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PyVol.py
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PyVol.py
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"""
Ernest Orlowski
Note: these two files require "holidays" & "py-vollib" to be pip installed.
"""
import numpy as np
from py_vollib.black.implied_volatility import implied_volatility
from py_vollib.black.greeks.numerical import delta, gamma, theta, vega
from py_vollib.black_scholes.implied_volatility import implied_volatility as bs_IV
from py_vollib.black_scholes.greeks.numerical import delta as bs_delta
from py_vollib.black_scholes_merton.greeks.numerical import gamma as bsm_gamma
# black calcs for futures
def blackIV(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
iv = implied_volatility(
discounted_option_price=df["Mid"],
F=F,
K=df["Strike"],
r=rf,
t=df["BDTE"] / 252,
flag=df["Flag"],
)
except:
iv = np.nan
return iv
def blackDelta(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
delt = delta(
flag=df["Flag"],
F=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
)
except:
delt = np.nan
return delt
def blackGamma(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
gam = gamma(
flag=df["Flag"],
F=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
)
except:
gam = np.nan
return gam
def blackVega(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
veg = vega(
flag=df["Flag"],
F=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
)
except:
veg = np.nan
return veg
def blackTheta(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
thet = theta(
flag=df["Flag"],
F=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
)
except:
thet = np.nan
return thet
# Black Scholes calcs for equities
def blackScholesIV(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
iv = bs_IV(
price=df["Mid"], # change to 'Mid'
S=F,
K=df["Strike"],
r=rf,
t=df["BDTE"] / 252,
flag=df["Flag"],
)
except:
iv = np.nan
return iv
def blackScholesDelta(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
delt = bs_delta(
flag=df["Flag"],
S=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
)
except:
delt = np.nan
return delt
def blackScholesGamma(df, F=None, rf=None):
if F is None:
F = df["F"]
if rf is None:
rf = 0.02
try:
gam = bsm_gamma(
flag=df["Flag"],
S=F,
K=df["Strike"],
t=df["BDTE"] / 252,
r=rf,
sigma=df["IV"],
q=0.015,
)
except:
gam = np.nan
return gam