  In this work, we aim to create a completely online algorithmic framework for
prediction with expert advice that is translation-free and scale-free of the
expert losses. Our goal is to create a generalized algorithm that is suitable
for use in a wide variety of applications. For this purpose, we study the
expected regret of our algorithm against a generic competition class in the
sequential prediction by expert advice problem, where the expected regret
measures the difference between the losses of our prediction algorithm and the
losses of the 'best' expert selection strategy in the competition. We design
our algorithm using the universal prediction perspective to compete against a
specified class of expert selection strategies, which is not necessarily a
fixed expert selection. The class of expert selection strategies that we want
to compete against is purely determined by the specific application at hand and
is left generic, which makes our generalized algorithm suitable for use in many
different problems. We show that no preliminary knowledge about the loss
sequence is required by our algorithm and its performance bounds, which are
second order, expressed in terms of sums of squared losses. Our regret bounds
are stable under arbitrary scalings and translations of the losses.
