With Alternative Data becoming more and more popular in the industry, quants are eager to adopt them into their investment processes. However, with a plethora of options, API standards, trying and evaluating datasets is a major hindrance to adoption of datasets. Join Yaacov, Sri, James and Brad discuss the opportunities, pitfalls and challenges of Alternative Data and its adoption in finance

1. Qu Speaker Series
The API Jungle
Brad Franklin, James Perkins, Yaacov Weinstock, Sri Krishnamurthy
2020 Copyright QuantUniversity LLC.
Hosted By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.qu.academy
12/22/2020
Qu.Academy
https://quspeakerseries15.splashthat.com/

2. 2
QuantUniversity
• Boston-based Data Science, Quant
Finance and Machine Learning
training and consulting advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Building a platform for AI
and Machine Learning Exploration
and Experimentation

3. 3
For registration information, go to
https://QuFallSchool.splashthat.com

4. 4
https://Quwinterschool.splashthat.com

5. 5
Next Week

6. 6

7. Empowering Quants and Coders
1. Developer Community
2. Data Platform
3. Labs
1851 2020

8. 8
1. Refinitiv Developer Community

9. 9
refinitiv.com/en/products/refinitiv-data-platform
2. Refinitiv Data Platform
CodeBook

10. 10
3. Refinitiv Labs
Data Exploration Thought Leadership
refinitiv.com/labs
AI/ML Projects

11. 11
FactSet API Suite
Copyright © 2020 FactSet Research Systems Inc. All rights reserved.

12. 12
Copyright © 2020 FactSet Research Systems Inc. All rights reserved.
developer.factset.c
om
Jupyter Notebook starter kits
Accelerate onboarding
“Getting Started” request examples
Sample SDK
Explore Live Documentation and Request/Response Models
Utilize “Try it Out” sandbox features
Manage authentication, monitor usage
Submit Issues directly to FactSet Support and Product Teams

13. 13
Climate
Local
Government
Education
Energy
Labor
ESG for Municipal Investments
Proprietary
Models
Fundamental
Research
Internally
Generated Data
Consume,Store,Process&
Exploit

14. 14 “Searching for an Edge with Alternative Data.” Quandl Blog, 13 May 2019, blog.quandl.com/gaining-an-edge-with-
alternative-data.
Speed of Discovery & Usage is the
Differentiator

15. 15
Architecture to Enable Speed of Discovery &
Usage
Layered
Component-oriented
Separation of concerns
Decoupling of tasks
Flexibility
Building of end-to-end data pipelines should be easy, frictionless, & self-service
GOAL:
ARCHITECTURE:

16. 16
https://gcp.solutions/diagram/bd-data-warehouse
https://aws.amazon.com/blogs/big-data/aws-serverless-data-analytics-pipeline-reference-architecture/
https://docs.microsoft.com/en-us/azure/architecture/example-scenario/dataplate2e/data-platform-end-to-end
Load
Store
Process
Distribute
Governance/Security
Architecture is Public (Cloud)

17. 17
Demos, slides and video available on QuAcademy
Go to www.qu.academy
17

18. 18
Instructions for the Lab:
1. Go to https://academy.qusandbox.com/#/register and register using the code:
"QUFALLSCHOOL"

19. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
19