Introduction to Quantitative Financial Risk Management with R
Evaluate risk vs. reward in stocks, consumer credit, and nontraditional markets
The course covers three financial markets and how to identify investment opportunities within each. First, participants will assess real stock data, then evaluate consumer credit and finally quantify aspects of a nontraditional investment market.
In this entrylevel workshop, you will learn how to download equity data, perform evaluations and visualize stocks using R. The course will explain basic trading indicators and visualizations giving attendees a foundation for more sophisticated analyses on their own. Next participants will model a consumer credit market to quantify risk versus reward thereby identifying the most lucrative opportunities. Lastly, we will explore a nontraditional market, simulate the reward in the market and put our findings to an actual test in a highly speculative and unregulated environment.
As a statistical programming language R is excellent for algorithmic trading, particularly nonhigh frequency trading. Further, R’s forgiving syntax means students can focus on concepts, trading indicators and application rather than esoteric coding. Using R in this course will help retail investors and financial professionals create trading workflows and evaluate risk.
What you'll learnand how you can apply it
By the end of this live, handson, online course, you’ll understand:
 What a stock indicator is and how to interpret it
 Common visualizations used by quantitative equity traders
 Basic risk modeling in consumer credit
 Simulating the return in an unregulated investment market
And you’ll be able to:
 Download stock data directly into R
 Build financial indications to identify stock buying opportunities
 Perform a “backtest” to evaluate an indicator before putting it into practice
 Build common stock visualizations mimicking online and paid services
 Model risk in consumer credit, visualize the risk vs reward in the market and make selections based on this perspective
 Structure an unregulated investment market for evaluation instead of speculation
This training course is for you because...
 You’re a retail investor looking to improve returns
 You work with financial information provided by professionals
 You want to improve your financial acumen and investment opportunities
 You want to apply your statistical and data science acumen in an investment workflow
Prerequisites
 Basic knowledge of R, RStudio & git
 Familiarity with data structures in R, particularly data frames and XTS (time series)
 Basics of ggplot2 visualization will improve learning outcomes
Recommended preparation:
 Please install R on your computer, following either the instructions in “Getting Started and Getting Help” or the official installation instructions for your operating system.
 Code examples and git repo will be provided near the start of the course.
 If possible, sign up for a free account with RStudio Cloud. Directions for setup and git connection will be provided. An RStudio Cloud account is optional, but recommended for the best experience.
 Chapters 1 “Getting Started and Getting Help” and 5 “Data Structures” in R Cookbook, 2nd Edition (book)
 Chapter 1 “Data Visualization with ggplot2” in R for Data Science (book)
Recommended followup:
 Take Algorithmic Risk Management In Trading And Investing (live online training course with Deepak Kanungo)
 Read Financial Modeling and Valuation: A Practical Guide to Investment Banking and Private Equity (book)
About your instructor

Ted Kwartler is a datadriven professional, author and instructor. He has held leadership roles at amazon.com, Liberty Mutual Insurance and was an early employee at DataRobot. He is also an adjunct professor at Harvard University’s Extension School where he teaches Data Science for Business. In addition, he teaches a seminar on Natural Language Processing (NLP) at St Gallen University, Switzerland. Ted holds an MBA from the University of Notre Dame with citations in marketing and analytics. As a result, his teaching and data science focuses on practical business and finance applications.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Introduction (10 minutes)
 Intro, review key terms & perform a “git pull” to ensure all participants have scripts, ppt, and data.
Obtaining Stock Data & Basic Visualization (15 minutes)
 Presentation: What is the financial market? Types of investing strategies.
 Presentation: What is an API? API access to stock data in R
 Exercise: Manipulate a time series object 1_TTR.R
 Presentation: How to understand basic financial plots
 Exercise: Make basic & dynamic plots of the data 1_TTR.R
 Q&A
Creating the first indicator (15 minutes)
 Presentation: Explain what a technical trading rule/indicator is
 Presentation: Learn what a lagged simple moving is
 Exercise: Calculate 3 SMAs to understand the “smoothing” effect on time series data 2_TTR.R
 Exercise: Visualize the SMA of an equity 2_TTR.R
 Q&A
Applying the SMA indicator (15 minutes)
 Exercise: Calculate 50 & 200 day SMAs 3_TTR.R
 Presentation: Calculate the investment return with SMAs “death” and “golden” cross pattern
 Exercise: Structure the SMAs as a trading indicator 3_TTR.R
 Presentation: Explain what a backtest is for evaluating an indicator
 Exercise: Visualize the historical performance using a backtest 3_TTR.R
 Q&A
 Break (5 minutes)
Creating the MACD indicator (20 minutes)
 Presentation: Walk through the moving avg convergence divergence indicator
 Exercise: Manually calculate a standard moving average convergence divergence indicator 4_TTR.R
 Exercise: Functional construction of the MACD 4_TTR.R
 Exercise: Apply the MACD signal as an indicator 4_TTR.R
 Exercise: Visualize the stock & MACD in a dynamic plot 4_TTR.R
 Exercise: Perform a backtest to understand & visualize the return using this indicator 4_TTR.R
 Debrief on MACD visuals & backtest
 Q&A
Creating & Applying the RSI indicator (20 minutes)
 Presentation: Learn what a relative strength indicator is
 Exercise: Functional construction of the RSI 5_TTR.R
 Presentation: Interpret RSI as an indication
 Exercise: Visualize the RSI in a dynamic plot 5_TTR.R
Compounding two or more indicators (10 minutes)
 Exercise: Calculate the MACD & RSI 5_TTR.R
 Exercise: Apply both as a single indicator for buying/selling actions 5_TTR.R
 Presentation: After backtest & visualizing debrief on results
 Q&A
 Break (5 minutes)
Consumer credit modeling (20 minutes)
 Presentation: Explain the investment scenario
 Presentation: Basics of a logistic regression
 Exercise: 6_CreditModeling.R Create a logistic regression modeling loan default probability
Predicting loan defaults (20 minutes)
 Exercise: Apply the logistic regression to new loans 7_CreditModeling.R
 Presentation: Model KPI & realistic investment scenario review
 Exercise: Evaluate the training, test partitions 7_CreditModeling.R
 Presentation: Introduce CAPM
 Exercise: Build a dynamic capital asset pricing visual to evaluate the investment opportunities 7_CreditModeling.R
 Q&A
Simulating a nontraditional market opportunity (15 minutes)
 Presentation: Review the trading card market as an investment vehicle
 Presentation: Structure problem for analysis
 Exercise: Examine how to sample with probability 8_nonTraditional_Mkt.R
 Exercise: Simulate market buying opportunity to quantify risk 8_nonTraditional_Mkt.R
 Q&A