Background
==========


Early online portfolio selection (OLPS) methods relied on a priori assumptions
about market dynamics and mathematical optimization. With increased computing
power and data, data-driven OLPS methods that directly learn from data have gained attention.

While various data-driven OLPS methods have shown promise, evaluating and
comparing them remains challenging. Without standardized datasets and fair
comparisons, it's unclear which method truly performs best in real-world
environments, hindering progress in this field.

Researchers often default to assuming newer methods are superior, but this
is irresponsible without consistent test conditions. Financial time series
are highly non-stationary, so the same method can exhibit drastically
different performance on different datasets.

To address this, we introduce ``FinOL``, a new finance benchmark platform
designed for data-driven OLPS research. ``FinOL`` provides diverse financial
datasets and extensive benchmark results for fair comparison.

Notation
--------

.. list-table::
   :header-rows: 1
   :class: ghost

   * - Notation
     - Description
   * - :math:`b_{t,i}`
     - Proportion of wealth invested in asset :math:`i` at the beginning of the :math:`t`-th trading period.
   * - :math:`\mathbf{b}_t`
     - :math:`m`-dimensional portfolio vector at the beginning of the :math:`t`-th trading period.
   * - :math:`{\mathbf{b}}_{1}`
     - Initial portfolio vector, typically a uniform distribution :math:`(1/m, \ldots, 1/m)`.
   * - :math:`\mathbf{b}_{t}^{\top} \mathbf{x}_t`
     - Daily return of the portfolio at the end of the :math:`t`-th trading period.
   * - :math:`m`
     - Number of assets in the financial market.
   * - :math:`n`
     - Number of discrete trading periods.
   * - :math:`S_n`
     - Final wealth achieved at the end of the :math:`n`-th trading period.
   * - :math:`S_0`
     - Initial value of the portfolio, normally set to 1.
   * - :math:`x_{t,i}`
     - Price relative of asset :math:`i` at the end of the :math:`t`-th trading period.
   * - :math:`\mathbf{x}_t`
     - :math:`m`-dimensional non-negative price relatives vector at the end of the :math:`t`-th trading period.
   * - :math:`\Delta_m`
     - Simplex constraint indicating the portfolio is fully self-financing without leverage or shorting.


Problem Formulation
-------------------

In the context of online portfolio selection, we analyze a financial market consisting
:math:`m` assets observed during a specified time horizon comprising :math:`n` discrete trading
periods (the term "periods" is defined flexibly). At the end of the :math:`t`-th trading period,
we use a :math:`m`-dimensional non-negative price relatives vector
:math:`\mathbf{x}_t=(x_{t,1},\ldots,x_{t,m}) \in \mathbb{R}_{+}^{m}` to represent the performance
of the :math:`m` assets, where each element :math:`x_{t,i}` equals the close price of asset
:math:`i` on the :math:`t`-th trading period divided by the close price of asset :math:`i` on
the :math:`(t-1)`-th trading period, i.e., :math:`x_{t,i}={C_{t,i}}/{C_{t-1,i}}`.

Before the start of the :math:`t`-th trading period, using past historical information,
we can construct a portfolio vector :math:`\mathbf{b}_t=(b_{t,1},\ldots,b_{t,m}) \in \mathbb{R}^m`
to allocate our wealth among :math:`m` assets. Each element :math:`b_{t,i}` represents the proportion
of wealth invested in asset :math:`i` at the beginning of the :math:`t`-th trading period. A portfolio
clearly needs to satisfy the simplex constraint :math:`\mathbf{b}_t \in \Delta_m`, where :math:`b_{t,i} \ge 0`
and :math:`\sum\nolimits_{i=1}^{m}{{{b}_{t,i}}}=1`. This constraint indicates the portfolio is fully
self-financing without leverage or shorting.

Therefore, at the end of the :math:`t`-th trading period, the daily return of the portfolio
is defined as :math:`\mathbf{b}_{t}^{\top} \mathbf{x}_t = \sum\nolimits_{i=1}^{m} b_{t,i}x_{t,i}`.
Based on this, the final wealth achieved at the end of the :math:`n`-th trading period is:

.. math::

    {{S}_{n}}={{S}_{0}}\prod\limits_{t=1}^{n}{\mathbf{b}_{t}^{\top}
		{\mathbf{x}_{t}}}={{S}_{0}}\prod\limits_{t=1}^{n}{\sum\limits_{i=1}^{m}{{{b}_{t,i}}{{x}_{t,i}}}},

where, without loss of generality, the initial value of the portfolio is normally set to 1, i.e.,
:math:`{S}_{0} = 1`; at the same time, the portfolio vector is initialized to a uniform distribution,
i.e., :math:`{\mathbf{b}}_{1} = (1/m, \ldots, 1/m)`.

Obviously, the goal of this task is to maximize the final portfolio wealth, which depends entirely
on the portfolio vector generated by the data-driven method in each period.