feat: the discount rate is an Effective Annual Rate (EAR)

discount_monthly_cash_flow() function is added and used in Portfolio.dcf

Author: chilango74

main.py

@@ -37,13 +37,13 @@
 # cf_strategy.frequency = "year"  # withdrawals frequency
 # cf_strategy.percentage = -0.40
 
-# # Indexation CF strategy
-# cf_strategy = ok.IndexationStrategy(pf)
-#
-# cf_strategy.initial_investment = 83_000_000
-# cf_strategy.frequency = "year"
-# cf_strategy.amount = 1_500_000 * 12
-# cf_strategy.indexation = 0.09
+# Indexation CF strategy
+cf_strategy = ok.IndexationStrategy(pf)
+
+cf_strategy.initial_investment = 10_000_000
+cf_strategy.frequency = "year"
+cf_strategy.amount = 10_000_000 * 0.05
+cf_strategy.indexation = 0.09
 
 # d = {
 #     "2015-06": -35_000_000,
@@ -52,16 +52,16 @@
 # cf_strategy.time_series_dic = d
 # cf_strategy.time_series_discounted_values = False
 
-# Fixed Percentage strategy
-cf_strategy = ok.VanguardDynamicSpending(pf)
-cf_strategy.initial_investment = 10_000_000
-cf_strategy.frequency = "year"
-cf_strategy.percentage = -0.15
-cf_strategy.indexation = 0.09
-cf_strategy.maximum_annual_withdrawal = 10_000_000 / 5  # 20%
-cf_strategy.minimum_annual_withdrawal = 10_000_000 / 10  # 10%
-cf_strategy.ceiling = 0.10
-cf_strategy.floor = -0.10
+# # VDS strategy
+# cf_strategy = ok.VanguardDynamicSpending(pf)
+# cf_strategy.initial_investment = 10_000_000
+# cf_strategy.frequency = "year"
+# cf_strategy.percentage = -0.15
+# cf_strategy.indexation = 0.09
+# cf_strategy.maximum_annual_withdrawal = 10_000_000 / 5  # 20%
+# cf_strategy.minimum_annual_withdrawal = 10_000_000 / 10  # 10%
+# cf_strategy.ceiling = 0.10
+# cf_strategy.floor = -0.10
 
 pf.dcf.cashflow_parameters = cf_strategy  # assign the cash flow strategy to portfolio
 
@@ -78,27 +78,28 @@
     number=100
 )
 
-# wi = pf.dcf.wealth_index(discounting="fv", include_negative_values=False)
-# cf = pf.dcf.cash_flow_ts(discounting="pv", remove_if_wealth_index_negative=True).resample("Y").sum()
-wi = pf.dcf.monte_carlo_wealth(discounting="fv", include_negative_values=False)
-# cf = pf.dcf.monte_carlo_cash_flow(discounting="fv", remove_if_wealth_index_negative=True)
+wi = pf.dcf.wealth_index(discounting="pv", include_negative_values=False)
+cf = pf.dcf.cash_flow_ts(discounting="pv", remove_if_wealth_index_negative=True)
+# wi = pf.dcf.monte_carlo_wealth(discounting="fv", include_negative_values=False)
+# cf = pf.dcf.monte_carlo_cash_flow(discounting="pv", remove_if_wealth_index_negative=True)
 # print(cf)
 
 wi.plot(
     # kind="bar",
     legend=False
 )
-plt.yscale('log')  # linear or log
+plt.yscale('linear')  # linear or log
 plt.show()
 
-# cf.plot(
-#     kind="bar",
-#     legend=False
-# )
-# plt.yscale('linear')  # linear or log
-# plt.show()
-
+df = cf
+df[df != 0].plot(
+    kind="bar",
+    legend=False
+)
+plt.yscale('linear')  # linear or log
+plt.show()
 
+print(df[df != 0])
 
 # df = pf.dcf.monte_carlo_wealth_fv
 # print(df)

main_notebook.ipynb

@@ -60,7 +60,7 @@ def __repr__(self):
     @property
     def discount_rate(self) -> float:
         """
-        Portfolio cash flow discount rate.
+        Annual effective discount rate for portfolio cash flow.
 
         Returns
         -------
@@ -182,14 +182,10 @@ def wealth_index(self, discounting: Literal["fv", "pv"], include_negative_values
         if discounting.lower() == "fv":
             return wealth_index_fv
         elif discounting.lower() == "pv":
-            n_rows = wealth_index_fv.shape[0]
-            discount_factors = (1.0 + self.discount_rate / settings._MONTHS_PER_YEAR) ** np.arange(n_rows)
-            wealth_index_pv = wealth_index_fv.div(discount_factors, axis=0)
-            return wealth_index_pv
+            return dcf_calculations.discount_monthly_cash_flow(wealth_index_fv, self.discount_rate)
         else:
             raise ValueError("'discounting' must be either 'fv' or 'pv'")
 
-
     def cash_flow_ts(self, discounting: Literal["fv", "pv"], remove_if_wealth_index_negative: bool = True) -> pd.Series:
         """
         Wealth index Future Values (FV) time series for the portfolio with cash flow (contributions and
@@ -240,10 +236,7 @@ def cash_flow_ts(self, discounting: Literal["fv", "pv"], remove_if_wealth_index_
         if discounting.lower() == "fv":
             return cash_flow_fv
         elif discounting.lower() == "pv":
-            n_rows = cash_flow_fv.shape[0]
-            discount_factors = (1.0 + self.discount_rate / settings._MONTHS_PER_YEAR) ** np.arange(n_rows)
-            cash_flow_pv = cash_flow_fv.div(discount_factors, axis=0)
-            return cash_flow_pv
+            return dcf_calculations.discount_monthly_cash_flow(cash_flow_fv, self.discount_rate)
         else:
             raise ValueError("'discounting' must be either 'fv' or 'pv'")
 
@@ -499,10 +492,7 @@ def monte_carlo_wealth(
         if discounting.lower() == "fv":
             return monte_carlo_wealth_fv
         elif discounting.lower() == "pv":
-            n_rows = monte_carlo_wealth_fv.shape[0]
-            discount_factors = (1.0 + self.discount_rate / settings._MONTHS_PER_YEAR) ** np.arange(n_rows)
-            monte_carlo_wealth_pv = monte_carlo_wealth_fv.div(discount_factors, axis=0)
-            return monte_carlo_wealth_pv
+            return dcf_calculations.discount_monthly_cash_flow(monte_carlo_wealth_fv, self.discount_rate)
         else:
             raise ValueError("'discounting' must be either 'fv' or 'pv'")
 
@@ -567,9 +557,7 @@ def monte_carlo_cash_flow(
         if discounting.lower() == "fv":
             return mc_cash_flow_fv
         elif discounting.lower() == "pv":
-            n_rows = mc_cash_flow_fv.shape[0]
-            discount_factors = (1.0 + self.discount_rate / settings._MONTHS_PER_YEAR) ** np.arange(n_rows)
-            return mc_cash_flow_fv.div(discount_factors, axis=0)
+            return dcf_calculations.discount_monthly_cash_flow(mc_cash_flow_fv, self.discount_rate)
         else:
             raise ValueError("'discounting' must be either 'fv' or 'pv'")
 
@@ -630,12 +618,12 @@ def plot_forecast_monte_carlo(
                     years = months / settings._MONTHS_PER_YEAR
                     periods = years / settings.frequency_periods_per_year[self.cashflow_parameters.frequency]
                     self.cashflow_parameters.amount *= (1.0 + self.cashflow_parameters.indexation) ** periods
-                s2 = self.monte_carlo_wealth_fv
+                s2 = self.monte_carlo_wealth(discounting="fv", include_negative_values=False)
                 for s in s2:
                     s2[s].plot(legend=None)
             self.cashflow_parameters = backup_obj
         else:
-            s2 = self.monte_carlo_wealth_fv
+            s2 = self.monte_carlo_wealth(discounting="fv", include_negative_values=False)
             s2.plot(legend=None)
         self.cashflow_parameters._clear_cf_cache()
 
@@ -684,7 +672,7 @@ def monte_carlo_survival_period(self, threshold: float = 0) -> pd.Series:
         >>> s.quantile(50 / 100)
         np.float64(17.5)
         """
-        s2 = self.monte_carlo_wealth_fv
+        s2 = self.monte_carlo_wealth(discounting="fv", include_negative_values=False)
         dates: pd.Series = helpers.Frame.get_survival_date(s2, self.discount_rate, threshold)
         return dates.apply(helpers.Date.get_period_length, args=(self.parent.last_date,))
 

okama/portfolios/dcf.py

@@ -85,7 +85,7 @@ def get_wealth_indexes_fv_with_cashflow(
         for n, x in enumerate(ror_cashflow_df.resample(rule=pandas_frequency, convention="start")):
             ror_ts = x[1].iloc[:, portfolio_position]  # select ror part of the grouped data
             cashflow_ts_local = x[1].loc[:, "cashflow_ts"].copy()
-            # CashFlow inside period
+            # CashFlow inside period (Extra withdrawals/contributions)
             if (cashflow_ts_local != 0).any():
                 period_wealth_index = pd.Series(dtype=float, name=portfolio_symbol)
                 for k, (date, r) in enumerate(ror_ts.items()):
@@ -245,4 +245,15 @@ def remove_negative_values(input_s: pd.Series) -> pd.Series:
         s[s.index > survival_date] = np.nan
     except IndexError:
         pass
-    return s
+    return s
+
+
+def discount_monthly_cash_flow(
+        cash_flow_fv: Union[pd.Series, pd.DataFrame],
+        annual_effective_discount_rate: float
+) -> Union[pd.Series, pd.DataFrame]:
+    number_of_months = cash_flow_fv.shape[0]
+    monlthly_discount_rate = (1 + annual_effective_discount_rate) ** (1 / settings._MONTHS_PER_YEAR) - 1
+    discount_factors = (1.0 + monlthly_discount_rate) ** np.arange(number_of_months)
+    cash_flow_pv = cash_flow_fv.div(discount_factors, axis=0)
+    return cash_flow_pv