Quant Investing for Portfolio Managers
525 Learners
15 Hours
Traditional asset managers allocate by sectors, but diversifying by factors can create a more balanced portfolio. Learn to backtest factors like skewness, and apply factor timing and tilting for portfolio adjustments. Evaluate performance using metrics like drawdown, Sharpe ratio, and factor Beta. Understand the limitations of advanced factor investing, including data mining, specification error and crowding. Start with a free preview today.
Level
Intermediate
Author
QuantInsti®
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₹22276₹28559(Additional 22% off)
Original Price: ₹50999
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Learn Advanced Factor Investing
- Optimise portfolios using quant techniques
- Backtest unconventional factors such as skewness for generating alphas
- Implement different factor timing approaches
- Apply factor tilting to adjust weights to different factors
- Evaluate portfolio performance and assess risk through metrics such as returns, Sharpe ratio, drawdowns and factor Beta
- Limitations of factor investing such as data mining, specification error, crowding
Skills for Quant Investing
Python
- Pandas
- NumPy
- TA-Lib
- Matplotlib
Concepts & Trading
- Non-Traditional Factors
- Factor Timing and Tilting
- Winsorization and Z-Score
- Factor Portfolio Risk
Strategies
- Skewness Factor Strategy
- Factor Timing Strategy
- Scoring-Based Factor Portfolio
- Rank-Based Factor Portfolio
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This course is a part of the Learning Track: Portfolio Management and Position Sizing using Quantitative Methods
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Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites for Quant Investing
To start with the course, you need to have a basic understanding of financial markets. You should also be familiar with some basic investment principles, including risk and return, portfolio, and asset allocation. A basic knowledge of Python, including pandas dataframe, matplotlib, and loops for strategy implementations.
Quantitative Investing Course
- Introduction to the CourseThis section serves as a preview of the course and introduces the course contents. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions regarding the use of machine learning algorithms for momentum trading. This section explains the course structure as well as the various teaching tools used in the course, such as videos, quizzes, coding exercises, and the capstone project. It also covers a few course-related frequently asked questions.
Fundamentals of Factor InvestingThis section tackles the first question which arises in every portfolio manager's mind, "Why should I create a portfolio using factors?" You will also explore the journey of factor investing, right from the CAPM to the newer models introduced in the 21st century.
Why Factor Investing?2m
Define Factor Investing3m
Factor Investing vs Actively Managed Funds3m
Multi-Factor Portfolio3m
Performance of Factor Based Portfolios3m
Journey of Factor Investing2m
CAPM and Influence on Returns3m
Three-Factor Model3m
Five-Factor Model3m
Define Factor Premium3m
Traditional Factor3m
Purpose of Identifying Factors3m
Types of Factors2m
Core Building Blocks of Factor Investing3m
Identify Non-Traditional Factor3m
Characteristics of Non-Traditional Factors3m
Additional Reading on Fundamentals of Factor Investing5m
Test on Fundamentals of Factors12m
- Why Traditional Factors Work?Traditional Factors have been around since decades, and yet they are still in practice among leading fund houses. Take a deep dive into the world of traditional factors and understand how they work. You will also get a brief overview on how you can create a traditional factor portfolio.Why Traditional Factors Work?2m
Identification of Style Factors3m
Stock Classification According to Quality3m
Reason of Momentum Factor3m
Focus of Value Factor3m
Size Factor Interpretation3m
Institutional Investors and Small-Cap Stocks3m
Belief in Value Investing3m
Analysis Based on Quality Factor3m
Analysis Based on Value Factor3m
Analysis Based on Size Factor3m
- Traditional Factor Investing and PerformanceThe popularity of traditional factors has made portfolio managers around the world to sit up and take notice. There are a number of thematic ETFs which are developed by institutions to cater to the retail trader. You can explore their performance in this section.Investing in Factors and Factor Based Fund Performance2m
Approaches to Factor Investing3m
Creation of Portfolio Using Value Factor3m
Limitation of P/E Ratio Based Value Investing3m
Flexibility of Factor Investing3m
Investing in Multiple Factors3m
FAQs on Traditional Factors5m
Summary of Traditional Factors5m
Additional Reading of Traditional Factors5m
Test on Traditional Factors12m
- Existence of Non-Traditional FactorsTraditional Factors are not the only factors in existence. Both academicians and professional traders are exploring the world of finance to uncover hidden sources of alphas, which are being called non-traditional factors. In this section, you will also understand how non-traditional factors can be discovered.
Why Do Non-Traditional Factors Exist?2m
Reason for Popularity of Non-Traditional Factors3m
Traditional Factors and Alpha3m
Example of Non-Traditional Factor3m
Non-Traditional Factors and Portfolio Performance3m
Difference Between Non-Traditional and Traditional Factors3m
How to Find Non-Traditional Factors?2m
Identification of Non-Traditional Factors3m
Example of Alternative Data for Identification of Factors3m
Skewness as Non-Traditional Factor3m
Action After Identification of Non-Traditional Factor3m
Research Papers and Non-Traditional Factors3m
FAQs on Non-Traditional Factors5m
Summary of Non-Traditional Factors5m
Additional Reading of Non-Traditional Factors5m
- Getting Data: Multiple AssetsIn this optional section, you will explore how to retrieve price data of multiple assets which can be used to create a factor portfolio.Uninterrupted Learning Journey with Quantra5m
Getting Data: Multiple Assets5m
- How to Calculate Skewness?In this optional section, you will recall the meaning of skewness and how it can be calculated from a dataset.How to Calculate Skewness?1m 39s
Skewness Measure3m
Indication of Positive Skew3m
Skewness and Normal Distribution3m
- Creation of Non-Traditional Factors PortfolioIn this section, you will focus on the skewness factor which can be used to create and rebalance a non-traditional factor based portfolio.Creation of Skewness Factor Based Portfolio2m
Relation Between Past Skewness and Future Returns3m
Data Frequency to Calculate Skewness3m
Type of Stocks Excluded in Portfolio Creation3m
Average Weekly Returns3m
Modification to Strategy in Research Paper3m
Restriction of Potential Stock Universe3m
Strategy Flow Diagram for Skewness Strategy5m
Creation of Non-Traditional Factor Based Portfolio5m
Calculate Skewness of Weekly Returns5m
Calculate Absolute Value of Skewness5m
Select 10 Assets with Lowest Skewness5m
- Challenges in Creation of Non-Traditional Factor PortfolioIn this section, you will list the reasons it is difficult to create a non-traditional factor portfolio.Challenges in Creation of Non-Traditional Factor Portfolio2m
Primary Challenge of Acquiring Data for Non-Traditional Factors3m
Sentiment Analysis and Bias3m
Method to Compare Non-Traditional Factor Data Across Assets3m
Traditional and Non-Traditional Factor Portfolio3m
FAQs on Creation of Non-Traditional Factors Portfolio5m
Summary of Creation of Non-Traditional Factors Portfolio5m
Additional Reading of Creation of Non-Traditional Factors Portfolio5m
Test on Creation of Non-Traditional Factors Portfolio10m
- Capstone Project on Non-Traditional Factor PortfolioIn this capstone project section, you will create a portfolio based on the kurtosis factor.Getting Started5m
Problem Statement5m
Capstone Project Model Solution2m
- Factor TimingExplore how market dynamics influence factor performance, the concept of factor timing, and practical approaches like economic cycle forecasting and price-based momentum for better portfolio management.
Factor Timing: Why and How?2m
Factor Outperformance3m
Purpose of Factor Timing3m
Timing is Money3m
Economic Cycles for Factor Timing3m
Momentum Approach3m
FAQs on Factor Timing5m
Additional Reading on Factor Timing5m
- Generalized Hurst ExponentUnderstand the Generalized Hurst Exponent as a tool to analyse market trends, identify momentum, and refine factor timing strategies.Generalized Hurst Exponent5m
GHE - Time Series3m
Time Series Behaviour3m
Interpretation of GHE3m
Variation with q3m
Estimating GHE3m
Generalized Hurst Exponent Calculation5m
Calculate the Logarithm of Lag Values5m
Perform a Linear Regression to Fit Log-Lags to Log-Moments5m
Calculate the Generalized Hurst Exponent5m
FAQs on Generalized Hurst Exponent5m
Additional Reading on Generalized Hurst Exponent5m
- Getting ETF DataDesigning and creating custom factors from data demands expertise, time, and resources. An alternative approach is to invest in ETFs that provide targeted exposure to specific factors. In this section, you will learn how to identify relevant ETF tickers, filter them according to your investment strategy, and retrieve their price data.How to Find ETF Data?5m
Selecting ETFs and Fetching Data15m
- Factor Timing StrategyLearn to develop factor timing strategy by combining the Generalized Hurst Exponent to detect trends and technical indicators like Momentum (MOM) for confirming positive momentum signals.Factor Timing Strategy2m
Purpose of Hurst Exponent3m
Trending Market3m
MOM Threshold3m
Long-Only Strategy3m
Lower MOM Threshold3m
Factor Timing Strategy Flow Diagram5m
Factor Timing Single ETF5m
Calculate the Momentum (MOM) Indicator5m
Trading Signal3m
Factor Timing Portfolio ETFs5m
Calculate the Benchmark Returns5m
Calculate the Portfolio Returns5m
FAQs on Factor Timing Strategy5m
Additional Reading on Factor Timing Strategy5m
- Effectiveness of Factor TimingExamine the strengths and limitations of factor timing strategies, including insights from recent research, and explore how timing can reduce drawdowns, align with economic conditions, and improve portfolio resilience.Effectiveness of Factor Timing2m
Another Look at Timing the Equity Premiums3m
Macroeconomic Data3m
Designing a Factor Timing Strategy3m
Effectiveness of Factor Timing Strategies3m
Rising Interest Rates3m
FAQs on Effectiveness of Factor Timing5m
Additional Reading on Effectiveness of Factor Timing5m
Test on Factor Timing14m
- Live Trading on BlueshiftLearn how you can take your backtested strategy live with some important steps. Learn about the code structure, the various functions used to create a strategy, and finally, paper or live trade on Blueshift.Section Overview2m 19s
Live Trading Overview2m 40s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateThis section includes a live trading strategy template. You can tweak the code by changing securities or the strategy parameters. You can also analyse the strategy's performance in more detail.FAQs for Live Trading on Blueshift5m
Paper/Live Trading Factor Timing Strategy5m
- Factor TiltingPortfolio managers utilise techniques like factor tilting to generate alpha by strategically allocating weights to key factors. In this section, you’ll learn what factor tilting is and the basic approach to implement it.Why Factor Tilting is Required2m
Strategy Enquiry3m
Weighting Approach Used3m
What is Factor Tilting?3m
Scoring Methodology2m
Purpose of Scoring3m
Process of Selecting Top Three Assets3m
Process for Creating Scores3m
Scoring Methodology5m
FAQs on Factor Tilting5m
Additional Reading on Factor Tilting5m
Summary of Factor Tilting5m
- Creation of Portfolio EngineIn this section, we will create a portfolio engine which will serve as the framework for backtesting a factor-based portfolio.Portfolio Engine: Backtesting Framework5m
Purpose of Portfolio Engine3m
Key Benefits of the Portfolio Engine3m
First Notebook3m
Second Notebook3m
Third Notebook3m
Fourth Notebook3m
Portfolio Engine - Importing Data & Finding Rebalance Dates.5m
Portfolio Engine - Portfolio Strategy5m
Portfolio Engine - Portfolio Returns5m
Portfolio Engine - Portfolio Performance5m
- Impact of Outliers on Scoring MethodologyThe scoring approach for tilting factors is intuitive, but it can lead to overly concentrated portfolios. In this section, you'll discover how outliers can have a significant impact on portfolio weights and overall performance.Impact of Outliers on Scoring Methodology2m
Primary Risk of Overexposure3m
Diversification Importance3m
Portfolio Returns Calculation3m
FAQs on Impact of Outliers on Scoring Methodology5m
Test on Factor Tilting12m
- Rank Based Weight AllocationRank based weight allocation is popular approach used for tilting weights.Rank Based Weight Allocation3m 17s
Ranks Order3m
Ranks Weight Diversification3m
Differences Between Consecutive Rank Weights3m
Implementing Rank Based Weight Allocation5m
Calculate Ranks5m
Rank Based Weight Allocation5m
Backtesting Rank Based Weight Allocation5m
Additional Reading on Rank Based Weight Allocation5m
- Limitations of Rank Based Weight AllocationRank based weight allocation is popular approach but comes with drawbacks.Limitations of Rank Based-Weight Allocation2m 36s
Limitation of Rank-Based Weight Allocation3m
When to prefer score-based weight allocation?3m
Drawback of Score-Based Weight Allocation3m
FAQs on Rank Based Weight Allocation5m
Test of Rank Based Weight Allocation14m
- Process of WinsorizationBoth the scoring and ranking methods have certain limitations. Wouldn't it be great if we could somehow the best parts of both methods? In this section, we will explore the process of winsorization and check how it will help us allocate weights efficiently.
Process of Winsorization2m
Identification of Outliers3m
Percentiles in Winsorization3m
Value Below 10th Percentile3m
Advantage of Using Percentiles3m
Reason for Choosing Winsorization3m
Application of Winsorization in ETF3m
Reducing Impact of Outliers Using Winzorization5m
Pivot the ETFs Dataframe5m
Apply Winsorization on 60-Day Returns5m
Plot the KDE of Winsorized Returns5m
Backtesting with Winsorization5m
FAQs on Process of Winsorization5m
Summary of Process of Winsorization5m
Additional Reading of Process of Winsorization5m
Test on Winsorization10m
- Capstone Project on Factor CombinationIn this capstone project you will construct a factor portfolio by assigning weights to factors based on the Sortino ratio as the performance metric.Problem Statement on Factor Combination5m
- Calculation of Multiple MetricsYou have been able to create and backtest a portfolio using a single metric. Now, you will try to calculate multiple metrics which can be used later for portfolio allocation.Calculation of Multiple Metrics2m
Importance of Multiple Metrics3m
Combining Metrics for Better Decision-Making3m
Types of Metrics for Weight Allocation3m
Avoiding Redundant Metrics3m
Choosing the Right Number of Metrics3m
Practical Application of Multiple Metrics3m
Calculation of Multiple Metrics5m
FAQs on Calculation of Multiple Metrics5m
Summary of Calculation of Multiple Metrics5m
- Z-Score StandardisationIn this section, you will be able to standardise the scoring metrics using z-score.Z-Score Standardisation2m
Need for Standardisation3m
Z-Score3m
Transformed Dataset3m
Z-Score Formula3m
Calculate Z-Score3m
Z-Score Standardisation of Scoring Metrics5m
Implement Z-Score Standardisation3m
FAQs on Z-Score Standardisation5m
Additional Reading on Z-Score Standardisation5m
- Use of Z-Score Values for Weight AllocationAfter combining the z-scores of multiple metrics we have to allocate weights in proportion of their z-score values. In this section, you will see how this can be achieved.Using Z-Score Values for Weight Allocation2m
Adjustment of Z-Score Value3m
Calculation of ETF Weights3m
Use of Z-Score3m
Effect of Shifted Z-Score on Weights3m
Overall Weight Allocation Process Using Z-Scores3m
Combined Z-Score Calculation3m
Using Z-Score Values for Weight Allocation5m
Find Minimum Z-Score Value of Day5m
Calculate Shift Value5m
Shift the Z-Score Values By Calculated Shift Value5m
Calculate Sum of Z-Score Values5m
Calculate Weight of Individual Assets5m
Backtesting Using Multiple Metrics Scoring Method5m
FAQs on Using Z-Score Values for Weight Allocation5m
Summary of Using Z-Score Values for Weight Allocation5m
Additional Reading on Using Z-Score Values for Weight Allocation5m
Test on Multiple Metric Based Scoring Methodology for Factor Portfolio Creation18m
- Analyse the Performance of Multi-Factor PortfolioThis section highlights the need for a deeper analysis of portfolio performance, focusing on return consistency, concentration risks, and distribution. It emphasises diversification and introduces drawdown and stress event analysis for better risk understanding.Part Overview: Risk Assessment of Multi-Factor Portfolio5m
Performance of a Multi-Factor Portfolio2m
Need to Analyse Portfolio Returns3m
Understanding Concentration Risk3m
Mitigating Concentration Risk3m
Interpreting Skewness in Returns3m
Importance of Kurtosis in Risk Analysis3m
Takeaway from Multi-Factor Portfolio Performance3m
Risks of Over-Allocation3m
Performance Analysis of Factor Portfolio5m
Calculate the Cumulative Metrics5m
Calculate Daily Volatility5m
Calculate the Returns Metrics5m
Plot the Returns Histogram3m
FAQs on the Performance of a Multi-Factor Portfolio5m
Additional Reading on Performance of a Multi-Factor Portfolio5m
Summary of Performance of a Multi-Factor Portfolio5m
Test on Performance of a Multi-Factor Portfolio16m
- Analyse the Drawdowns of Multi-Factor PortfolioThis section discusses in-depth risk analysis, focusing on drawdowns, recovery times, and the impact of market events. It concludes with an introduction to analysing portfolio sensitivity to factors.Drawdown Analysis of a Multi-Factor Portfolio2m
Importance of Drawdown Analysis3m
Evaluating Top 5 Drawdowns3m
Understanding Recovery Times3m
Analysing Recovery of the October 2022 Drawdown3m
Identifying Market Stress Events3m
Sensitivity to Factors3m
Drawdown Analysis of Factor Portfolio5m
Calculate the Drawdown5m
Calculate the Max Drawdown Date5m
Calculating Recovery Date3m
Calculating Recovery Duration3m
FAQs on the Drawdown Analysis of a Multi-Factor Portfolio5m
Additional Reading on Drawdown Analysis of a Multi-Factor Portfolio5m
Summary of Drawdown Analysis of a Multi-Factor Portfolio5m
Test on Drawdown Analysis of a Multi-Factor Portfolio14m
- Analyse the Sensitivity of Portfolio to its FactorsThis section explains how to measure portfolio sensitivity using factor beta, which shows how returns change with factor shifts. It uses linear regression for calculation and highlights that growth and momentum factors are key drivers of portfolio returns.Sensitivity of Portfolio to its Factors2m
Importance of Factor Beta3m
Interpreting Factor Beta Value3m
Method for Measuring Factor Beta3m
Linear Regression Equation3m
Factor Beta for a Multi-Factor Portfolio3m
Interpreting Factor Beta Plot3m
Calculate and Interpret Factor Beta5m
Check Index Alignment5m
Calculate the Factor Beta5m
FAQs on the Sensitivity of Portfolio to its Factors5m
Additional Reading on Sensitivity of Portfolio to its Factors5m
Summary of Sensitivity of Portfolio to its Factors5m
Test on Sensitivity of Portfolio to its Factors14m
- Core and Satellite InvestingCore and Satellite investing blends stability and growth by combining a diversified core for steady returns with satellite investments for higher growth potential.Core and Satellite Investing2m
Approaches in Core and Satellite Investing3m
Fast-moving vs Slow-moving satellite3m
Best Composition3m
FAQs on Core and Satellite Investing5m
Additional Reading on Core and Satellite Investing5m
- Factor Investing in Emerging MarketsIn this section, we will examine how factors in developing markets behave differently compared to the developed markets. We will discuss the unique challenges and opportunities, including market inefficiencies, liquidity constraints, and macroeconomic influences.Factor Investing in Emerging Markets2m
Key Reason for Factor Investing3m
Rapid Growth of Emerging Markets3m
Major Challenge3m
Effectiveness of Factor Investing3m
Unique Growth Theme3m
FAQs on Factor Investing in Emerging Markets5m
Additional Reading on Factor Investing in Emerging Markets5m
- Limitations of Factor InvestingUnderstand the key challenges of factor investing, including data mining, specification errors, crowding, regime shifts, and overfitting. Learn strategies to mitigate these issues.Limitations of Factor Investing2m
Historical Data3m
Mitigate the Risk of Data Mining3m
Specification Error3m
Price-To-Earnings Ratio3m
Low-Volatility Factor Strategy3m
FAQs on Limitations of Factor Investing5m
Additional Reading on Limitations of Factor Investing5m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra5m
Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips for Setting-up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Summary of the CourseIn this section, you will go through the summary of the course and access the data files and notebooks which were used in the course.Summary of the Course2m
Summary of the Course and Next Steps5m
Python Codes and Data2m
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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.
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Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.