feat: get_wealth_indexes_with_cashflow has use_discounted_values parameters

Author: chilango74

main.py

@@ -28,7 +28,7 @@
 ind = ok.IndexationStrategy(pf)
 ind.initial_investment = 10_000
 ind.frequency = "year"
-ind.amount = -1_000
+ind.amount = -500
 ind.indexation = "inflation"
 
 # # TimeSeries strategy
@@ -44,12 +44,12 @@
 # Assign a strategy
 pf.dcf.cashflow_parameters = ind
 pf.dcf.discount_rate = 0.10
-pf.dcf.use_discounted_values = False
+pf.dcf.use_discounted_values = True
 
 # Set Monte Carlo
 pf.dcf.set_mc_parameters(distribution="t", period=50, number=100)
 
-pf.dcf.plot_forecast_monte_carlo(backtest=False)
+pf.dcf.plot_forecast_monte_carlo(backtest=True)
 
 plt.yscale("log")  # log or linear
 plt.legend("")

okama/common/helpers/helpers.py

@@ -197,19 +197,19 @@ def get_wealth_indexes_with_cashflow(
         portfolio_symbol: Optional[str],
         inflation_symbol: Optional[str],
         cashflow_parameters: type[CashFlow],
+        use_discounted_values: bool,
     ) -> Union[pd.Series, pd.DataFrame]:
         """
         Returns wealth index for a series of returns with cash flows (withdrawals/contributions).
 
         Values of the wealth index correspond to the beginning of the month.
         """
-        # TODO: add use_discounted_values parameters to setup initial conditions without modifying cashflow_parameters
         pf_object = cashflow_parameters.parent
         dcf_object = cashflow_parameters.parent.dcf
         amount = getattr(cashflow_parameters, "amount", None)
         period_initial_amount = (
             dcf_object.initial_investment_pv
-            if dcf_object.use_discounted_values
+            if use_discounted_values
             else cashflow_parameters.initial_investment
         )
         period_initial_amount_cached = period_initial_amount
@@ -218,7 +218,7 @@ def get_wealth_indexes_with_cashflow(
         else:
             try:
                 # amount is not defined in TimeSeriesStrategy & PercentageStrategy
-                amount = dcf_object.cashflow_pv if dcf_object.use_discounted_values else cashflow_parameters.amount
+                amount = dcf_object.cashflow_pv if use_discounted_values else cashflow_parameters.amount
             except AttributeError:
                 pass
             if isinstance(ror, pd.DataFrame):
@@ -240,7 +240,7 @@ def get_wealth_indexes_with_cashflow(
                     elif cashflow_parameters.NAME == "time_series":
                         try:
                             cashflow = cashflow_parameters.time_series[date]
-                            if dcf_object.use_discounted_values:
+                            if use_discounted_values:
                                 last_date = pf_object.last_date
                                 first_date = date.to_timestamp(how="End")
                                 period_length = Date.get_period_length(last_date, first_date)

okama/portfolio.py

@@ -2505,6 +2505,7 @@ def wealth_index(self) -> pd.DataFrame:
                 portfolio_symbol=self.parent.symbol,
                 inflation_symbol=infl,
                 cashflow_parameters=self.cashflow_parameters,
+                use_discounted_values=self.use_discounted_values,
             )
             self._wealth_index = self.parent._make_df_if_series(df)
         return self._wealth_index
@@ -2553,6 +2554,7 @@ def wealth_index_with_assets(self) -> pd.DataFrame:
                 None,  # symbol
                 None,  # inflation_symbol
                 self.cashflow_parameters,
+                self.use_discounted_values
             ),
         )
         return wealth_df
@@ -2776,6 +2778,7 @@ def monte_carlo_wealth(self) -> pd.DataFrame:
                     None,  # portfolio_symbol
                     None,  # inflation_symbol
                     self.cashflow_parameters,
+                    False,  # use_discounted_values
                 ),
             )
 
@@ -2876,12 +2879,12 @@ def plot_forecast_monte_carlo(
         >>> plt.yscale("log")  # Y-axis has logarithmic scale
         >>> plt.show()
         """
-        backup_obj = self.cashflow_parameters
-        backup = self.use_discounted_values
-        self.use_discounted_values = False  # we need to start with not discounted values
         if backtest:
             if self.cashflow_parameters is None:
                 raise AttributeError("'cashflow_parameters' is not defined.")
+            backup_obj = self.cashflow_parameters
+            backup = self.use_discounted_values
+            self.use_discounted_values = False  # we need to start with not discounted values
             s1 = self.wealth_index[self.parent.symbol]
             s1.plot(legend=None, figsize=figsize)
             last_backtest_value = s1.iloc[-1]
@@ -2895,12 +2898,12 @@ def plot_forecast_monte_carlo(
                 s2 = self.monte_carlo_wealth
                 for s in s2:
                     s2[s].plot(legend=None)
+            self.cashflow_parameters = backup_obj
+            self.use_discounted_values = backup
+            self.cashflow_parameters._clear_cf_cache()
         else:
             s2 = self.monte_carlo_wealth
             s2.plot(legend=None)
-        self.cashflow_parameters = backup_obj
-        self.cashflow_parameters._clear_cf_cache()
-        self.use_discounted_values = backup
 
     def monte_carlo_survival_period(self, threshold: float = 0) -> pd.Series:
         """