“All You Need to Know to Win an Algotrading Competition” by Elena Tverdokhlebova, Tatiana Shpakova & Karina Ashurbekova Abstract: We would like to share the story of winning an international algotrading championship. In our presentation we will explain how algotrading works and cover some technical aspects and as well describe our team strategy and how collaborate during all steps of competition. Our goal is to inspire girls to start participate in data science competitions, since we believe it is possible to achieve great results even when you don't have much experience

1. All You Need to Know
How To Win
an Algotrading
Competition
Tatiana Shpakova Elena Tverdokhlebova Karina Ashurbekova

2. We are standing right here! Why?
What? International Quant Championship
How long? 5 months of contest
Where? Final in Singapore
Why we? We became Champions

3. Elena Tverdokhlebova
Education: Moscow Institute of Physics and Technology (MIPT)
Ecole Polytechnique (X)
Current Position: Data Scientist at Enablon, Paris, France
More about us

4. Tatiana Shpakova
Education: Moscow Institute of Physics and Technology (MIPT)
Current Position: PhD student in Machine Learning
at ENS d’Ulm & INRIA, Paris, France
More about us

5. Karina Ashurbekova
Education: Moscow Institute of Physics and Technology (MIPT)
Grenoble Institute of Technology (INP)
Current Position: PhD student in Statistics
at UGA & INRIA & GIPSA-lab, Grenoble, France
More about us

6. February 2018, WIMLDS meetup
We need to start participate
in Data Science
competitions

7. Introduction
to trading

8. How does it work?
Day 1
Day2
0.001 0.003 0.0009
...
0.02 -0.08 0.0005

9. How does it work?

10. How does it work?
Day 1
Day2
...
0.001 0.003 0.0009 0.02 -0.08 0.0005
0.001 0.003 0.0009 0.03 -0.08 0.0005
...
...

11. What is algorithmic trading?
Goal: creation of trading strategies called alphas
alpha is a vector of weights = distribution of cash besides stocks

12. What is alpha? Example.
Alpha expression:
- delta(close, 2)=
=
Close price of the day before yesterday
-
Close price of today

13. Metrics
mean(PnL)
std(PnL)
PnL - Profit & LossSharpe =
|Return|
TurnoverFitness =Sharpe

14. Types of Data
fundamental data
Assets
Debt
Sales
Price/Volume data
Stock price
Volume
Capitalization
News data
social nets
news
positive buzz
negative buzz
Relation data
# of
competitors
# of customers
# of partners

15. Our techniques

16. ★ research papers
★ investopedia
★ books & google
★ WORLDQUANT site
Alpha Ideas
number of our alphas: 101

17. Idea -> Model -> Fitted Robust Model
how to find, grow an alpha and succeed?
alpha life style cycle

18. Some tricks
★ data coverage
★ look at distribution of weights
★ normalization, get rid of the scale
★ prevent overfitting
★ do not trade too frequently
★ trade right stocks in the right moment … (next slide)

19. Example:
Mean reversion -delta(close,2)
on
established VS non-established
market

20. Portfolio
optimization

21. Next goal: construct a portfolio
Strategy: don't put all your eggs in one basket
Portfolio construction

22. 1 step: Diversity. Heatmap correlation and histogram
ana fun news pv rel snt

23. Step 2: Adjacency matrix and correlation graph
group alphas by inner correlation
values

24. Motivation!

25. We started from the zero and our journey was...
difficult sometimes

26. But don’t give up!
...
after 1 stage place 47 among 10500 teams
after 2 stage place 23 among 500 teams
after 3 stage place 3 among 15 teams
final place 1 !!!

27. Financial competitions and platforms
1. Two Sigma: Using News to Predict Stock Movements [finished]
2. Women Who Quant [soon]
3. International Quant Championship 2019 [starts March-April]
4. www.quantopian.com
5. www.worldquantvrc.com

28. Thank you!
Questions?
Elena Tverdokhlebova: elochk@gmail.com
Tatiana Shpakova: shpakova.tany@gmail.com
Karina Ashurbekova: karinaashurbekova131644@gmail.com