Metadata-Version: 2.1
Name: option-wiz
Version: 0.0.1
Summary: Package for option calculations
Home-page: https://github.com/nisaac21/option_py
Author: Neil Isaac et al.
Author-email: neilisaac08@gmail.com
License: MIT
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development
Classifier: Topic :: Office/Business :: Financial
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# Derivatives_Pricing_Library


Future Ideas:

Strategy Performance: https://medium.com/@rgaveiga/optionlab-a-python-library-for-evaluating-option-trading-strategies-50551ba7e578

* FIX MONTE CARLO THETA 


* Customization and Extensibility: Design the library to be modular and customizable, allowing users to extend its functionality to suit their specific needs.

* Optimization: Potential optimization technique at  http://www.jaeckel.org/LetsBeRational.pdf

* Option Contract Representation: Define a class or data structure to represent option contracts, including attributes like underlying asset, expiration date, strike price, option type (call/put), etc.

* Pricing Models: Implement common options pricing models like Black-Scholes, Binomial, and Monte Carlo simulations.
Allow users to calculate option prices, Greeks (Delta, Gamma, Theta, Vega, Rho), and implied volatility.

* Volatility Analysis: Provide tools to analyze historical and implied volatility. Offer functions to compute volatility smile, volatility skews, term structures, and volatility cones.

* Volatility Forecasting: Implement features to forecast future implied or historical volatility using different models, such as GARCH

* Risk Management: Offer tools for risk assessment and portfolio management involving options positions.
Calculate portfolio Greeks, value-at-risk (VaR), and stress testing scenarios.

* Visualization: Include visualization capabilities to plot option-related data, such as payoff diagrams, volatility charts, and strategy performance graphs.

* Strategy Analysis: Enable users to analyze option strategies like covered calls, protective puts, straddles, strangles, iron condors, and more. Calculate potential profit/loss, risk-reward ratios, break-even points, and visualize payoff diagrams.

* Historical Data and Market Analysis: Integrate with financial data providers to retrieve historical and real-time market data.
Allow users to analyze and visualize option chains, historical price trends, and volume/open interest data.

* Backtesting and Simulation:Provide the ability to backtest option strategies using historical data.Simulate various market conditions and assess strategy performance over time.

* Option Chains and Expirations: Implement methods to retrieve and display available option contracts for a given underlying asset.

* Error Handling and Documentation: Include comprehensive documentation for each function and class.
Implement proper error handling with meaningful error messages to assist users in troubleshooting.

* Educational Resources: Document the library with clear explanations and examples of its functionalities. Provide educational resources on options trading and analysis concepts for users who are new to options.
