Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

Overview

Binomial Option Pricing Calculator

Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

Background

A derivative is a financial instrument that derives its value from the price of an underlying asset. An option gives the owner the ability to buy or sell the underlying asset at pre-determined price. An option that allows the holder to buy the asset at the pre-determined price (also known as the exercise or strike price) is called a call option. An option that lets the owner sell the underlying asset at the strike price is called a put option. There are three key types of options, a European option allows the holder to exercise ('redeem') the option only at maturity of the option. An American option can be exercised any time before maturity. A Bermudan option is exercisable at pre-deteremined dates decided at the creation of the option.

The binomial pricing method is one of the three most common methods used to value options - the others being the Black-Scholes model and a Monte Carlo simulation. The method predicts the price of the underlying asset at intervals (branches) between now and maturity of the option contract. This creates a tree showing the price movements of the asset, which can be used to find the fair value of the option. Unlike Black-Scholes, the binomial method allows the intrinsic value of the option to be calculated prior to maturity, better representing the value of American and Bermudan options which have the option of early exercise.

Pricing options using this method is done by:

  1. Determining the magnitude that stock prices will rise or fall between each branch.
  2. Calculating the probability that the stock price will move upwards or downward.
  3. Forming the binomial stock price tree with the specified number of branches.
  4. Calculate the payoff of the option at maturity.
  5. Working backwards, value the option by discounting the value of the option at the following nodes using. If the option is American or Bermudan and exercisible at that branch, then the value of the option if it was exercised is calculated, if it is greater than the discoutned value, it becomes the calculated value of the branch.
  6. The value derived at the top of the tree is the fair value of the option today.

Features of the Script

  • Does not require any libraries - it will work in base python3 and immune to changes in libraries
  • Option type is specified as a parameter allowing easy implementations
  • Returns and displays the calculated stock tree

The following assumptions are made by the model:

  • No dividends are paid across the option's life
  • Risk-Free rate is constant across the option's life
  • The price will move up or down each period

Variables and Paramaters

The variables required are:

Name Symbol Description
Stock Price s The current price of the underlying asset (time 0)
Exercise Price x The strike price of the option contract
Time to Maturity t The time until maturity of the option contract (in years)
Risk-Free Rate r The current risk-free rate
Branches/Steps b The number of branches used to value the option
Volatility v The volatility of the price movements in the underlying asset

Optional variables are:

Name Symbol Description
Option Nationality nat 'A' for American (default), 'B' for Bermudan, 'E' for European
Option Type typ 'C' for Call (default), 'P' for Put
Print Results prnt True to enable printing (default), False to disable
Exercisible Periods exP The branches that a Bermudan option can be exercised

Related Projects

Beta calculator with stock data downloader: https://github.com/sammuhrai/beta-calculator

Disclaimer

Script is for educational purposes and is not to be taken as financial advice.

Owner
sammuhrai
sammuhrai
Working Time Statistics of working hours and working conditions by industry and company

Working Time Statistics of working hours and working conditions by industry and company

Feng Ruohang 88 Nov 04, 2022
Handle, manipulate, and convert data with units in Python

unyt A package for handling numpy arrays with units. Often writing code that deals with data that has units can be confusing. A function might return

The yt project 304 Jan 02, 2023
Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

Overview dataflow-mvp provides a basic example pipeline that pulls data from an API and writes it to a BigQuery table using GCP's Dataflow (i.e., Apac

Chris Carbonell 1 Dec 03, 2021
SparseLasso: Sparse Solutions for the Lasso

SparseLasso: Sparse Solutions for the Lasso Introduction SparseLasso provides a Scikit-Learn based estimation of the Lasso with cross-validation tunin

Gabriel Okasa 1 Nov 08, 2021
A fast, flexible, and performant feature selection package for python.

linselect A fast, flexible, and performant feature selection package for python. Package in a nutshell It's built on stepwise linear regression When p

88 Dec 06, 2022
Bearsql allows you to query pandas dataframe with sql syntax.

Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine

14 Jun 22, 2022
Python Practicum - prepare for your Data Science interview or get a refresher.

Python-Practicum Python Practicum - prepare for your Data Science interview or get a refresher. Data Data visualization using data on births from the

Jovan Trajceski 1 Jul 27, 2021
ASOUL直播间弹幕抓取&&数据分析

ASOUL直播间弹幕抓取&&数据分析(更新中) 这些文件用于爬取ASOUL直播间的弹幕(其他直播间也可以)和其他信息,以及简单的数据分析生成。

159 Dec 10, 2022
Elementary is an open-source data reliability framework for modern data teams. The first module of the framework is data lineage.

Data lineage made simple, reliable, and automated. Effortlessly track the flow of data, understand dependencies and analyze impact. Features Visualiza

898 Jan 09, 2023
PyPSA: Python for Power System Analysis

1 Python for Power System Analysis Contents 1 Python for Power System Analysis 1.1 About 1.2 Documentation 1.3 Functionality 1.4 Example scripts as Ju

758 Dec 30, 2022
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

gg I wasn't satisfied with any of the other available Gemini clients, so I wrote my own. Requires Python 3.9 (maybe older, I haven't checked) and opti

RAFAEL RODRIGUES 5 Jan 03, 2023
Hydrogen (or other pure gas phase species) depressurization calculations

HydDown Hydrogen (or other pure gas phase species) depressurization calculations This code is published under an MIT license. Install as simple as: pi

Anders Andreasen 13 Nov 26, 2022
Containerized Demo of Apache Spark MLlib on a Data Lakehouse (2022)

Spark-DeltaLake-Demo Reliable, Scalable Machine Learning (2022) This project was completed in an attempt to become better acquainted with the latest b

8 Mar 21, 2022
Collections of pydantic models

pydantic-collections The pydantic-collections package provides BaseCollectionModel class that allows you to manipulate collections of pydantic models

Roman Snegirev 20 Dec 26, 2022
CaterApp is a cross platform, remotely data sharing tool created for sharing files in a quick and secured manner.

CaterApp is a cross platform, remotely data sharing tool created for sharing files in a quick and secured manner. It is aimed to integrate this tool with several more features including providing a U

Ravi Prakash 3 Jun 27, 2021
A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Thomas Träff 2 Dec 22, 2021
Aggregating gridded data (xarray) to polygons

A package to aggregate gridded data in xarray to polygons in geopandas using area-weighting from the relative area overlaps between pixels and polygons. Check out the binder link above for a sample c

Kevin Schwarzwald 42 Nov 09, 2022
Analyzing Covid-19 Outbreaks in Ontario

My group and I took Covid-19 outbreak statistics from ontario, and analyzed them to find different patterns and future predictions for the virus

Vishwaajeeth Kamalakkannan 0 Jan 20, 2022
Time ranges with python

timeranges Time ranges. Read the Docs Installation pip timeranges is available on pip: pip install timeranges GitHub You can also install the latest v

Micael Jarniac 2 Sep 01, 2022
An orchestration platform for the development, production, and observation of data assets.

Dagster An orchestration platform for the development, production, and observation of data assets. Dagster lets you define jobs in terms of the data f

Dagster 6.2k Jan 08, 2023