handled issues with frequency validation

pyfacts/core.py

@@ -180,7 +180,7 @@ class Series(UserList):
             if len(self) != len(other):
                 raise ValueError("Length of Series must be same for comparison")
 
-        elif (self.dtype != float and isinstance(other, Number)) or not isinstance(other, self.dtype):
+        elif self.dtype != float and isinstance(other, Number):
             raise Exception(f"Cannot compare type {self.dtype.__name__} to {type(other).__name__}")
 
         return other
@@ -300,7 +300,9 @@ class Series(UserList):
 
 
 def _validate_frequency(
-    data: List[Tuple[datetime.datetime, float]], provided_frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None
+    data: List[Tuple[datetime.datetime, float]],
+    provided_frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
+    raise_error: bool = True,
 ):
     """Checks the data and returns the expected frequency."""
     if provided_frequency is not None:
@@ -325,7 +327,10 @@ def _validate_frequency(
             expected_frequency = frequency
             break
     else:
+        if raise_error:
             raise ValueError("Data does not match any known frequency. Perhaps you have too many missing data points.")
+        else:
+            expected_frequency = provided_frequency.symbol
 
     expected_data_points = expected_data_points[expected_frequency]
     if provided_frequency is None:
@@ -387,7 +392,7 @@ class TimeSeriesCore:
 
         ts_data = _preprocess_timeseries(ts_data, date_format=date_format)
 
-        validation = _validate_frequency(data=ts_data, provided_frequency=frequency)
+        validation = _validate_frequency(data=ts_data, provided_frequency=frequency, raise_error=validate_frequency)
         if frequency is None:
             frequency = validation["expected_frequency"]
 
@@ -508,7 +513,7 @@ class TimeSeriesCore:
         """Helper function to retrieve items using a list"""
 
         data_to_return = [self._get_item_from_key(key) for key in date_list]
-        return self.__class__(data_to_return, frequency=self.frequency.symbol)
+        return self.__class__(data_to_return, frequency=self.frequency.symbol, validate_frequency=False)
 
     def _get_item_from_series(self, series: Series):
         """Helper function to retrieve item using a Series object

pyfacts/pyfacts.py

@@ -344,8 +344,8 @@ class TimeSeries(TimeSeriesCore):
     @date_parser(1, 2)
     def calculate_rolling_returns(
         self,
-        from_date: datetime.date | str,
-        to_date: datetime.date | str,
+        from_date: datetime.date | str = None,
+        to_date: datetime.date | str = None,
         frequency: Literal["D", "W", "M", "Q", "H", "Y"] = None,
         as_on_match: str = "closest",
         prior_match: str = "closest",
@@ -429,6 +429,13 @@ class TimeSeries(TimeSeriesCore):
                 frequency = getattr(AllFrequencies, frequency)
             except AttributeError:
                 raise ValueError(f"Invalid argument for frequency {frequency}")
+        if from_date is None:
+            from_date = self.start_date + relativedelta(
+                days=int(_interval_to_years(return_period_unit, return_period_value) * 365 + 1)
+            )
+
+        if to_date is None:
+            to_date = self.end_date
 
         dates = create_date_series(from_date, to_date, frequency.symbol)
         if frequency == AllFrequencies.D:

pyfacts/statistics.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import datetime
 import statistics
+from cmath import sqrt
 from typing import Literal
 
 from pyfacts.core import date_parser
@@ -472,13 +473,14 @@ def sortino_ratio(
     closest: Literal["previous", "next"] = "previous",
     date_format: str = None,
 ) -> float:
-    """Calculate the Sharpe ratio of any time series
+    """Calculate the Sortino ratio of any time series
 
-    Sharpe ratio is a measure of returns per unit of risk,
-    where risk is measured by the standard deviation of the returns.
+    Sortino ratio is a variation of the Sharpe ratio,
+    where risk is measured as standard deviation of negative returns only.
+    Since deviation on the positive side is not undesirable, hence sortino ratio excludes positive deviations.
 
-    The formula for Sharpe ratio is:
-        (average asset return - risk free rate)/volatility of asset returns
+    The formula for Sortino ratio is:
+        (average asset return - risk free rate)/volatility of negative asset returns
 
     Parameters
     ----------
@@ -528,7 +530,7 @@ def sortino_ratio(
 
     Returns
     -------
-        Value of Sharpe ratio as a float.
+        Value of Sortino ratio as a float.
 
     Raises
     ------
@@ -559,11 +561,13 @@ def sortino_ratio(
         "closest": closest,
         "date_format": date_format,
     }
-    average_rr_ts = time_series_data.calculate_rolling_returns(**common_params, annual_compounded_returns=True)
+    average_rr_ts = time_series_data.calculate_rolling_returns(**common_params, annual_compounded_returns=False)
     average_rr = statistics.mean(average_rr_ts.values)
+    annualized_average_rr = (1 + average_rr) ** (365 / interval_days) - 1
 
-    excess_returns = average_rr - risk_free_rate
+    excess_returns = annualized_average_rr - risk_free_rate
     sd = statistics.stdev([i for i in average_rr_ts.values if i < 0])
+    sd *= sqrt(365 / interval_days)
 
     sortino_ratio_value = excess_returns / sd
     return sortino_ratio_value

test_series.py

@@ -1,6 +1,6 @@
 import datetime
 
-from fincal.core import Series
+from pyfacts.core import Series
 
 s1 = Series([2.5, 6.2, 5.6, 8.4, 7.4, 1.5, 9.6, 5])
 
@@ -19,7 +19,7 @@ dt_lst = [
     datetime.datetime(2020, 6, 19, 0, 0),
     datetime.datetime(2016, 3, 16, 0, 0),
     datetime.datetime(2017, 4, 25, 0, 0),
-    datetime.datetime(2016, 7, 10, 0, 0)
+    datetime.datetime(2016, 7, 10, 0, 0),
 ]
 
 s2 = Series(dt_lst)