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.
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 complex data types, high-dimensional feature spaces, and multi-source data fusion. To solve
this problem, we propose FinOL, an open finance database for advancing research in data-driven OLPS. FinOL expands
and enriches the previous OLPS database, containing 8 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. Importantly, we are
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.
For inquiries, please get in touch with us at finol.official@gmail.com (Monday to Friday, 9:00 AM to 6:00 PM)