[Quant Lecture] Imperfect Models

Probability For Algorithmic Trading

Why "all models are wrong" isn’t an excuse—it’s your edge

You’ve seen it: a strategy crushes backtests, then implodes live. The culprit isn’t bad luck—it’s unmanaged model imperfection. From structural biases to regime shifts, your models are leaking alpha like a sieve.

In this tactical chapter you’ll learn to diagnose flaws, exploit useful errors, and build systems that profit despite uncertainty.

What you’ll master:
🔹 The profitability paradox: Why "wrong" models still print money (and when to ditch them).
🔹 Structural vs. parametric traps: How Black-Scholes-style assumptions silently poison your forecasts.
🔹 Domain landmines: Why 99% of models fail in crises (and how to spot "off-road" conditions before they wreck you).
🔹 Overfitting decoded: Residual analysis that exposes curve-fitting—not guesswork.
🔹 Python battle drills: Simulate regime shifts, stress-test extrapolation, and debug bias/variance tradeoffs.

This isn’t theory. It’s your toolkit for turning model flaws into managed risk factors.