Independent research for algorithmic investing.

Our mission

The Institute for Algorithmic Investing applies rigorous quantitative analysis and data science to the independent evaluation of algorithmic investment systems. Our mission is to provide investors with a trusted, independent source of institutional-grade research in a space defined by sophisticated opportunities and rapid growth.

We exist to help independent investors understand, evaluate, and engage with algorithmic investment systems with the same analytical depth available to institutional allocators.

What we evaluate

We evaluate algorithmic investment systems across multiple dimensions:

Our proprietary scoring framework produces ratings that allow investors to compare systems on a consistent, objective basis. The specific methodologies, scoring models, and factor weightings that produce our ratings are developed internally and represent the Institute's core intellectual property.

Our standards

The Institute's evaluation framework is built to institutional specifications. It applies the same categories of analysis and risk-detection standards that professional allocators use when evaluating algorithmic systems for institutional capital deployment. The standards we apply are not exotic or arbitrarily demanding. Structural soundness, honest risk management, transparent performance reporting, and basic operational integrity are not luxuries reserved for elite hedge funds — they are the minimum requirements that should apply to any system an investor is trusting to execute their strategy.

The analyst behind the ratings

Brian Devens is the founding analyst at the Algo Institute and Managing Director of SkySail Strategies, a quantitative research firm based in Tampa, Florida.

He studied systems engineering at Virginia Tech with a specialization in probabilistic and stochastic modeling — the mathematics of decision-making under uncertainty. Before transitioning to quantitative finance, he spent years as a discretionary trader managing eight-figure positions across live markets and multiple economic regimes.

The shift from discretionary to systematic came after working alongside institutional quant researchers, including a mentor operating algorithmic systems generating consistent daily returns on portfolios worth north of $80 million. That exposure to institutional-grade quantitative infrastructure — and the gap between what institutional allocators could evaluate and what retail investors were left to figure out on their own — became the basis for both SkySail and the Institute.

At SkySail, Brian's firm has spent over seven years and more than $1 million in quantitative trading system research and development. The firm's flagship architecture, the GRIP Alpha Model, runs nearly 20 independent algorithms and has been independently audited by a CIPM-certified verification firm — the same standard professional fund allocators use to evaluate hedge funds. The model has been used in multiple institutional funds, and SkySail works with investors across the United States, Europe, and the Middle East.

The Algo Institute's evaluation methodology grew directly out of this quantitative research background. The four-pillar framework — warehoused risk, latent risk, inherited risk, and vendor credibility — reflects the same analytical categories institutional allocators apply when conducting due diligence on algorithmic strategies. Brian built the framework because these tools existed inside institutions but were completely unavailable to individual investors deploying five- and six-figure allocations into the same types of systems.

Brian conducts all vendor evaluations published by the Institute. Each Analyst Rating Report and its companion video analysis reflect his direct analytical work, not aggregated opinions or AI-generated assessments.

Disclosure: SkySail Strategies is the Institute's founding research partner. SkySail's own systems are included in the coverage universe and evaluated under the same methodology applied to every other vendor. The relationship is disclosed in full on our disclosure page.

Core values