semvec/pypi-packages huggingface dataset:
paref 0.2.0
Pareto reflection based multi-objective optimization
License: MIT
Requires Python: >=3.10

[![Python Test & Lint](https://github.com/nicolaipalm/paref/actions/workflows/python-test.yml/badge.svg)](https://github.com/nicolaipalm/paref/actions/workflows/python-test.yml)
[![Python Version](https://img.shields.io/badge/python-3.9+-blue.svg?style=plastic)](https://www.python.org/downloads/)

[documentation](https://paref.readthedocs.io/en/latest/)//[notebooks](https://github.com/nicolaipalm/paref/tree/main/docs/notebooks)//[demo]()//[paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4668407)

# Paref - problem tailored MOO for expensive black-box functions

A multi-objective optimization (MOO) problem comes with an idea of what properties the identified
(Pareto) points must satisfy.
The fact that these properties are satisfied is what makes a MOO successful in the first place.
Why not construct MOO algorithms that search for exactly these properties and,
by their very nature, use only a minimum number of evaluations?
With the language of PAreto REFlections this is now possible.
This package contains...

- a series of ready-to-use [MOO algorithms](https://github.com/nicolaipalm/paref/tree/main/paref/moo_algorithms)
  corresponding to frequently targeted properties
- a framework for you to implement your problem tailored MOO algorithm
- generic and intuitive [interfaces](https://github.com/nicolaipalm/paref/tree/main/paref/interfaces) for MOO
  algorithms, black-box functions and more, so solving a MOO problem with user-defined properties with Paref requires
  only minimal effort

See the official [documentation](https://paref.readthedocs.io/en/latest/) for more information.

The official release is available at PyPi:

```
pip install paref
```
quantbullet 0.1.1
Toolkit for swift quant analysis
License: MIT
Requires Python: >=3.9

# quantbullet

`quantbullet` is a toolkit designed for streamlined quantitative analysis in finance. The goals for this package are:

1. To provide a practical set of tools for prototyping quantitative research ideas.
2. To integrate and test contemporary research findings, primarily from academic sources, ensuring they're actionable.

While I initially developed this package for my own needs, I intend to maintain it consistently. If it assists others in their endeavors, I consider that a success.

## Installation

```bash
$ pip install quantbullet
```

## Usage

1. Statistical Jump Models. See [this notebook](./docs/research/jump_model_prod.ipynb) for an example. Statistical jump models are a type of regime-switching model that applies clustering algorithms to temporal financial data, explicitly penalizing jumps between different financial regimes to capture true persistence in underlying regime-switching processes.

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`quantbullet` was created by Yiming Zhang. It is licensed under the terms of the MIT license.

## Credits

This project developement is generously supported by JetBrains softwares with their Open Source development license.

<img src="https://resources.jetbrains.com/storage/products/company/brand/logos/jb_beam.png" alt="JetBrains Logo (Main) logo." width=200>

`quantbullet` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter). [Python Packages](https://py-pkgs.org/) is an excellent resource for learning how to create and publish Python packages.