_action = (deal_tag != 0);
if(iteration > 0 && UpdatePolicy > 0 && iteration % UpdatePolicy == 0)
{
if(resolvable_action)
{
//--- Fresh Critic activations on the same observation AFTER the Critic
//--- weight update: the policy gradient must not mix pre-update
//--- activations with post-update weights.
if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1) ||
!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1))
{ Print("TrainTransition stage=policy_critic_forward"); ReturnFalse; }
const int pick = SelectPolicyCritic(int(PolicyEvents++));
CNet *policy_critic = (pick == 0 ? GetPointer(Q1) : GetPointer(Q2));
float policy_srm = 0.0f;
if(!CriticSRMValue(*policy_critic, policy_srm))
{
if(CriticLastSRMInvalid(*policy_critic))
{
PolicySkippedInvalid++;
actor_update_blocked = true;
if(trace_td)
PrintFormat("ACSRM policy skipped iteration=%d reason=invalid_srm_distribution", iteration);
}
else
{ Print("TrainTransition stage=policy_critic_srm"); ReturnFalse; }
}
else
{
const bool policy_ok = PolicyOutputGradientACSRM(Actor, *policy_critic, 1.0f);
if(!policy_ok)
{
if(CriticLastSRMInvalid(*policy_critic))
{
PolicySkippedInvalid++;
actor_update_blocked = true;
if(trace_td)
PrintFormat("ACSRM policy skipped iteration=%d reason=invalid_srm_gradient", iteration);
}
else
{ Print("TrainTransition stage=policy_backward"); ReturnFalse; }
}
else
{
PolicyTransitions++;
actor_update_needed = true;
}
}
}
else
PolicySkippedInvalid++;
}
//--- Restored original-project Actor signals: a profitable realized-future
//--- teacher and a profitable random executable action re-enter the action
//--- manifold; their deals also expand Owner-Critic coverage. Only
//--- RESOLVABLE labels (tag != 0) train anything: with the SL sign fixed
//--- the reward > 0 gate now stands for a genuine TP profit.
double teacher_reward = 0;
int teacher_tag = 0;
if(!BuildTeacherAction(position, GetPointer(Account), GetPointer(TeacherAction),
teacher_reward, InpDealHorizon, teacher_tag))
{ Print("TrainTransition stage=teacher_action"); ReturnFalse; }
if(trace_td)
PrintFormat("ACSRM teacher iteration=%d reward=%.8f tag=%d", iteration, teacher_reward, teacher_tag);
if(teacher_reward > 0.0 && teacher_tag != 0)
{
if(!AddActorSupervisedGradient(Actor, GetPointer(TeacherAction)))
{
if(IsStopped())
{
LogStage03StopRequested("teacher_actor_backward");
return(false);
}
Print("TrainTransition stage=teacher_actor_backward");
ReturnFalse;
}
TeacherTransitions++;
actor_update_needed = true;
}
if(teacher_tag == 0)
UnresolvedLabels++;
else
{
const int teacher_owner = SkillObservationOwner(position, 1, InpSeed);
CNet *teacher_critic = (teacher_owner == 0 ? GetPointer(Q1) : GetPointer(Q2));
if(!BuildCriticInput(context_layer.getOutput(), GetPointer(TeacherAction), GetPointer(CriticInput)) ||
!teacher_critic.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1) ||
!UpdateCriticDistribution(teacher_critic, teacher_reward, (teacher_critic == GetPointer(Q1))))
{
if(IsStopped())
{
LogStage03StopRequested("teacher_critic_backward");
return(false);
}
Print("TrainTransition stage=teacher_critic_backward");
ReturnFalse;
}
TeacherCriticTransitions++;
if(teacher_critic == GetPointer(Q1))
CriticQ1Total++;
else
CriticQ2Total++;
}
double random_reward = 0;
int random_tag = 0;
if(!BuildRandomAction(position, GetPointer(Account), GetPointer(RandomAction),
random_reward, InpDealHorizon, random_tag))
{ Print("TrainTransition stage=random_action"); ReturnFalse; }
if(trace_td)
PrintFormat("ACSRM random iteration=%d reward=%.8f tag=%d", iteration, random_reward, random_tag);
if(random_reward > 0.0 && random_tag != 0)
{
if(!AddActorSupervisedGradient(Actor, GetPointer(RandomAction)))
{
if(IsStopped())
{
LogStage03StopRequested("random_actor_backward");
return(false);
}
Print("TrainTransition stage=random_actor_backward");
ReturnFalse;
}
actor_update_needed = true;
}
if(random_tag == 0)
UnresolvedLabels++;
else
{
const int random_owner = SkillObservationOwner(position, 2, InpSeed);
CNet *random_critic = (random_owner == 0 ? GetPointer(Q1) : GetPointer(Q2));
if(!BuildCriticInput(context_layer.getOutput(), GetPointer(RandomAction), GetPointer(CriticInput)) ||
!random_critic.feedForward(GetPointer(CriticInput), 1, false, GetPointer(SkillMarket), -1) ||
!UpdateCriticDistribution(random_critic, random_reward, (random_critic == GetPointer(Q1))))
{
if(IsStopped())
{
LogStage03StopRequested("random_critic_backward");
return(false);
}
Print("TrainTransition stage=random_critic_backward");
ReturnFalse;
}
RandomCriticTransitions++;
if(random_critic == GetPointer(Q1))
CriticQ1Total++;
else
CriticQ2Total++;
}
//--- ONE coherent Actor weight update with the combined gradient (if any).
//--- An invalid SRM distribution blocks the whole Actor update for this
//--- observation, matching srm_invalid_policy=skip_actor_update.
if(actor_update_needed && !actor_update_blocked &&
!Actor.backPropGradient(GetPointer(SkillMarket), -1, -1, true))
{
if(IsStopped())
{
LogStage03StopRequested("actor_combined_backward");
return(false);
}
Print("TrainTransition stage=actor_combined_backward");
ReturnFalse;
}
return(true);
}
//+------------------------------------------------------------------+
//| Implements TrainACSRMActorCritic. |
//+------------------------------------------------------------------+
void TrainACSRMActorCritic(void)
{
Stage03StopRequestedLogged = false;
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;
PolicyTransitions = 0;
PolicyEvents = 0;
PolicySkippedInvalid = 0;
CriticTransitions = 0;
CriticQ1Transitions = 0;
CriticQ2Transitions = 0;
TeacherTransitions = 0;
TeacherCriticTransitions = 0;
RandomCriticTransitions = 0;
CriticQ1Total = 0;
CriticQ2Total = 0;
UnresolvedLabels = 0;
TagTP = 0;
TagSL = 0;
TagHorizon = 0;
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 RewardSum = 0;
CriticQ1Error = 0;
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 CriticQ2Error = 0;
for(int iteration = 0; iteration < Iterations && !IsStopped(); iteration++)
{
//--- Fresh episode: seed the sampler from the observation Owner.
if(episode == 0)
{
if(!SkillForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
break;
MathSrand(InpSeed + position);
const vector<float> sampled = SampleAccount(GetPointer(State), Rates[position].time,
EtalonBalance, MinBalance);
if(sampled.Size() != AccountDescr || !Account.AssignArray(sampled) ||
(Account.GetIndex() < 0 && !Account.BufferCreate(SkillMarket.GetOpenCL())) ||
!Account.BufferWrite() || !Actor.Clear())
break;
}
bool terminal = false;
if(!TrainTransition(position, iteration, terminal))
{
if(IsStopped())
{
LogStage03StopRequested("train_transition");
break;
}
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++;
//--- Close the episode on range exhaustion, its bar limit or a
//--- terminal account state.
if(position < first || episode >= MathMax(1, EpisodeBars) || terminal)
{
if(iteration % 10000 == 0)
PrintFormat("ACSRM episode closed iteration=%d episode=%d position=%d terminal=%s",
iteration, episode, position, (terminal ? "true" : "false"));
if(position < first)
position = last;
episode = 0;
}
//--- Refresh the progress panel without delaying training updates.
if(GetTickCount() - shown > 500)
{
const double r_mean = (CriticTransitions > 0 ? RewardSum / double(CriticTransitions) : 0.0);
Comment(StringFormat("ACSRM AC %6.2f%% Q1 err %.8f Q2 err %.8f critic %I64u " +
"policy %I64u skip %I64u Rmean %.4f",
100.0 * iteration / MathMax(Iterations, 1), CriticQ1Error, CriticQ2Error,
CriticTransitions, PolicyTransitions, PolicySkippedInvalid,
r_mean));
shown = GetTickCount();
}
}
if(IsStopped())
{
//--- A stop can land between parameter updates; never publish a partial step.
LogStage03StopRequested("training_loop");
Comment("");
PrintFormat("ACSRM_STAGE03_STOP_NO_PUBLISH completed=%d/%d critic=%I64u q1=%I64u q2=%I64u policy=%I64u",
completed_iterations, Iterations, CriticTransitions,
CriticQ1Transitions, CriticQ2Transitions, PolicyTransitions);
return;
}
Comment("");
//--- Reject publication unless every requested iteration completed safely.
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);
return;
}
if(!SavePolicies())
PrintFormat("%s -> %d save failed", __FUNCTION__, __LINE__);
else
PrintFormat("ACSRM_STUDY_PASS iterations=%d critic=%I64u q1=%I64u q2=%I64u " +
"q1_total=%I64u q2_total=%I64u policy=%I64u skip=%I64u teacher=%I64u " +
"teacher_critic=%I64u random_critic=%I64u unresolved=%I64u " +
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 "tags_tp=%I64u tags_sl=%I64u tags_horizon=%I64u " +
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 "q1_err=%.8f q2_err=%.8f published=canonical",
completed_iterations, CriticTransitions, CriticQ1Transitions, CriticQ2Transitions,
CriticQ1Total, CriticQ2Total,
PolicyTransitions, PolicySkippedInvalid, TeacherTransitions,
TeacherCriticTransitions, RandomCriticTransitions, UnresolvedLabels,
TagTP, TagSL, TagHorizon, CriticQ1Error, CriticQ2Error);
}
//+------------------------------------------------------------------+
//| Initializes the Stage 03 BasePolicy Expert. |
//+------------------------------------------------------------------+
int OnInit()
{
ResetLastError();
//--- Pin the deal-evaluation horizon for manifest writer/validator symmetry.
SkillManifestDealHorizon = MathMax(1, InpDealHorizon);
SkillManifestTargetTau = double(InpTauT);
SkillManifestTargetUpdatePeriod = InpUpdateTargets;
if(InpSelector < 0 || InpSelector > 2 ||
InpUpdateTargets < 0 || InpTauT < 0.0f || InpTauT > 1.0f)
{
PrintFormat("ACSRM input validation failed at line %d error=%d",
__LINE__, GetLastError());
return(INIT_FAILED);
}
//--- Confirm Stage 03 and that the frozen Forecast checkpoint is intact.
if(InpOMPBStage != OMPB_STAGE_BASE_POLICY ||
!SkillInitIndicators() || !SkillLoadForecastInference() ||
!SkillConfigureProductionACSRMCheckpoint() ||
!SkillValidateProductionACSRMCheckpoint() ||
!SkillCaptureProductionACSRMFingerprints() || !LoadOrCreatePolicies() ||
!SkillVerifyFrozenForecastExact() ||
!SkillVerifyProductionACSRMFingerprints())
{
PrintFormat("ACSRM Actor-Critic initialization failed at line %d error=%d",
__LINE__, GetLastError());
return(INIT_FAILED);
}
if(!EventSetMillisecondTimer(1))
{
PrintFormat("ACSRM Actor-Critic timer initialization failed at line %d error=%d",
__LINE__, GetLastError());
return(INIT_FAILED);
}
//--- Finalize initialization only after the Stage 03 timer is armed.
return(INIT_SUCCEEDED);
}
//+------------------------------------------------------------------+
//| Function OnDeinit. |
//+------------------------------------------------------------------+
void OnDeinit(const int reason)
{
EventKillTimer();
//--- Only a completed explicit training loop persists all three artifacts
//--- and writes the hash manifest last. Deinit must not create a mixed
//--- generation.
if(SkillForecast != NULL && !SkillVerifyFrozenForecastExact())
PrintFormat("%s -> %d forecast mutation", __FUNCTION__, __LINE__);
if(SkillProductionSignatureReady && !SkillVerifyProductionACSRMFingerprints())
PrintFormat("%s -> %d ACSRM production signature mutation", __FUNCTION__, __LINE__);
SkillForecast = NULL;
}
//+------------------------------------------------------------------+
//| Runs the one-shot training lifecycle. |
//+------------------------------------------------------------------+
void OnTimer(void)
{
TrainACSRMActorCritic();
ExpertRemove();
}
//+------------------------------------------------------------------
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//+---
在新议题中引用复制固定链接