fincal/fincal.py

@@ -407,7 +407,7 @@ class TimeSeries(TimeSeriesCore):
         if_not_found: Literal["fail", "nan"] = "fail",
         annual_compounded_returns: bool = None,
         date_format: str = None,
-    ) -> float:
+    ):
         """Calculates the volatility of the time series.add()
 
         The volatility is calculated as the standard deviaion of periodic returns.
@@ -431,20 +431,6 @@ class TimeSeries(TimeSeriesCore):
             Number of traded days per year to be considered for annualizing volatility.
             Only used when annualizing volatility for a time series with daily frequency.
             If not provided, will use the value in FincalOptions.traded_days.
-
-        Remaining options are passed on to rolling_return function.
-
-        Returns:
-        -------
-            Returns the volatility number as float
-
-        Raises:
-        -------
-            ValueError: If frequency string is outside valid values
-
-        Also see:
-        --------
-            TimeSeries.calculate_rolling_returns()
         """
 
         if frequency is None:
@@ -487,54 +473,6 @@ class TimeSeries(TimeSeriesCore):
 
         return sd
 
-    def average_rolling_return(self, **kwargs) -> float:
-        """Calculates the average rolling return for a given period
-
-        Parameters
-        ----------
-        kwargs: parameters to be passed to the calculate_rolling_returns() function
-
-        Returns
-        -------
-        float
-            returns the average rolling return for a given period
-
-        Also see:
-        ---------
-        TimeSeries.calculate_rolling_returns()
-        """
-
-        kwargs["return_period_unit"] = kwargs.get("return_period_unit", self.frequency.freq_type)
-        kwargs["return_period_value"] = kwargs.get("return_period_value", 1)
-        kwargs["to_date"] = kwargs.get("to_date", self.end_date)
-
-        if kwargs.get("from_date", None) is None:
-            start_date = self.start_date + relativedelta(
-                **{kwargs["return_period_unit"]: kwargs["return_period_value"]}
-            )
-            kwargs["from_date"] = start_date
-
-        rr = self.calculate_rolling_returns(**kwargs)
-        return statistics.mean(rr.values)
-
-    def max_drawdown(self):
-        max_val_dict = {}
-
-        prev_val = 0
-        prev_date = list(self.data)[0]
-
-        for dt, val in self.data.items():
-            if val > prev_val:
-                max_val_dict[dt] = (dt, val, 0)
-                prev_date, prev_val = dt, val
-            else:
-                max_val_dict[dt] = (prev_date, prev_val, val / prev_val - 1)
-
-        max_drawdown = min(max_val_dict.items(), key=lambda x: x[1][2])
-        max_drawdown = dict(start_date=max_drawdown[1][0], end_date=max_drawdown[0], drawdown=max_drawdown[1][2])
-
-        return max_drawdown
-
 
 if __name__ == "__main__":
     date_series = [

tests/test_fincal2.py

@@ -168,12 +168,3 @@ class TestVolatility:
         assert round(sd, 6) == 0.023164
         sd = ts.volatility(from_date="2017-10-01", to_date="2019-08-31", annualize_volatility=True)
         assert round(sd, 6) == 0.050559
-        sd = ts.volatility(from_date="2017-02-01", frequency="M", return_period_unit="months")
-        assert round(sd, 6) == 0.050884
-        sd = ts.volatility(
-            frequency="M",
-            return_period_unit="months",
-            return_period_value=3,
-            annualize_volatility=False,
-        )
-        assert round(sd, 6) == 0.020547