In this series, the Omphalos AI Research Team want to discuss the key and fundamental aspects of quantitative investing in detail and depth. In particular, our series will not be a beautiful story of how to build the holy grail of investing, but rather a long list of pitfalls that can be encountered when building such systems. It will not be a complete and exhaustive list of pitfalls, but we will try to describe those we discuss in great depth so that their significance is clear to everyone. And importantly, this will not be a purely theoretical discussion. We will provide a practical view on all of these aspects — shaped by real-world lessons and, in many cases, by our own painful and sometimes even traumatic experiences in building and testing systematic investment strategies. These hard-earned lessons are precisely why Omphalos Fund has been designed as a resilient, modular, and diversified platform — built to avoid the traps that have undone so many before.
At Omphalos Fund, we have always been clear about one thing: artificial intelligence is not magic. It is a powerful tool, but its value depends entirely on the system it operates in and the rules that guide it. When applied to asset management, this means that even the most advanced AI can only be effective if it is built on a deep understanding of how markets work — with all their complexities, inefficiencies, and risks.
That is why our latest Behind the Cloud white paper takes a step back from the technology itself. Instead, it examines the foundations of quantitative investing — the real-world mechanics, pitfalls, and paradoxes that shape investment strategies. The aim is not to present a flawless “holy grail” of investing, but to show the challenges and traps that every systematic investor must navigate.
We believe this is essential for anyone working with AI in finance. Without appreciating the underlying business of investing, AI models risk becoming black boxes that look impressive in theory but fail in practice. By shedding light on the subtle but critical issues in quantitative investment design — from overfitting to diversification, from the illusion of normal distributions to the reality of risk of ruin — we provide context for why our platform is built the way it is: modular, transparent, and resilient.
The goal of this white paper is simple:
To help readers understand that using AI in asset management is not only about smarter algorithms — it’s about building systems that are grounded in strong investment fundamentals and designed to survive the real world of markets.
In investing, intuition often tells us that a high win rate must equal success. If a strategy is right nine times out of ten, surely it’s a winner. But in systematic investing, this is one of the most deceptive illusions. A strategy can win 90% of the time and still lose money overall - sometimes spectacularly.
This paradox highlights one of the most important lessons in quantitative finance: it’s not how often you win, but how much you win when you’re right, and how much you lose when you’re wrong.
Let’s start with a question:
"With a 90% win rate, how many losing trades could you face in a row in the worst-case scenario?"
The intuitive guess might be “not many.” The uncomfortable truth? The answer is infinity. Even with a 90% win rate, nothing prevents markets from delivering an unbroken streak of losses. That is why risk-of-ruin thinking matters more than win rates.
Consider a simple example: a strategy that makes $1 on 9 trades in a row, then loses $20 on the 10th. The win rate is 90%, but the net result is –$11. Many short-volatility, carry, or arbitrage strategies fall into this trap. They provide a stream of small, steady gains until a rare but massive loss erases years of progress.
This is the danger of asymmetric payoff structures. Strategies optimized for high win rates often expose themselves to “fat tail” risks - low-probability but high-impact losses. Investors, attracted by the comfort of frequent wins, underestimate the risk of ruin.
The opposite is also true: a strategy with only 30–40% winning trades can be highly profitable if its average gains vastly outweigh its losses. This is why systematic investors often pay less attention to win ratios and more to payoff distributions, tail risks, and risk-adjusted returns.
Classic and modern research illustrates why win rate is a misleading metric:
At Omphalos Fund, we design agents to think in terms of distribution, not frequency. Each trading agent is evaluated not only on how often it is correct, but on the shape of its return profile.
Key principles we apply include:
This approach means that even if individual agents show low win rates, their contribution to the total portfolio can still be strongly positive. The system isn’t about being right most of the time - it’s about surviving and compounding over time.
From 2012 to early 2018, many investors embraced strategies that sold volatility, betting on the persistence of calm markets. These strategies showed win rates well above 80%. Every day, they earned small, steady profits - until “Volmageddon” in February 2018, when volatility spiked and billions were wiped out in hours.
The lesson is stark: frequency of success is meaningless without magnitude of risk. A 90% winning record is not an edge if the 10% losses are fatal.
👉 In the next chapter, we’ll tackle another dangerous illusion: the “lie of normal distribution.” While many financial models assume bell curves and stable probabilities, markets are dominated by fat tails and outliers - the very risks most backtests and naive statistics fail to capture.
Stay tuned for Behind The Cloud, where we’ll continue to explore the frontiers of AI in finance and investing.
If you missed our former editions of "Behind The Cloud", please check out our BLOG.
© The Omphalos AI Research Team - October 2025
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