Metadata-Version: 2.2
Name: tiny_pricing_utils
Version: 1.0.3
Summary: A set of utility functions for my project
Home-page: https://github.com/MichaelCarloH/Option-Pricing/tree/main/tiny_pricing_utils
Author: Michael Carlo
Author-email: michael.carlo@outlook.it
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
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# tiny-pricing-utils Package

tiny_pricing_utils is a Python package for option pricing and financial modeling. It includes implementations of the Black-Scholes and Heston models, Monte Carlo simulations, and characteristic functions for Fourier-based pricing.

Working examples can be found at: https://github.com/MichaelCarloH/Option-Pricing/tree/main/Option%20pricing

Key Features: 
- Black-Scholes Model: European option pricing.
- Heston Model: Class-based implementation with FFT pricing methods.
- Monte Carlo Simulations: Simulates stock price paths under different models.
- Characteristic Functions: Fourier-based characteristic functions for pricing using BS and Heston.
- Stock paths generation with Geometric Brownian Motion, Euler and Milstein Monetcarlo methods


## Features
- Utility functions for pricing options
- Helper functions for computing characteristic function
- Easy-to-use and modular

## Installation

To install the package, you can clone the repository or use `pip`:

## Documentation

The full documentation can be found at: 

https://option-pricing-1ld1qd386-michael-carlos-projects.vercel.app/


### Clone the repository:
```bash
git clone https://github.com/MichaelCarloH/Option-Pricing.git
python setup.py install
```

### Aknowledgements:
Part of the Theory and code snippets are based on the course “Financial Engineering” by Peter Leon and Wim Schoutens and course collaborators at KU Leuven.
