Warrior_EA/Signals/SignalCONV.mqh

//+------------------------------------------------------------------+
//|                                                       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;
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)
  {
//--- Convolutional Layer
   CLayerDescription *desc = new CLayerDescription();
   if(CheckPointer(desc) == POINTER_INVALID)
      return false;
   desc.count = m_hiddenLayersCount;
   desc.type = defNeuronConv;
   // PRELU, not TANH: matches what CNeuronConv's CPU path (Network.mqh) has always hardcoded
   // regardless of this setting (its activationFunction() override ignores `activation` entirely) -
   // this used to silently diverge from the GPU/DirectML tier, which DOES honor this field and was
   // therefore actually running tanh instead of the intended PReLU whenever hardware accel was active.
   desc.activation = PRELU;
   desc.optimization = (ENUM_OPTIMIZATION)m_optimizationAlgo;
   desc.window = m_neuronsCount;
   desc.step = m_neuronsCount;
   if(!topology.Add(desc))
      return false;
//--- Pooling Layer
   desc = new CLayerDescription();
   if(CheckPointer(desc) == POINTER_INVALID)
      return false;
   desc.count = m_hiddenLayersCount;
   desc.type = defNeuronPool;
   // Unused either way - CNeuronPool::feedForward (CPU) does plain average pooling and never calls
   // activationFunction() at all, and the GPU FeedForwardProof kernel (max pooling) doesn't even take
   // an activation argument. NONE documents that accurately instead of implying an activation applies.
   desc.activation = NONE;
   desc.optimization = (ENUM_OPTIMIZATION)m_optimizationAlgo;
   desc.window = 3;
   desc.step = 2;
   return topology.Add(desc);
  }
//+------------------------------------------------------------------+