| //+------------------------------------------------------------------+
|
| //| GateLearningSystem.mqh - Gate Learning and Auto-Tuning |
|
| //| Manages gate threshold learning and adaptive adjustments |
|
| //+------------------------------------------------------------------+
|
| #ifndef GATELEARNINGSYSTEM_MQH
|
| #define GATELEARNINGSYSTEM_MQH
|
|
|
| #include "CGateBase.mqh"
|
|
|
| //+------------------------------------------------------------------+
|
| //| Gate Learning System Class |
|
| //+------------------------------------------------------------------+
|
| class CGateLearningSystem
|
| {
|
| private:
|
| bool m_autoAdjust;
|
| double m_learningRate;
|
| int m_minSamples;
|
| int m_sampleCounts[8];
|
| double m_successRates[8];
|
|
|
| public:
|
| // Constructor
|
| CGateLearningSystem(bool autoAdjust = true, double learningRate = 0.05)
|
| {
|
| m_autoAdjust = autoAdjust;
|
| m_learningRate = learningRate;
|
| m_minSamples = 20;
|
|
|
| for (int i = 0; i < 8; i++)
|
| {
|
| m_sampleCounts[i] = 0;
|
| m_successRates[i] = 0.5;
|
| }
|
| }
|
|
|
| // Destructor
|
| ~CGateLearningSystem() {}
|
|
|
| // Record gate outcome for learning
|
| void RecordOutcome(int gateIndex, bool passed, double profitLoss = 0)
|
| {
|
| if (gateIndex < 0 || gateIndex >= 8) return;
|
|
|
| m_sampleCounts[gateIndex]++;
|
|
|
| // Update success rate with exponential moving average
|
| double alpha = m_learningRate;
|
| if (passed && profitLoss > 0)
|
| m_successRates[gateIndex] = (1 - alpha) * m_successRates[gateIndex] + alpha * 1.0;
|
| else if (!passed)
|
| m_successRates[gateIndex] = (1 - alpha) * m_successRates[gateIndex] + alpha * 0.0;
|
| }
|
|
|
| // Get recommended threshold adjustment for a gate
|
| double GetThresholdAdjustment(int gateIndex)
|
| {
|
| if (gateIndex < 0 || gateIndex >= 8) return 0.0;
|
| if (m_sampleCounts[gateIndex] < m_minSamples) return 0.0;
|
|
|
| // Adjust threshold based on success rate
|
| double targetRate = 0.6; // Target 60% pass rate
|
| double currentRate = m_successRates[gateIndex];
|
|
|
| // If too strict, lower threshold; if too loose, raise threshold
|
| return (currentRate - targetRate) * 0.1;
|
| }
|
|
|
| // Print learning report
|
| void PrintReport()
|
| {
|
| Print("=== Gate Learning Report ===");
|
| for (int i = 0; i < 8; i++)
|
| {
|
| Print(StringFormat("Gate %d: samples=%d, success_rate=%.2f%%, adjustment=%.4f",
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| i + 1, m_sampleCounts[i], m_successRates[i] * 100,
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| GetThresholdAdjustment(i)));
|
| }
|
| }
|
|
|
| // Save learning data
|
| void SaveLearningData()
|
| {
|
| // Stub - would save to file in full implementation
|
| }
|
|
|
| // Enable/disable auto-adjustment
|
| void SetAutoAdjust(bool enabled) { m_autoAdjust = enabled; }
|
| bool GetAutoAdjust() const { return m_autoAdjust; }
|
|
|
| // Set learning rate
|
| void SetLearningRate(double rate) { m_learningRate = rate; }
|
| double GetLearningRate() const { return m_learningRate; }
|
|
|
| // Set minimum samples before adjustment
|
| void SetMinSamples(int samples) { m_minSamples = samples; }
|
| int GetMinSamples() const { return m_minSamples; }
|
| };
|
|
|
| #endif // GATELEARNINGSYSTEM_MQH
|
