Vkhomyakov (Sourcery refactored)

okama/asset_list.py

@@ -728,20 +728,20 @@ def describe(self, years: Tuple[int, ...] = (1, 5, 10), tickers: bool = True) ->
         for ti in self.symbols:
             # short_ticker = ti.split(".", 1)[0]
             value = self.assets_first_dates[ti].strftime("%Y-%m")
-            row.update({ti: value})
+            row[ti] = value
         row.update(period=None, property="Inception date")
         if hasattr(self, "inflation"):
-            row.update({self.inflation: self.inflation_first_date.strftime("%Y-%m")})
+            row[self.inflation] = self.inflation_first_date.strftime("%Y-%m")
         description = pd.concat([description, pd.DataFrame(row, index=[0])], ignore_index=True)
         # last asset date
         row = {}
         for ti in self.symbols:
             # short_ticker = ti.split(".", 1)[0]
             value = self.assets_last_dates[ti].strftime("%Y-%m")
-            row.update({ti: value})
+            row[ti] = value
         row.update(period=None, property="Last asset date")
         if hasattr(self, "inflation"):
-            row.update({self.inflation: self.inflation_last_date.strftime("%Y-%m")})
+            row[self.inflation] = self.inflation_last_date.strftime("%Y-%m")
         description = pd.concat([description, pd.DataFrame(row, index=[0])], ignore_index=True)
         # last data date
         row = {x: self.last_date.strftime("%Y-%m") for x in df.columns}
@@ -1157,16 +1157,15 @@ def tracking_difference_annualized(self, rolling_window: Optional[int] = None) -
         >>> x.tracking_difference_annualized(rolling_window = 12*5).plot()
         >>> plt.show()
         """
-        if rolling_window:
-            rolling_cagr = helpers.Frame.get_rolling_fn(
-                self.assets_ror,
-                window=rolling_window,
-                fn=helpers.Frame.get_cagr,
-                window_below_year=False,  # small windows below 12 months are not allowed (CAGR is not defined)
-            )
-            return rolling_cagr.subtract(rolling_cagr.iloc[:, 0], axis=0).iloc[:, 1:]
-        else:
+        if not rolling_window:
             return helpers.Index.tracking_difference_annualized(self.tracking_difference())
+        rolling_cagr = helpers.Frame.get_rolling_fn(
+            self.assets_ror,
+            window=rolling_window,
+            fn=helpers.Frame.get_cagr,
+            window_below_year=False,  # small windows below 12 months are not allowed (CAGR is not defined)
+        )
+        return rolling_cagr.subtract(rolling_cagr.iloc[:, 0], axis=0).iloc[:, 1:]
 
     @property
     def tracking_difference_annual(self) -> pd.DataFrame:

okama/portfolio.py

@@ -1145,9 +1145,7 @@ def recovery_period(self) -> int:
         s1 = s.where(s == 0).notnull().astype(int)
         s1_1 = s.where(s == 0).isnull().astype(int).cumsum()
         s2 = s1.groupby(s1_1).cumsum()
-        # Max recovery period date should not be in the border (means it's not recovered)
-        max_period = s2.max() if s2.idxmax().to_timestamp() != self.last_date else np.NAN
-        return max_period
+        return s2.max() if s2.idxmax().to_timestamp() != self.last_date else np.NAN
 
     def describe(self, years: Tuple[int] = (1, 5, 10)) -> pd.DataFrame:
         """
@@ -1359,7 +1357,7 @@ def percentile_inverse_cagr(
         """
         if distr == "hist":
             cagr_distr = self.get_rolling_cagr(years * settings._MONTHS_PER_YEAR).loc[:, [self.symbol]].squeeze()
-        elif distr in ["norm", "lognorm"]:
+        elif distr in {"norm", "lognorm"}:
             if not n:
                 n = 1000
             cagr_distr = self._get_cagr_distribution(distr=distr, years=years, n=n)
@@ -1408,7 +1406,7 @@ def percentile_history_cagr(self, years: int, percentiles: List[int] = [10, 50,
                 self.get_rolling_cagr(years * 12).loc[:, self.symbol].quantile(percentile / 100)
                 for years in period_range
             ]
-            returns_dict.update({percentile: percentile_returns_list})
+            returns_dict[percentile] = percentile_returns_list
         df = pd.DataFrame(returns_dict, index=list(period_range))
         df.index.rename("years", inplace=True)
         return df
@@ -1640,7 +1638,7 @@ def percentile_distribution_cagr(
         results = {}
         for percentile in percentiles:
             value = cagr_distr.quantile(percentile / 100)
-            results.update({percentile: value})
+            results[percentile] = value
         return results
 
     def percentile_wealth(
@@ -1696,12 +1694,12 @@ def percentile_wealth(
         """
         if distr == "hist":
             results = self.percentile_wealth_history(years=years, percentiles=percentiles).iloc[-1].to_dict()
-        elif distr in ["norm", "lognorm"]:
+        elif distr in {"norm", "lognorm"}:
             results = {}
             wealth_indexes = self._monte_carlo_wealth(distr=distr, years=years, n=n)
             for percentile in percentiles:
                 value = wealth_indexes.iloc[-1, :].quantile(percentile / 100)
-                results.update({percentile: value})
+                results[percentile] = value
         else:
             raise ValueError('distr should be "norm", "lognorm" or "hist".')
         if today_value: