Merge pull request #264 from ta-oliver/v0.0.62

Version 0.0.62

infertrade/_version.py

@@ -18,4 +18,4 @@
 File for storing current version of package.
 """
 
-__version__ = "0.0.61"
+__version__ = "0.0.62"

infertrade/api.py

@@ -159,8 +159,11 @@ def _get_raw_callable(name_of_strategy_or_signal: str) -> callable:
 
     @staticmethod
     def calculate_allocations(
-        df: pd.DataFrame, name_of_strategy: str, name_of_price_series: str = PandasEnum.MID.value,
-            allocation_lower_limit: float = -1.0, allocation_upper_limit: float = 1.0
+        df: pd.DataFrame,
+        name_of_strategy: str,
+        name_of_price_series: str = PandasEnum.MID.value,
+        allocation_lower_limit: float = -1.0,
+        allocation_upper_limit: float = 1.0,
     ) -> pd.DataFrame:
         """Calculates the allocations using the supplied strategy."""
         if name_of_price_series is not PandasEnum.MID.value:

infertrade/base.py

@@ -27,6 +27,7 @@
 
 # TODO - migrate last function to utilities.
 
+
 def get_signal_calc(func: callable, adapter: callable = None) -> callable or pd.DataFrame:
     """An adapter to calculate a signal prior to usage within a trading rule."""
     if adapter:

infertrade/utilities/operations.py

@@ -617,7 +617,7 @@ def calculate_regression_with_kelly_optimum(
 
                     if volatility < recent_fractional_realised_vol:
                         volatility = recent_fractional_realised_vol
-                        
+
                     # Absolute floor for volatility at 0.01% == 1bp
                     minimum_volatility = 0.0001
                     if volatility < minimum_volatility:

tests/test_allocations.py

@@ -122,4 +122,3 @@ def test_all_allocations_list_required_series():
     """Checks that all allocation rules list required series."""
     for ii_rule in Api.available_algorithms(filter_by_category="allocation"):
         assert isinstance(Api.required_inputs_for_algorithm(ii_rule), list)
-

tests/test_api.py

@@ -174,8 +174,8 @@ def test_return_representations(algorithm):
         )
     for representation in dict_of_properties[algorithm]["available_representation_types"]:
         assert (
-                returned_representations[representation]
-                == dict_of_properties[algorithm]["available_representation_types"][representation]
+            returned_representations[representation]
+            == dict_of_properties[algorithm]["available_representation_types"][representation]
         )
 
     # Check if the if the function returns the correct representation when given a string
@@ -187,8 +187,8 @@ def test_return_representations(algorithm):
                 type(returned_representations),
             )
         assert (
-                returned_representations[representation]
-                == dict_of_properties[algorithm]["available_representation_types"][representation]
+            returned_representations[representation]
+            == dict_of_properties[algorithm]["available_representation_types"][representation]
         )
 
     # Check if the function returns the correct representations when given a list
@@ -200,8 +200,8 @@ def test_return_representations(algorithm):
         )
     for representation in algorithm_representations:
         assert (
-                returned_representations[representation]
-                == dict_of_properties[algorithm]["available_representation_types"][representation]
+            returned_representations[representation]
+            == dict_of_properties[algorithm]["available_representation_types"][representation]
         )
 
 
@@ -367,15 +367,21 @@ def test_allocation_limit(test_df):
 
     test_df_copy = copy.deepcopy(test_df)
     df_with_allocations = Api.calculate_allocations(
-        df=test_df_copy, name_of_strategy=available_allocation_algorithms[0], name_of_price_series="close",
-        allocation_lower_limit=0, allocation_upper_limit=0
+        df=test_df_copy,
+        name_of_strategy=available_allocation_algorithms[0],
+        name_of_price_series="close",
+        allocation_lower_limit=0,
+        allocation_upper_limit=0,
     )
     if not all(df_with_allocations["allocation"] == 0.0):
         raise ValueError("Allocation limits breached")
 
     df_with_allocations = Api.calculate_allocations(
-        df=test_df_copy, name_of_strategy=available_allocation_algorithms[0], name_of_price_series="close",
-        allocation_lower_limit=-0.1, allocation_upper_limit=0.1
+        df=test_df_copy,
+        name_of_strategy=available_allocation_algorithms[0],
+        name_of_price_series="close",
+        allocation_lower_limit=-0.1,
+        allocation_upper_limit=0.1,
     )
     if any(-0.1 > df_with_allocations["allocation"]) or any(df_with_allocations["allocation"] > 0.1):
         raise ValueError("Allocation limits breached")