Resolve flake8 errors in docstrings related to whitespace

qf_lib/containers/__init__.py

@@ -13,9 +13,9 @@
 #     limitations under the License.
 
 """
-This module offers a comprehensive set of containers for financial data analysis, including series, dataframes, data arrays, and futures-related objects. 
+This module offers a comprehensive set of containers for financial data analysis, including series, dataframes, data arrays, and futures-related objects.
 
-Each container type is designed with specialized classes to handle various aspects of financial data, such as price series, return calculations, and futures contract management. 
+Each container type is designed with specialized classes to handle various aspects of financial data, such as price series, return calculations, and futures contract management.
 These classes streamline the processing and analysis of complex financial datasets, providing a robust framework for quantitative finance research.
 
 Notes:

qf_lib/data_providers/abstract_price_data_provider.py

@@ -34,20 +34,19 @@
 class AbstractPriceDataProvider(DataProvider, metaclass=ABCMeta):
     """
     Interface for data providers that supply historical data for various asset classes, including stocks, indices, and futures.
-    
-    This base class is designed for simple data providers, which are linked to a single data source (e.g., Quandl, Bloomberg, Yahoo). 
+
+    This base class is designed for simple data providers, which are linked to a single data source (e.g., Quandl, Bloomberg, Yahoo).
     It defines the standard structure and methods that any specific data provider implementation must adhere to in order to access and retrieve historical market data.
-    
+
     Notes:
         - When implementing the get_history method (which drivers a large portion of backtesting capabilities) careful consideration must be taken
-        to ensure the data is returned in the expected format depending on the specific input tickers and fields. 
+        to ensure the data is returned in the expected format depending on the specific input tickers and fields.
         For Example:
             - isinstance(tickers, str) and isinstance(fields, str) it is expected to return a PriceSeries object
             - isinstance(tickers, str) and isinstance(fields, list) it is expected to return a PricesDataframe object
             - otherwise it is expected to return a QFDataArray object
-            
-    """
 
+    """
 
     def get_price(self, tickers: Union[Ticker, Sequence[Ticker]], fields: Union[PriceField, Sequence[PriceField]],
                   start_date: datetime, end_date: datetime = None, frequency: Frequency = Frequency.DAILY, **kwargs) -> \

qf_lib/portfolio_construction/covariance_estimation/robust_covariance.py

@@ -22,9 +22,9 @@
 class RobustCovariance:
     """
     Class for volatility forecasting and covariance matrix estimation.
-    
-    This class provides methods for estimating future volatility and the covariance matrix 
-    of financial assets. Note that the forecasting methods implemented here may have a tendency 
+
+    This class provides methods for estimating future volatility and the covariance matrix
+    of financial assets. Note that the forecasting methods implemented here may have a tendency
     to reduce the estimated volatility.
 
     Parameters