forked from animatedread/Warrior_EA
4eae763849
The plateau/regression line printed balancedOosEra as the current value while comparing against m_bestBalancedOos, which has held the SELECTION score sincea142749. 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.1039ad9relabelled 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>
63 lines
3.4 KiB
MQL5
63 lines
3.4 KiB
MQL5
//+------------------------------------------------------------------+
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//| Warrior_EA |
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//| AnimateDread |
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//| |
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//+------------------------------------------------------------------+
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#include "..\Expert\ExpertSignalAIBase.mqh"
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// wizard description start
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//+------------------------------------------------------------------+
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//| Description of the class |
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//| Title=Signals of indicator 'LSTM AI' |
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//| Type=SignalAdvanced |
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//| Name=LSTM AI |
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//| ShortName=LSTM |
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//| Class=CSignalLSTM |
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//| Page=signal_lstm |
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//+------------------------------------------------------------------+
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// wizard description end
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//+------------------------------------------------------------------+
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//| Class CSignalLSTM. |
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//| Purpose: Class of generator of trade signals based on |
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//| the 'LSTM AI' indicator. |
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//| Is derived from the CExpertSignalAIBase class. |
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//| Only the network topology differs from the other AI signals: an |
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//| input layer feeds a single LSTM layer before the common tapering |
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//| hidden-layer stack (see AddCustomLayers). |
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//+------------------------------------------------------------------+
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class CSignalLSTM : public CExpertSignalAIBase
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{
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protected:
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virtual bool AddCustomLayers(CArrayObj *topology) override;
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//--- keep in step with AddCustomLayers below - see the base declarations.
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virtual bool UsesLstmStage(void) const override { return true; }
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//--- keep the tapering Dense stack TANH-bounded, matching the LSTM layer's own bounded
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//--- output, instead of the base class's default PRELU (see the base declaration's comment)
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virtual ENUM_ACTIVATION HiddenLayerActivation(void) override { return TANH; }
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public:
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CSignalLSTM(void);
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//--- method of creating the indicator and timeseries
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virtual bool InitIndicators(CIndicators *indicators) override;
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};
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//+------------------------------------------------------------------+
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//| Constructor |
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//+------------------------------------------------------------------+
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CSignalLSTM::CSignalLSTM(void)
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{
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SetIdentity("LSTM", "LSTM");
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}
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//+------------------------------------------------------------------+
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//| Create indicators and bootstrap/load the network. |
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//+------------------------------------------------------------------+
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bool CSignalLSTM::InitIndicators(CIndicators *indicators)
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{
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return InitNeuralNetwork(indicators);
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}
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//+------------------------------------------------------------------+
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//| LSTM layer inserted between the input layer and the common |
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//| tapering hidden-layer stack. |
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//+------------------------------------------------------------------+
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bool CSignalLSTM::AddCustomLayers(CArrayObj *topology)
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{
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return AddLstmStage(topology);
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}
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//+------------------------------------------------------------------+
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