pyBlackScholesAnalytics is a Python package implementing analytics for options and option strategies under the Black-Scholes Model for educational purposes.
pyBlackScholesAnalytics package is a Python package designed to use the well known Black-Scholes model to evaluate price, P&L and greeks of European options (both plain-vanilla and simple equity exotics such as cash-or-nothing Digital options), as well as simple option strategies built on them.
The package has been developed as a spin-off project of the "IT for Business and Finance" class held at the University of Siena for the Master degree in Finance in 2020.
pyBlackScholesAnalytics places itself in the middle between the coding style and level of a master student and that required for a junior quant at an investment bank. The aim is to address the gap between the two providing a playground for students to master financial concepts related to options and option strategies and implementing a dedicated comprehensive object-oriented architecture.
The package itself tries to follow the style guide for Python code PEP8. Intentional as well as unintentional departures from this style may occur in code. I'd like to thank in advance anyone who will make me aware of possible improvements in style and clarity of code.
You can install pyBlackScholesAnalytics simply typing
pip install pyBlackScholesAnalytics
Latest version of the package is available on PyPI. If you encounter problems during installation please share with me.
The current version of the package features the following components:
options: definitions for
EuropeanOption abstract base-class as well
DigitalOption derived classes
portfolio: definition of
Portfolio class implementing analytics for portfolios of options
plotter: definitions for
Plotter abstract base-class as well
PortfolioPlotter derived classes
utils: definition of general utility functions
numeric_routines: definition of
NumericGreeks class implementing option greeks through finite-difference methods
As far as the educational purpose is concerned, I find the pyBlackScholesAnalytics package itself helpful as much as the way in which its final version can be progressively built. In my experience, the constructive approach is ubiquitous in the real life of a Quant/Strat: a business need is first formulated by a trader or another stakeholder, then tackled by the Quant/Strat team with an ad hoc analysis, then a tactic short-term implementation of the response is produced and, finally, a strategic and robust long-term solution is designed and implemented. For this reason, the package is complemented by a series of 4 Tutorials in the form of Jupyter Notebooks and Youtube Videos. These tutorial aim to present the package step-by step in a constructive way building on the ideas of the Object-Oriented paradygm as improvements over sequential implementations of the same financial concepts.
Jupyter Notebook are made interactive thanks to Binder (click on the badge here below and start playing with the notebooks from your web browser)!
|Jupyter Notebook||Youtube Video Playlist|
|Derivatives Analytics - Introduction to Object Oriented Programming: in this tutorial we introduce Object-Oriented Programming in Python. We first make a non-financial example, developing the intuition behind the need of a change of programming paradigm to be able to cohordinate together different pieces of code. Once we have established the intuition, we then leverage on some basic financial knowledge to develop a Black-Scholes pricer for European call Options, first, and then a more general pricer for Plain-Vanilla put Options as well.||.ipynb||watch|
|Derivatives Analytics - Inheritance and Polymorphism: in this tutorial we introduce Inheritance and Polymorphism in Python which are two milestones in Object-Oriented programming. We present these concepts introducing Digital cash-or-nothing Options and observing their similarities with Plain-Vanilla Options. Inheritance and Polymorphism allow us to leverage on the financial analogies between these two contracts and eventually represent them more efficiently as derived classes of a unique ||.ipynb||watch|
|Derivatives Analytics - Objects Composition: in this tutorial we introduce Composition which is an additional way to model relationships among objects, alternatively to Inheritance. We present this relationship introducing a common ||.ipynb||watch|
|Derivatives Analytics - Options Greeks: in this tutorial we introduce Option Greeks. That is, the derivatives of an option price with respect to its pricing parameters. We provide both a numeric computation using finite-difference methods implemented in ||.ipynb||watch|
|options.py||This example shows basic usage of |
|options_other_params.py||This example shows usage of |
|options_IV.py||This example shows usage of |
|options_plot.py||This example shows basic integration of |
|options_plot_other_params.py||This example shows integration of |
|options_plot_IV.py||This example shows integration of |
|options_plot_surface.py||This example shows integration of |
|options_numeric_greeks.py||This example provides an example of first-order numeric greeks implemented in the |
|options_numeric_analytic_greeks_comparison.py||This example provides a comparison of first-order greeks for Plain-Vanilla and Digital Option contracts implemented either through finite-difference methods in |
|portfolio.py||This example shows basic usage of |
|portfolio_single_strike.py||This example shows basic usage of |
|portfolio_multi_strikes.py||This example shows basic usage of |
|bull_spread.py||This example shows usage of |
|bull_spread_other_params.py||This example shows usage of |
|calendar_spread.py||This example shows usage of |
|calendar_spread_other_params.py||This example shows usage of |
See pyBlackScholesAanlytics Github repository for gallery of images.
This is still the first version of this package, so if you find errors, have comments or suggestions you can reach Gabriele Pompa (firstname.lastname@example.org_). If you wish to contribute, please contact me through GitHub/gabrielepompa88. If you are interested but feel a bit new to Python, I can recommend the open "IT for Business and Finance" as a reasonable starting point.
Thank you in advance for your attention.