Enhance strategy output standardization and improve plotting logic

- Introduced a new method to standardize output column names across different strategies, ensuring consistency in data handling and plotting.
- Updated plotting logic in test_bbrsi.py to utilize standardized column names, improving clarity and maintainability.
- Enhanced error handling for missing data in plots and adjusted visual elements for better representation of trading signals.
- Improved the overall structure of strategy implementations to support additional indicators and metadata for better analysis.

cycles/Analysis/strategies.py

@@ -16,14 +16,66 @@ class Strategy:
 
     def run(self, data, strategy_name):
         if strategy_name == "MarketRegimeStrategy":
-            return self.MarketRegimeStrategy(data)
+            result = self.MarketRegimeStrategy(data)
+            return self.standardize_output(result, strategy_name)
         elif strategy_name == "CryptoTradingStrategy":
-            return self.CryptoTradingStrategy(data)
+            result = self.CryptoTradingStrategy(data)
+            return self.standardize_output(result, strategy_name)
         else:
             if self.logging is not None:
                 self.logging.warning(f"Strategy {strategy_name} not found. Using no_strategy instead.")
             return self.no_strategy(data)
 
+    def standardize_output(self, data, strategy_name):
+        """
+        Standardize column names across different strategies to ensure consistent plotting and analysis
+        
+        Args:
+            data (DataFrame): Strategy output DataFrame
+            strategy_name (str): Name of the strategy that generated this data
+            
+        Returns:
+            DataFrame: Data with standardized column names
+        """
+        if data.empty:
+            return data
+            
+        # Create a copy to avoid modifying the original
+        standardized = data.copy()
+        
+        # Standardize column names based on strategy
+        if strategy_name == "MarketRegimeStrategy":
+            # MarketRegimeStrategy already has standard column names for most fields
+            # Just ensure all standard columns exist
+            pass
+        elif strategy_name == "CryptoTradingStrategy":
+            # Map strategy-specific column names to standard names
+            column_mapping = {
+                'UpperBand_15m': 'UpperBand',
+                'LowerBand_15m': 'LowerBand',
+                'SMA_15m': 'SMA',
+                'RSI_15m': 'RSI',
+                'VolumeMA_15m': 'VolumeMA',
+                # Keep StopLoss and TakeProfit as they are
+            }
+            
+            # Add standard columns from mapped columns
+            for old_col, new_col in column_mapping.items():
+                if old_col in standardized.columns and new_col not in standardized.columns:
+                    standardized[new_col] = standardized[old_col]
+                    
+            # Add additional strategy-specific data as metadata columns
+            if 'UpperBand_1h' in standardized.columns:
+                standardized['UpperBand_1h_meta'] = standardized['UpperBand_1h']
+            if 'LowerBand_1h' in standardized.columns:
+                standardized['LowerBand_1h_meta'] = standardized['LowerBand_1h']
+        
+        # Ensure all strategies have BBWidth if possible
+        if 'BBWidth' not in standardized.columns and 'UpperBand' in standardized.columns and 'LowerBand' in standardized.columns:
+            standardized['BBWidth'] = (standardized['UpperBand'] - standardized['LowerBand']) / standardized['SMA'] if 'SMA' in standardized.columns else np.nan
+            
+        return standardized
+
     def no_strategy(self, data):
         """No strategy: returns False for both buy and sell conditions"""
         buy_condition = pd.Series([False] * len(data), index=data.index)
@@ -74,6 +126,7 @@ class Strategy:
             DataFrame: A unified DataFrame containing original data, BB, RSI, and signals.
         """
 
+        # data = aggregate_to_hourly(data, 4)
         data = aggregate_to_daily(data)
         
         # Calculate Bollinger Bands