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vectorized_black gives the undiscounted option price #13 #19

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vectorized_black gives the undiscounted option price #13 #19

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19f4e51
vectorized_black gives the undiscounted option price #13
ortap Nov 25, 2024
c7ef3c2
bump up setup py version
ortap Nov 25, 2024
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6 changes: 3 additions & 3 deletions py_vollib_vectorized/_model_calls.py
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Original file line number Diff line number Diff line change
Expand Up @@ -87,10 +87,10 @@ def _black_scholes_vectorized_call(flags, Ss, Ks, ts, rs, sigmas):


@maybe_jit()
def _black_vectorized_call(Fs, Ks, sigmas, ts, flag):
def _black_vectorized_call(Fs, Ks, sigmas, ts, rs, flag):
prices = []
for F, K, sigma, T, q in zip(Fs, Ks, sigmas, ts, flag):
prices.append(black(F, K, sigma, T, q))
for F, K, sigma, T, r, q in zip(Fs, Ks, sigmas, ts, rs, flag):
prices.append(np.exp(-r*T)*black(F, K, sigma, T, q))
return prices


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9 changes: 5 additions & 4 deletions py_vollib_vectorized/models.py
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Expand Up @@ -14,10 +14,11 @@ def vectorized_black(flag, F, K, t, r, sigma, *, return_as="dataframe", dtype=np
:param K: The strike price.
:param sigma: The Implied Volatility (as a decimal, i.e. 0.10 for 10%).
:param t: The annualized time to expiration. Must be positive. For small TTEs, use a small value (1e-3).
:param r: The risk-free rate
:param flag: For each contract, this should be specified as `c` for a call option and `p` for a put option.
:param return_as: To return as a :obj:`pd.Series` object, use "series". To return as a :obj:`pd.DataFrame` object, use "dataframe". Any other value will return a :obj:`numpy.array` object.
:param dtype: Data type.
:return: The price of the option.
:return: The price of the option (after discounting)
>>> import py_vollib.black
>>> import py_vollib_vectorized
>>> flag = ['c', 'p']
Expand All @@ -32,10 +33,10 @@ def vectorized_black(flag, F, K, t, r, sigma, *, return_as="dataframe", dtype=np
array([1.53408169, 1.38409245])
"""
flag = _preprocess_flags(flag, dtype=dtype)
F, K, sigma, t, flag = maybe_format_data_and_broadcast(F, K, sigma, t, flag, dtype=dtype)
_validate_data(F, K, sigma, t, flag)
F, K, sigma, t, r, flag = maybe_format_data_and_broadcast(F, K, sigma, t, r, flag, dtype=dtype)
_validate_data(F, K, sigma, t, r, flag)

prices = _black_vectorized_call(F, K, sigma, t, flag)
prices = _black_vectorized_call(F, K, sigma, t, r, flag)
prices = np.ascontiguousarray(prices)

if return_as == "series":
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