Bug fix

financepy/market/curves/discount_curve.py

@@ -97,13 +97,18 @@ def dfs(self):
     ###########################################################################
     def _zero_to_df(
         self,
+        value_dt: Date,
         rates: float | np.ndarray,
         times: float | np.ndarray,
         freq_type: FrequencyTypes,
+        dc_type: DayCountTypes,
     ):
         """Convert a zero rate with a specified compounding frequency to a discount
         factor for a single maturity date or a list of dates."""
 
+        # This is not used in the code so we set it to None
+        dc_type = None
+
         if isinstance(times, float):
             times = np.array([times])
 
@@ -131,8 +136,8 @@ def _zero_to_df(
 
     def _df_to_zero(
         self,
-        dfs: (float, np.ndarray),
-        maturity_dts: (Date, list),
+        dfs: float | np.ndarray,
+        maturity_dts: Date | list,
         freq_type: FrequencyTypes,
         dc_type: DayCountTypes,
     ):
@@ -310,7 +315,7 @@ def df(self, dt: (list, Date), day_count=DayCountTypes.ACT_ACT_ISDA):
 
     ###########################################################################
 
-    def _df(self, t: (float, np.ndarray)):
+    def _df(self, t: float | np.ndarray):
         """Hidden function to calculate a discount factor from a time or a
         vector of times. Discourage usage in favour of passing in dates."""
 

financepy/market/curves/discount_curve_flat.py

@@ -32,7 +32,7 @@ class DiscountCurveFlat(DiscountCurve):
 
     def __init__(self,
                  value_dt: Date,
-                 flat_rate: (float, np.ndarray),
+                 flat_rate: float | np.ndarray,
                  freq_type: FrequencyTypes = FrequencyTypes.CONTINUOUS,
                  dc_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA):
         """ Create a discount curve which is flat. This is very useful for
@@ -79,7 +79,7 @@ def bump(self,
 ###############################################################################
 
     def df(self,
-           dts: (Date, list)):
+           dts: Date | list):
         """ Return discount factors given a single or vector of dts. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency, and it defaults to continuous compounding. It

financepy/market/curves/discount_curve_ns.py

@@ -97,7 +97,7 @@ def zero_rate(self,
     ###########################################################################
 
     def _zero_rate(self,
-                   times: (float, np.ndarray)):
+                   times: float | np.ndarray):
         """ Zero rate for Nelson-Siegel curve parametrisation. This means that
         the t vector must use the curve day count."""
 
@@ -113,7 +113,7 @@ def _zero_rate(self,
     ###########################################################################
 
     def df(self,
-           dates: (Date, list)):
+           dates: Date | list):
         """ Return discount factors given a single or vector of dates. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency and it defaults to continuous compounding. It

financepy/market/curves/discount_curve_nss.py

@@ -107,7 +107,7 @@ def zero_rate(self,
     ###########################################################################
 
     def _zero_rate(self,
-                   times: (float, np.ndarray)):
+                   times: float | np.ndarray):
         """ Calculation of zero rates given a single time or a numpy vector of
         times. This function can return a single zero rate or a vector of zero
         rates. The compounding frequency must be provided. """
@@ -127,7 +127,7 @@ def _zero_rate(self,
     ###########################################################################
 
     def df(self,
-           dates: (Date, list)):
+           dates: Date | list):
         """ Return discount factors given a single or vector of dates. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency and it defaults to continuous compounding. It

financepy/market/curves/discount_curve_poly.py

@@ -27,7 +27,7 @@ class DiscountCurvePoly(DiscountCurve):
 
     def __init__(self,
                  value_dt: Date,
-                 coefficients: (list, np.ndarray),
+                 coefficients: list | np.ndarray,
                  freq_type: FrequencyTypes = FrequencyTypes.CONTINUOUS,
                  dc_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA):
         """ Create zero rate curve parametrised using a cubic curve from
@@ -83,7 +83,7 @@ def zero_rate(self,
     ###########################################################################
 
     def _zero_rate(self,
-                   times: (float, np.ndarray)):
+                   times: float | np.ndarray):
         """ Calculate the zero rate to maturity date but with times as inputs.
         This function is used internally and should be discouraged for external
         use. The compounding frequency defaults to that specified in the

financepy/market/curves/discount_curve_pwf.py

@@ -27,7 +27,7 @@ class DiscountCurvePWF(DiscountCurve):
     def __init__(self,
                  value_dt: Date,
                  zero_dts: list,
-                 zero_rates: (list, np.ndarray),
+                 zero_rates: list | np.ndarray,
                  freq_type: FrequencyTypes = FrequencyTypes.CONTINUOUS,
                  day_count_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA):
         """ Creates a discount curve using a vector of times and zero rates
@@ -121,7 +121,7 @@ def _fwd(self,
     ###########################################################################
 
     def df(self,
-           dates: (Date, list)):
+           dates: Date | list):
         """ Return discount factors given a single or vector of dates. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency and it defaults to continuous compounding. It

financepy/market/curves/discount_curve_pwl.py

@@ -26,8 +26,8 @@ class DiscountCurvePWL(DiscountCurve):
 
     def __init__(self,
                  value_dt: Date,
-                 zero_dts: (Date, list),
-                 zero_rates: (list, np.ndarray),
+                 zero_dts: Date | list,
+                 zero_rates: list | np.ndarray,
                  freq_type: FrequencyTypes = FrequencyTypes.CONTINUOUS,
                  dc_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA):
         """ Curve is defined by a vector of increasing times and zero rates."""
@@ -102,7 +102,7 @@ def _zero_rate(self,
     ###########################################################################
 
     def df(self,
-           dates: (Date, list)):
+           dates: Date | list):
         """ Return discount factors given a single or vector of dates. The
         discount factor depends on the rate and this in turn depends on its
         compounding frequency and it defaults to continuous compounding. It
@@ -127,7 +127,7 @@ def df(self,
     ###########################################################################
 
     # def _df(self,
-    #         t: (float, np.ndarray)):
+    #         t: float | np.ndarray):
     #     """ Returns the discount factor at time t taking into account the
     #     piecewise flat zero rate curve and the compunding frequency. """
 

financepy/market/curves/discount_curve_zeros.py

@@ -20,25 +20,28 @@
 # TODO: Fix up __repr__ function
 ###############################################################################
 
+
 class DiscountCurveZeros(DiscountCurve):
-    """ This is a curve calculated from a set of dates and zero rates. As we
+    """This is a curve calculated from a set of dates and zero rates. As we
     have rates as inputs, we need to specify the corresponding compounding
     frequency. Also to go from rates and dates to discount factors we need to
     compute the year fraction correctly and for this we require a day count
     convention. Finally, we need to interpolate the zero rate for the times
     between the zero rates given and for this we must specify an interpolation
-    convention. The class inherits methods from FinDiscountCurve. """
-
-###############################################################################
-
-    def __init__(self,
-                 value_dt: Date,
-                 zero_dts: list,
-                 zero_rates: (list, np.ndarray),
-                 freq_type: FrequencyTypes = FrequencyTypes.ANNUAL,
-                 dc_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA,
-                 interp_type: InterpTypes = InterpTypes.FLAT_FWD_RATES):
-        """ Create the discount curve from a vector of dates and zero rates
+    convention. The class inherits methods from FinDiscountCurve."""
+
+    ###############################################################################
+
+    def __init__(
+        self,
+        value_dt: Date,
+        zero_dts: list,
+        zero_rates: list | np.ndarray,
+        freq_type: FrequencyTypes = FrequencyTypes.ANNUAL,
+        dc_type: DayCountTypes = DayCountTypes.ACT_ACT_ISDA,
+        interp_type: InterpTypes = InterpTypes.FLAT_FWD_RATES,
+    ):
+        """Create the discount curve from a vector of dates and zero rates
         factors. The first date is the curve anchor. Then a vector of zero
         dates and then another same-length vector of rates. The rate is to the
         corresponding date. We must specify the compounding frequency of the
@@ -59,8 +62,7 @@ def __init__(self,
             raise FinError("Unknown Frequency type " + str(freq_type))
 
         if dc_type not in DayCountTypes:
-            raise FinError("Unknown Cap Floor DayCountRule type " +
-                           str(dc_type))
+            raise FinError("Unknown Cap Floor DayCountRule type " + str(dc_type))
 
         self.value_dt = value_dt
         self.freq_type = freq_type
@@ -74,38 +76,36 @@ def __init__(self,
         if test_monotonicity(self._times) is False:
             raise FinError("Times or dates are not sorted in increasing order")
 
-        dfs = self._zero_to_df(self.value_dt,
-                               self._zero_rates,
-                               self._times,
-                               self.freq_type,
-                               self.dc_type)
+        dfs = self._zero_to_df(
+            self.value_dt, self._zero_rates, self._times, self.freq_type, self.dc_type
+        )
 
         self._dfs = np.array(dfs)
 
         self._interp_type = interp_type
         self._interpolator = Interpolator(self._interp_type)
         self._interpolator.fit(self._times, self._dfs)
 
-# ###############################################################################
+    # ###############################################################################
 
-#     def bump(self, bump_size):
-#         """ Calculate the continuous forward rate at the forward date. """
+    #     def bump(self, bump_size):
+    #         """ Calculate the continuous forward rate at the forward date. """
 
-#         times = self.times.copy()
-#         discount_factors = self._discount_factors.copy()
+    #         times = self.times.copy()
+    #         discount_factors = self._discount_factors.copy()
 
-#         n = len(self.times)
-#         for i in range(0, n):
-#             t = times[i]
-#             discount_factors[i] = discount_factors[i] * np.exp(-bump_size*t)
+    #         n = len(self.times)
+    #         for i in range(0, n):
+    #             t = times[i]
+    #             discount_factors[i] = discount_factors[i] * np.exp(-bump_size*t)
 
-#         disc_curve = FinDiscountCurve(self.value_dt, times,
-#                                      discount_factors,
-#                                      self._interp_type)
+    #         disc_curve = FinDiscountCurve(self.value_dt, times,
+    #                                      discount_factors,
+    #                                      self._interp_type)
 
-#         return disc_curve
+    #         return disc_curve
 
-###############################################################################
+    ###############################################################################
 
     def __repr__(self):
 
@@ -118,15 +118,17 @@ def __repr__(self):
         s += label_to_string("DATES", "ZERO RATES")
         num_points = len(self._times)
         for i in range(0, num_points):
-            s += label_to_string("%12s" % self._zero_dts[i],
-                                 "%10.7f" % self._zero_rates[i])
+            s += label_to_string(
+                "%12s" % self._zero_dts[i], "%10.7f" % self._zero_rates[i]
+            )
 
         return s
 
-###############################################################################
+    ###############################################################################
 
     def _print(self):
-        """ Simple print function for backward compatibility. """
+        """Simple print function for backward compatibility."""
         print(self)
 
+
 ###############################################################################

financepy/market/curves/interpolator.py

@@ -34,7 +34,7 @@ class InterpTypes(Enum):
 # TODO: GET RID OF THIS FUNCTION !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
 ###############################################################################
 
-def interpolate(t: (float, np.ndarray),  # time or array of times
+def interpolate(t: float | np.ndarray,  # time or array of times
                 times: np.ndarray,  # Vector of times on grid
                 dfs: np.ndarray,  # Vector of discount factors
                 method: int):  # Interpolation method which is value of enum

financepy/market/volatility/equity_vol_surface.py

@@ -268,8 +268,8 @@ def __init__(
         discount_curve: DiscountCurve,
         dividend_curve: DiscountCurve,
         expiry_dts: list,
-        strikes: (list, np.ndarray),
-        volatility_grid: (list, np.ndarray),
+        strikes: list | np.ndarray,
+        volatility_grid: list | np.ndarray,
         vol_func_type=VolFuncTypes.CLARK,
         fin_solver_type=FinSolverTypes.NELDER_MEAD,
     ):

financepy/market/volatility/fx_vol_surface.py

@@ -540,9 +540,9 @@ def __init__(
         domestic_curve: DiscountCurve,
         foreign_curve: DiscountCurve,
         tenors: list,
-        atm_vols: (list, np.ndarray),
-        ms_25_delta_vols: (list, np.ndarray),
-        rr_25_delta_vols: (list, np.ndarray),
+        atm_vols: list | np.ndarray,
+        ms_25_delta_vols: list | np.ndarray,
+        rr_25_delta_vols: list | np.ndarray,
         atm_method: FinFXATMMethod = FinFXATMMethod.FWD_DELTA_NEUTRAL,
         delta_method: FinFXDeltaMethod = FinFXDeltaMethod.SPOT_DELTA,
         vol_func_type: VolFuncTypes = VolFuncTypes.CLARK,

financepy/market/volatility/fx_vol_surface_plus.py

@@ -1219,11 +1219,11 @@ def __init__(
         domestic_curve: DiscountCurve,
         foreign_curve: DiscountCurve,
         tenors: list,
-        atm_vols: (list, np.ndarray),
-        ms_25_delta_vols: (list, np.ndarray),
-        rr_25_delta_vols: (list, np.ndarray),
-        ms_10_delta_vols: (list, np.ndarray),
-        rr_10_delta_vols: (list, np.ndarray),
+        atm_vols: list | np.ndarray,
+        ms_25_delta_vols: list | np.ndarray,
+        rr_25_delta_vols: list | np.ndarray,
+        ms_10_delta_vols: list | np.ndarray,
+        rr_10_delta_vols: list | np.ndarray,
         alpha: float,
         atm_method: FinFXATMMethod = FinFXATMMethod.FWD_DELTA_NEUTRAL,
         delta_method: FinFXDeltaMethod = FinFXDeltaMethod.SPOT_DELTA,

financepy/market/volatility/swaption_vol_surface.py

@@ -387,7 +387,7 @@ def __init__(
         self,
         value_dt: Date,
         expiry_dts: list,
-        fwd_swap_rates: (list, np.ndarray),
+        fwd_swap_rates: list | np.ndarray,
         strike_grid: np.ndarray,
         vol_grid: np.ndarray,
         vol_func_type: VolFuncTypes = VolFuncTypes.SABR,

financepy/models/black_scholes.py

@@ -42,7 +42,7 @@ class BlackScholesTypes(Enum):
 class BlackScholes(Model):
 
     def __init__(self,
-                 volatility: (float, np.ndarray),
+                 volatility: float | np.ndarray,
                  bs_type: BlackScholesTypes = BlackScholesTypes.DEFAULT,
                  num_steps_per_year=52,
                  num_paths=10000,

financepy/products/equity/equity_barrier_option.py

@@ -57,7 +57,7 @@ def __init__(
     def value(
         self,
         value_dt: Date,
-        stock_price: (float, np.ndarray),
+        stock_price: float | np.ndarray,
         discount_curve: DiscountCurve,
         dividend_curve: DiscountCurve,
         model,

financepy/products/equity/equity_digital_option.py

@@ -67,7 +67,7 @@ def __init__(
     def value(
         self,
         value_dt: Date,
-        s: (float, np.ndarray),
+        s: float | np.ndarray,
         discount_curve: DiscountCurve,
         dividend_curve: DiscountCurve,
         model,

financepy/products/equity/equity_one_touch_option.py

@@ -154,7 +154,7 @@ def __init__(
     def value(
         self,
         value_dt: Date,
-        stock_price: (float, np.ndarray),
+        stock_price: float | np.ndarray,
         discount_curve: DiscountCurve,
         dividend_curve: DiscountCurve,
         model,