Executive Q&A: Julius Franck-Oberaspach on Using Quantitative Trading to Level the Playing Field in Finance

In an industry where institutional giants dominate the trading landscape, Julius Franck-Oberaspach is working to rewrite the rules. As Co-Founder and Co-CEO of Vertus, he’s built a fintech platform that gives retail investors and smaller institutions access to the kinds of advanced, risk-mitigating trading strategies once reserved for the biggest players.

With a background in quantitative finance and a deep understanding of market inefficiencies, Franck-Oberaspach and his team are developing adaptive systems that prioritize resilience over speed—and transparency over opacity. The goal? To arm clients with tools that not only outperform during market booms but protect against devastating downturns.

In this conversation, Franck-Oberaspach shares how Vertus got its start, what it takes to win the trust of institutional clients, and why he believes machine learning—not classical financial theory—is the key to unlocking the next era of alpha.

Julius Franck-Oberaspach, Co-Founder and Co-CEO of Vertus.

Q: Can you tell me how the idea for your fintech platform first came about? What gap in the market were you seeing?

Vertus was created to bring democratization to a field of extremely high competition and dominance of institutional players. Beside the pursuit for efficiency in our personal investments, we were acutely aware of the lack of transparent and effective investment platforms for retail and smaller institutional clients. On a playing field where retail clients face counterparties such as BlackRock, etc. 90% of traders lose 90% of their money in 90 days. By providing large scale quantitative trading systems that combine efficient execution with diversification, we aim to counter that statistic.

Q: What were some of the earliest challenges you faced getting the attention of major investment houses?

Trust. As in any field that touches investment and finance, trust plays a big, if not the biggest part in the process of acquiring clients. A lot of our current client relationships had to be built for years before being able to close them. Even if track records and backgrounds are adequate, especially family offices and smaller funds need a lot of attention and time to be comfortable with such important sourcing decisions.

Q: How do you balance the need for speed and precision when it comes to executing high-stakes institutional trades?

As we are not engaging in high-frequency trading, our systems are mainly focused on achieving the best medium-term execution v.s. the fastest entry on a given quote. Our models are not reliant on a single edge that decays if we were not the fastest to act upon it. Instead, our systems benefit from a large number of edges, which are of less capacity each but allow slight latencies. Most of it comes down to the average expected holding period of 24-48h. That is also reflected in our infrastructure, which does not rely on co-location of direct fiber connections to exchanges.

Q: There’s so much competition in fintech — what do you think truly sets your company apart?

Our unique relationships with broker/dealers, regulated entities, and sales networks / family offices, gives us an edge first and foremost in data, but beyond that in business. This in combination with a highly adaptive, talented team at the frontier of innovation sets Vertus apart. The usage of AI helps our team stay small and nimble, but punch way above our weight class in terms of models and technologies.

Q: Can you walk me through a typical client use case? How does your platform actually improve their trading operations?

Typically, our clients already have an investment business / are a regulated asset manager that is already running some investment strategy. They seek additional products and/or diversification in their existing offering. We help them by offering active trading systems with low correlation to equities, high diversification, and tail risk mitigation. This helps them diversify their risk and improve overall profitability.

Q: How involved are you personally in product development and the tech side of the business?

Very much - I am the head of product development at Vertus, my role is 100% focused on technology and product.

Q: How do you stay ahead of the constantly changing regulatory environment in the financial sector?

We work very closely with the regulators in the jurisdictions we operate in and seek close relationships with our regulated partners. Furthermore, we have a part of our team dedicated to compliance.

Q: What role does data security play in your platform, especially given the scale of your institutional clients?

Data security is of highest importance for us. We make sure that our technology integrates fully with all of our clients existing solutions and encryption / security standards. We use encryption across our systems for any flow of data. Also, we maintain a dedicated infrastructure for all

Q: How do you see AI and machine learning shaping the future of institutional trading?

AI and machine learning are already at the heart of institutional trading, and we firmly believe that their role will only grow in importance over the coming years. Trading linear, rule-based models in the current date and time just does not make sense from many angles. Machine learning models allow us to find complex linear and non-linear relationships across thousands of factors, allowing more accurate modeling of the financial markets than ever before. My perspective on why unsupervised machine learning on big data has a higher chance of success and will continue to dominate classical models is the following:

The main argument crystalizes on my belief that alpha does not need to be explainable. While this brings challenges in dealing with regime changes and unexpected outliers, in application, lack of explainability can be beneficial. The less obvious and linear a source of alpha, the more sustainable. Hidden, unexplainable sources of alpha attract less capital and therefore have higher capacity with less decay. I think that classical models based on a partial differential equation are more or less exhausted in their discovery of new alpha. To find new, undiscovered alpha, practitioners have to resort to non-linear models such as ML based systems.

Q: Are you seeing different needs between traditional investment houses and newer institutional players like crypto funds or alternative asset managers?

Yes, definitely. While the prior are harder to convince as clients, they are stickier, but more sensitive to risk exposure. With the latter, we see much quicker client acquisition, a higher risk profile and often faster churn in case our system does not fit their expectations.

Q: Can you share an example of a major client success story that really illustrates the value you bring?

My favorite examples of client successes are instances where our systems prevented large losses for clients. Most recently, during the major market downturn of April, markets fell double digit %´s within days, even without leverage. While our systems also took losses during these day, they were much lower than any of our competitors. Importantly, most systems had a lower realized loss than the S&P 500 despite running on leverage and with a higher return profile.

Q: When you pitch to large investment firms, what tends to resonate with them the most about your approach?

The tail-risk protection. Investment firms are all very acquainted with the issues that come from tail-risk and recognize the importance of it. As our product targets that essential worry of our clients directly, it resonates them the most. Interestingly, clients love our methodology of caring more about risk, than about return.

Q: How do you build trust with clients who are, by nature, cautious and deeply analytical?

With full transparency (as far as IP protection allows us to go). It helps to put them on a call with some of our engineers or me directly and talk them through. Most funds have their own quant teams, allowing them to go into a deep discussion about the tech with us directly dissolves most of their concerns.

Q: What’s been the most surprising thing you’ve learned since starting the company?

The sad reality that in leveraged trading, almost 99% of retail traders lose their deposit, given enough time.

Q: How are you thinking about expansion — is your focus global from the start, or do you see more regional priorities?

We started regional in the UK and Europe, but are now targeting US institutional clients and the Middle East / Latin America.

Q: How does your platform help clients manage market volatility, especially in fast-moving environments?

Using our active diversification in multiple non-correlated subsystems means, that clients are invested into a large variety of trading styles and assets. As these are designed to offset each other in their exposure, the final impact of volatility on the equity of the user is minimized.

Q: What’s your take on the growing trend of passive investing? Does it impact your business model?

I think passive investing reflects a broader desire for simplicity and cost efficiency in portfolio construction. However, we believe it has also created opportunities particularly because passive strategies often lead to crowding and structural inefficiencies. Our business model thrives in precisely those areas where passive capital creates blind spots.

Q: With such large institutional clients, how do you handle customization and client-specific needs?

While our technology is modular and generalized, we do provide the ability to tailor strategies to institutional needs. Our systems are designed to integrate with a client’s existing infrastructure, and we often deploy customized versions of our strategies based on their liquidity preferences, and risk appetites.

Q: How do you see your technology evolving over the next 3–5 years?

Over the next few years, we plan to increase the coordination between our subsystems, extend our edge into new data domains like satellite or behavioral data, and continue building self-adaptive models that learn as markets evolve. Ultimately, we’re aiming for an architecture that is both massively scalable and resilient to black swan events. Beyond that, we are taking on new asset classes to extend the benefit of diversification even further.

Q: Are there any misconceptions about fintech in the institutional trading space that you often encounter?

That markets are efficient. Meaning that all public and private information at any given time is immediately reflected in the prices of underlying assets, making price itself the best indication for the fair value of an asset. In other words: markets are unpredictable. It is usually the biggest concern people have when seeing our systems: how can we achieve above market returns if market are efficient?

Inefficiency, on the other hand, suggests non-random asset returns and persistent market opportunities that can be exploited for alpha. Even academia is arguing about whether markets follow strong, semi-strong or weak form efficiency.

From our observations, markets are in a constant shift between inefficiency, and efficiency, leaving room to navigate given enough high-quality data and models.

Q: How do you attract top engineering and data science talent in such a competitive market?

By offering a stimulating environment with challenging and exciting tasks. Hard problems attract smart people by design. The problem of solving the financial markets has somehow always been like a magnet to elite talent. While the competition for talent is fierce, our company stand out with its nimble, small but elite team, and by offering hands-on experience with nearly all areas of the business, which many quant jobs lack. We also foster a culture that encourages experimentation, learning from other fields, and working across domains qualities that resonate deeply with the kind of people we want on board.

Q: What’s your approach to partnerships with other fintech providers or legacy financial institutions?

We value cooperation and strategic partnerships very highly. It allows us to leverage expertise outside of our venture effectively and bridges gaps for us. For example, we cooperate closely with regulated asset managers all over the world, to be able to deliver our products compliantly. Furthermore, as we offer an integrated product that needs adjustment to the clients trading venue, we are in close relationship with major trading software companies and exchanges. Valuing these relationships has never failed to bring extraordinary value, sometimes beyond the initial intent.

Q: Can you speak to any ethical considerations you take into account when developing new features or tools?

Our core ethical stance is that our technology should help level the playing field. That’s why we are currently securitizing our systems so they can be accessed in a broader, standardized, compliant way through channels people already trust, rather than being focused on mostly institutional clients.

We also believe that alpha doesn’t need to be extractive. Our systems aim to generate returns through pattern recognition and adaptability instead of predatory practices. Ultimately, we hold ourselves to the principle that better technology should not only yield higher risk-adjusted returns but empower more responsible investing.

Q: How do you measure success internally? Is it strictly client acquisition, trade volume, or something else?

For my team, the core metrics are centered around robustness and longevity of performance across market regimes. Meaning, we track how our systems perform not just in up markets, but in stress environments and make sure that the risk-adjusted returns are matching our expectations. Our biggest benchmark is ourselves from previous months. Additionally, we measure internal success by our ability to stay lean, scale efficiently, and build infrastructure that improves with each iteration.

Q: Finally, what’s your vision for the future of institutional trading, and how do you see your company playing a role in shaping it?

As we see technology advance further and further, we expect the barriers to institutional grade data and models being lowered. This will cause a big shift in the space, as smaller firms will be able to compete with giants – at least outside of high frequency trading. This influx in competition will require a large degree of adaptability and creativity for all parties in order to maintain the highest degree of trading efficiency.

We expect this shift to play into our hands. With adaptability built at the very core of our technology, we are built for change by design. Everything in quantitative finance is about competition, in prices, liquidity and on the business level, we embrace that and acknowledge the benefit in the progress tough competition brings. We expect lots of innovation in the space to come out of small firms such as ours, with big players being challenged on their less nimble infrastructures.

We´re not here to beat the game as it is, we’re here to change how the game is being played.