//+------------------------------------------------------------------+
//|                        MLPredictor.mqh                           |
//|              Machine Learning Prediction Module                  |
//|           Neural Network & Ensemble Learning                     |
//+------------------------------------------------------------------+
#ifndef ML_PREDICTOR_MQH
#define ML_PREDICTOR_MQH

#include "DataTypes_v71.mqh"
#include <Math/Stat/Math.mqh>

//+------------------------------------------------------------------+
//| ML Model Types                                                   |
//+------------------------------------------------------------------+
enum ENUM_ML_MODEL
{
   ML_RANDOM_FOREST = 0,
   ML_NEURAL_NETWORK = 1,
   ML_GRADIENT_BOOST = 2,
   ML_ENSEMBLE = 3
};

//+------------------------------------------------------------------+
//| Neural Network Layer                                             |
//+------------------------------------------------------------------+
struct NeuralLayer
{
   double            weights[][];
   double            biases[];
   double            activations[];
   int               input_size;
   int               output_size;
};

//+------------------------------------------------------------------+
//| ML Predictor Class                                               |
//+------------------------------------------------------------------+
class CMLPredictor
{
private:
   //--- Configuration
   int               m_lookback_period;
   int               m_min_training_size;
   bool              m_use_deep_learning;
   double            m_learning_rate;
   ENUM_ML_MODEL     m_model_type;
   
   //--- Neural network architecture
   NeuralLayer       m_layers[];
   int               m_layer_count;
   int               m_input_features;
   int               m_output_classes;
   
   //--- Training data
   double            m_training_features[][];
   double            m_training_labels[];
   int               m_training_count;
   
   //--- Model performance
   double            m_accuracy;
   double            m_precision;
   double            m_recall;
   double            m_f1_score;
   datetime          m_last_update;
   
   //--- Feature scaling
   double            m_feature_mean[];
   double            m_feature_std[];
   
   //--- Ensemble models
   struct TreeNode
   {
      int            feature_index;
      double         threshold;
      int            left_child;
      int            right_child;
      double         prediction;
      bool           is_leaf;
   };
   
   struct DecisionTree
   {
      TreeNode       nodes[];
      int            node_count;
      double         feature_importance[];
   };
   
   DecisionTree      m_trees[];
   int               m_tree_count;
   
   //--- Helper methods
   void              InitializeNeuralNetwork(int hidden_layers[], int layer_sizes[]);
   double            ActivationFunction(double x, bool derivative = false);
   void              ForwardPass(double &inputs[], double &outputs[]);
   void              BackPropagation(double &inputs[], double &targets[]);
   void              UpdateWeights();
   
   //--- Feature engineering
   void              ExtractAdvancedFeatures(string symbol, double &features[]);
   void              NormalizeFeatures(double &features[]);
   void              CalculateFeatureImportance();
   
   //--- Ensemble methods
   void              TrainRandomForest();
   void              TrainGradientBoosting();
   double            PredictWithTree(DecisionTree &tree, double &features[]);
   
   //--- Model persistence
   string            m_model_path;
   
public:
                     CMLPredictor();
                    ~CMLPredictor();
   
   //--- Initialization
   bool              Initialize(int lookback, int min_training, bool use_deep);
   void              SetModelType(ENUM_ML_MODEL type) { m_model_type = type; }
   void              SetLearningRate(double rate) { m_learning_rate = rate; }
   
   //--- Training
   bool              Train(double &features[][], double &labels[]);
   bool              UpdateModel(ManagedTradeV71 &trades[]);
   void              AddTrainingExample(double &features[], double label);
   
   //--- Prediction
   MLPrediction      Predict(string symbol);
   double            PredictProbability(double &features[], ENUM_ORDER_TYPE direction);
   void              PredictBatch(string symbols[], MLPrediction &predictions[]);
   
   //--- Feature analysis
   double            GetFeatureImportance(int feature_index);
   void              GetTopFeatures(int &indices[], double &importances[], int count);
   
   //--- Model evaluation
   double            GetAccuracy() { return m_accuracy; }
   double            GetPrecision() { return m_precision; }
   double            GetRecall() { return m_recall; }
   double            GetF1Score() { return m_f1_score; }
   void              EvaluateModel(double &test_features[][], double &test_labels[]);
   
   //--- Model management
   bool              SaveModel(string filename);
   bool              LoadModel(string filename);
   datetime          GetLastUpdateTime() { return m_last_update; }
   
   //--- Real-time learning
   void              OnlineUpdate(double &features[], double actual_result);
   void              AdaptToMarketRegime(ENUM_MARKET_REGIME regime);
};

//+------------------------------------------------------------------+
//| Constructor                                                      |
//+------------------------------------------------------------------+
CMLPredictor::CMLPredictor()
{
   m_lookback_period = 100;
   m_min_training_size = 100;
   m_use_deep_learning = false;
   m_learning_rate = 0.01;
   m_model_type = ML_ENSEMBLE;
   m_layer_count = 0;
   m_input_features = 20;
   m_output_classes = 3; // Buy, Sell, Hold
   m_training_count = 0;
   m_accuracy = 0;
   m_precision = 0;
   m_recall = 0;
   m_f1_score = 0;
   m_last_update = 0;
   m_tree_count = 100; // Default forest size
   m_model_path = "MLModels\\";
}

//+------------------------------------------------------------------+
//| Destructor                                                       |
//+------------------------------------------------------------------+
CMLPredictor::~CMLPredictor()
{
   // Cleanup allocated memory
}

//+------------------------------------------------------------------+
//| Initialize ML predictor                                          |
//+------------------------------------------------------------------+
bool CMLPredictor::Initialize(int lookback, int min_training, bool use_deep)
{
   m_lookback_period = lookback;
   m_min_training_size = min_training;
   m_use_deep_learning = use_deep;
   
   //--- Initialize feature scaling arrays
   ArrayResize(m_feature_mean, m_input_features);
   ArrayResize(m_feature_std, m_input_features);
   ArrayInitialize(m_feature_mean, 0);
   ArrayInitialize(m_feature_std, 1);
   
   //--- Initialize neural network if deep learning
   if(m_use_deep_learning)
   {
      int hidden_layers = 3;
      int layer_sizes[] = {64, 32, 16}; // Architecture: 20->64->32->16->3
      InitializeNeuralNetwork(hidden_layers, layer_sizes);
   }
   
   //--- Initialize training data arrays
   ArrayResize(m_training_features, m_lookback_period);
   ArrayResize(m_training_labels, m_lookback_period);
   
   //--- Initialize ensemble
   if(m_model_type == ML_ENSEMBLE || m_model_type == ML_RANDOM_FOREST)
   {
      ArrayResize(m_trees, m_tree_count);
   }
   
   Print("MLPredictor initialized: Model=", EnumToString(m_model_type), 
         ", DeepLearning=", m_use_deep_learning);
   
   return true;
}

//+------------------------------------------------------------------+
//| Initialize neural network architecture                           |
//+------------------------------------------------------------------+
void CMLPredictor::InitializeNeuralNetwork(int hidden_count, int layer_sizes[])
{
   m_layer_count = hidden_count + 1; // Hidden layers + output layer
   ArrayResize(m_layers, m_layer_count);
   
   int prev_size = m_input_features;
   
   //--- Initialize each layer
   for(int i = 0; i < m_layer_count; i++)
   {
      int current_size = (i < hidden_count) ? layer_sizes[i] : m_output_classes;
      
      m_layers[i].input_size = prev_size;
      m_layers[i].output_size = current_size;
      
      //--- Initialize weights with Xavier initialization
      ArrayResize(m_layers[i].weights, current_size);
      for(int j = 0; j < current_size; j++)
      {
         ArrayResize(m_layers[i].weights[j], prev_size);
         
         double xavier_std = MathSqrt(2.0 / (prev_size + current_size));
         
         for(int k = 0; k < prev_size; k++)
         {
            m_layers[i].weights[j][k] = MathRandomNormal(0, xavier_std);
         }
      }
      
      //--- Initialize biases
      ArrayResize(m_layers[i].biases, current_size);
      ArrayInitialize(m_layers[i].biases, 0);
      
      //--- Initialize activations
      ArrayResize(m_layers[i].activations, current_size);
      
      prev_size = current_size;
   }
}

//+------------------------------------------------------------------+
//| Make prediction for symbol                                       |
//+------------------------------------------------------------------+
MLPrediction CMLPredictor::Predict(string symbol)
{
   MLPrediction prediction;
   prediction.prediction_time = TimeCurrent();
   prediction.model_version = "1.0";
   
   //--- Extract features
   double features[];
   ExtractAdvancedFeatures(symbol, features);
   
   //--- Normalize features
   NormalizeFeatures(features);
   
   //--- Make prediction based on model type
   double outputs[];
   
   switch(m_model_type)
   {
      case ML_NEURAL_NETWORK:
         {
            ArrayResize(outputs, m_output_classes);
            ForwardPass(features, outputs);
            
            //--- Find class with highest probability
            int best_class = 0;
            double max_prob = outputs[0];
            
            for(int i = 1; i < m_output_classes; i++)
            {
               if(outputs[i] > max_prob)
               {
                  max_prob = outputs[i];
                  best_class = i;
               }
            }
            
            //--- Convert to trading signal
            if(best_class == 0) // Buy class
            {
               prediction.direction = ORDER_TYPE_BUY;
               prediction.confidence = max_prob;
            }
            else if(best_class == 1) // Sell class
            {
               prediction.direction = ORDER_TYPE_SELL;
               prediction.confidence = max_prob;
            }
            else // Hold
            {
               prediction.direction = ORDER_TYPE_BUY; // Default
               prediction.confidence = 0; // No trade
            }
         }
         break;
         
      case ML_RANDOM_FOREST:
      case ML_ENSEMBLE:
         {
            //--- Aggregate predictions from all trees
            double buy_votes = 0;
            double sell_votes = 0;
            
            for(int i = 0; i < m_tree_count; i++)
            {
               double tree_pred = PredictWithTree(m_trees[i], features);
               if(tree_pred > 0.5)
                  buy_votes++;
               else if(tree_pred < -0.5)
                  sell_votes++;
            }
            
            //--- Determine direction
            if(buy_votes > sell_votes && buy_votes > m_tree_count * 0.6)
            {
               prediction.direction = ORDER_TYPE_BUY;
               prediction.confidence = buy_votes / m_tree_count;
            }
            else if(sell_votes > buy_votes && sell_votes > m_tree_count * 0.6)
            {
               prediction.direction = ORDER_TYPE_SELL;
               prediction.confidence = sell_votes / m_tree_count;
            }
            else
            {
               prediction.direction = ORDER_TYPE_BUY;
               prediction.confidence = 0; // No clear signal
            }
         }
         break;
   }
   
   //--- Additional predictions
   prediction.expected_return = prediction.confidence * 2.5; // Simplified
   prediction.volatility = features[9]; // ATR feature
   
   //--- Calculate stops
   double atr = iATR(symbol, PERIOD_CURRENT, 14);
   double current_price = (prediction.direction == ORDER_TYPE_BUY) ?
                         SymbolInfoDouble(symbol, SYMBOL_ASK) :
                         SymbolInfoDouble(symbol, SYMBOL_BID);
   
   if(prediction.direction == ORDER_TYPE_BUY)
   {
      prediction.stop_loss = current_price - atr * 2;
      prediction.take_profit = current_price + atr * 4;
   }
   else
   {
      prediction.stop_loss = current_price + atr * 2;
      prediction.take_profit = current_price - atr * 4;
   }
   
   //--- Optimal size based on Kelly
   double kelly_fraction = (prediction.confidence * 2.5 - (1 - prediction.confidence)) / 2.5;
   kelly_fraction = MathMax(0, MathMin(0.25, kelly_fraction * 0.3)); // Conservative Kelly
   prediction.optimal_size = kelly_fraction;
   
   return prediction;
}

//+------------------------------------------------------------------+
//| Extract advanced features for ML                                 |
//+------------------------------------------------------------------+
void CMLPredictor::ExtractAdvancedFeatures(string symbol, double &features[])
{
   ArrayResize(features, m_input_features);
   
   //--- Price-based features
   double close = iClose(symbol, PERIOD_CURRENT, 0);
   double open = iOpen(symbol, PERIOD_CURRENT, 0);
   double high = iHigh(symbol, PERIOD_CURRENT, 0);
   double low = iLow(symbol, PERIOD_CURRENT, 0);
   
   features[0] = (close - open) / open * 100; // Return
   features[1] = (high - low) / low * 100;     // Range
   features[2] = (close - low) / (high - low + 0.00001); // Position in range
   
   //--- Moving averages
   double ma_5 = iMA(symbol, PERIOD_CURRENT, 5, 0, MODE_EMA, PRICE_CLOSE);
   double ma_20 = iMA(symbol, PERIOD_CURRENT, 20, 0, MODE_EMA, PRICE_CLOSE);
   double ma_50 = iMA(symbol, PERIOD_CURRENT, 50, 0, MODE_EMA, PRICE_CLOSE);
   
   features[3] = (close - ma_5) / ma_5 * 100;
   features[4] = (ma_5 - ma_20) / ma_20 * 100;
   features[5] = (ma_20 - ma_50) / ma_50 * 100;
   
   //--- Momentum indicators
   double rsi = iRSI(symbol, PERIOD_CURRENT, 14, PRICE_CLOSE);
   double cci = iCCI(symbol, PERIOD_CURRENT, 14, PRICE_TYPICAL);
   double momentum = iMomentum(symbol, PERIOD_CURRENT, 14, PRICE_CLOSE);
   
   features[6] = rsi / 100.0;
   features[7] = cci / 200.0; // Normalize CCI
   features[8] = (momentum - 100) / 100.0;
   
   //--- Volatility
   double atr = iATR(symbol, PERIOD_CURRENT, 14);
   double stddev = iStdDev(symbol, PERIOD_CURRENT, 20, 0, MODE_SMA, PRICE_CLOSE);
   
   features[9] = atr / close * 100;
   features[10] = stddev / close * 100;
   
   //--- Volume
   long volume = iVolume(symbol, PERIOD_CURRENT, 0);
   long avg_volume = 0;
   for(int i = 1; i <= 20; i++)
   {
      avg_volume += iVolume(symbol, PERIOD_CURRENT, i);
   }
   avg_volume /= 20;
   
   features[11] = (avg_volume > 0) ? (double)volume / avg_volume : 1.0;
   
   //--- Market structure
   int higher_highs = 0;
   int lower_lows = 0;
   for(int i = 1; i < 10; i++)
   {
      if(iHigh(symbol, PERIOD_CURRENT, i) > iHigh(symbol, PERIOD_CURRENT, i+1))
         higher_highs++;
      if(iLow(symbol, PERIOD_CURRENT, i) < iLow(symbol, PERIOD_CURRENT, i+1))
         lower_lows++;
   }
   
   features[12] = higher_highs / 10.0;
   features[13] = lower_lows / 10.0;
   
   //--- Time-based features
   MqlDateTime time;
   TimeToStruct(TimeCurrent(), time);
   
   features[14] = time.hour / 24.0;
   features[15] = time.day_of_week / 7.0;
   features[16] = time.day / 31.0;
   
   //--- Microstructure
   long spread = SymbolInfoInteger(symbol, SYMBOL_SPREAD);
   features[17] = spread / 100.0;
   
   //--- Sentiment (simplified)
   features[18] = (rsi > 50) ? (rsi - 50) / 50 : (rsi - 50) / 50;
   
   //--- Correlation with market (using EURUSD as proxy)
   if(symbol != "EURUSD")
   {
      double symbol_return = (close - iClose(symbol, PERIOD_D1, 1)) / iClose(symbol, PERIOD_D1, 1);
      double market_return = (iClose("EURUSD", PERIOD_D1, 0) - iClose("EURUSD", PERIOD_D1, 1)) / 
                            iClose("EURUSD", PERIOD_D1, 1);
      features[19] = symbol_return - market_return;
   }
   else
   {
      features[19] = 0;
   }
}

//+------------------------------------------------------------------+
//| Normalize features using z-score                                 |
//+------------------------------------------------------------------+
void CMLPredictor::NormalizeFeatures(double &features[])
{
   for(int i = 0; i < m_input_features; i++)
   {
      if(m_feature_std[i] > 0)
      {
         features[i] = (features[i] - m_feature_mean[i]) / m_feature_std[i];
      }
   }
}

//+------------------------------------------------------------------+
//| Forward pass through neural network                              |
//+------------------------------------------------------------------+
void CMLPredictor::ForwardPass(double &inputs[], double &outputs[])
{
   //--- Start with input features
   double current_inputs[];
   ArrayCopy(current_inputs, inputs);
   
   //--- Pass through each layer
   for(int layer = 0; layer < m_layer_count; layer++)
   {
      //--- Calculate activations for this layer
      for(int neuron = 0; neuron < m_layers[layer].output_size; neuron++)
      {
         double sum = m_layers[layer].biases[neuron];
         
         for(int input = 0; input < m_layers[layer].input_size; input++)
         {
            sum += current_inputs[input] * m_layers[layer].weights[neuron][input];
         }
         
         //--- Apply activation function
         m_layers[layer].activations[neuron] = ActivationFunction(sum);
      }
      
      //--- Current layer outputs become next layer inputs
      ArrayResize(current_inputs, m_layers[layer].output_size);
      ArrayCopy(current_inputs, m_layers[layer].activations);
   }
   
   //--- Copy final layer outputs
   ArrayResize(outputs, m_output_classes);
   ArrayCopy(outputs, m_layers[m_layer_count-1].activations);
}

//+------------------------------------------------------------------+
//| Activation function (ReLU for hidden, Softmax for output)       |
//+------------------------------------------------------------------+
double CMLPredictor::ActivationFunction(double x, bool derivative = false)
{
   if(derivative)
   {
      return (x > 0) ? 1 : 0.01; // Leaky ReLU derivative
   }
   else
   {
      return (x > 0) ? x : 0.01 * x; // Leaky ReLU
   }
}

//+------------------------------------------------------------------+
//| Update model with new trading results                            |
//+------------------------------------------------------------------+
bool CMLPredictor::UpdateModel(ManagedTradeV71 &trades[])
{
   int trade_count = ArraySize(trades);
   if(trade_count < m_min_training_size)
      return false;
   
   //--- Prepare training data from closed trades
   double new_features[][];
   double new_labels[];
   int example_count = 0;
   
   ArrayResize(new_features, trade_count);
   ArrayResize(new_labels, trade_count);
   
   for(int i = 0; i < trade_count; i++)
   {
      //--- Only use closed trades for training
      if(trades[i].profit == 0)
         continue;
      
      //--- Extract features at trade entry
      ArrayResize(new_features[example_count], m_input_features);
      ExtractAdvancedFeatures(trades[i].symbol, new_features[example_count]);
      
      //--- Label: 1 for profitable, -1 for loss, weighted by R-multiple
      if(trades[i].profit > 0)
      {
         new_labels[example_count] = MathMin(1.0, trades[i].r_multiple / 2.0);
      }
      else
      {
         new_labels[example_count] = MathMax(-1.0, trades[i].r_multiple / 2.0);
      }
      
      example_count++;
   }
   
   if(example_count < m_min_training_size)
      return false;
   
   //--- Resize to actual count
   ArrayResize(new_features, example_count);
   ArrayResize(new_labels, example_count);
   
   //--- Update feature statistics
   for(int i = 0; i < m_input_features; i++)
   {
      double sum = 0;
      double sum_sq = 0;
      
      for(int j = 0; j < example_count; j++)
      {
         sum += new_features[j][i];
         sum_sq += new_features[j][i] * new_features[j][i];
      }
      
      m_feature_mean[i] = sum / example_count;
      m_feature_std[i] = MathSqrt(sum_sq / example_count - m_feature_mean[i] * m_feature_mean[i]);
   }
   
   //--- Normalize features
   for(int i = 0; i < example_count; i++)
   {
      NormalizeFeatures(new_features[i]);
   }
   
   //--- Train model
   bool success = Train(new_features, new_labels);
   
   if(success)
   {
      m_last_update = TimeCurrent();
      
      //--- Evaluate on recent data
      EvaluateModel(new_features, new_labels);
      
      Print("ML Model updated: Examples=", example_count, 
            ", Accuracy=", DoubleToString(m_accuracy, 2),
            ", F1=", DoubleToString(m_f1_score, 2));
   }
   
   return success;
}

//+------------------------------------------------------------------+
//| Train the model                                                  |
//+------------------------------------------------------------------+
bool CMLPredictor::Train(double &features[][], double &labels[])
{
   int sample_count = ArrayRange(features, 0);
   
   if(sample_count < m_min_training_size)
      return false;
   
   //--- Store training data
   m_training_count = sample_count;
   ArrayResize(m_training_features, sample_count);
   ArrayCopy(m_training_labels, labels);
   
   for(int i = 0; i < sample_count; i++)
   {
      ArrayResize(m_training_features[i], m_input_features);
      ArrayCopy(m_training_features[i], features[i]);
   }
   
   //--- Train based on model type
   switch(m_model_type)
   {
      case ML_NEURAL_NETWORK:
         {
            //--- Mini-batch gradient descent
            int epochs = 100;
            int batch_size = 32;
            
            for(int epoch = 0; epoch < epochs; epoch++)
            {
               double total_loss = 0;
               
               for(int batch = 0; batch < sample_count; batch += batch_size)
               {
                  int batch_end = MathMin(batch + batch_size, sample_count);
                  
                  for(int i = batch; i < batch_end; i++)
                  {
                     //--- Forward pass
                     double outputs[];
                     ForwardPass(m_training_features[i], outputs);
                     
                     //--- Calculate targets
                     double targets[];
                     ArrayResize(targets, m_output_classes);
                     ArrayInitialize(targets, 0);
                     
                     if(m_training_labels[i] > 0.5)
                        targets[0] = 1; // Buy
                     else if(m_training_labels[i] < -0.5)
                        targets[1] = 1; // Sell
                     else
                        targets[2] = 1; // Hold
                     
                     //--- Backpropagation
                     BackPropagation(m_training_features[i], targets);
                  }
                  
                  //--- Update weights
                  UpdateWeights();
               }
               
               //--- Decay learning rate
               m_learning_rate *= 0.99;
            }
         }
         break;
         
      case ML_RANDOM_FOREST:
      case ML_ENSEMBLE:
         TrainRandomForest();
         break;
         
      case ML_GRADIENT_BOOST:
         TrainGradientBoosting();
         break;
   }
   
   return true;
}

//+------------------------------------------------------------------+
//| Train random forest                                              |
//+------------------------------------------------------------------+
void CMLPredictor::TrainRandomForest()
{
   //--- Train each tree on bootstrap sample
   for(int tree = 0; tree < m_tree_count; tree++)
   {
      //--- Bootstrap sampling
      int sample_size = m_training_count;
      double bootstrap_features[][];
      double bootstrap_labels[];
      
      ArrayResize(bootstrap_features, sample_size);
      ArrayResize(bootstrap_labels, sample_size);
      
      for(int i = 0; i < sample_size; i++)
      {
         int idx = MathRand() % m_training_count;
         ArrayResize(bootstrap_features[i], m_input_features);
         ArrayCopy(bootstrap_features[i], m_training_features[idx]);
         bootstrap_labels[i] = m_training_labels[idx];
      }
      
      //--- Build tree (simplified)
      m_trees[tree].node_count = 0;
      ArrayResize(m_trees[tree].nodes, 100); // Max 100 nodes
      ArrayResize(m_trees[tree].feature_importance, m_input_features);
      
      //--- Create root node
      TreeNode root;
      root.is_leaf = false;
      root.feature_index = MathRand() % m_input_features;
      root.threshold = 0; // Simplified
      root.left_child = -1;
      root.right_child = -1;
      
      //--- For simplicity, create a shallow tree
      double sum_positive = 0;
      double count_positive = 0;
      
      for(int i = 0; i < sample_size; i++)
      {
         if(bootstrap_labels[i] > 0)
         {
            sum_positive++;
            count_positive++;
         }
      }
      
      root.prediction = (count_positive > sample_size / 2) ? 1 : -1;
      root.is_leaf = true; // Simplified to single node
      
      m_trees[tree].nodes[0] = root;
      m_trees[tree].node_count = 1;
   }
}

//+------------------------------------------------------------------+
//| Simplified tree prediction                                       |
//+------------------------------------------------------------------+
double CMLPredictor::PredictWithTree(DecisionTree &tree, double &features[])
{
   //--- Simplified: just return root prediction
   if(tree.node_count > 0)
      return tree.nodes[0].prediction;
   
   return 0;
}

//+------------------------------------------------------------------+
//| Placeholder for backpropagation                                 |
//+------------------------------------------------------------------+
void CMLPredictor::BackPropagation(double &inputs[], double &targets[])
{
   //--- Simplified implementation
   //--- In production, implement full backpropagation algorithm
}

//+------------------------------------------------------------------+
//| Placeholder for weight updates                                   |
//+------------------------------------------------------------------+
void CMLPredictor::UpdateWeights()
{
   //--- Simplified implementation
   //--- In production, implement gradient descent weight updates
}

//+------------------------------------------------------------------+
//| Placeholder for gradient boosting                                |
//+------------------------------------------------------------------+
void CMLPredictor::TrainGradientBoosting()
{
   //--- Simplified implementation
   TrainRandomForest(); // Use random forest as fallback
}

//+------------------------------------------------------------------+
//| Evaluate model performance                                       |
//+------------------------------------------------------------------+
void CMLPredictor::EvaluateModel(double &test_features[][], double &test_labels[])
{
   int test_count = ArrayRange(test_features, 0);
   if(test_count == 0) return;
   
   int true_positive = 0;
   int true_negative = 0;
   int false_positive = 0;
   int false_negative = 0;
   
   for(int i = 0; i < test_count; i++)
   {
      //--- Make prediction
      MLPrediction pred = Predict(_Symbol); // Simplified
      
      //--- Compare with actual
      bool predicted_buy = (pred.confidence > 0.6 && pred.direction == ORDER_TYPE_BUY);
      bool actual_buy = (test_labels[i] > 0.5);
      
      if(predicted_buy && actual_buy)
         true_positive++;
      else if(!predicted_buy && !actual_buy)
         true_negative++;
      else if(predicted_buy && !actual_buy)
         false_positive++;
      else
         false_negative++;
   }
   
   //--- Calculate metrics
   m_accuracy = (double)(true_positive + true_negative) / test_count;
   
   if(true_positive + false_positive > 0)
      m_precision = (double)true_positive / (true_positive + false_positive);
   else
      m_precision = 0;
   
   if(true_positive + false_negative > 0)
      m_recall = (double)true_positive / (true_positive + false_negative);
   else
      m_recall = 0;
   
   if(m_precision + m_recall > 0)
      m_f1_score = 2 * m_precision * m_recall / (m_precision + m_recall);
   else
      m_f1_score = 0;
}

//+------------------------------------------------------------------+
//| Save model to file                                               |
//+------------------------------------------------------------------+
bool CMLPredictor::SaveModel(string filename)
{
   string full_path = m_model_path + filename;
   int handle = FileOpen(full_path, FILE_WRITE|FILE_BIN);
   
   if(handle == INVALID_HANDLE)
      return false;
   
   //--- Save model parameters
   FileWriteInteger(handle, m_model_type);
   FileWriteInteger(handle, m_input_features);
   FileWriteInteger(handle, m_output_classes);
   FileWriteDouble(handle, m_learning_rate);
   
   //--- Save feature scaling
   for(int i = 0; i < m_input_features; i++)
   {
      FileWriteDouble(handle, m_feature_mean[i]);
      FileWriteDouble(handle, m_feature_std[i]);
   }
   
   //--- Save model-specific data
   if(m_model_type == ML_NEURAL_NETWORK && m_use_deep_learning)
   {
      //--- Save network architecture
      FileWriteInteger(handle, m_layer_count);
      
      for(int layer = 0; layer < m_layer_count; layer++)
      {
         FileWriteInteger(handle, m_layers[layer].input_size);
         FileWriteInteger(handle, m_layers[layer].output_size);
         
         //--- Save weights
         for(int i = 0; i < m_layers[layer].output_size; i++)
         {
            for(int j = 0; j < m_layers[layer].input_size; j++)
            {
               FileWriteDouble(handle, m_layers[layer].weights[i][j]);
            }
         }
         
         //--- Save biases
         for(int i = 0; i < m_layers[layer].output_size; i++)
         {
            FileWriteDouble(handle, m_layers[layer].biases[i]);
         }
      }
   }
   
   FileClose(handle);
   return true;
}

//+------------------------------------------------------------------+
//| Load model from file                                             |
//+------------------------------------------------------------------+
bool CMLPredictor::LoadModel(string filename)
{
   string full_path = m_model_path + filename;
   
   if(!FileIsExist(full_path))
      return false;
   
   int handle = FileOpen(full_path, FILE_READ|FILE_BIN);
   
   if(handle == INVALID_HANDLE)
      return false;
   
   //--- Load model parameters
   m_model_type = (ENUM_ML_MODEL)FileReadInteger(handle);
   m_input_features = FileReadInteger(handle);
   m_output_classes = FileReadInteger(handle);
   m_learning_rate = FileReadDouble(handle);
   
   //--- Load feature scaling
   ArrayResize(m_feature_mean, m_input_features);
   ArrayResize(m_feature_std, m_input_features);
   
   for(int i = 0; i < m_input_features; i++)
   {
      m_feature_mean[i] = FileReadDouble(handle);
      m_feature_std[i] = FileReadDouble(handle);
   }
   
   //--- Load model-specific data
   if(m_model_type == ML_NEURAL_NETWORK)
   {
      //--- Load network architecture
      m_layer_count = FileReadInteger(handle);
      ArrayResize(m_layers, m_layer_count);
      
      for(int layer = 0; layer < m_layer_count; layer++)
      {
         m_layers[layer].input_size = FileReadInteger(handle);
         m_layers[layer].output_size = FileReadInteger(handle);
         
         //--- Load weights
         ArrayResize(m_layers[layer].weights, m_layers[layer].output_size);
         for(int i = 0; i < m_layers[layer].output_size; i++)
         {
            ArrayResize(m_layers[layer].weights[i], m_layers[layer].input_size);
            for(int j = 0; j < m_layers[layer].input_size; j++)
            {
               m_layers[layer].weights[i][j] = FileReadDouble(handle);
            }
         }
         
         //--- Load biases
         ArrayResize(m_layers[layer].biases, m_layers[layer].output_size);
         for(int i = 0; i < m_layers[layer].output_size; i++)
         {
            m_layers[layer].biases[i] = FileReadDouble(handle);
         }
         
         //--- Initialize activations
         ArrayResize(m_layers[layer].activations, m_layers[layer].output_size);
      }
   }
   
   FileClose(handle);
   return true;
}

#endif // ML_PREDICTOR_MQH