Code refeactoring

Author: andrewmurphy353

curo/calculator.py

@@ -97,14 +97,21 @@ def add(self, series: Series) -> None:
             series.amount = gauss_round(series.amount, self.precision)
         self._series.append(series)
 
-    def solve_value(self, convention: Convention, interest_rate: float) -> float:
+    def solve_value(
+        self,
+        convention: Convention,
+        interest_rate: float,
+        start_date: Optional[pd.Timestamp] = None) -> float:
         """
         Solves for one or more unknown payment or advance cash flow amounts to achieve
         a net future value (NFV) of zero.
 
         Args:
             convention (Convention): Day count convention (e.g., US30360, USAppendixJ).
             interest_rate (float): Annualized interest rate (e.g., 0.12 for 12%).
+            start_date (pd.Timestamp, optional): The start date for constructing the
+                cash flow profile for `undated` series. Defaults to the current system 
+                date if None.
 
         Returns:
             float: The raw cash flow amount (before weightings), unrounded.
@@ -130,7 +137,6 @@ def solve_value(self, convention: Convention, interest_rate: float) -> float:
         else:
             if not self._series:
                 raise ValidationError("No cash flow series provided")
-            start_date = min(s.post_date_from for s in self._series)
             cash_flows = self._build_profile(start_date)
 
         sort_by = SortColumn.POST_DATE if convention.use_post_dates else SortColumn.VALUE_DATE
@@ -141,11 +147,6 @@ def solve_value(self, convention: Convention, interest_rate: float) -> float:
             mode=ValidationMode.SOLVE_VALUE
         )
 
-        # Check for at least one unknown amount
-        # unknown_mask = ~cash_flows[Column.IS_KNOWN.value] | cash_flows[Column.AMOUNT.value].isna()
-        # if not unknown_mask.any():
-        #     raise ValidationError("At least one unknown amount required")
-
         # Assign day count factors
         cash_flows = self._assign_factors(cash_flows, convention)
 
@@ -180,19 +181,26 @@ def nfv_function(value: float) -> float:
         self.profile = self._update_unknowns(
             cash_flows, value, precision=self.precision, is_rounded=True
         )
-        
+
         # Amortise interest if not using XIRR method
         if not convention.use_xirr_method:
             self.profile = self._amortise_interest(self.profile, original_interest_rate)
-        
-        return round(value, self.precision)
 
-    def solve_rate(self, convention: Convention, upper_bound: float = 10.0) -> float:
+        return gauss_round(value, self.precision)
+
+    def solve_rate(
+        self,
+        convention: Convention,
+        start_date: Optional[pd.Timestamp] = None,
+        upper_bound: float = 10.0) -> float:
         """
         Computes the effective interest rate that results in a net future value (NFV) of zero.
 
         Args:
             convention (Convention): Day count convention (e.g., US30360, USAppendixJ).
+            start_date (pd.Timestamp, optional): The start date for constructing the
+                cash flow profile for `undated` series. Defaults to the current system 
+                date if None.
             upper_bound (float): Upper bound for the interest rate search (default: 10.0, or 1000%).
 
         Returns:
@@ -219,7 +227,6 @@ def solve_rate(self, convention: Convention, upper_bound: float = 10.0) -> float
         else:
             if not self._series:
                 raise ValidationError("No cash flow series provided")
-            start_date = min(s.post_date_from for s in self._series) #FIXME
             cash_flows = self._build_profile(start_date)
 
         sort_by = SortColumn.POST_DATE if convention.use_post_dates else SortColumn.VALUE_DATE
@@ -723,16 +730,16 @@ def _calculate_nfv(self, cash_flows: pd.DataFrame, day_count: Convention, rate:
                 amount = row[Column.AMOUNT.value] or 0.0
 
                 if isinstance(day_count, USAppendixJ):
-                    principal_factor = (1 + rate) ** factor.year_fraction
-                    fractional_adjustment = (
-                        1.0 + (factor.fractional_period * rate)
-                        if (factor.fractional_period is not None and
-                            factor.fractional_period / 12.0 > 0.0)
+                    primary_period_factor = (1 + rate) ** factor.primary_period_fraction
+                    partial_period_factor = (
+                        1.0 + (factor.partial_period_fraction * rate)
+                        if (factor.partial_period_fraction is not None and
+                            factor.partial_period_fraction / 12.0 > 0.0)
                         else 1.0
                     )
-                    capital_balance += amount / (principal_factor * fractional_adjustment)
+                    capital_balance += amount / (primary_period_factor * partial_period_factor)
                 else:
-                    capital_balance += amount * (1 + rate) ** (-factor.year_fraction)
+                    capital_balance += amount * (1 + rate) ** (-factor.primary_period_fraction)
         else:  # NEIGHBOUR
             accrued_interest = 0.0
             for _, row in cash_flows.iterrows():
@@ -742,7 +749,7 @@ def _calculate_nfv(self, cash_flows: pd.DataFrame, day_count: Convention, rate:
                 factor = row['factor']
                 amount = row[Column.AMOUNT.value] or 0.0
                 is_payment = row[Column.IS_INTEREST_CAPITALISED.value] is not None
-                period_interest = capital_balance * rate * factor.year_fraction
+                period_interest = capital_balance * rate * factor.primary_period_fraction
 
                 if is_payment:
                     if row[Column.IS_INTEREST_CAPITALISED.value]:
@@ -797,7 +804,7 @@ def _update_unknowns(
 
         # Apply rounding if requested
         if is_rounded:
-            adjusted_values = adjusted_values.round(precision)
+            adjusted_values = adjusted_values.apply(lambda x: gauss_round(x, precision))
 
         # Update amounts for unknown cash flows
         cash_flows.loc[mask, Column.AMOUNT.value] = adjusted_values
@@ -838,13 +845,13 @@ def _amortise_interest(
             if row.get(Column.IS_CHARGE.value, False):
                 continue
 
-            factor = row['factor'].year_fraction
+            factor = row['factor'].primary_period_fraction
             amount = row[Column.AMOUNT.value] or 0.0
-            period_interest = round(capital_balance * interest_rate * factor, precision)
+            period_interest = gauss_round(capital_balance * interest_rate * factor, precision)
 
             if row.get(Column.IS_INTEREST_CAPITALISED.value) is not None:  # Payment
                 if row[Column.IS_INTEREST_CAPITALISED.value]:
-                    interest = round(accrued_interest + period_interest, precision)
+                    interest = gauss_round(accrued_interest + period_interest, precision)
                     capital_balance += interest + amount
                     accrued_interest = 0.0
                 else:
@@ -864,7 +871,7 @@ def _amortise_interest(
         if not payment_indices.empty:
             last_payment_idx = payment_indices[-1]
             current_interest = cash_flows.at[last_payment_idx, 'interest']
-            cash_flows.at[last_payment_idx, 'interest'] = round(
+            cash_flows.at[last_payment_idx, 'interest'] = gauss_round(
                 current_interest - capital_balance, precision
             )
 

curo/daycount/actual_360.py

@@ -29,7 +29,7 @@ class Actual360(Convention):
         ...     pd.Timestamp('2020-02-28', tz='UTC')
         ... )
         >>> print(factor)
-        31/360 = 0.08611111
+        f = 31/360 = 0.08611111
     """
     def __init__(
         self,
@@ -63,16 +63,16 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
             ...     pd.Timestamp('2020-01-28', tz='UTC'),
             ...     pd.Timestamp('2020-02-28', tz='UTC')
             ... )
-            >>> factor.year_fraction
+            >>> factor.primary_period_fraction 
             0.08611111111111111
-            >>> factor.year_fraction_operands
+            >>> factor.discount_factor_log
             ['31/360']
         """
         if end < start:
             raise ValueError("end must be after start")
         days = (end - start).days if end > start else 0  # Exclude end date, 0 for same day
         factor = days / 360
         return DayCountFactor(
-            year_fraction=factor,
-            year_fraction_operands=[f"{days}/360"]
+            primary_period_fraction=factor,
+            discount_factor_log=[f"{days}/360"]
         )

curo/daycount/actual_365.py

@@ -29,7 +29,7 @@ class Actual365(Convention):
         ...     pd.Timestamp('2020-02-28', tz='UTC')
         ... )
         >>> print(factor)
-        31/365 = 0.08493151
+        f = 31/365 = 0.08493151
     """
     def __init__(
         self,
@@ -63,16 +63,16 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
             ...     pd.Timestamp('2020-01-28', tz='UTC'),
             ...     pd.Timestamp('2020-02-28', tz='UTC')
             ... )
-            >>> factor.year_fraction
+            >>> factor.primary_period_fraction
             0.08493150684931507
-            >>> factor.year_fraction_operands
+            >>> factor.discount_factor_log
             ['31/365']
         """
         if end < start:
             raise ValueError("end must be after start")
         days = (end - start).days if end > start else 0  # Exclude end date, 0 for same day
         factor = days / 365
         return DayCountFactor(
-            year_fraction=factor,
-            year_fraction_operands=[f"{days}/365"]
+            primary_period_fraction=factor,
+            discount_factor_log=[f"{days}/365"]
         )

curo/daycount/actual_isda.py

@@ -31,7 +31,7 @@ class ActualISDA(Convention):
         ...     pd.Timestamp('2020-02-28', tz='UTC')
         ... )
         >>> print(factor)
-        31/366 = 0.08469945
+        f = 31/366 = 0.08469945
     """
     def __init__(
         self,
@@ -65,15 +65,15 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
             ...     pd.Timestamp('2020-01-28', tz='UTC'),
             ...     pd.Timestamp('2020-02-28', tz='UTC')
             ... )
-            >>> factor.year_fraction
+            >>> factor.primary_period_fraction
             0.08469945355191257
-            >>> factor.year_fraction_operands
+            >>> factor.discount_factor_log
             ['31/366']
         """
         if end < start:
             raise ValueError("end must be after start")
         if end == start:
-            return DayCountFactor(year_fraction=0.0, year_fraction_operands=["0/365"])
+            return DayCountFactor(primary_period_fraction=0.0, discount_factor_log=["0/365"])
 
         start_year = start.year
         end_year = end.year
@@ -85,12 +85,12 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
             denominator = 366 if calendar.isleap(start_year) else 365
             factor = days / denominator
             return DayCountFactor(
-                year_fraction=factor,
-                year_fraction_operands=[f"{days}/{denominator}"]
+                primary_period_fraction=factor,
+                discount_factor_log=[f"{days}/{denominator}"]
             )
 
         # Multi-year: split by year
-        operand_log = []
+        discount_factor_log = []
         current_date = start
         current_year = start_year
 
@@ -99,7 +99,7 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
             days = (year_end - current_date).days + 1 if year_end >= current_date else 0
             denominator = 366 if calendar.isleap(current_year) else 365
             factor += days / denominator
-            operand_log.append(f"{days}/{denominator}")
+            discount_factor_log.append(f"{days}/{denominator}")
             current_date = year_end + pd.Timedelta(days=1)  # Move to Jan 1 next year
             current_year += 1
 
@@ -108,9 +108,9 @@ def compute_factor(self, start: pd.Timestamp, end: pd.Timestamp) -> DayCountFact
         denominator = 366 if calendar.isleap(end_year) else 365
         if days > 0:
             factor += days / denominator
-            operand_log.append(f"{days}/{denominator}")
+            discount_factor_log.append(f"{days}/{denominator}")
 
         return DayCountFactor(
-            year_fraction=factor,
-            year_fraction_operands=operand_log
+            primary_period_fraction=factor,
+            discount_factor_log=discount_factor_log
         )