Why "all models are wrong" isnβt an excuseβitβs your edge
Youβve seen it: a strategy crushes backtests, then implodes live. The culprit isnβt bad luckβitβs unmanaged model imperfection. From structural biases to regime shifts, your models are leaking alpha like a sieve.
In this tactical chapter youβll learn to diagnose flaws, exploit useful errors, and build systems that profit despite uncertainty.
What youβll master:
πΉ The profitability paradox: Why "wrong" models still print money (and when to ditch them).
πΉ Structural vs. parametric traps: How Black-Scholes-style assumptions silently poison your forecasts.
πΉ Domain landmines: Why 99% of models fail in crises (and how to spot "off-road" conditions before they wreck you).
πΉ Overfitting decoded: Residual analysis that exposes curve-fittingβnot guesswork.
πΉ Python battle drills: Simulate regime shifts, stress-test extrapolation, and debug bias/variance tradeoffs.
This isnβt theory. Itβs your toolkit for turning model flaws into managed risk factors.