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NewsMaterial3 min readDisconfirmAI Editorial

Systematic Investor Rob Carver: Choosing Strategies Using Left-Tail Percentiles, More Conservative Than Kelly Optimal

In a blog post, Rob Carver proposes that when choosing between multiple trading strategies or leverage levels, one is essentially selecting among N distributions of outcomes. He compares three methods—optimizing the median (equivalent to Kelly optimal), optimizing the right tail (the gambler's approach), and optimizing the left tail (a conservative approach)—and argues that optimizing for the 10th or 25th percentile of the left tail is a reasonable practical compromise, while acknowledging that historical backtesting cannot predict true extreme outcomes.

2026-11-04 22:10 GMT+8

What is the real question when a systematic investor faces N trading strategies or N leverage levels? Rob Carver provides a clear reframing in his blog: whether bootstrapping historical returns to obtain a distribution of final Compound Annual Growth Rate (CAGR), or bootstrapping different leverage levels to generate a series of discrete possible outcomes, both are fundamentally about choosing which of N outcome distributions you prefer. You are not selecting the average performance of a specific strategy, but rather which complete outcome distribution you favor.

Carver distinguishes three selection methods. The first is the strict expectation method: selecting the distribution with the best median value, i.e., the best 50th percentile. As he has previously argued, this is mathematically equivalent to Kelly optimal, which maximizes long-term compound growth. The second is the gambler's method: selecting the distribution with the best right tail, such as the option with the most favorable 1% right-tail point. This corresponds to high-leverage, cryptocurrency, or short-gamma strategies—paths that do not blow up in the best possible alternative histories generated by the bootstrap. Carver's assessment is that nobody should be a gambler, except occasionally. The third is the conservative method he favors: selecting the distribution with the best left tail, i.e., the option with the most favorable 1st, 10th, or 25th percentile point, meaning the option that yields the best outcome if bad things happen.

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The extreme version of the conservative method is MinMax from game theory: selecting the option with the best worst-case outcome. Carver points out that this is exactly what all chess-playing software does—treating the market as an adversary and striving for the least bad result in the worst possible state. However, he immediately acknowledges that if the number of bootstrap iterations is sufficiently large, the lower bound of all distributions will converge toward zero (since compounded returns and absolute returns greater than -100% cannot mathematically fall below zero), making them hard to distinguish. Therefore, the most realistic extreme version in practice is selecting the option with the most optimal 1st-percentile left-tail point. Given that this is likely to be negative, it would be the option with the least worst 1st-percentile tail.

Carver does not suggest directly optimizing for the 1st-percentile point. He cites an old saying in the trading community: there are no old, bold traders. Good traders know how to quantify risk and when to take risks, but they also understand the importance of keeping their capital mostly intact for the future. His conclusion is that optimizing around the 10th or 25th percentile point is a reasonable compromise in practice. The 1st-percentile point is hard to distinguish in bootstrapping, and more extreme results likely exceed the range predictable from historical samples; while the 50th-percentile point (Kelly optimal) maximizes long-term growth, the corresponding path volatility may exceed the psychological tolerance of most people. The 10% to 25% interval implies that in every four to ten possible outcomes, you are optimizing for the worse-performing instance rather than the worst-case scenario.

Carver explicitly marks the boundaries of this method in the article: more extreme outcomes are highly uncertain and will likely go beyond what a historical bootstrapping exercise could predict. This means that if the market experiences structural breaks never seen in historical samples, the left-tail percentiles themselves will underestimate actual risk. The value of this method lies in providing an actionable compromise point, but it does not solve the problem of new risks that models cannot foresee.

Sources

  • qoppac.blogspot.com↗
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