We have long posted decision-making links for trading. The following excerpts from “Subconsciously: Athletes May Play Like Statisticians”, by David Leonhardt of The New York Times continues that tradition:
When Justine Henin-Hardenne rips a cross-court forehand at the Australian Open or Tom Brady, the New England Patriots quarterback, dodges an onrushing defender, each looks like the very definition of living in the moment. Like other great athletes, they often appear to rely on speed, strength and lightning-fast reactions. There seems to be little time for highly advanced quantitative analysis that weighs current observations against past experiences to suggest a plan of attack. But this kind of analysis is precisely what the human brain does when facing a physical challenge, according to a study by two European scientists published in the current issue of Nature. The more uncertainty that people face — be it caused by wind on a tennis court, snow on a football field or darkness on a country highway — the more they make decisions based on their subconscious memory and the less they depend on what they see.
The great trend followers follow this path. It can be described, as “practice makes perfect”. However, people have long known about these concepts:
In everyday life, of course, people have been using the ideas underlying Bayesian analysis since well before it became the vogue in science labs, or even before Thomas Bayes, an 18th-century British minister and mathematician, formalized the method in a paper that was published two years after he died. When crossing a street, people rely on both what they see and what they remember about the speed of cars on similar roads. When deciding whether to take a sick child to a doctor, parents consider the current symptoms as well as the child’s history and their general knowledge of illness. “The human brain knows about Bayes’s rule,” said Konrad P. Körding, a postdoctoral researcher.
What Athletes, Bayesian Analysis, and Trend Following Share
The Nature study finding is that the brain, under uncertainty, shifts from relying on current sensory input toward relying on accumulated prior experience. The tennis player in fog relies less on seeing the ball and more on their developed sense of where the ball tends to go given the conditions. The systematic trend follower in a choppy, uncertain market relies less on reading current market conditions and more on the rules developed through years of testing across diverse market conditions. Both are applying accumulated pattern knowledge under conditions of reduced informational clarity.
The Bayesian framework makes this mathematically precise. A prior probability distribution, the accumulated knowledge from past experience, is updated by new observations to produce a posterior probability. In uncertain conditions, where new observations are noisy, the prior distribution dominates. In clear conditions, current observations dominate. The athlete in fog and the systematic trader in a low-signal market are both operating in regimes where the prior, the rules built from extensive testing, should dominate the posterior, the current market reading.
The “practice makes perfect” formulation is the learning mechanism that builds the prior. A tennis player who has hit ten thousand forehands has a rich prior distribution over where the ball will land given conditions, spin, and court position. A systematic trader who has backtested a system across decades of market data has a rich prior distribution over how the system performs across different market conditions. Both priors are built through accumulated experience rather than explicit calculation, and both produce faster, more robust decisions under uncertainty than explicit real-time analysis would allow.
Körding’s observation that “the human brain knows about Bayes’s rule” is the foundational justification for systematic rules-based trading at a deeper level than most practitioners articulate. The brain is already doing Bayesian updating. The question is whether the prior it is updating from is calibrated on accurate data or on the cognitive biases documented by Kahneman and Tversky. Systematic rules that are built on historical market data rather than intuitive pattern recognition are providing the brain with a correctly calibrated prior rather than a bias-distorted one.
Frequently Asked Questions
What is the connection between Bayesian analysis and systematic trading?
Bayesian analysis combines prior probability distributions, built from accumulated past experience, with current observations to produce updated probability estimates. Systematic trading rules encode the prior: the accumulated evidence about how price signals produce returns across diverse market conditions. When new market data arrives, the system evaluates it against the prior and produces a trading signal. This is the same process the brain performs when an athlete in uncertain conditions relies on accumulated experience rather than current sensory input.
Why does the brain rely more on prior experience under uncertainty?
Because current observations become noisier and less reliable as uncertainty increases. Under high uncertainty, the information content of current observations is low relative to the information content of accumulated prior experience. Bayesian analysis weights the posterior toward the prior when current observations are noisy. Athletes in poor visibility and traders in choppy, unclear markets are both in high-uncertainty regimes where the prior, accumulated pattern knowledge, should dominate the decision.
How does “practice makes perfect” apply to systematic trading?
Through the development and testing of trading rules across large historical samples. A system that has been tested across decades of diverse market conditions has built a rich prior distribution about how different market conditions relate to future returns. This prior allows the system to make consistently good decisions across market environments it has not seen before, just as a highly practiced athlete makes consistently good decisions in novel game situations by drawing on the pattern knowledge accumulated through thousands of repetitions.
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