Quantitative Research & Trading

Systematic trading is the most honest feedback loop I have worked in. A model in a notebook can look brilliant for years. A strategy with real capital behind it tells you the truth within weeks. Everything here comes from that discipline: building strategies that survive contact with a live market, not just a backtest.

Start with the edge, not the code

The first question is never "which model". It is "what is the inefficiency, and why does it persist". I lay out that sequence in The Quantitative Trading Playbook: goals first, then a hypothesis with a reason to exist, then testing, then sizing. Skip the "why does this edge exist" step and you end up trading noise with conviction.

A backtest is a hypothesis, not a result

Most backtests overstate future performance, and the gap is predictable. Building a Backtesting Framework That Doesn't Lie to You is about closing it: walk-forward windows instead of one in-sample fit, execution costs modeled explicitly, and a measure of how sensitive an edge is to small parameter changes. A strategy that only works at one precise parameter value is fragile by construction.

Volatility and the Greeks as instruments

Options give you a richer surface to read than price alone. Volatility Surfaces and What They Tell You About the Market covers what skew and term structure reveal about positioning and regime, and Options Greeks as Risk Dials treats the Greeks not as exam trivia but as the controls you actually turn to manage a book.

The part that is not math

The hardest part of trading is rarely the model. It is holding discipline when a strategy that looked good starts to slip, and knowing the difference between ordinary variance and a broken edge. The Trader Mindset and my notes on asset classes and algorithmic trading paradigms are about that operating discipline: monitoring the right statistics, and deciding in advance what would make you stop.

This is the work I take on directly through Quantitative Research & Trading Systems, and you can see it applied end to end in the backtesting framework case study.