https://github.com/marcdemers/py_vollib_vectorized
A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.
https://github.com/marcdemers/py_vollib_vectorized
finance finance-application greeks implied-volatility pandas py-vollib speedups trading trading-bot vectorization volatility volatility-modeling
Last synced: 11 days ago
JSON representation
A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.
- Host: GitHub
- URL: https://github.com/marcdemers/py_vollib_vectorized
- Owner: marcdemers
- License: mit
- Created: 2020-11-03T01:41:49.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-12-02T23:22:23.000Z (about 1 year ago)
- Last Synced: 2025-08-23T07:23:30.227Z (5 months ago)
- Topics: finance, finance-application, greeks, implied-volatility, pandas, py-vollib, speedups, trading, trading-bot, vectorization, volatility, volatility-modeling
- Language: Python
- Homepage:
- Size: 281 KB
- Stars: 136
- Watchers: 7
- Forks: 35
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# py_vollib_vectorized
## Introduction
The `py_vollib_vectorized` package makes pricing thousands of option contracts and calculating greeks fast and effortless.
It is built on top of the `py_vollib` library.
Upon import, it will automatically patch the corresponding `py_vollib` functions so as to support vectorization.
Inputs can then be passed as floats, tuples, lists, `numpy.array`, or `pandas.Series`.
Automatic broadcasting is performed on the inputs.
On top of vectorization, modifications to py_vollib include additional `numba` speedups; as such, `numba` is required.
These speedups make `py_vollib_vectorized` the fastest library for pricing option contracts.
See the [documentation](https://py-vollib-vectorized.readthedocs.io/en/latest) for more details.
## Installation
pip install py_vollib_vectorized
## Requirements
* Written for Python 3.5+
* Requires py_vollib, numba, numpy, pandas, scipy
## Code samples
The library can be used in two ways.
Upon import, it monkey-patches (i.e. replaces) the corresponding functions in `py_vollib`.
As a more versatile alternative, users that would prefer to work with a dedicated option pricing API can make use of the utility functions provided by the library.
#### Patching `py_vollib`
```python
# The usual py_vollib syntax
import numpy as np
import pandas as pd
import py_vollib.black_scholes
flag = 'c' # 'c' for call, 'p' for put
S = 100 # Underlying asset price
K = 90 # Strike
t = 0.5 # (Annualized) time-to-expiration
r = 0.01 # Interest free rate
iv = 0.2 # Implied Volatility
option_price = py_vollib.black_scholes.black_scholes(flag, S, K, t, r, iv) # 12.111581435
# This library keeps the same syntax, but you can pass as input any iterable of values.
# This includes list, tuple, numpy.array, pd.Series, pd.DataFrame (with only a single column).
# Note that you must pass a value for each contract as *no broadcasting* is done on the inputs.
# Patch the original py_vollib library by importing py_vollib_vectorized
import py_vollib_vectorized # The same functions now accept vectors as input!
# Note that the input arguments are broadcasted.
# You can specify ints, floats, tuples, lists, numpy arrays or Series.
flag = ['c', 'p'] # 'c' for call, 'p' for put
S = (100, 100) # Underlying asset prices
K = [90] # Strikes
t = pd.Series([0.5, 0.6]) # (Annualized) times-to-expiration
r = np.array([0.01]) # Interest free rates
iv = 0.2 # Implied Volatilities
option_price = py_vollib.black_scholes.black_scholes(flag, S, K, t, r, iv, return_as='array')
# array([12.11158143, 2.02418536])
```
#### Utility functions
We also define other utility functions to get all contract prices, implied volatilities, and greeks in a single call.
```python
import pandas as pd
from py_vollib_vectorized import price_dataframe, get_all_greeks
# Using the data above, we can calculate all contracts greeks in a single call
greeks = get_all_greeks(flag, S, K, t, r, iv, model='black_scholes', return_as='dict')
# {'delta': array([ 0.80263679, -0.21293214]),
# 'gamma': array([0.0196385, 0.01875498]),
# 'theta': array([-0.01263557, -0.00964498]),
# 'rho': array([0.34073321, -0.13994668]),
# 'vega': array([0.19626478, 0.22493816])}
# We can also price a dataframe easily by specifying a dataframe and the corresponding columns
df = pd.DataFrame()
df['Flag'] = ['c', 'p']
df['S'] = 95
df['K'] = [100, 90]
df['T'] = 0.2
df['R'] = 0.2
df['IV'] = 0.2
result = price_dataframe(df, flag_col='Flag', underlying_price_col='S', strike_col='K', annualized_tte_col='T',
riskfree_rate_col='R', sigma_col='IV', model='black_scholes', inplace=False)
# Price delta gamma theta rho vega
# 2.895588 0.467506 0.046795 -0.045900 0.083035 0.168926
# 0.611094 -0.136447 0.025739 -0.005335 -0.027151 0.092838
```
See the [documentation](https//py-vollib-vectorized.readthedocs.io/en/latest) for more details.
## Benchmarking
Compared to looping through contracts or to using built-in pandas functionality, this library is very memory efficient and scales fast and well to a large number of contracts.
## Acknowledgements
This library optimizes the `py_vollib` codebase, itself built upon Peter Jäckel's *Let's be rational* methodology.