PEP 8 CHANGES

Author: domokane

financepy/market/curves/discount_curve.py

@@ -10,7 +10,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import g_days_in_year, g_small
+from ...utils.global_vars import G_DAYS_IN_YEARS, G_SMALL
 from ...utils.frequency import annual_frequency, FrequencyTypes
 from ...utils.day_count import DayCount, DayCountTypes
 from ...utils.math import test_monotonicity
@@ -65,7 +65,7 @@ def __init__(
                 start_index = 1
 
         for i in range(start_index, num_points):
-            t = (df_dates[i] - value_dt) / g_days_in_year
+            t = (df_dates[i] - value_dt) / G_DAYS_IN_YEARS
             self._times.append(t)
             self._dfs.append(df_values[i])
 
@@ -114,7 +114,7 @@ def _zero_to_df(
         if isinstance(times, float):
             times = np.array([times])
 
-        t = np.maximum(times, g_small)
+        t = np.maximum(times, G_SMALL)
 
         f = annual_frequency(freq_type)
 
@@ -180,7 +180,7 @@ def _df_to_zero(
 
             df = df_list[i]
 
-            t = max(times[i], g_small)
+            t = max(times[i], G_SMALL)
 
             if freq_type == FrequencyTypes.CONTINUOUS:
                 r = -np.log(df) / t
@@ -293,7 +293,7 @@ def swap_rate(
                 pv01 += alpha * df
                 prev_dt = next_dt
 
-            if abs(pv01) < g_small:
+            if abs(pv01) < G_SMALL:
                 par_rate = 0.0
             else:
                 df_start = self.df(effective_dt)
@@ -373,7 +373,7 @@ def fwd(self, dts: Date):
 
         df1 = self.df(dts)
         df2 = self.df(dts_plus_one_days)
-        dt = 1.0 / g_days_in_year
+        dt = 1.0 / G_DAYS_IN_YEARS
         fwd = np.log(df1 / df2) / (1.0 * dt)
 
         if isinstance(dts, Date):

financepy/market/curves/discount_curve_ns.py

@@ -8,7 +8,7 @@
 
 from ...utils.date import Date
 from ...utils.frequency import FrequencyTypes
-from ...utils.global_vars import g_small
+from ...utils.global_vars import G_SMALL
 from ...utils.error import FinError
 from ...market.curves.discount_curve import DiscountCurve
 from ...utils.helpers import check_argument_types
@@ -99,7 +99,7 @@ def _zero_rate(self, times: Union[float, np.ndarray]):
         """Zero rate for Nelson-Siegel curve parametrisation. This means that
         the t vector must use the curve day count."""
 
-        t = np.maximum(times, g_small)
+        t = np.maximum(times, G_SMALL)
 
         theta = t / self._tau
         e = np.exp(-theta)

financepy/market/curves/discount_curve_nss.py

@@ -8,7 +8,7 @@
 
 from ...utils.date import Date
 from ...utils.frequency import FrequencyTypes
-from ...utils.global_vars import g_small
+from ...utils.global_vars import G_SMALL
 from ...utils.helpers import label_to_string
 from ...utils.error import FinError
 from ...market.curves.discount_curve import DiscountCurve
@@ -110,7 +110,7 @@ def _zero_rate(self, times: Union[float, np.ndarray]):
         times. This function can return a single zero rate or a vector of zero
         rates. The compounding frequency must be provided."""
 
-        t = np.maximum(times, g_small)
+        t = np.maximum(times, G_SMALL)
 
         theta_1 = t / self._tau_1
         theta_2 = t / self._tau_2

financepy/market/curves/discount_curve_poly.py

@@ -8,7 +8,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import g_small
+from ...utils.global_vars import G_SMALL
 from ...utils.helpers import label_to_string
 from ...market.curves.discount_curve import DiscountCurve
 from ...utils.helpers import check_argument_types
@@ -91,7 +91,7 @@ def _zero_rate(self, times: Union[float, np.ndarray]):
         use. The compounding frequency defaults to that specified in the
         constructor of the curve object. Which may be annual to continuous."""
 
-        t = np.maximum(times, g_small)
+        t = np.maximum(times, G_SMALL)
 
         zero_rate = 0.0
         for n in range(0, len(self._coefficients)):

financepy/market/curves/discount_curve_pwf.py

@@ -8,7 +8,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import g_small
+from ...utils.global_vars import G_SMALL
 from ...utils.math import test_monotonicity
 from ...utils.frequency import FrequencyTypes
 from ...utils.helpers import label_to_string
@@ -70,7 +70,7 @@ def _zero_rate(self, times: Union[float, np.ndarray, list]):
         if np.any(times < 0.0):
             raise FinError("All times must be positive")
 
-        times = np.maximum(times, g_small)
+        times = np.maximum(times, G_SMALL)
 
         zero_rates = []
 

financepy/market/curves/discount_curve_pwf_onf.py

@@ -8,7 +8,7 @@
 
 from ...utils.date import Date
 from ...utils.error import FinError
-from ...utils.global_vars import g_small, g_basis_point
+from ...utils.global_vars import G_SMALL, G_BASIS_POINT
 from ...utils.math import test_monotonicity
 from ...utils.frequency import FrequencyTypes
 from ...utils.helpers import label_to_string
@@ -81,15 +81,15 @@ def brick_wall_curve(
         valuation_date: Date,
         start_dt: Date,
         end_dt: Date,
-        level: float = 1.0 * g_basis_point,
+        level: float = 1.0 * G_BASIS_POINT,
     ):
         """Generate a discount curve of the shape f(t) = level*1_{startdate < t <= enddate} where f(.) is the instantaneous forward rate
             Mostly useful for applying bumps to other discount_curve's, see composite_discount_curve.py
         Args:
             valuation_date (Date): valuation date for the discount_curve
             start_dt (Date): start of the non-zero ON forward rate
             end_dt (Date): end of the non-zero ON forward rate
-            level (float, optional): ON forward rate between the start and end dates. Defaults to 1.0*g_basis_point.
+            level (float, optional): ON forward rate between the start and end dates. Defaults to 1.0*G_BASIS_POINT.
 
         Returns:
             DiscountCurve: discount curve of the required shape
@@ -101,7 +101,7 @@ def brick_wall_curve(
     ###############################################################################
 
     @classmethod
-    def flat_curve(cls, valuation_date: Date, level: float = 1.0 * g_basis_point):
+    def flat_curve(cls, valuation_date: Date, level: float = 1.0 * G_BASIS_POINT):
         knot_dts = [valuation_date.add_tenor("1Y")]
         onfwd_rates = [level]
         return cls(valuation_date, knot_dts, onfwd_rates)
@@ -118,7 +118,7 @@ def _zero_rate(self, times: Union[float, np.ndarray, list]):
         if np.any(times < 0.0):
             raise FinError("All times must be positive")
 
-        times = np.maximum(times, g_small)
+        times = np.maximum(times, G_SMALL)
         ldfs = self._logdfs_interp(times)
         zero_rates = -ldfs / times
         return zero_rates

financepy/market/curves/interpolator.py

@@ -10,7 +10,7 @@
 from scipy.interpolate import CubicSpline
 from scipy.interpolate import InterpolatedUnivariateSpline
 from ...utils.error import FinError
-from ...utils.global_vars import g_small
+from ...utils.global_vars import G_SMALL
 from ...utils.tension_spline import TensionSpline
 
 ###############################################################################
@@ -236,8 +236,8 @@ def fit(self, times: np.ndarray, dfs: np.ndarray):
 
         elif self._interp_type == InterpTypes.PCHIP_ZERO_RATES:
 
-            g_small_vector = np.ones(len(self._times)) * g_small
-            zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
+            G_SMALL_vector = np.ones(len(self._times)) * G_SMALL
+            zero_rates = -np.log(self._dfs) / (self._times + G_SMALL_vector)
 
             if self._times[0] == 0.0:
                 zero_rates[0] = zero_rates[1]
@@ -256,8 +256,8 @@ def fit(self, times: np.ndarray, dfs: np.ndarray):
 
             """Second derivatives at left is zero and first derivative at
             right is clamped to zero."""
-            g_small_vector = np.ones(len(self._times)) * g_small
-            zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
+            G_SMALL_vector = np.ones(len(self._times)) * G_SMALL
+            zero_rates = -np.log(self._dfs) / (self._times + G_SMALL_vector)
 
             if self._times[0] == 0.0:
                 zero_rates[0] = zero_rates[1]
@@ -275,8 +275,8 @@ def fit(self, times: np.ndarray, dfs: np.ndarray):
         elif self._interp_type == InterpTypes.NATCUBIC_ZERO_RATES:
 
             """Second derivatives are clamped to zero at end points"""
-            g_small_vector = np.ones(len(self._times)) * g_small
-            zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
+            G_SMALL_vector = np.ones(len(self._times)) * G_SMALL
+            zero_rates = -np.log(self._dfs) / (self._times + G_SMALL_vector)
 
             if self._times[0] == 0.0:
                 zero_rates[0] = zero_rates[1]
@@ -323,8 +323,8 @@ def fit(self, times: np.ndarray, dfs: np.ndarray):
 
         elif self._interp_type == InterpTypes.TENSION_ZERO_RATES:
             tension_sigma = self._optional_interp_params.get("sigma", 1.0)
-            g_small_vector = np.ones(len(self._times)) * g_small
-            zero_rates = -np.log(self._dfs) / (self._times + g_small_vector)
+            G_SMALL_vector = np.ones(len(self._times)) * G_SMALL
+            zero_rates = -np.log(self._dfs) / (self._times + G_SMALL_vector)
 
             if self._times[0] == 0.0:
                 zero_rates[0] = zero_rates[1]
@@ -349,7 +349,7 @@ def interpolate(self, t: float):
                 print(t)
                 raise FinError("Interpolate times must all be >= 0")
 
-            if np.abs(t) < g_small:
+            if np.abs(t) < G_SMALL:
                 return 1.0
 
             tvec = np.array([t])

financepy/market/volatility/equity_vol_surface.py

@@ -12,7 +12,7 @@
 
 from ...utils.error import FinError
 from ...utils.date import Date
-from ...utils.global_vars import g_days_in_year
+from ...utils.global_vars import G_DAYS_IN_YEARS
 from ...utils.global_types import OptionTypes
 from ...models.option_implied_dbn import option_implied_dbn
 from ...utils.helpers import check_argument_types, label_to_string
@@ -319,7 +319,7 @@ def vol_from_strike_date(self, K, expiry_dt):
         interpolation is done in variance space and then converted back to a
         lognormal volatility."""
 
-        t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+        t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         vol_type_value = self._vol_func_type.value
 
@@ -394,7 +394,7 @@ def vol_from_strike_date(self, K, expiry_dt):
     #     """ Interpolates the strike at a delta and expiry date. Linear
     #     interpolation is used in strike."""
 
-    #     t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+    #     t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
     #     vol_type_value = self._vol_func_type.value
 
@@ -496,7 +496,7 @@ def vol_from_delta_date(self, call_delta, expiry_dt, delta_method=None):
         interpolation is done in variance space and then converted back to a
         lognormal volatility."""
 
-        t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+        t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         vol_type_value = self._vol_func_type.value
 
@@ -653,7 +653,7 @@ def _build_vol_surface(self, fin_solver_type=FinSolverTypes.NELDER_MEAD):
         for i in range(0, num_expiry_dts):
 
             expiry_dt = self._expiry_dts[i]
-            t_exp = (expiry_dt - spot_dt) / g_days_in_year
+            t_exp = (expiry_dt - spot_dt) / G_DAYS_IN_YEARS
 
             dis_df = self._discount_curve.df_t(t_exp)
             div_df = self._dividend_curve.df_t(t_exp)

financepy/market/volatility/fx_vol_surface.py

@@ -12,7 +12,7 @@
 
 from ...utils.error import FinError
 from ...utils.date import Date
-from ...utils.global_vars import g_days_in_year
+from ...utils.global_vars import G_DAYS_IN_YEARS
 from ...utils.global_types import OptionTypes
 from ...products.fx.fx_vanilla_option import FXVanillaOption
 from ...models.option_implied_dbn import option_implied_dbn
@@ -619,7 +619,7 @@ def volatility(self, K, expiry_dt):
         index0 = 0
         index1 = 0
 
-        t = (expiry_dt - self.value_dt) / g_days_in_year
+        t = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         num_curves = self.num_vol_curves
 
@@ -718,7 +718,7 @@ def build_vol_surface(self):
         for i in range(0, num_vol_curves):
 
             expiry_dt = self.expiry_dts[i]
-            t_exp = (expiry_dt - spot_dt) / g_days_in_year
+            t_exp = (expiry_dt - spot_dt) / G_DAYS_IN_YEARS
 
             dom_df = self.domestic_curve.df_t(t_exp)
             for_df = self.foreign_curve.df_t(t_exp)

financepy/market/volatility/fx_vol_surface_plus.py

@@ -12,7 +12,7 @@
 
 from ...utils.error import FinError
 from ...utils.date import Date
-from ...utils.global_vars import g_days_in_year
+from ...utils.global_vars import G_DAYS_IN_YEARS
 from ...utils.global_types import OptionTypes
 from ...products.fx.fx_vanilla_option import FXVanillaOption
 from ...models.option_implied_dbn import option_implied_dbn
@@ -1346,7 +1346,7 @@ def vol_from_strike_date(self, K, expiry_dt):
         interpolation is done in variance space and then converted back to a
         lognormal volatility."""
 
-        t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+        t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         vol_type_value = self.vol_func_type.value
 
@@ -1437,7 +1437,7 @@ def delta_to_strike(self, call_delta, expiry_dt, delta_method):
         """Interpolates the strike at a delta and expiry date. Linear
         time to expiry interpolation is used in strike."""
 
-        t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+        t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         vol_type_value = self.vol_func_type.value
 
@@ -1548,7 +1548,7 @@ def vol_from_delta_date(self, call_delta, expiry_dt, delta_method=None):
         interpolation is done in variance space and then converted back to a
         lognormal volatility."""
 
-        t_exp = (expiry_dt - self.value_dt) / g_days_in_year
+        t_exp = (expiry_dt - self.value_dt) / G_DAYS_IN_YEARS
 
         vol_type_value = self.vol_func_type.value
 
@@ -1734,7 +1734,7 @@ def _build_vol_surface(self, fin_solver_type=FinSolverTypes.NELDER_MEAD, tol=1e-
         for i in range(0, num_vol_curves):
 
             expiry_dt = self.expiry_dts[i]
-            t_exp = (expiry_dt - spot_dt) / g_days_in_year
+            t_exp = (expiry_dt - spot_dt) / G_DAYS_IN_YEARS
 
             dom_df = self.domestic_curve.df(expiry_dt)
             for_df = self.foreign_curve.df(expiry_dt)