For the Webinar video, you can also visit: https://blog.quantinsti.com/machine-learning-webinar-11-june-2019/ Overview: In our webinars, we try to cater to all the questions/queries sent in by the attendees. Some questions are common to a lot of people while some are exclusive and niche. Both types of questions bring new perspectives to all the participants and shed more clarity on the topic. This Q&A session on Machine Learning in Trading, with Dr. Ernest Chan was the perfect opportunity to ask him any query pertaining to this topic. It was helpful to those who wish to apply their technical skills in AI, Cloud, Machine Learning etc. to Financial Markets or aspire to belong to the algorithmic trading community. Event Specifications: - Questions were shortlisted prior to the webinar. - Questions were specific to the following categories: Artificial Intelligence, Machine Learning, Deep Learning, and their application in the Financial Markets. Speaker Profile: Dr. Ernest P Chan is the Managing Member of QTS Capital Management, LLC. He has worked for various investment banks (Morgan Stanley, Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium Partners, MANE) since 1997. Dr. Chan received his PhD in Physics from Cornell University and was a member of IBM’s Human Language Technologies group before joining the financial industry. He was a co-founder and principal of EXP Capital Management, LLC, a Chicago-based investment firm. Chan is also the author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business (Wiley), Algorithmic Trading: Winning Strategies and Their Rationale and his third and latest book is on Machine Trading: Deploying Computer Algorithms to Conquer the Markets. He is also a popular financial blogger. Tuesday, June 11, 2019 at 11:00 AM ET | 8:30 PM IST | 10:00 PM SGT For the Webinar video, you can also visit: https://blog.quantinsti.com/machine-learning-webinar-11-june-2019/ Learn more about our EPAT® course here: https://www.quantinsti.com/epat/ OR Visit us at: https://www.quantinsti.com/ Like and Follow us on: Facebook: https://www.facebook.com/quantinsti/ LinkedIn: https://www.linkedin.com/company/quantinsti Twitter: https://twitter.com/QuantInsti

1. MACHINE LEARNING
IN TRADING Q&A
Dr. Ernest Chan

2. Dr. Chan is the Managing Member of QTS Capital Management,
LLC. He has worked for various investment banks (Morgan Stanley,
Credit Suisse, Maple) and hedge funds (Mapleridge, Millennium
Partners, MANE) since 1997.
Dr. Chan received his PhD in physics from Cornell University and
was a member of IBM’s Human Language Technologies group
before joining the financial industry. He was a co-founder and
principal of EXP Capital Management, LLC, a Chicago-based
investment firm. Chan is also the author of Quantitative Trading:
How to Build Your Own Algorithmic Trading Business (Wiley),
Algorithmic Trading: Winning Strategies and Their Rationale and his
third and latest book is on Machine Trading: Deploying Computer
Algorithms to Conquer the Markets.
At QuantInsti, he is one of our esteemed EPAT faculty members and
the author of three advanced self-learning courses on our
interactive learning platform Quantra.
Speaker
2
Dr. Ernest Chan

3. Categories
3
Limitations
FutureDataset
Optimization
Prerequisites
Risk management
Getting started
Capabilities
MACHINE LEARNING
IN TRADING

4. Questions
4
Some researchers have said that stock market prices are like a random walk.
Is it even possible or done by someone when it comes to a prices prediction?
What are the suitable input parameters for a model tasked to do this?

5. Questions
5
What are the unique benefits I get from using ML as a trading tool, that no other tool
can provide? Can I build a profitable trading strategy (not investment) using AI and
ML? What is the best financial market in term of suitability with AI and ML? What are
the true capabilities and limitations of using AI and ML in trading?

6. Questions
6
How to detect and avoid fake news or information that may affect the prediction
models?

7. Questions
7
How to use ML/AI for quantity management (number of shares to be bought/sold) so
as to maximize profit?
How to create an ML model which can work well with the uncertainty of future stock
prices based on historical trends?
How can a model learn to improve its operations based on trading decisions taken by
it earlier?

8. Questions
8
Basics, prerequisites to learn this. How much of stats and coding are required?

9. Questions
9
Which is the better tool for trading, R or Python? Do I need to be a good programmer
for learning Python? Is this really for retail or intraday trader like me?

10. Questions
10
What would you say are the biggest obstacles for machine learning models to
perform well in the markets?
And the biggest contributing factors to good performing models?
If you could go back in time and coach yourself when you just started with machine
learning, what are the 5 points you would tell the younger Dr. Chan?
What would your advice be to someone who is new in ML, and has no university
mathematics, computers or science background?

11. Questions
11
If I am pursuing the education that is outside of this program, what knowledge would
be most beneficial for me prior to embarking on implementing ML in trading? Any
specific courses or topics?

12. Questions
12
Where do I start? What models should I explore (simple modes/deep learning)? What
preprocessing is required for simple models/deep learning models?
I have read that using deep learning you can forecast based on other time series
values (for example series t1 and t2 help in forecasting required series t3), how to
extend this for normal data for example if Trump tweets that trade war between the
US and China is over then the markets will improve in another X days. In this example,
what President Trump tweets is not forecastable but still an independent event and
cause the market to fluctuate.

13. 13
Live Questions

14. Webinar Video
14
Webinar Video Link

15. 15
Thank You!