Warrior_EA/Expert/Training/ITrainingData.mqh

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//|                                                       Warrior_EA |
//|                                                     AnimateDread |
//|                                                                  |
//| A READ-ONLY VIEW of one model's training data.                   |
//+------------------------------------------------------------------+
#ifndef WARRIOR_TRAINING_ITRAININGDATA_MQH
#define WARRIOR_TRAINING_ITRAININGDATA_MQH
//+------------------------------------------------------------------+
//| WHAT A TRAINING-SIDE COLLABORATOR IS ALLOWED TO SEE.              |
//|                                                                  |
//| Everything that analyses a model's data - baselines, geometry     |
//| scans, redundancy reports - needs the same handful of things: a   |
//| feature row, a label, an outcome, an excursion, and the shape     |
//| they all share. Before this, each one reached straight into       |
//| CExpertSignalAIBase's members, which is why a 951-line diagnostic |
//| could not be moved, tested or replaced without moving the signal  |
//| class with it.                                                    |
//|                                                                  |
//| Every accessor is BOUNDS-CHECKED and returns false rather than    |
//| reading past a cache. The caller asks "is there a label at r?"    |
//| and never "how long is the label array?", so a short cache is a   |
//| missing row here instead of an out-of-range read at each of the   |
//| thirty-odd call sites that used to do the test themselves.        |
//|                                                                   |
//| MQL5 has interfaces, but a class may implement one only if it     |
//| inherits nothing else, and CExpertSignalAIBase is already a       |
//| CExpertSignalCustom. So this is an abstract class and the signal  |
//| exposes itself through a small ADAPTER that does inherit it - see |
//| CAIBaseTrainingData. Collaborators depend on this and never on    |
//| the signal.                                                       |
//+------------------------------------------------------------------+
class CTrainingDataView
  {
public:
                    ~CTrainingDataView(void) { }
   //--- SHAPE: the geometry every row shares.
   virtual int       HistoryBars(void) = 0;        // bars per feature window
   virtual int       FeaturesPerBar(void) = 0;     // columns per bar
   virtual int       LabelResolutionBars(void) = 0; // mean label resolution lag; also the declustering gap
   virtual int       PurgeBars(void) = 0;          // purge width between fitted and held-out spans
   virtual int       CalibrationHiIndex(const int totalIter, const int oosCutoff) = 0;
   //--- ROWS: false means "this bar has nothing to say", never a partial answer.
   virtual bool      HasLabel(const int bar) = 0;
   virtual bool      IsBuyLabel(const int bar) = 0;
   virtual bool      IsSellLabel(const int bar) = 0;
   virtual bool      RowFeatures(const int bar, const int width, double &x[]) = 0;
   //--- THE MODEL'S OWN CALL on a bar, as the chart drew it: which way and how strongly.
   //--- False = it said nothing (never scored, or scored Neutral). Direction rather than a raw
   //--- double on purpose - turning one into the other needs the head's output width, which is the
   //--- model's business and not a reader's.
   virtual bool      DirectionalCall(const int bar, bool &isBuy, double &magnitude) = 0;
   //--- IDENTITY, for log lines and for the once-per-chart guards.
   virtual string    Id(void) = 0;
   virtual bool      IsEnsembleMember(void) = 0;
   virtual int       EnsembleIndex(void) = 0;
   //--- The net's own gate on this chart, for the row every baseline is read against. False when
   //--- the gate has not scored yet, which is NOT the same as a gate that scored zero.
   virtual bool      GateReference(double &precPct, int &calls, double &chancePct) = 0;
   //--- POLICY the collaborator must not re-derive: overlapping labels are worth less than their
   //--- count, and only the model knows its own overlap - see EffectiveSampleSize.
   virtual double    EffectiveSampleSize(const double rawN) = 0;
   virtual bool      Stopping(void) = 0;
  };
#endif // WARRIOR_TRAINING_ITRAININGDATA_MQH
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