This is a practical guidebook that shows quants and finance professionals how to use Python and machine-learning techniques to design, test, and improve trading and risk models.
Helps financial professionals and students learn how to systematically apply machine learning with Python to quantitative finance problems such as pricing, risk management, and trading strategy design, instead of relying only on traditional models or ad-hoc spreadsheets.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Data quality and feature engineering for financial time-series, not raw compute or LLM context limits.
Early Majority
Positioned as a hands-on Python + ML textbook specifically for financial engineering, bridging traditional quant finance and modern machine-learning workflows rather than focusing purely on theory or purely on generic data science.