RSI-Stoch-MA-EA/Aisignalconfirmation.mqh

//+------------------------------------------------------------------+
//|                                        AISignalConfirmation.mqh |
//|              AI-Powered Signal Confirmation System              |
//|   Combines OpenAI API + MQL5 Neural Network capabilities       |
//|   Validates signal clusters before trading                      |
//+------------------------------------------------------------------+

//================ AI CONFIRMATION STRUCTURE ==================//
struct AIConfirmation
{
   bool     approved;           // Final decision
   double   confidence;         // 0-100
   string   reasoning;          // Why approved/rejected
   string   ai_bias;           // "bullish", "bearish", "neutral"
   string   pattern_detected;  // Pattern identified by AI
   double   risk_score;        // 0-100 (higher = riskier)
   string   recommendation;    // Specific trading advice
};

//================ GLOBAL AI STATE ==================//
AIConfirmation LastAIDecision;
datetime       LastAICallTime = 0;
int            AI_Call_Cooldown = 60;  // 60 seconds between AI calls
string         AI_Context_Memory = "";  // Stores recent market context

//================ NEURAL NETWORK VARIABLES ==================//
// These would connect to MQL5's neural network library
// For full implementation, see: https://www.mql5.com/en/neurobook

bool           UseNeuralNetwork = false;  // Enable when NN model is trained
string         NeuralModelPath = "";      // Path to trained model
double         NNConfidence = 0;          // NN model confidence

//+------------------------------------------------------------------+
//| Initialize AI Confirmation System                                |
//+------------------------------------------------------------------+
void InitializeAIConfirmation()
{
   LastAIDecision.approved = false;
   LastAIDecision.confidence = 0;
   
   // Check if we have a trained neural network model
   NeuralModelPath = "Files\\TrainingModels\\QuarterTheory_NN.nnw";
   if(FileIsExist(NeuralModelPath))
   {
      UseNeuralNetwork = true;
      Print("✅ Neural Network model found: ", NeuralModelPath);
   }
   else
   {
      Print("ℹ️ No Neural Network model - using OpenAI only");
   }
   
   Print("✅ AI Signal Confirmation initialized");
}

//+------------------------------------------------------------------+
//| Request AI Confirmation for Signal Cluster                       |
//+------------------------------------------------------------------+
AIConfirmation RequestAIConfirmation(SignalCluster &cluster)
{
   AIConfirmation decision;
   
   // Rate limiting - don't call AI too frequently
   if(TimeCurrent() - LastAICallTime < AI_Call_Cooldown)
   {
      Print("⏱️ AI cooldown active - using last decision");
      return LastAIDecision;
   }
   
   //==============================================================
   // PHASE 1: PREPARE MARKET CONTEXT
   //==============================================================
   string context = BuildMarketContext(cluster);
   
   //==============================================================
   // PHASE 2: GET NEURAL NETWORK ANALYSIS (if available)
   //==============================================================
   double nn_confidence = 0;
   string nn_pattern = "";
   
   if(UseNeuralNetwork)
   {
      // This would call the trained NN model
      // For now, placeholder
      nn_confidence = AnalyzeWithNeuralNetwork(context);
      nn_pattern = GetNNPatternPrediction();
      
      Print("🧠 Neural Network Confidence: ", nn_confidence, "%");
      Print("🧠 NN Pattern: ", nn_pattern);
   }
   
   //==============================================================
   // PHASE 3: GET OPENAI ANALYSIS
   //==============================================================
   string ai_response = "";
   
   if(Use_OpenAI && AI_Initialized)
   {
      ai_response = GetOpenAIAnalysis(context, cluster, nn_pattern);
      Print("🤖 OpenAI Analysis received");
   }
   else
   {
      // Fallback to rule-based decision
      Print("ℹ️ OpenAI not available - using rules-based decision");
      decision = MakeRuleBasedDecision(cluster, nn_confidence);
      LastAIDecision = decision;
      LastAICallTime = TimeCurrent();
      return decision;
   }
   
   //==============================================================
   // PHASE 4: PARSE AI RESPONSE
   //==============================================================
   decision = ParseAIResponse(ai_response, cluster);
   
   //==============================================================
   // PHASE 5: COMBINE WITH NEURAL NETWORK (if available)
   //==============================================================
   if(UseNeuralNetwork && nn_confidence > 0)
   {
      // Weighted combination: 70% OpenAI, 30% Neural Network
      double combined_confidence = (decision.confidence * 0.7) + (nn_confidence * 0.3);
      decision.confidence = combined_confidence;
      
      // Only approve if both agree on direction
      if(decision.ai_bias != GetNNBias() && nn_confidence > 60)
      {
         decision.approved = false;
         decision.reasoning = "OpenAI and Neural Network disagree on direction";
         Print("⚠️ AI CONFLICT: OpenAI says ", decision.ai_bias, 
               " but NN says ", GetNNBias());
      }
   }
   
   //==============================================================
   // PHASE 6: FINAL DECISION LOGGING
   //==============================================================
   Print("========================================");
   Print("🤖 AI CONFIRMATION RESULT:");
   Print("  Decision: ", decision.approved ? "APPROVED ✅" : "REJECTED ❌");
   Print("  Confidence: ", decision.confidence, "%");
   Print("  AI Bias: ", decision.ai_bias);
   Print("  Pattern: ", decision.pattern_detected);
   Print("  Risk Score: ", decision.risk_score);
   Print("  Reasoning: ", decision.reasoning);
   Print("  Recommendation: ", decision.recommendation);
   Print("========================================");
   
   LastAIDecision = decision;
   LastAICallTime = TimeCurrent();
   
   return decision;
}

//+------------------------------------------------------------------+
//| Build Comprehensive Market Context for AI                        |
//+------------------------------------------------------------------+
string BuildMarketContext(SignalCluster &cluster)
{
   string context = "";
   
   // Current market conditions
   context += "MARKET CONDITIONS:\n";
   context += "Symbol: " + _Symbol + "\n";
   context += "Timeframe: " + EnumToString((ENUM_TIMEFRAMES)_Period) + "\n";
   context += "Mode: " + CurrentMode + "\n";
   context += "State: " + CurrentState + "\n";
   context += "Price: " + DoubleToString(SymbolInfoDouble(_Symbol, SYMBOL_BID), 5) + "\n";
   context += "\n";
   
   // Signal cluster information
   context += "SIGNAL CLUSTER:\n";
   context += "Count: " + IntegerToString(cluster.count) + " signals\n";
   context += "Direction: " + cluster.consensus_direction + "\n";
   context += "Type: " + cluster.consensus_type + "\n";
   context += "Strength: " + DoubleToString(cluster.combined_strength, 1) + "/100\n";
   context += "Conflict Score: " + DoubleToString(cluster.conflict_score, 1) + "/100\n";
   context += "\n";
   
   // Individual signals
   context += "ACTIVE SIGNALS:\n";
   for(int i = 0; i < cluster.count; i++)
   {
      CoordinatedSignal sig = cluster.signals[i];
      context += StringFormat("- %s: %s %s at %s (strength: %.0f)\n",
                             sig.source, sig.direction, sig.action,
                             sig.location, sig.strength);
   }
   context += "\n";
   
   // Technical levels
   context += "KEY LEVELS:\n";
   context += "MFIB Levels:\n";
   context += "  1.000: " + DoubleToString(MFIB_1000, 5) + "\n";
   context += "  0.786: " + DoubleToString(MFIB_786, 5) + "\n";
   context += "  0.618: " + DoubleToString(MFIB_618, 5) + "\n";
   context += "  0.500: " + DoubleToString(MFIB_500, 5) + "\n";
   context += "  0.382: " + DoubleToString(MFIB_382, 5) + "\n";
   context += "  0.236: " + DoubleToString(MFIB_236, 5) + "\n";
   context += "\n";
   
   // Moving averages
   context += "Moving Averages:\n";
   context += "  MA500: " + DoubleToString(MA_500_Value, 5) + "\n";
   context += "  MA230: " + DoubleToString(MA_230_Value, 5) + "\n";
   context += "  MA140: " + DoubleToString(MA_140_Value, 5) + "\n";
   context += "  MA50: " + DoubleToString(MA_50_Value, 5) + "\n";
   context += "  MA20: " + DoubleToString(MA_20_Value, 5) + "\n";
   context += "\n";
   
   // Recent patterns
   if(PatternCount > 0)
   {
      context += "CANDLESTICK PATTERNS:\n";
      for(int i = 0; i < MathMin(PatternCount, 5); i++)
      {
         PatternSignal pattern = ActivePatterns[i];
         context += StringFormat("- %s at %s (strength: %.0f)\n",
                                pattern.name, pattern.location, 
                                pattern.adjusted_strength);
      }
      context += "\n";
   }
   
   // Stochastic
   context += "STOCHASTIC:\n";
   context += "  Main: " + DoubleToString(StochBuffer_Main[0], 1) + "\n";
   context += "  Signal: " + DoubleToString(StochBuffer_Signal[0], 1) + "\n";
   context += "\n";
   
   // Add context memory (recent AI insights)
   if(AI_Context_Memory != "")
   {
      context += "RECENT AI INSIGHTS:\n";
      context += AI_Context_Memory + "\n\n";
   }
   
   return context;
}

//+------------------------------------------------------------------+
//| Get OpenAI Analysis                                              |
//+------------------------------------------------------------------+
string GetOpenAIAnalysis(string context, SignalCluster &cluster, string nn_pattern)
{
   string prompt = "You are an expert forex trader analyzing the following market conditions.\n\n";
   prompt += context;
   
   if(nn_pattern != "")
   {
      prompt += "NEURAL NETWORK DETECTED PATTERN: " + nn_pattern + "\n\n";
   }
   
   prompt += "Based on this information, please provide:\n";
   prompt += "1. Your directional bias (bullish/bearish/neutral)\n";
   prompt += "2. Confidence level (0-100)\n";
   prompt += "3. Approval (YES/NO) for taking this trade\n";
   prompt += "4. Risk score (0-100, higher = riskier)\n";
   prompt += "5. Key reasoning (2-3 sentences)\n";
   prompt += "6. Specific recommendation\n\n";
   prompt += "Format your response EXACTLY as:\n";
   prompt += "BIAS: [bullish/bearish/neutral]\n";
   prompt += "CONFIDENCE: [0-100]\n";
   prompt += "APPROVAL: [YES/NO]\n";
   prompt += "RISK: [0-100]\n";
   prompt += "REASONING: [your reasoning]\n";
   prompt += "RECOMMENDATION: [your recommendation]\n";
   
   // Call OpenAI (from OpenAI.mqh)
   string response = CallOpenAI(prompt);
   
   return response;
}

//+------------------------------------------------------------------+
//| Parse AI Response into Structured Decision                       |
//+------------------------------------------------------------------+
AIConfirmation ParseAIResponse(string response, SignalCluster &cluster)
{
   AIConfirmation decision;
   
   // Extract BIAS
   int bias_pos = StringFind(response, "BIAS:");
   if(bias_pos >= 0)
   {
      int bias_end = StringFind(response, "\n", bias_pos);
      string bias_line = StringSubstr(response, bias_pos + 5, bias_end - bias_pos - 5);
      StringTrimLeft(bias_line);
      StringTrimRight(bias_line);
      decision.ai_bias = StringToLower(bias_line);
   }
   else
   {
      decision.ai_bias = "neutral";
   }
   
   // Extract CONFIDENCE
   int conf_pos = StringFind(response, "CONFIDENCE:");
   if(conf_pos >= 0)
   {
      int conf_end = StringFind(response, "\n", conf_pos);
      string conf_line = StringSubstr(response, conf_pos + 11, conf_end - conf_pos - 11);
      StringTrimLeft(conf_line);
      StringTrimRight(conf_line);
      decision.confidence = StringToDouble(conf_line);
   }
   else
   {
      decision.confidence = 0;
   }
   
   // Extract APPROVAL
   int appr_pos = StringFind(response, "APPROVAL:");
   if(appr_pos >= 0)
   {
      int appr_end = StringFind(response, "\n", appr_pos);
      string appr_line = StringSubstr(response, appr_pos + 9, appr_end - appr_pos - 9);
      StringTrimLeft(appr_line);
      StringTrimRight(appr_line);
      decision.approved = (StringFind(StringToUpper(appr_line), "YES") >= 0);
   }
   else
   {
      decision.approved = false;
   }
   
   // Extract RISK
   int risk_pos = StringFind(response, "RISK:");
   if(risk_pos >= 0)
   {
      int risk_end = StringFind(response, "\n", risk_pos);
      string risk_line = StringSubstr(response, risk_pos + 5, risk_end - risk_pos - 5);
      StringTrimLeft(risk_line);
      StringTrimRight(risk_line);
      decision.risk_score = StringToDouble(risk_line);
   }
   else
   {
      decision.risk_score = 50;
   }
   
   // Extract REASONING
   int reas_pos = StringFind(response, "REASONING:");
   if(reas_pos >= 0)
   {
      int reas_end = StringFind(response, "RECOMMENDATION:", reas_pos);
      if(reas_end < 0) reas_end = StringLen(response);
      decision.reasoning = StringSubstr(response, reas_pos + 10, reas_end - reas_pos - 10);
      StringTrimLeft(decision.reasoning);
      StringTrimRight(decision.reasoning);
   }
   else
   {
      decision.reasoning = "No reasoning provided";
   }
   
   // Extract RECOMMENDATION
   int rec_pos = StringFind(response, "RECOMMENDATION:");
   if(rec_pos >= 0)
   {
      decision.recommendation = StringSubstr(response, rec_pos + 15);
      StringTrimLeft(decision.recommendation);
      StringTrimRight(decision.recommendation);
   }
   else
   {
      decision.recommendation = "Follow standard trade management";
   }
   
   // Validate that AI bias matches cluster direction
   if(decision.ai_bias != cluster.consensus_direction && 
      decision.ai_bias != "neutral")
   {
      Print("⚠️ AI DISAGREES: Cluster says ", cluster.consensus_direction,
            " but AI says ", decision.ai_bias);
      decision.approved = false;
      decision.reasoning += " AI directional bias conflicts with signal cluster.";
   }
   
   return decision;
}

//+------------------------------------------------------------------+
//| Fallback Rule-Based Decision (when AI not available)            |
//+------------------------------------------------------------------+
AIConfirmation MakeRuleBasedDecision(SignalCluster &cluster, double nn_conf)
{
   AIConfirmation decision;
   
   // Use cluster consensus
   decision.ai_bias = cluster.consensus_direction;
   decision.confidence = cluster.combined_strength;
   
   // Incorporate NN confidence if available
   if(nn_conf > 0)
   {
      decision.confidence = (decision.confidence * 0.7) + (nn_conf * 0.3);
   }
   
   // Approval criteria
   decision.approved = (cluster.conflict_score < 40 && 
                       decision.confidence >= 60 &&
                       cluster.consensus_direction != "neutral");
   
   // Risk score (inverse of confidence, adjusted for conflict)
   decision.risk_score = 100 - decision.confidence + cluster.conflict_score;
   
   decision.reasoning = "Rule-based decision: " + 
                       IntegerToString(cluster.count) + " signals, " +
                       DoubleToString(cluster.conflict_score, 1) + "% conflict";
   
   decision.recommendation = decision.approved ? 
                            "Proceed with standard position size" :
                            "Skip this setup";
   
   return decision;
}

//+------------------------------------------------------------------+
//| Neural Network Analysis (Placeholder for MQL5 NN implementation)|
//+------------------------------------------------------------------+
double AnalyzeWithNeuralNetwork(string context)
{
   // This is a placeholder for the actual NN implementation
   // Full implementation requires:
   // 1. Training a model using MQL5's neural network library
   // 2. Saving the model to file
   // 3. Loading and running inference here
   
   // See: https://www.mql5.com/en/neurobook for full implementation guide
   
   // For now, return 0 (not implemented)
   return 0;
}

//+------------------------------------------------------------------+
//| Get Neural Network Pattern Prediction                            |
//+------------------------------------------------------------------+
string GetNNPatternPrediction()
{
   // Placeholder - would return pattern identified by NN
   return "";
}

//+------------------------------------------------------------------+
//| Get Neural Network Bias                                          |
//+------------------------------------------------------------------+
string GetNNBias()
{
   // Placeholder - would return directional bias from NN
   return "neutral";
}

//+------------------------------------------------------------------+
//| Update AI Context Memory (for continuity between calls)         |
//+------------------------------------------------------------------+
void UpdateAIContextMemory(string new_insight)
{
   // Keep only last 3 insights
   if(AI_Context_Memory != "")
   {
      int newline_count = 0;
      for(int i = 0; i < StringLen(AI_Context_Memory); i++)
      {
         if(StringGetCharacter(AI_Context_Memory, i) == '\n')
            newline_count++;
         
         if(newline_count >= 3)
         {
            AI_Context_Memory = StringSubstr(AI_Context_Memory, 0, i);
            break;
         }
      }
   }
   
   AI_Context_Memory = new_insight + "\n" + AI_Context_Memory;
}

//+------------------------------------------------------------------+
//| Calculate Defensive Multiplier Based on AI Risk Score           |
//+------------------------------------------------------------------+
double GetAIRiskMultiplier(AIConfirmation &decision)
{
   // Convert risk score to position size multiplier
   // Low risk (0-30) = 1.2x multiplier
   // Medium risk (31-60) = 1.0x multiplier
   // High risk (61-100) = 0.7x multiplier
   
   double multiplier = 1.0;
   
   if(decision.risk_score <= 30)
      multiplier = 1.2;  // Low risk - can be aggressive
   else if(decision.risk_score <= 60)
      multiplier = 1.0;  // Medium risk - standard size
   else if(decision.risk_score <= 80)
      multiplier = 0.8;  // High risk - defensive
   else
      multiplier = 0.6;  // Very high risk - very defensive
   
   // Adjust based on confidence
   if(decision.confidence >= 85)
      multiplier += 0.2;
   else if(decision.confidence < 60)
      multiplier -= 0.1;
   
   return MathMax(0.5, MathMin(multiplier, 1.5));
}

//+------------------------------------------------------------------+
//| Get AI Decision Summary for Display                              |
//+------------------------------------------------------------------+
string GetAIDecisionSummary()
{
   if(LastAIDecision.confidence == 0)
      return "No AI decision yet";
   
   string summary = "";
   summary += LastAIDecision.approved ? "✅ APPROVED" : "❌ REJECTED";
   summary += " | " + LastAIDecision.ai_bias;
   summary += " | " + DoubleToString(LastAIDecision.confidence, 0) + "% conf";
   summary += " | " + DoubleToString(LastAIDecision.risk_score, 0) + "% risk";
   
   return summary;
}