Fix/improve documentation for users (#188)

qf_lib/containers/__init__.py

@@ -11,3 +11,16 @@
 #     WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #     See the License for the specific language governing permissions and
 #     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.
+
+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:
+  - When constructing a container object, the input data is typically expected to be in a wide format. For example:
+    - A `PricesSeries` object might be initialized with a DataFrame where each column represents the price history of a different asset.
+    - A `QFDataArray` can be created using multi-dimensional arrays to represent time, asset, and other axes of data.
+
+"""

qf_lib/data_providers/abstract_price_data_provider.py

@@ -33,9 +33,19 @@
 
 class AbstractPriceDataProvider(DataProvider, metaclass=ABCMeta):
     """
-    An interface for data providers containing historical data of stocks, indices, futures
-    and other asset classes. This is a base class of any simple data provider (a data provider that is associated with
-    single data base, for example: Quandl, Bloomberg, Yahoo.)
+    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).
+    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.
+        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]],

qf_lib/portfolio_construction/covariance_estimation/robust_covariance.py

@@ -21,8 +21,11 @@
 
 class RobustCovariance:
     """
-    Creates a class used for vol forecasting and covariance matrix estimation
-    NOTE: this method has a tendency to make decrease the volatility
+    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
+    to reduce the estimated volatility.
 
     Parameters
     ----------