Lab

The online platform to learn modern quantitative finance with diverse learning channels for diverse learning styles

Img Frame

SIGN UP NOW
An encyclopedia of Modern Quantitative Finance
Your resource to learn by abstract principles
Theory   2,088 pages of explanations with formulas

Applications to practice beyond the Theory
Your tool to learn by real-world examples
Case studies   343 large scale analytics with real data

Simulations from data using code
Your device to learn by visualization
Data animations   206 visualizations of complex models in motion

One pseudo-code for all coding languages
Your medium to learn step by step
Documentation   683 pages of language-neutral pseudo-code
Three coding languages, no installation
Your environment to learn by doing
Code   110,005 lines of code in Python, MATLAB and R
The code "documentation"
Full control over your scripts
Each function, script and param explained in detail
Summary of all the learnings
Your source to learn by key items
Slides   3,155 multimedia summaries of all the materials

Video clips on the topics
Your aid to learn by listening
Video lectures   547 walk-throughs of ARPM Lab topics

Derivations to support the analytical results
Your guide to learn by induction
Exercises   1,259 analytical derivations to support the Theory

Overview

The ARPM (Advanced Risk and Portfolio Management) Lab is a constantly updated online platform for learning and teaching modern quantitative finance. The ARPM Lab spans the entire spectrum of Modern Quantitative Finance, across asset management, banking, and insurance, from the foundations to the most advanced developments.
In one framework and relying on one consistent notation, the ARPM Lab facilitates connections across disparate topics, and covers:
  • All the major asset classes: equities (public/private), fixed income, credit, currencies, alternatives, high-frequency, enterprise, etc.
  • The most advanced techniques: data science and machine learning, factor modeling, portfolio construction, algorithmic trading, investment risk measurement, liquidity modeling, enterprise risk management, etc.
While most materials on quantitative finance focus on asset pricing and risk neutral valuation (“Q”), the ARPM Lab focuses on the much broader applications to real world probability (“P”, learn more).

Organization of the ARPM Lab

Figure coverage
The ARPM Lab is developed "bottom-up" to address the practitioners' needs, but is organized and presented "top-down" in a structured, academic manner, built around the sequential steps of the business process (see the figure): Valuation, ex-ante Risk/Portfolio Management (the "Checklist"), and ex-post Performance Analysis.
The quantitative business process is enabled by Factor Models and Learning and supported by a Quant Toolbox of techniques necessary to tackle these topics.

Learning the ARPM Lab by topic

The exhaustive body of knowledge of the ARPM Lab is best learned through four Learning Modules

Data Science
for Finance
Financial Engineering
for Investment
Quantitative Risk
Management
Quantitative Portfolio
Management

This is what we do in the ARPM Quant Bootcamp and in the ARPM Quant Marathon. The same four modules are tested in the exams of the ARPM Certificate, which proves proficiency across all the parts of the ARPM Lab.

Learning the ARPM Lab by channel

Different people learn in different ways. To facilitate the different learning styles of disparate audiences, the ARPM Lab is accessible via 8 interconnected Learning Channels:
iconVideo lectures iconTheory iconCase studies iconData animations
iconCode iconDocumentation iconSlides iconExercises
The interconnections among the channels maximize the effectiveness of unstructured, “bottom-up” learning, which does not follow the recommended dependencies of the four Learning Modules.
For instance, one may land on a piece of interactive code, and follow the code forward and backward across different topics; or switch to the theory on that topic to deepen understanding, or watch a video first, and then try an exercise.



This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.
Review our cookies policy for more information.