Who is Serenity @aleabitoreddit? Former AI research scientist, author of a Nature paper, member of the RISC-V Foundation, and former prominent retail trader on Reddit’s WallStreetBets forum—permanently banned from the platform due to his overwhelming influence. His real identity remains anonymous; he does not reveal his face, grant interviews, charge fees, or sell courses. All of his research is freely and openly shared. Yet this anonymous blogger’s performance is so extraordinary it defies belief: Second half of 2025 (6 months): Return of 630%. Since 2026 (as of May 28): Return of 4,502.45%, or 45x. Cumulative return over the past two years: 225x. Stock selection accuracy: Of 35 publicly recommended small- and mid-cap tech stocks, only four declined; the rest rose between 1x and 19.6x. This week, I carefully studied Serenity’s investment logic. My first thought was Bayesian theorem. His approach is essentially a direct application of Bayesian methods to investing. Step One: Initial Research — Defining the Prior Probability Begin by reading academic papers and analyzing industry Bill of Materials (BOMs) to deconstruct and map the global supply chain. Within the Bayesian framework, this is a systematic, intensive process of building initial beliefs (prior probability P(H)). Mediocre investors are mediocre because their priors are based on hearsay or linear extrapolation. By dissecting BOMs and studying academic literature, Serenity directly models the “physical reality” of the industry. This gives his priors a quality far superior to market consensus from the outset, granting him a massive probabilistic edge in decision-making. Step Two: Betting Under Incomplete Information Once a bottleneck is identified, rapidly translate research into actual trades. This is the essence of Bayesian decision-making: acting decisively under uncertainty. Having calculated through deep research that the probability of a certain “hidden champion” becoming the “Strait of Hormuz” is significantly higher than market perception, there’s no need to wait for 100% certainty—act immediately. This trade is essentially using real capital to price the discrepancy between high-quality prior probability P(H) and the market’s distorted low probability. Step Three: Updating Beliefs Through Evidence During Holding Period Conduct thorough due diligence while holding positions to validate the investment thesis. This is the classic Bayesian update process (P(H|E)). The hypothesis H is: “This company is a critical future bottleneck.” Each fact uncovered during due diligence is evidence E: - Positive evidence (high likelihood P(E|H)): Discovery of exclusive patents, long-term contracts with downstream giants, or significantly superior product yields. These dramatically strengthen confidence—posterior probability P(H|E) surges, prompting position increases. - Negative evidence (low likelihood P(E|¬H)): Discovery that the technology may be obsolete, core customers are developing in-house alternatives, or management is dishonest. These cause posterior probability to collapse, triggering decisive exit. What matters is not whether the stock price rose or fell—but whether the original thesis continues to be reinforced by facts. Step Four: Continuous Iteration Within a Total Probability Framework Dynamically capture and rotate among the most active bottlenecks. This touches on deeper Bayesian principles: the law of total probability and sequential updating. The market is a dynamic probability field, where the “bottleneck probability” P(Blockage_i) evolves over time. We must function like a continuously operating Bayesian machine: 1. Use the posterior probability from the previous round of due diligence on “Section A” as the prior for the next evaluation. 2. Continuously compare the posterior probability of Section A with the prior probability of Section B—wherever “bottleneck certainty” is higher and more active, allocate capital there. 3. Keep the portfolio dynamically allocated to opportunities with the highest posterior probabilities, completely abandoning rigid “buy-and-hold” strategies that create inflexibility and path dependence—maximizing capital efficiency. This logic embodies the core philosophical foundation of Bayes’ theorem: What you believe in is not conviction—but probability; The anchor of probability is not history—but structure; The basis for decision-making is not certainty—but expected value. And his username is also telling. Serenity means: A state of complete calm, tranquility, undisturbed by any interference. Translated literally—it is “calm without thought.”