delost

the model in that clip has no good signal in it. it still put up +17% against the index's +5% the formula is doing the work R(t) = (Rmax / 7) · Σ s_i(t) seven separate signals, each scored, averaged into one number that's the entire model. no genius indicator anywhere in it and that's the part retail keeps missing retail hunts for the one signal that works a desk assumes every individual signal is weak and builds around that assumption here's why that assumption wins take N signals, each with sharpe s, and average them if they're uncorrelated, the combined sharpe is: s · √N seven weak signals at sharpe 0.3 each 0.3 × √7 = 0.79 nothing in that stack survives a backtest alone. together they clear the bar the noise in each signal is independent, so averaging cancels it the edge in each points the same way, so averaging keeps it that asymmetry is the whole mechanism but there's a catch, and it's the one that kills retail attempts correlation. the real formula is: s · √( N / (1 + (N−1)ρ) ) at ρ = 0.5 those same seven signals give: 0.3 × √(7 / 4) = 0.40 half the benefit, gone seven versions of momentum with different lookbacks aren't seven signals. they're one signal, repeated so the search isn't for better signals it's for signals that are wrong at different times grinold formalized this in 1989. the fundamental law of active management: IR = IC × √breadth skill per bet times the square root of how many independent bets you take you can be barely right, as long as you're barely right about many uncorrelated things renaissance doesn't run one model. it runs thousands of weak ones that's not a compromise. that's the design retail asks "is this signal good enough to trade" a desk asks "what does this add that i don't already have" the math is public. grinold's paper, every portfolio theory textbook the correlation matrix that tells you whether your signals are actually distinct is three lines of python they weren't finding better signals they were finding signals that disagree full breakdown in the article below

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