  The artificial segmentation of an investment management process into a
workflow with silos of offline human operators can restrict silos from
collectively and adaptively pursuing a unified optimal investment goal. To meet
the investor's objectives, an online algorithm can provide an explicit
incremental approach that makes sequential updates as data arrives at the
process level. This is in stark contrast to offline (or batch) processes that
are focused on making component level decisions prior to process level
integration. Here we present and report results for an integrated, and online
framework for algorithmic portfolio management. This article provides a
workflow that can in-turn be embedded into a process level learning framework.
The workflow can be enhanced to refine signal generation and asset-class
evolution and definitions. Our results confirm that we can use our framework in
conjunction with resampling methods to outperform naive market capitalisation
benchmarks while making clear the extent of back-test over-fitting. We consider
such an online update framework to be a crucial step towards developing
intelligent portfolio selection algorithms that integrate financial theory,
investor views, and data analysis with process-level learning.
