Warrior_EA/Signals/SignalCONV.mqh
AnimateDread 4eae763849 fix(ai): report the metric actually compared; surface the derived front-end
The plateau/regression line printed balancedOosEra as the current value while
comparing against m_bestBalancedOos, which has held the SELECTION score since
a142749. Two different metrics in one sentence, so HYBRID logged "regressed
from best 14.4% to 34.0%" a hundred times - a regression to a higher number,
which is not a thing. The comparison itself was right (selectionScore, coverage
weighted, genuinely below best); only the print was wrong. 1039ad9 relabelled
these strings but missed that this site passes the wrong variable.

The startup config line had the same shape of gap: it printed the dense taper
and called itself self-verifying while the DERIVED conv and recurrent stages -
the ones that dominate CONV/LSTM/HYBRID - were invisible. It now shows the
width into and out of each front-end stage, and flags the case where the dense
stack is wider than the vector reaching it (a linear fan-out cannot recover
what the bottleneck discarded; it only adds parameters). Flagged, not silently
reshaped - that would re-key trained topologies mid-comparison.

UsesConvStage()/UsesLstmStage() replace HasConvBeforeLstm() as the primitive,
so each subclass declares its composition once and both the capacity budget and
the config line derive from it rather than restating it.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 15:20:30 -04:00

60 lines
3.2 KiB
MQL5

//+------------------------------------------------------------------+
//| Warrior_EA |
//| AnimateDread |
//| |
//+------------------------------------------------------------------+
#include "..\Expert\ExpertSignalAIBase.mqh"
// wizard description start
//+------------------------------------------------------------------+
//| Description of the class |
//| Title=Signals of indicator 'Convolutional AI' |
//| Type=SignalAdvanced |
//| Name=Convolutional AI |
//| ShortName=CONV |
//| Class=CSignalCONV |
//| Page=signal_conv |
//+------------------------------------------------------------------+
// wizard description end
//+------------------------------------------------------------------+
//| Class CSignalCONV. |
//| Purpose: Class of generator of trade signals based on |
//| the 'Convolutional AI Neural Network. |
//| Is derived from the CExpertSignalAIBase class. |
//| Only the network topology differs from the other AI signals: an |
//| input layer feeds a Conv+Pool stage before the common tapering |
//| hidden-layer stack (see AddCustomLayers). |
//+------------------------------------------------------------------+
class CSignalCONV : public CExpertSignalAIBase
{
protected:
virtual bool AddCustomLayers(CArrayObj *topology) override;
//--- keep in step with AddCustomLayers below - see the base declarations.
virtual bool UsesConvStage(void) const override { return true; }
public:
CSignalCONV(void);
//--- method of creating the indicator and timeseries
virtual bool InitIndicators(CIndicators *indicators) override;
};
//+------------------------------------------------------------------+
//| Constructor |
//+------------------------------------------------------------------+
CSignalCONV::CSignalCONV(void)
{
SetIdentity("Convolutional", "CONV");
}
//+------------------------------------------------------------------+
//| Create indicators and bootstrap/load the network. |
//+------------------------------------------------------------------+
bool CSignalCONV::InitIndicators(CIndicators *indicators)
{
return InitNeuralNetwork(indicators);
}
//+------------------------------------------------------------------+
//| Conv + Pool layers inserted between the input layer and the |
//| common tapering hidden-layer stack. |
//+------------------------------------------------------------------+
bool CSignalCONV::AddCustomLayers(CArrayObj *topology)
{
return AddConvStage(topology);
}
//+------------------------------------------------------------------+