The groundbreaking work of Fisher Black, Myron Scholes, and Robert Merton in the 1970s led to the development of the Black-Scholes-Merton option pricing model, which revolutionized the valuation of European options. Building on these foundations, the binomial option pricing model was introduced by John Cox, Stephen Ross, and Mark Rubinstein in 1979, followed by the trinomial model by Phelim Boyle in 1986. These models provide a solid theoretical basis for the valuation of financial derivatives.
This project aims to implement the binomial and trinomial models in Python to demonstrate their practical application in the modern financial world. The development of a user-friendly Python-based application enables users to flexibly adjust model parameters and analyze a variety of derivatives. A particular focus is on the visual representation of model calculations and the integration of educational resources to enhance the understanding of fundamental financial mathematical concepts.
The project offers a platform that serves both as a powerful analysis tool and as an educational resource, contributing to the expansion and deepening of the application of financial mathematical models.
This section provides a detailed examination of the FinanceApp, divided into three main areas: user interface, implementation, and future extensions. The goal is to provide a comprehensive understanding of the application by illuminating both the interaction interface and the technical structures and processes that support these interactions. Additionally, an outlook on potential further developments and the expansion of functionalities is given.
The user interface of the FinanceApp is designed to translate the complexity of financial mathematical models into an accessible and intuitive form. It allows users to conduct analyses and evaluations of derivative financial instruments without extensive prior knowledge. The interface includes:
- Model Selection and Configuration: Users can choose between different valuation models and specify parameters to evaluate a variety of derivatives under different market conditions.
- Visualization: The application offers comprehensive visualizations of calculation results, including model trees and convergence behavior, to provide deeper insights into the evaluated financial instruments.
- LearningHub: An interactive platform to promote the understanding of fundamental financial concepts.
- Settings and Support Functions: Users can set default values and track their learning progress. A help section provides access to the documentation of the bachelor's thesis.
- Concept Area: This area allows users to explore potential extensions such as the integration of real-time financial data.
The implementation of the app ensures stability and efficiency by enabling the calculations and analyses on which user interactions are based.
An outlook on the expansion of the app with advanced features, such as the integration of real-time financial data, is provided.
Finally, a UML class diagram offers further insights into the structure of the project and illustrates the underlying conceptual and technical aspects.
The development of this application was accomplished using Python in conjunction with the Qt Designer, which facilitated the creation of a robust and intuitive user interface. The following screenshots provide a closer look at the user interface, showcasing its design and functionality. These visuals illustrate how the complex financial models are made accessible to users, allowing them to interact with the application seamlessly and effectively. By leveraging the capabilities of Qt Designer, the interface is both visually appealing and functionally rich, ensuring a smooth user experience.
Please note that the user interface and all comments are in German, as the accompanying bachelor's thesis was also written in German. This linguistic consistency ensures seamless integration between the application and the written work.