Workshop: Anatomy of a Quant Strategy, Pt. 1

Ever wondered how a real quant research pipeline actually starts? This is Part 1 of a hands-on workshop series walking through a traditional quantitative trading workflow from the ground up: data, features, alpha models, portfolio construction, and execution.
In this first session, we go from raw market data to a working backtest engine, live in a shared Colab notebook. You'll learn:

• How to build a liquidity-filtered, tradeable universe from a stock index (and the survivorship bias trap almost every beginner falls into)
• The performance metrics every quant report uses — Sharpe, Sortino, drawdown, Calmar — and what each one actually tells you
• How a backtest engine works mechanically, by building one from scratch
• Why the train/test split is the single habit that separates rigorous research from wishful thinking

No finance background required — just comfort with Python and pandas. Bring a laptop; we'll be coding live together in Google Colab (free, runs in your browser, nothing to install).

This is Workshop 1 of a multi-part series. Future sessions build on this notebook to add predictive features, ML alpha models, and RL-based execution — but this session stands alone if you can only make it to one.

Note: this event is hosted independently and is not affiliated with or sponsored by Brooklyn Public Library; we're simply using their public meeting room space.