Metadata-Version: 2.1
Name: finol
Version: 0.0.2
Summary: A plug-and-play Python library for data-driven OLPS research
Home-page: https://github.com/jiahaoli57/FinOL
Author: Jiahao Li
Author-email: finol.official@gmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.9.0
Description-Content-Type: text/markdown
License-File: LICENSE


# FinOL: Towards Open Benchmarking for Data-Driven Online Portfolio Selection

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<img align="center" src=figure/logo.png width="20%"/> 

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[//]: # ([![GitHub stars]&#40;https://img.shields.io/github/stars/ai4finol/finol?color=orange&#41;]&#40;https://github.com/ai4finol/finol/stargazers&#41;)

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***
``FinOL`` represents a pioneering open database for facilitating data-driven financial research. As an
ambitious project, it collects and organizes extensive assets from global markets over half a century,
it provides a long-awaited unified platform to advance data-driven OLPS research.

## :star: **What's NEW!** 

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| Update                     | Status                                                                                      |
|----------------------------|---------------------------------------------------------------------------------------------|
| Release  ``FinOL``  v0.0.1 | [Released v0.0.1](https://github.com/jiahaoli57/finol/releases/tag/v0.0.1) on 17 March 2024 |

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## Outline

- [FinOL: Towards Open Benchmarking for Data-Driven Online Portfolio Selection](#finol)
  - [Outline](#outline)
  - [About](#about)
  - [Why should I use FinOL?](#why-should-I-use-FinOL?)
  - [Contact Us](#contact-us)

[//]: # (  - [Installation]&#40;#installation&#41;)

[//]: # (  - [Tutorial]&#40;#tutorial&#41;)

[//]: # (  - [Useful Script]&#40;#useful-script&#41;)

[//]: # (  - [Dataset]&#40;#dataset&#41;)

[//]: # (  - [Model Zoo]&#40;#model-zoo&#41;)

[//]: # (  - [Visualization Toolkit]&#40;#visualization-toolkit&#41;)

[//]: # (  - [File Structure]&#40;#file-structure&#41;)

[//]: # (  - [Publications]&#40;#publications&#41;)

[//]: # (  - [News]&#40;#news&#41;)

[//]: # (  - [Team]&#40;#team&#41;)

[//]: # (  - [Competition]&#40;#competition&#41;)

## About

Online portfolio selection (OLPS) is an important issue in operations research community that studies how to dynamically
adjust portfolios according to market changes. In the past, OLPS research relied on a general database called ``OLPS`` 
containing price relatives data of financial assets across different markets. However, with the widespread adoption of 
data-driven technologies like machine learning in finance, ``OLPS`` can no longer meet the needs of OLPS research because 
due to the lack of support for high-dimensional feature spaces. To solve 
this problem, we propose ``FinOL``, an open finance platform for advancing research in data-driven OLPS. ``FinOL`` expands 
and enriches the previous ``OLPS`` database, containing 9 benchmark financial datasets from 1962 to present across global 
markets. To promote fair comparisons, we evaluate a large number of past classic OLPS methods on ``FinOL``, providing 
reusable benchmark results for future ``FinOL`` users and effectively supporting OLPS research. More importantly, to 
lower the barriers to research, ``FinOL`` provides a complete data-training-testing suite with just three lines of 
command. We are also committed to regularly updating ``FinOL`` with new data and benchmark results reflecting the latest 
developments and trends in the field. This ensures ``FinOL`` remains a valuable resource as data-driven OLPS methods 
continue evolving.

![image](figure/FinOL.png)

## Why should I use FinOL?

1. ``FinOL`` contributes comprehensive datasets spanning diverse market conditions and asset classes to enable large-scale empirical validation;
2. ``FinOL`` contributes the most extensive benchmark results to date for portfolio selection methods, providing the academic community an unbiased performance assessment;
3. ``FinOL`` contributes a user-friendly Python library for data-driven OLPS research, providing a comprehensive toolkit for academics to develop, test, and validate new OLPS methods.

## Contact Us

For inquiries, please get in touch with us at finol.official@gmail.com (Monday to Friday, 9:00 AM to 6:00 PM)


[//]: # (## Useful Links)

 
