refactor: rename Rebalance.return_ts to return_ror_ts

mbk-dev · Feb 22, 2024 · ed5245e · ed5245e
1 parent ed18fb8
commit ed5245e
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Showing 5 changed files with 27 additions and 27 deletions.
diff --git a/main.py b/main.py
@@ -14,22 +14,22 @@
                      discount_rate=0.01
                      )
 
-print(portf.discount_rate)
-print(portf)
-print(f"{portf.get_cagr()}")
-print(f"{portf.initial_amount_pv=}, {portf.cashflow_pv=}")
-print(f"{portf.survival_period=}")
-print(f"{portf.survival_date=}")
-portf.wealth_index.plot()
-
-portf.plot_forecast_monte_carlo(distr="norm", years=30, backtest=True, n=100)
+# print(portf.discount_rate)
+# print(portf)
+# print(f"{portf.get_cagr()}")
+# print(f"{portf.initial_amount_pv=}, {portf.cashflow_pv=}")
+# print(f"{portf.survival_period=}")
+# print(f"{portf.survival_date=}")
+# portf.wealth_index.plot()
+#
+# portf.plot_forecast_monte_carlo(distr="norm", years=30, backtest=True, n=100)
 
-s_periods = portf.monte_carlo_survival_period(distr="lognorm", years=25, n=100)
+s_periods = portf.monte_carlo_survival_period(distr="lognorm", years=25, n=10)
 print(f"медиана {s_periods.quantile(50 / 100)}")
-print(f"первый порцентиль {s_periods.quantile(1 / 100)}")
-print(f"99й порцентиль {s_periods.quantile(99 / 100)}")
-print(f"минимум {s_periods.min()}")
-print(f"среднее {s_periods.mean()}")
+# print(f"первый порцентиль {s_periods.quantile(1 / 100)}")
+# print(f"99й порцентиль {s_periods.quantile(99 / 100)}")
+# print(f"минимум {s_periods.min()}")
+# print(f"среднее {s_periods.mean()}")
 
 plt.show()
 

diff --git a/okama/common/helpers/helpers.py b/okama/common/helpers/helpers.py
@@ -500,7 +500,7 @@ def assets_weights_ts(self, weights: list, ror: pd.DataFrame) -> pd.DataFrame:
         portfolio_wealth_index = self.wealth_ts(weights=weights, ror=ror)
         return assets_wealth_indexes.divide(portfolio_wealth_index, axis=0)
 
-    def return_ts(self, weights: Union[list, np.ndarray], ror: pd.DataFrame) -> pd.Series:
+    def return_ror_ts(self, weights: Union[list, np.ndarray], ror: pd.DataFrame) -> pd.Series:
         """
         Return monthly rate of return time series of rebalanced portfolio given returns time series of the assets.
         Default rebalancing period is a Year (end of year)

diff --git a/okama/common/make_asset_list.py b/okama/common/make_asset_list.py
@@ -81,7 +81,7 @@ def __init__(
             # Add inflation to the date range dict
             self.assets_first_dates.update({self.inflation: macro.Inflation(self.inflation).first_date})
             self.assets_last_dates.update({self.inflation: macro.Inflation(self.inflation).last_date})
-        self.assets_ror: pd.DataFrame = self.assets_ror[self.first_date : self.last_date]
+        self.assets_ror: pd.DataFrame = self.assets_ror[self.first_date: self.last_date]
         self.period_length: float = round((self.last_date - self.first_date) / np.timedelta64(365, "D"), ndigits=1)
         self.pl = settings.PeriodLength(
             self.assets_ror.shape[0] // settings._MONTHS_PER_YEAR,

diff --git a/okama/frontier/multi_period.py b/okama/frontier/multi_period.py
@@ -228,7 +228,7 @@ def gmv_monthly_weights(self) -> np.ndarray:
 
         # Set the objective function
         def objective_function(w):
-            risk = helpers.Rebalance(period=period).return_ts(w, ror).std()
+            risk = helpers.Rebalance(period=period).return_ror_ts(w, ror).std()
             return risk
 
         # construct the constraints
@@ -272,7 +272,7 @@ def gmv_annual_weights(self) -> np.ndarray:
 
         # Set the objective function
         def objective_function(w):
-            ts = helpers.Rebalance(period=period).return_ts(w, ror)
+            ts = helpers.Rebalance(period=period).return_ror_ts(w, ror)
             mean_return = ts.mean()
             risk = ts.std()
             return helpers.Float.annualize_risk(risk=risk, mean_return=mean_return)
@@ -297,13 +297,13 @@ def _get_gmv_monthly(self) -> Tuple[float, float]:
         """
         return (
             helpers.Rebalance(period=self.rebalancing_period)
-            .return_ts(
+            .return_ror_ts(
                 self.gmv_monthly_weights,
                 self.assets_ror,
             )
             .std(),
             helpers.Rebalance(period=self.rebalancing_period)
-            .return_ts(
+            .return_ror_ts(
                 self.gmv_monthly_weights,
                 self.assets_ror,
             )
@@ -332,7 +332,7 @@ def gmv_annual_values(self) -> Tuple[float, float]:
         >>> frontier.gmv_annual_values
         (0.03695845106087943, 0.04418318557516887)
         """
-        returns = helpers.Rebalance(period=self.rebalancing_period).return_ts(self.gmv_annual_weights, self.assets_ror)
+        returns = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(self.gmv_annual_weights, self.assets_ror)
         return (
             helpers.Float.annualize_risk(returns.std(), returns.mean()),
             (returns + 1.0).prod() ** (settings._MONTHS_PER_YEAR / returns.shape[0]) - 1.0,
@@ -369,7 +369,7 @@ def global_max_return_portfolio(self) -> dict:
         # Set the objective function
         def objective_function(w):
             # Accumulated return for rebalanced portfolio time series
-            objective_function.returns = helpers.Rebalance(period=period).return_ts(w, ror)
+            objective_function.returns = helpers.Rebalance(period=period).return_ror_ts(w, ror)
             accumulated_return = (objective_function.returns + 1.0).prod() - 1.0
             return -accumulated_return
 
@@ -395,7 +395,7 @@ def objective_function(w):
         return point
 
     def _get_cagr(self, weights):
-        ts = helpers.Rebalance(period=self.rebalancing_period).return_ts(weights, self.assets_ror)
+        ts = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(weights, self.assets_ror)
         acc_return = (ts + 1.0).prod() - 1.0
         return (1.0 + acc_return) ** (settings._MONTHS_PER_YEAR / ts.shape[0]) - 1.0
 
@@ -426,7 +426,7 @@ def minimize_risk(self, target_value: float) -> Dict[str, float]:
 
         def objective_function(w):
             # annual risk
-            ts = helpers.Rebalance(period=self.rebalancing_period).return_ts(w, self.assets_ror)
+            ts = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(w, self.assets_ror)
             risk_monthly = ts.std()
             mean_return = ts.mean()
             return helpers.Float.annualize_risk(risk_monthly, mean_return)
@@ -482,7 +482,7 @@ def _maximize_risk(self, target_return: float) -> Dict[str, float]:
 
         def objective_function(w):
             # annual risk
-            ts = helpers.Rebalance(period=self.rebalancing_period).return_ts(w, self.assets_ror)
+            ts = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(w, self.assets_ror)
             risk_monthly = ts.std()
             mean_return = ts.mean()
             result = -helpers.Float.annualize_risk(risk_monthly, mean_return)
@@ -799,7 +799,7 @@ def get_monte_carlo(self, n: int = 100) -> pd.DataFrame:
 
         # Portfolio risk and cagr for each set of weights
         portfolios_ror = weights_df.aggregate(
-            helpers.Rebalance(period=self.rebalancing_period).return_ts,
+            helpers.Rebalance(period=self.rebalancing_period).return_ror_ts,
             ror=self.assets_ror,
         )
         random_portfolios = pd.DataFrame()

diff --git a/okama/portfolio.py b/okama/portfolio.py
@@ -353,7 +353,7 @@ def ror(self) -> pd.Series:
         if self.rebalancing_period == "month":
             s = helpers.Frame.get_portfolio_return_ts(self.weights, self.assets_ror)
         else:
-            s = helpers.Rebalance(period=self.rebalancing_period).return_ts(self.weights, self.assets_ror)
+            s = helpers.Rebalance(period=self.rebalancing_period).return_ror_ts(self.weights, self.assets_ror)
         return s.rename(self.symbol, inplace=True)
 
     @property