fix: DCF discount rate for a short history (<12 months)

Author: chilango74

okama/common/helpers/helpers.py

@@ -189,7 +189,8 @@ def get_cagr(ror: Union[pd.DataFrame, pd.Series]) -> Union[pd.Series, float]:
         Return Compound Annual Rate of Return (CAGR) for each asset given returns time series DataFrame.
         """
         if ror.shape[0] < 12:
-            return pd.Series({x: None for x in ror.columns})  # CAGR is not defined for periods < 1 year
+            # CAGR is not defined for periods < 1 year. Return None or Series with NaNs.
+            return pd.Series({x: None for x in ror.columns}) if isinstance(ror, pd.DataFrame) else None
         return ((ror + 1.0).prod()) ** (settings._MONTHS_PER_YEAR / ror.shape[0]) - 1.0
 
     @staticmethod
@@ -360,13 +361,14 @@ def condition(y):
         return df
 
     @staticmethod
-    def skewness(ror: Union[pd.DataFrame, pd.Series]) -> Union[pd.Series, float]:
+    def skewness(ror: Union[pd.DataFrame, pd.Series]) -> Union[pd.DataFrame, pd.Series]:
         """
         Calculate expanding skewness.
         The shape of time series should be at least 12. In the opposite case empty time series is returned.
         """
-        sk = ror.expanding(min_periods=1).skew()
-        return sk.iloc[settings._MONTHS_PER_YEAR :]
+        sk = ror.expanding(min_periods=12).skew()
+        sk.dropna(inplace=True)
+        return sk
 
     @staticmethod
     def skewness_rolling(ror: Union[pd.DataFrame, pd.Series], window: int = 60) -> Union[pd.Series, float]:
@@ -385,8 +387,9 @@ def kurtosis(ror: Union[pd.Series, pd.DataFrame]):
         Calculate expanding Fisher (normalized) kurtosis time series.
         Kurtosis should be close to zero for normal distribution.
         """
-        kt = ror.expanding(min_periods=1).kurt()
-        return kt.iloc[settings._MONTHS_PER_YEAR :]
+        kt = ror.expanding(min_periods=12).kurt()
+        kt.dropna(inplace=True)
+        return kt
 
     @staticmethod
     def kurtosis_rolling(ror: Union[pd.Series, pd.DataFrame], window: int = 60):

okama/portfolios/dcf.py

@@ -74,6 +74,9 @@ def discount_rate(self, discount_rate: Optional[float]):
         self._monte_carlo_wealth_fv = pd.DataFrame()
         if discount_rate is None and hasattr(self.parent, "inflation"):
             self._discount_rate = helpers.Frame.get_cagr(self.parent.inflation_ts)
+            if self._discount_rate is None:
+                # if time series is too short (<12 month) CAGR is not defined
+                self._discount_rate = settings.DEFAULT_DISCOUNT_RATE
         elif discount_rate is None and not hasattr(self.parent, "inflation"):
             self._discount_rate = settings.DEFAULT_DISCOUNT_RATE
         else:
@@ -493,9 +496,7 @@ def monte_carlo_wealth(
         if self.cashflow_parameters is None:
             raise AttributeError("'cashflow_parameters' is not defined.")
         if self._monte_carlo_wealth_fv.empty:
-            return_ts = self.parent.monte_carlo_returns_ts(
-                distr=self.mc.distribution, parameters = self.mc.distribution_parameters, years=self.mc.period, n=self.mc.number
-            )
+            return_ts = self.parent.mc.monte_carlo_returns_ts()
             self._monte_carlo_wealth_fv = return_ts.apply(
                 dcf_calculations.get_wealth_indexes_fv_with_cashflow,
                 axis=0,