Free Download Quantitative Portfolio Management By QuantInsti
Recommended For
This program is ideal for portfolio managers and quantitative analysts who want to build portfolios systematically, enhance returns, and manage risks with precision. Throughout the course, you’ll explore advanced portfolio construction methods such as Factor Investing, Risk Parity, the Kelly Criterion, and Modern Portfolio Theory.
LIVE TRADING
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Write code and backtest a multi-factor portfolio model.
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Estimate expected returns for assets.
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Allocate funds using Kelly criterion, modern portfolio theory, and risk parity approaches.
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Understand and explain CAPM as well as the Fama-French factor framework.
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Identify factors like momentum, value, size, and quality.
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Assess performance with Sharpe ratio, maximum drawdown, and monthly results.
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Paper trade strategies and test them in live markets without the need for additional installations.
SKILLS COVERED
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Portfolio Construction & Management
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Multi-Factor Investment Models
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Kelly Criterion Application
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Risk Parity Allocation
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Fama-French Three-Factor Analysis
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Modern Portfolio Theory
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Core Mathematics
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Linear Regression, Maximum Drawdown
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Annualized Volatility Measures
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Covariance & Beta Calculations
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Skewness & Kurtosis Analysis
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Treynor & Information Ratios
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Computational Proficiency
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Python with Pandas, NumPy, Math
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Ordinary Least Squares (OLS)
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CVXPY Optimization
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Data Import Techniques
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Data Visualization Skills
PREREQUISITES
Participants should already have some exposure to trading and a basic understanding of financial terminology such as long and short positions. To code strategies in Python, familiarity with handling, storing, and visualizing data using Pandas and DataFrames is necessary. These foundational skills are included in the Python for Trading course.
SYLLABUS
Introduction
Overview of portfolio management using quantitative techniques.
- Introduction to the Course
- Course Structure
- Quantra Features and Guidance
Basics of Portfolio Construction
Understand mathematical terms, such as covariance, returns and standard deviation of a portfolio, that are required to construct a portfolio.
- Mathematical Terms for Portfolio Construction
- Calculate Covariance
- Interpret the Covariance Value
- Calculate Portfolio Returns
- Calculate Portfolio Standard Deviation
- How to Use Jupyter Notebook?
- Basics of Portfolio Construction
- Calculate Portfolio Returns in Python
- Calculate Covariance in Python
- Calculate Portfolio Std Deviation in Python
- Frequently Asked Questions
Modern Portfolio Theory
Calculate optimal weights by maximising mean-variance of the portfolio. Maximize returns per unit risk of the portfolio choosing stocks with less covariance. Simulate random weights and plot the Efficient Frontier.
- Construct Two-Stock Portfolio using MPT
- Objective of MPT
- Choose the Portfolio Based on Covariance
- Equi-Weighted Portfolio
- Efficient Frontier
- Targeted Risk
- Implement Modern Portfolio Theory in Python
- Choose the Portfolio – MPT
- Plot the Efficient Frontier
- Calculate Optimal Weights
- Construct Multiple Stocks Portfolio using MPT
- Returns of Portfolio with Multiple Stocks
- Portfolio Standard Deviation – Matrix Form
- Covariance Matrix
Kelly Criterion
Apply the Kelly Criterion to optimise the capital allocation
- What is Utility?
- The concept of Utility
- The Utility Curve
- The Kelly Criterion
- The Kelly Criterion: Derivation
- The Final Portfolio Value
- The Daily Portfolio Value
- Create a Portfolio Based on Kelly Criterion
- Create an Array of Weights
- Calculate the Final Portfolio Value
- Create the Kelly Criterion
- Create a Kelly Portfolio
Live Trading on Blueshift
This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.
- Section Overview
- Live Trading Overview
- Vectorised vs Event Driven
- Process in Live Trading
- Real-Time Data Source
- Blueshift Code Structure
- Important API Methods
- Schedule Strategy Logic
- Fetch Historical Data
- Place Orders
- Backtest and Live Trade on Blueshift
- Additional Reading
- Blueshift Data FAQs
Live Trading Template
Blueshift Live Trading Template
- Paper/Live Trading Kelly Criterion Strategy
- FAQs for Live Trading on Blueshift
Risk Parity
Allocate capital to the securities in the portfolio such that each security contributes equally to the overall risk of the portfolio.
- Construct Two-Stock Portfolio using Risk Parity
- Risk Parity Approach
- Basis of Risk Parity
- Calculate Percentage Capital Allocation
- Risk Parity
- Calculate Weights using Risk Parity Approach
- Portfolio with Multiple Stocks
- Risk Parity for Multiple Stocks
- Data Handling
- Extension to ‘n’ Stocks
- Portfolio Metrics
- Sharpe Ratio
- Risk Parity Relationship
- Risk Parity vs Traditional Portfolio
- Risk Parity Failure
- Test on Capital Allocation
Beta
Understand and interpret beta of an asset. Calculate beta of an asset using different methods.
- What is Beta?
- Risk Exposure
- Market Beta
- Interpretation of Beta
- Movement of Asset with Positive Beta
- Movement of Asset with Negative Beta
- Beta of an Asset in Python
- Calculate Daily Returns
- Calculate Beta
Capital Asset Pricing Model (CAPM)
Understand the Capital Asset Pricing Model and its limitations. Calculate expected returns of an asset using the capital asset pricing model.
- Introduction to CAPM
- Factors Affecting Expected Return
- Calculate Expected Return on Asset
- What is Security Market Line?
- SML Characteristic
- Stocks lie on the SML
- Stocks lie above SML
- Calculate Jensen’s Alpha
- Fama-French Three- Factor Model
- Understand the Fama-French three-factor model. Calculate expected returns using the Fama-French Three-Factor Model.
Fama-French Three-Factor Model
- Factors of the Fama-French Model
- Size Factor Exposure
- High Book to Market Ratio Stock
- Calculation of SMB and HML Factor
- SMB Calculation
- HML Calculation
- Expected Returns using Fama-French Model
- Calculate Beta of Fama-French Factors
Fama-French Five-Factor Model
Understand the Fama-French Five-Factor Model and its factors.
- Fama-French Five-Factor Model
- Profitability Factor
- Investment Factor
- Test on Beta, CAPM, and Fama-French
Factor Investing
Understand factor investing and different types of factors. How different factors work and their application in trading.
- Factor Investing
- Macroeconomic Factors
- Good Factors
- Applications of Factor Investing
- Choose Factor Strategy
- Benefits of Factor Investing
- Which Factor Works Best?
Multi Factor Model
Understand momentum and short-term reversal factors. Create multiple factors and then combine them to form a multi-factor portfolio.
- Multi-Factor Model: Momentum Factor
- Stock Selection in Factor Model
- Selection Criterion
- Benefits of Negative Correlation
- Assumption of Momentum Factor
- Timeframe of a Factor
- Interpretation of Momentum Factor
- Multi-Factor Model: Reversal Factor
- Short-Term Reversal Factor
- Determination of Existing Trend
- Interpretation of Short-Term Reversal Factor
- The Momentum Factor in Python
- Create the Momentum Factor
- Stocks to Buy/Sell using Momentum Factor
- Paper/Live Trading Momentum Factor Strategy
- The Short-Term Reversal Factor in Python
- Create the Short-Term Reversal Factor
- Stocks to Buy/Sell using Short-Term Factor
- Combine the Factors
- Paper/Live Trading Multi-Factor Strategy
- Test on Factors and Multi-Factor Investing
Portfolio Performance Analysis
Learn to analyze the portfolio using multiple performance measures such as Sharpe ratio, maximum drawdowns, Sortino ratio and many more metrics. Python code is provided to calculate all these performance metrics with an example.
- Portfolio Performance Analysis
- Calculate Sharpe Ratio in Python
- Calculate Sortino Ratio in Python
- Calculate Skewness in Python
- Annualised Volatility
- Calculate the Sortino Ratio
- Calculate the Information Ratio
- Calculate the Maximum Drawdown
- Test on Performance Analysis and Paper Trading.
Run Codes Locally on Your Machine
Learn to install the Python environment in your local machine.
- Python Installation Overview
- Flow Diagram
- Install Anaconda on Windows
- Install Anaconda on Mac
- Know your Current Environment
- Troubleshooting Anaconda Installation Problems
- Creating a Python Environment
- Changing Environments
- Quantra Environment
- Troubleshooting Tips For Setting Up Environment
- How to Run Files in Downloadable Section?
- Troubleshooting For Running Files in Downloadable Section
Capstone Project
In this section, you will undertake a capstone project on real-world data. This project will require you to apply and practice the concepts learnt throughout this course.
- Capstone Project: Getting Started
- Problem Statement
- Frequently Asked Questions
- Template Code Files
- Model Solution: QPM Capstone Project
- Capstone Solution Downloadable
Summary
This section includes a downloadable zipped folder with all the codes and notebooks for easy access.
- Summary
- Python Codes and Data
ABOUT AUTHOR
QuantInsti®
QuantInsti 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, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
