715 pages; A Gentle Introduction to Convexity; Quantitative and Qualitative Treatments to Capital Markets
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Our last market notes was some treatments to advanced topics in linear algebra, looking at Rayleigh Quotients, Schur complements and their implications. We will transition to some applied mathematics, looking at convex optimization, which plays a big role in portfolio optimization problems. This is our next core topic in the lecture notes series.
Our last formulaic alpha report was here:
and in the next post, we will continue the quant dev series, and look at integrating the alpha tree to our Russian Doll backtesting engine. We are slowly arriving at a near-no code solution to quantitative backtesting. This continues from our last quantdev post on recursive thinking:
Our quant notes are nicely evolving into an invaluable quant handbook for any professional/aspirer in the quant space. After we go through discussions on convexity, we will branch into two core routes, namely implementing the convex optimization libraries for systematic portfolios in Python and adding extensive Python code annotations to the vast array of topics we already have on the lecture notes.
Happy reading! Full Notes (715 pages, paid):