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Applied Investment & Risk Analytics with R (Intermediate)

Event Description

Applied Investment & Risk Analytics with R (Intermediate) 

4 & 5 May 2017

Trainer: Mark C. Hoogendijk​


Learn advanced Efficient Frontier calculations, the Black-Litterman model, algorithmic back-testing, Option Pricing, Monte Carlo simulations, Machine Learning for Finance and have the opportunity to build screeners & market dash-boards for large global portfolio data sets.

This 2 day module course is a great stepping stone to the more advanced Investment & Risk Analytics in R. As a direct extension of the “Investment Analytics & Data Visualization with R” course, which successfully ran last November, the course will build upon participants’ basic understanding of R and take them to the next level. Focusing on Advanced Data Mining techniques required for portfolio and risk management of large portfolios, the modules are great for expanding ones understanding of the R language, whilst learning more about Portfolio & Risk Analytics together with Data Visualization. (Running simulations in the cloud will also be covered)

All modules are based on up to date & historical market prices focusing on Stocks, ETF’s, FX Rates and Macro-Economic Indicators. Packages such as dplyr, tidyr, tibble and purrr will be used for fast Data Manipulation, required for the amount of data that will be analyzed. (The modules will work with a database of 4000 stocks including historical price data, balance-sheet data, option data, splits and dividends and calculated technical indicators). The goal of the course is to provide the participants with an end product that they can directly implement or quickly alter for their own purposes.

Requirements :  Basic understanding of R is required.

The course is a beginner to intermediate level course for R.

All code will be shared and provided to the participants. Full overview of available online materials will be shared too. Including an overview of additional financial packages to be used for further Portfolio Analytics.

Who Should attend?

This training course is specifically designed for all level experiences within the investment management sector and tailored specially for:

  - ​ ​Research & Sector Analysts (Bottom-Up and Top Down)

  - ​  Investment Analysts

  - ​ (Hedge) Fund managers

  - ​ CIO’s with Life Insurance Companies

  - ​ Investment Actuaries

  -  Risk Managers

 - (Associate) Professors within Computational Finance & Risk Management departments

Course Outline :

Day 1:

Morning Session:

 - Intermediate Data Mining. Building your portfolio database.

 - Running Performance & Risk Analytics over a 4000 large stock database.

 - Adding technical & fundamental indicators to the database. Building sophisticated screeners and capturing the outcomes in powerful interactive visuals & tables using Shiny.

Afternoon Session:

 - Intermediate Efficient Frontier Analytics, incorporating multi-core back-test, running large back-tests with Amazon AWS. Option Pricing in R (including Monte Carlo). Back-testing algorithmic trading strategies.

 - Working with Big Data in the cloud, running optimizations with Amazon AWS

This module is completely focused on creating your portfolio database efficiently and executing Performance and Risk Analytics on the database. Technical Indicators are calculated for all 4000 stocks. Based on the information Dashboards will be created. Given the incorporated Option Data, pricing with Black-Scholes and Monte Carlo simulations are covered in the afternoon session. Algorithmic back-testing will also be covered and executed on the database.

Building & Analyzing Your Portfolio DataBase

 - Importing the data from public databases.

 - Creating one large database without losing oversight

- Categorizing the data according to MarketCap, Volume & Liquidit

 - Calculating Risk metrics for all stocks and adding to the database

 - Calculating Technical Indicators and adding to the database

 - Running your reports overnight with a Batch File & Windows task scheduler

Risk & Portfolio Interactive Dashboards

 - Revisiting RMD files.

 - Foundation of flexdashboard package

 - Creating a daily market summary dashboard incorporating World Indices, FX Rates, Interest Rates and selected ETF’s.

 - Running a dashboard for your portfolio

 - Introduction to Shiny (Creating an Interactive tool for the 4000 Stock dataset)

Black & Scholes in R & intermediate Portfolio Analytics and Option Pricing

 - Importing Option Data & Quick introduction to Black & Scholes with R

 - Creating Sensitivity Analysis for all greeks based on ITM-ness

 - Running a simple Monte Carlo for Option pricing

 - Setting up your computer for Multi-Core Efficient Frontier Calculations

 - Using your own moment functions for Efficient Frontier

 - The Black-Litterman model

 - Running Efficient Frontier back-test in the cloud with Amazon AWS

Algorithmic Backtesting

 - Introduction to the quantstrat package

 - Back-Testing various strategies. 

 Day 2

Morning Session

 - Introduction to Machine Learning for Finance.

 - A comprehensive introduction will be given to Machine Learning, including covering the various research topics within Finance.

 - Participants will be introduced to different R packages required for Machine learning and apply Machine Learning algorithms. 

Afternoon Session:

 - Applied Machine learning for Finance. In this session participants will apply the algorithms to Investment Analytics covering regression, classification and clustering.​

This module is an introductory course to Machine learning for Investment Analytics. An overview of Machine Learning is provided covering various algorithms. The course covers the split between Supervised and Unsupervised learning, the three categories of regression, classification and clustering and applies this to Investment Analytics.

Overview Machine Learning

 - Understanding Supervised versus Unsupervised, parametric and non-parametric models, looking into Regression, Classification and clustering

 - Overview of Research on the topic of Machine Learning for Finance

 - Introduction to various Machine Learning packages in R

 - Implementing Machine Learning algorithms in R, understanding the fundamentals.

Applied Machine Learning for Finance

 - Linear and Logistic Regression

 -  Lasso & Ridge Regression

 - Support Vector Machines

 - Decision Trees and Random Forest

 - Kaggle Competitions

 - Future Learning 

This workshop is eligible for 14 CE credit hours.


Course Fee:

CFA Singapore member price : S$1,580* (Early Bird fee) / S$1,780* (Standard fee)

Non-member price : S$1,780* (Early Bird fee) / S$1,980* (Standard fee)

Early bird fee valid till 10 April 2017

* Prices before 7% GST

* 10% off Group Discount available​

Event Type


Education Topic

Portfolio Management

Start Time

5/4/2017 9:00

End Time

5/5/2017 17:30





Event Country


Event Region

Asia Pacific

Location Info

​TBC, Singapore


Mark C. Hoogendijk CFA, CAIA

CE Credits


SER Credit




Member Price

S$1,580* (Early Bird fee) / S$1,780* (Standard fee)

Non-Member Price

S$1,780* (Early Bird fee) / S$1,980* (Standard fee)

Candidate Price



​​​Please send your registration form to​

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Applied Investment and Risk Analytice with R (Intermediate) - 4 and 5 May 2017.pdf    
Content Type: MyCFA Calendar
Created at 3/15/2017 10:23 by[CASMSTS:username]
Last modified at 3/16/2017 13:09 by EVENTMANAGEMENT@CFASINGAPORE.ORG