Merge pull request #88 from PyFE/heston_fft

Name refactoring

Author: jaehyukchoi

pyfeng/__init__.py

@@ -12,7 +12,7 @@
     SabrChoiWu2021P,
 )
 from .sabr_int import SabrUncorrChoiWu2021
-from .sabr_mc import SabrCondMc
+from .sabr_mc import SabrMcCond
 from .nsvh import Nsvh1, NsvhMc
 from .multiasset import (
     BsmSpreadKirk,
@@ -27,10 +27,6 @@
     BsmBasketJsu,
 )
 from .multiasset_mc import BsmNdMc, NormNdMc
-from .ousv import OusvIFT, OusvCondMC
-
-# SV models
-from .heston_mc import HestonCondMcQE
 
 # Asset Allocation
 from .assetalloc import RiskParity

pyfeng/ex.py

@@ -1,10 +1,14 @@
-# SV models (CMC, AE) from ASP 2021
-from .heston_mc import HestonMcAe, HestonMcExactGK
 #from .heston_fft import HestonFft
-from .sv32_mc import Sv32CondMcQE, Sv32McAe
-from .garch import GarchCondMC, GarchApproxUncor
+
+# SV models (CMC, AE) from ASP 2021
+from .heston_mc import HestonMcAndersen2008, HestonMcAe, HestonMcExactGK
+from .sv32_mc import Sv32McCondQE, Sv32McAe
+from .garch import GarchMcCond, GarchUncorrBaroneAdesi2004
+
+# SABR / OUSV models for research
 from .sabr_int import SabrCondQuad
 from .sabr_mc import SabrMcExactCai2017
+from .ousv import OusvSchobelZhu1998, OusvMcCond
 
 # Basket-Asian from ASP 2021
 from .multiasset_Ju2002 import BsmBasketAsianJu2002, BsmContinuousAsianJu2002

pyfeng/garch.py

@@ -5,7 +5,7 @@
 from . import bsm
 
 
-class GarchApproxUncor(sv.SvABC):
+class GarchUncorrBaroneAdesi2004(sv.SvABC):
     """
     The implementation of Barone-Adesi et al (2004)'s approximation pricing formula for European
     options under uncorrelated (rho=0) GARCH diffusion model.
@@ -71,7 +71,7 @@ def price(self, strike, spot, texp, cp=1):
         return c_ga_2
 
 
-class GarchCondMC(sv.SvABC, sv.CondMcBsmABC):
+class GarchMcCond(sv.SvABC, sv.CondMcBsmABC):
     """
     Garch model with conditional Monte-Carlo simulation
     The SDE of SV is: dv_t = mr * (theta - v_t) dt + vov * v_t dB_T

pyfeng/heston_mc.py

@@ -9,7 +9,7 @@
 from . import sv_abc as sv
 
 
-class HestonCondMcQE(sv.SvABC, sv.CondMcBsmABC):
+class HestonMcAndersen2008(sv.SvABC, sv.CondMcBsmABC):
     """
     Heston model with conditional Monte-Carlo simulation
 
@@ -26,7 +26,7 @@ class HestonCondMcQE(sv.SvABC, sv.CondMcBsmABC):
         >>> strike = np.array([60, 100, 140])
         >>> spot = 100
         >>> sigma, vov, mr, rho, texp = 0.04, 1, 0.5, -0.9, 10
-        >>> m = pf.HestonCondMcQE(sigma, vov=vov, mr=mr, rho=rho)
+        >>> m = pf.HestonMcAndersen2008(sigma, vov=vov, mr=mr, rho=rho)
         >>> m.set_mc_params(n_path=1e5, dt=1/8, rn_seed=123456)
         >>> m.price(strike, spot, texp)
         >>> # true price: 44.330, 13.085, 0.296

pyfeng/ousv.py

@@ -3,7 +3,7 @@
 from . import sv_abc as sv
 
 
-class OusvIFT(sv.SvABC):
+class OusvSchobelZhu1998(sv.SvABC):
     """
     The implementation of Schobel & Zhu (1998)'s inverse FT pricing formula for European
     options the Ornstein-Uhlenbeck driven stochastic volatility process.
@@ -12,10 +12,10 @@ class OusvIFT(sv.SvABC):
 
     Examples:
         >>> import pyfeng as pf
-        >>> model = pf.OusvIFT(0.2, mr=4, vov=0.1, rho=-0.7, intr=0.09531)
+        >>> model = pf.OusvSchobelZhu1998(0.2, mr=4, vov=0.1, rho=-0.7, intr=0.09531)
         >>> model.price(100, 100, texp=np.array([1, 5, 10]))
         array([13.21493, 40.79773, 62.76312])
-        >>> model = pf.OusvIFT(0.25, mr=8, vov=0.3, rho=-0.6, intr=0.09531)
+        >>> model = pf.OusvSchobelZhu1998(0.25, mr=8, vov=0.3, rho=-0.6, intr=0.09531)
         >>> model.price(np.array([90, 100, 110]), 100, texp=1)
         array([21.41873, 15.16798, 10.17448])
     """
@@ -103,7 +103,7 @@ def price(self, strike, spot, texp, cp=1):
         return df * fwd * price
 
 
-class OusvCondMC(sv.SvABC, sv.CondMcBsmABC):
+class OusvMcCond(sv.SvABC, sv.CondMcBsmABC):
     """
     OUSV model with conditional Monte-Carlo simulation
     The SDE of SV is: d sigma_t = mr (theta - sigma_t) dt + vov dB_T

pyfeng/sabr_mc.py

@@ -10,7 +10,7 @@
 from . import cev
 
 
-class SabrCondMc(sabr.SabrABC, sv.CondMcBsmABC):
+class SabrMcCond(sabr.SabrABC, sv.CondMcBsmABC):
     """
     Conditional MC for SABR model (beta=0,1 or rho=0) with conditional Monte-Carlo simulation
 

pyfeng/sv32_mc.py

@@ -16,7 +16,7 @@
 from .norm import Norm
 
 
-class Sv32CondMcQE:
+class Sv32McCondQE:
     """
     Conditional MC for 3/2 model based on QE discretization scheme by Andersen(2008)
 
@@ -30,7 +30,7 @@ class Sv32CondMcQE:
         >>> forward = 100
         >>> delta = [1, 1/2, 1/4, 1/8, 1/16, 1/32]
         >>> vov, kappa, rho, texp, theta, sigma = [1, 0.5, -0.9, 10, 0.04, np.sqrt(0.04)]
-        >>> sv32_cmc_qe = pf.Sv32CondMcQE(vov=vov, kappa=kappa, rho=rho, theta=theta)
+        >>> sv32_cmc_qe = pf.Sv32McCondQE(vov=vov, kappa=kappa, rho=rho, theta=theta)
         >>> price_cmc = np.zeros([len(delta), len(strike)])
         >>> for d in range(len(delta)):
         >>>     price_cmc[d, :] = sv32_cmc_qe.price(strike, forward, texp, sigma=sigma, delta=delta[d], path=1e5, seed=123456)

tests/test_sabr.py

@@ -63,7 +63,7 @@ def test_UnCorrChoiWu2021(self):
 
     def test_CondMc(self):
         for k in [19, 20]:  # can test 22 (Korn&Tang) also, but difficult to pass
-            m, df, rv = pf.SabrCondMc.init_benchmark(k)
+            m, df, rv = pf.SabrMcCond.init_benchmark(k)
             m.set_mc_params(n_path=5e4, dt=0.05, rn_seed=1234)
             p = m.price(**rv["args_pricing"])
             np.testing.assert_almost_equal(p, rv["val"], decimal=4)