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:
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options: definitions forEuropeanOptionabstract base-class as well asPlainVanillaOptionandDigitalOptionderived classes -
portfolio: definition ofPortfolioclass implementing analytics for portfolios of options -
plotter: definitions forPlotterabstract base-class as well asOptionPlotterandPortfolioPlotterderived classes -
utils: definition of general utility functions -
numeric_routines: definition ofNumericGreeksclass 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 tutorials 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.
Moreover several examples showcase the features of pyBlackScholesAnalytics package and can be used as entry-point to begin the exploration of the package.
Jupyter Notebooks are available in GitHub, rendered statically thanks to Jupyter nbviewer and interactively on your web browser thanks to Binder. Each Jupyter Notebook is complemented with a dedicated Youtube video playlist.
| Jupyter Notebook | GitHub | nbviewer | Binder | Youtube Video |
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| 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. |  |
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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 EuropeanOption abstract base class. |
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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 MarketEnvironment under which Option contracts are emitted, a Portfolio class is designed to aggregate Options and another Plotter class is designed to provide visualization routines. Finally, we examine several Option Strategies which uses the architecture implemented so far. |
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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 NumericGreeks class and their analytic expression using the Black-Scholes model. Finally, all features introduced in this and previous tutorial are collected in the pyBlackScholesAnalytics Python package which is hereby introduced. |
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| options.py | This example shows basic usage of PlainVanillaOption and DigitalOption classes. Basic instantiation examples are provided with combinations of the underlying level (S) and time parameter (t/tau). Price, P&L, first-order greeks as well as Black-Scholes implied-volatility are computed for Plain-Vanilla and Digital Option contracts. |
| options_other_params.py | This example shows usage of PlainVanillaOption and DigitalOption classes. Instantiation examples are provided involving combinations of the underlying level (S), strike-price (K), time parameter (t/tau), as well as underlying volatility (sigma) and short-rate (r) parameters. Price, P&L, first-order greeks as well as Black-Scholes implied-volatility are computed for Plain-Vanilla and Digital Option contracts. |
| options_IV.py | This example shows usage of PlainVanillaOption and DigitalOption classes to compute of Black-Scholes implied volatility surfaces for Plain-Vanilla and Digital Option contracts. |
| options_plot.py | This example shows basic integration of PlainVanillaOption and DigitalOption classes with Plotter class. Price, P&L and first-order greeks plots are shown for Plain-Vanilla and Digital Option contracts. |
| options_plot_other_params.py | This example shows integration of PlainVanillaOption and DigitalOption classes with Plotter class. Price, P&L and first-order greeks plots are shown for Plain-Vanilla and Digital Option contracts against underlying level (S), strike-price (K), volatility (sigma) and short-rate (r). |
| options_plot_IV.py | This example shows integration of PlainVanillaOption and DigitalOption classes with Plotter class (in particular .plot_IV() method). Focus is on the visualization of Black-Scholes implied-volatility for Plain-Vanilla and Digital Option contracts both as a line plot and surface-plot. |
| options_plot_surface.py | This example shows integration of PlainVanillaOption and DigitalOption classes with Plotter class. Price, P&L and first-order greeks plots are shown for Plain-Vanilla and Digital Option contracts as underlying level (S), strike-price (K), volatility (sigma) and short-rate (r) surface-plots Vs time parameter. |
| options_numeric_greeks.py | This example provides an example of first-order numeric greeks implemented in the NumericGreeks class using finite-difference methods for Plain-Vanilla and Digital Option contracts. |
| 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 NumericGreeks class or using their analytic expression implemented in PlainVanillaOption and DigitalOption classes. |
| portfolio.py | This example shows basic usage of Portfolio class to construct a derivative portfolio of Plain-Vanilla and Digital Option contracts. Basic instantiation examples are provided with combinations of the underlying level (S) and time parameter (t/tau). Price, P&L, first-order greeks are computed for constructed portfolio and benchmarked with the corresponding metrics calculated combining constituent options metrics. |
| portfolio_single_strike.py | This example shows basic usage of Portfolio class to construct a derivative portfolio of Plain-Vanilla and Digital Option contracts. Basic instantiation examples are provided with combinations of the underlying level (S), strike-price (K), time parameter (t/tau) as well as underlying volatility (sigma) and short-rate (r) parameters. Price, P&L, first-order greeks are computed for single-strike portfolio. |
| portfolio_multi_strikes.py | This example shows basic usage of Portfolio class to construct a derivative portfolio of Plain-Vanilla and Digital Option contracts. Basic instantiation examples are provided with combinations of the underlying level (S), time parameter (t/tau) as well as underlying volatility (sigma) and short-rate (r) parameters. Price, P&L, first-order greeks are computed for multi-strike portfolio. |
| bull_spread.py | This example shows usage of Portfolio class to create a Bull-Spread option strategy. Basic instantiation examples are provided with combinations of the underlying level (S) and time parameter (t/tau). Price, P&L, first-order greeks are computed and plotted using the Plotter class as line plots and surface-plots Vs time parameter. |
| bull_spread_other_params.py | This example shows usage of Portfolio class to create a Bull-Spread option strategy. Instantiation examples are provided with combinations of the underlying level (S) and time parameter (t/tau) as well as underlying volatility (sigma) and short-rate (r) parameters. Price, P&L, first-order greeks are computed and plotted using the Plotter class as line plots and surfaces-plots Vs time parameter. |
| calendar_spread.py | This example shows usage of Portfolio class to create a Calendar-Spread option strategy. Basic instantiation examples are provided with combinations of the underlying level (S) and time parameter (t). Price, P&L, first-order greeks are computed and plotted using the Plotter class as line plots and surface-plots Vs time parameter. |
| calendar_spread_other_params.py | This example shows usage of Portfolio class to create a Calendar-Spread option strategy. Instantiation examples are provided with combinations of the underlying level (S) and time parameter (t) as well as underlying volatility (sigma) and short-rate (r) parameters. Price, P&L, first-order greeks are computed and plotted using the Plotter class as line plots and surfaces-plots Vs time parameter. |
Below something you can do using pyBlackScholesAnalytics package to whet your appetite:
- See options_plot.py example for Plain-Vanilla and Cash-Or-Nothing Digital Options price, P&L and Greeks examples
- See options_plot_surface.py example for Option surface plots examples
- See options_plot_other_params.py example for more pricing parameters dependency examples
- See options_plot_IV.py example for Black-Scholes implied volatility examples
- See bull_spread.py example for Bull-Spread Strategy price, P&L and Greeks examples
- See calendar_spread.py example for Calendar-Spread Strategy price, P&L and Greeks examples
This project is still at early stages of development, so if you find errors, have comments or suggestions you can reach Gabriele Pompa ([email protected]). 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" course as a reasonable starting point.
Thank you in advance for your attention.