Systematic trading: statistics, signal design, algo execution, APIs and infrastructure.
Quant trading replaces gut feeling with data, math, and code. Here's what actually separates it from discretionary trading.
How to tell a real edge from noise: p-values, sample size, and the statistical traps that make bad strategies look good.
Raw price data isn't a signal. Turning it into predictive features is where most of a quant's real work happens.
The two pillars of quant strategy design pull in opposite directions — and knowing which regime favors which is the real edge.
Combining strategies isn't about picking the best one — it's about picking ones that lose money at different times.
A great signal can still lose money if it's executed badly. Execution algorithms exist to minimize what a trade costs to place.
Every algorithmic strategy runs on top of a data and execution pipeline. Get this plumbing wrong and nothing above it matters.
A trading bot is a distributed system with money on the line. Its architecture needs to be boring, testable, and hard to break.
An algorithmic strategy left unmonitored is a liability waiting to happen. Here's how professionals watch their bots.
ML looks powerful on financial data — and that's exactly the problem. Most ML trading models fail for the same handful of reasons.