Quantitative Portfolio Management – QuantInsti

Course overview
Recommended for portfolio managers and quants who wish to construct their portfolio quantitatively, generate returns and manage risks effectively. In this course, you will learn different portfolio management techniques such as Factor Investing, Risk Parity and Kelly Portfolio, and Modern Portfolio Theory.
Live Trading

Code and backtest multi-factor portfolio strategy.
Calculate the expected returns of an asset.
Allocate capital using Kelly criterion, modern portfolio theory, and risk parity.
Explain the CAPM and the Fama-french framework.
Define different factors such as momentum, value, size and quality.
Evaluate portfolio performance using Sharpe ratio, maximum drawdown and monthly performance.
Paper trade and analyze the strategies and apply in live markets without any installations or downloads

Skills Required To Learn Quantitative Portfolio Management
Portfolio Management

Multi-Factor Strategy
Kelly Criterion
Risk Parity
Fama-French Three-Factor Model
Modern Portfolio Theory

Underlying Math

Linear Regression, Maximum
Drawdown
Annualised Volatility
Covariance, Beta
Skewness, Kurtosis
Treynor Ratio, Information Ratio

Computation Skills

Pandas, NumPy, Math
OLS
CVXPY
Data lImporting
Data Visualisation

Learning Track 7
This course is a part of the Learning Track: Portfolio Management and Position Sizing using Quantitative Methods. Enroll to the entire track to enable 10% discount.
Prerequisites
It is expected that you have some trading experience and understand basic financial markets terminology like ‘going long and short’. If you want to be able to code strategies in Python, then experience to store, visualise and manage data using Pandas and DataFrame is required. These skills are covered in our course ‘Python for Trading’.
Syllabus
Introduction

Basics of Portfolio Construction

Modern Portfolio Theory

Kelly Criterion

Live Trading on Blueshift

Live Trading Template

Risk Parity

Beta

Capital Asset Pricing Model (CAPM)

Fama-French Three- Factor Model

Fama-French Five-Factor Model

Factor Investing

Multi Factor Model

Portfolio Performance Analysis

Run Codes Locally on Your Machine

Capstone Project

Summary
About Author
Quantlnsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, Quantlnsti has been helping its users grow in this domain throupgh its learning & financial applications based ecosystem

for 10+ years.
Why Quantra ?

Gain more in less time
Get taught by practitioners
Learn at your own pace
Get data & strategy models to practice on your own