HangukQuant Research

HangukQuant Research

Share this post

HangukQuant Research
HangukQuant Research
715 pages; A Gentle Introduction to Convexity; Quantitative and Qualitative Treatments to Capital Markets
Copy link
Facebook
Email
Notes
More

715 pages; A Gentle Introduction to Convexity; Quantitative and Qualitative Treatments to Capital Markets

HangukQuant's avatar
HangukQuant
Jul 24, 2023
∙ Paid
4

Share this post

HangukQuant Research
HangukQuant Research
715 pages; A Gentle Introduction to Convexity; Quantitative and Qualitative Treatments to Capital Markets
Copy link
Facebook
Email
Notes
More
Share

.. preview

..

Toc:

Market Notes 1 50
556KB ∙ PDF file
Download
Download

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.

702 pages; Quantitative and Qualitative Treatments to Capital Markets; Some Notes on Matrices

702 pages; Quantitative and Qualitative Treatments to Capital Markets; Some Notes on Matrices

HangukQuant
·
July 20, 2023
Read full story

Our last formulaic alpha report was here:

Formulaic Alphas

Formulaic Alphas

HangukQuant
·
July 22, 2023
Read full story

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:

Traversal Algorithms for Alpha Tree/Graph and Recursion Exercises

Traversal Algorithms for Alpha Tree/Graph and Recursion Exercises

HangukQuant
·
July 17, 2023
Read full story

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):

This post is for paid subscribers

Already a paid subscriber? Sign in
© 2025 QUANTA GLOBAL PTE. LTD. 202328387H.
Privacy ∙ Terms ∙ Collection notice
Start writingGet the app
Substack is the home for great culture

Share

Copy link
Facebook
Email
Notes
More