@UnicornBitcoin: A Stanford professor took two utterly ordinary stocks, did one thing every day—rebalance—and turned $100,000 into $7 mi…

A Stanford professor took two utterly ordinary stocks, did one thing every day—rebalance—and turned $100,000 into $7 million, without ever predicting a single stock price move. The method, when laid out, is so simple it's hard to believe. Two stocks, and after the market closes each day, adjust the positions back to equal halves. Sell whoever's up, buy whoever's down, forcing yourself to buy low and sell high. You're earning the money from volatility, not from ups and downs. The two stocks he picked—neither one really goes up much on its own, but they're volatile and uncorrelated with each other. One rises while the other falls, and you low-buy high-sell back and forth every day, eating the spread; over decades of compounding, $100K turns into $7M. There's a counterintuitive principle hidden here. The goal of investing shouldn't be to maximize expected returns—that way, you'd bet everything on the one with the highest projection, and risk would explode. The right goal is to maximize the long-term growth rate of wealth, which mathematically is the geometric mean, calculated with logarithms; compounding is all about that. Take the simplest example and you'll get it. Cash plus one stock: the stock either doubles or halves each day, 50/50 odds. On its own, that stock just treads water long-term. But if you keep cash and stock at half each every day—on double days, you sell to top up cash; on halve days, you use cash to buy the dip—you steadily earn 6% per period. The crowning achievement of this approach is called the Universal Portfolio. It requires no predictions, no modeling, no learning—just take historical data and rebalance daily, and it performs as well as any hindsight genius, with error within √N, even holding up in extreme scenarios like the 1929 crash. The nuts and bolts: each day, calculate a weight based on the stocks' past performance, and adjust positions accordingly. Turn rebalancing into an automatic machine that only looks backward, never forward. The hardest part of this whole thing has never been the algorithm—it's the discipline. Mechanical rebalancing every day, unwavering for decades; that's a hundred times harder than understanding it now