3.9 Enhance chart functionality with indicator management and data export features

- Updated `ChartBuilder` to support dynamic indicator integration, allowing users to specify overlay and subplot indicators for enhanced chart analysis.
- Implemented a new `get_indicator_data` method in `MarketDataIntegrator` for fetching indicator data based on user configurations.
- Added `create_export_controls` in `chart_controls.py` to facilitate data export options (CSV/JSON) for user analysis.
- Enhanced error handling and logging throughout the chart and data analysis processes to improve reliability and user feedback.
- Updated documentation to reflect new features and usage guidelines for indicator management and data export functionalities.

data/common/indicators.py

@@ -415,6 +415,67 @@ class TechnicalIndicators:
         
         return results
 
+    def calculate(self, indicator_type: str, candles: Union[pd.DataFrame, List[OHLCVCandle]], **kwargs) -> Optional[Dict[str, Any]]:
+        """
+        Generic method to calculate any supported indicator by type.
+        
+        Args:
+            indicator_type: The type of indicator to calculate (e.g., 'sma', 'ema').
+            candles: The input data, either a DataFrame or a list of OHLCVCandle objects.
+            **kwargs: Keyword arguments for the specific indicator function.
+            
+        Returns:
+            A dictionary containing the indicator results, or None if the type is unknown.
+        """
+        # If input is a DataFrame, convert it to list of OHLCVCandle objects.
+        # This is a temporary adaptation to the existing methods.
+        # Future optimization should standardize on DataFrames.
+        if isinstance(candles, pd.DataFrame):
+            from .data_types import OHLCVCandle
+            
+            # Ensure required columns are present
+            required_cols = {'open', 'high', 'low', 'close', 'volume'}
+            if not required_cols.issubset(candles.columns):
+                if self.logger:
+                    self.logger.error("Indicators: DataFrame missing required columns for OHLCVCandle conversion.")
+                return None
+
+            symbol = kwargs.get('symbol', 'UNKNOWN')
+            timeframe = kwargs.get('timeframe', 'UNKNOWN')
+
+            candles_list = [
+                OHLCVCandle(
+                    symbol=symbol,
+                    timeframe=timeframe,
+                    start_time=row['timestamp'],
+                    end_time=row['timestamp'],
+                    open=Decimal(str(row['open'])),
+                    high=Decimal(str(row['high'])),
+                    low=Decimal(str(row['low'])),
+                    close=Decimal(str(row['close'])),
+                    volume=Decimal(str(row['volume'])),
+                    trade_count=int(row.get('trade_count', 0))
+                ) for _, row in candles.iterrows()
+            ]
+            candles = candles_list
+
+        indicator_method = getattr(self, indicator_type, None)
+        if indicator_method and callable(indicator_method):
+            # We need to construct a proper IndicatorResult object here
+            # For now, let's adapt to what the methods return
+            raw_result = indicator_method(candles, **kwargs)
+            
+            # The methods return List[IndicatorResult], let's package that
+            if raw_result:
+                return {
+                    "data": raw_result
+                }
+            return None
+        
+        if self.logger:
+            self.logger.warning(f"TechnicalIndicators: Unknown indicator type '{indicator_type}'")
+        return None
+
 
 def create_default_indicators_config() -> Dict[str, Dict[str, Any]]:
     """