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Quantitative analysis is a commitment that every decision is written down in advance — measurable, testable, and reviewable. Here's what that means, and why it beats trading on impression.
Every quant strategy — however complex — is six explicit parts. We take one real backtest apart, from its economic hypothesis to the fee that outgrew the starting capital, to show how a hunch becomes a rule a machine can test and trade.
A portfolio is not judged by return alone. Modern Portfolio Theory plots every allocation as a point in risk-return space — and the efficient frontier is the edge where you stop leaving return on the table.
"Algorithmic trading" is not one thing — it's six families, each with a different answer to why the return exists, a real backtest, and a precise environment where it stops working.
Every strategy earns because someone on the other side loses — or accepts less for something else. Name that party and why they stay, or your edge is a coincidence. There are four sources, and each has a half-life.
Every strategy is sold with one shiny number — total return. It never tells you how long it took, what you endured, or whether it repeats. Six metrics break "profit" into its parts.
The return metrics told you what happened. These six risk-adjusted numbers ask the harder question — was it repeatable skill, or luck in a single sample? — and every one has a blind spot.
A backtest is a tool of proof and of deception at once. Its six traps don't make bad results — they make beautiful false ones, which is worse, because a bad result is rejected while a beautiful one is believed and funded.
The discretionary investor asks how much they'll make and finds out the risk when it lands. The quant reverses it — decide the most pain you'll endure first, then build what fits. Here's how to read a full risk dashboard.
Everything in the series stays inert until you test your first hypothesis. These four steps are one complete work cycle you can run today — from a written question to a result that holds or fails.