The document provides an overview of a guided tour of machine learning techniques for traders given by Tucker Balch, a professor of interactive computing at Georgia Tech. It discusses decision trees, k-nearest neighbors, and reinforcement learning algorithms. It also highlights an example trading strategy that uses decision trees to classify stock sentiment data and technical factors to generate long and short signals. Backtests show the combined strategy achieved annual returns over 30% with lower maximum drawdowns than the market.

1. A GUIDED TOUR OF
MACHINE LEARNING FOR
TRADERS
TUCKER BALCH, PH.D.
PROFESSOR, GEORGIA TECH
CO-FOUNDER AND CTO, LUCENA RESEARCH

2. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
WHO THIS IS FOR
People who are…
• familiar with quantitative techniques
• interested to know what’s under the “hood”
with ML techniques.
• No Machine Learning knowledge assumed.

3. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
ABOUT THE SPEAKER
• Professor of Interactive Computing at
Georgia Institute of Technology.
• Teach courses in Artificial Intelligence and
Finance.
• Teach MOOCs on Machine Learning for
Trading
• Published over 120 research publications
related to Robotics and Machine Learning.
• Co-founder of Lucena Research.

4. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
ABOUT MY COURSE

5. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
ABOUT LUCENA
RESEARCH
• Fin-tech company who employ
experts in Computational
Finance, Quantitative Analysis,
and Software Development.
• We deliver investment decision
support technology to hedge
funds and wealth managers:
• Price forecasting
• Hedging
• ML-based stock screening
• Model portfolios
• Python-based infrastructure.
• http://lucenaresearch.com

6. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
TALK OVERVIEW
• Machine Learning: Big Picture
• Decision Trees: Classification
• Decision Trees: Regression
• Decision Trees Example: Sentiment-based strategy
• kNN: Classification
• kNN: Regression
• Reinforcement Learning

7. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
THE BIG PICTURE
“Machine Learning” goes by many names:
• Machine Learning
• Big Data
• Predictive Analytics
Focus: Supervised Learning
• Start with examples: Factor values & outcomes
• Build model from examples
• Use model to predict outcomes

8. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
HOW TO BUILD A
PREDICTIVE MODEL
Factors (X1, X2, … XN)
Predict outcome: Y

9. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
HOW TO BUILD A
PREDICTIVE MODEL
Factors (X1, X2, … XN)
Predict outcome: Y
Classification: One of several outcomes
Regression: Numerical outcome

10. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
HOW TO BUILD A
PREDICTIVE MODEL
Factors (X1, X2, … XN)
Predict outcome: Y
Classification: One of several outcomes
Regression: Numerical outcome
Lots of methods solve same problem
• kNN
• Decision Trees
• Support Vector Machines (SVM)
• Artificial Neural Networks (ANN)
• Deep Learning

11. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
WHO SHOULD I VOTE
FOR?

12. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
PREDICT VOTING
BEHAVIOR
Factors:
• Do you believe the country is “broken”?
• If so, what caused the country to become broken?
• Where do you stand on a woman’s right to chose?
• What are your religious views?
Outcomes:
• Trump
• Clinton
• Cruz
• Sanders
• Kasich

13. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
PREDICT VOTING
BEHAVIOR
Model: Decision Tree

14. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
PREDICT STOCK
BEHAVIOR
Model: Decision Tree

15. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
TREES ALSO WORK
FOR REGRESSION
Model: Decision Tree

16. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
LOTS OF TREES =
FOREST

17. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
HOW TO BUILD A
TREE
• Gather data <X1, X2, X3, Y>
• Find most predictive factor Xi of Y
• Find threshold Ti that splits data most effectively
• Decision node: Xi < Ti?
• Left tree: Xi < Ti
• Right tree: Xi >= Ti
• Recurse until only one data item left: Leaf

18. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
DECISION TREES
RECAP
• A decision tree is a flow chart of yes/no questions
• When you reach a leaf, that is your prediction
• Can be used for classification or regression
• Training:
• find most predictive factor
• split data based on that factor
• Recurse
• Query:
• Follow path through decision nodes until leaf
• Forest: An ensemble learner with multiple trees
• Training: Build trees with sampled data
• Query: Query each tree: Vote, or average to find result
• Less susceptible to overfitting

19. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
USING DECISION TREES
FOR STOCK SCANS
• CHECKMATE: Trading strategy developed by Lucena Research,
Inc. in partnership with PsychSignal.com
• Classification-based strategy
• Separate scans for long and short positions
• Factors:
• PyschSignal: Sentiment data: stocktwits, twitter analysis
• Lucena: 400+ technical & fundamental factors per stock
• Outcomes: Up/Down/Neutral

20. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
BACKTEST OF LONG
SCAN
Backtest simulation performance from QuantDesk® – Past performance is no guarantee of future
results. In-sample training period: 2011.

21. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
BACKTEST OF SHORT
SCAN
Backtest simulation performance from QuantDesk® – Past performance is no guarantee of future
results. In-sample training period: 2011.

22. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
BACKTEST OF LONG &
SHORT COMBINED
Backtest simulation performance from QuantDesk® – Past performance is no guarantee of future
results. In-sample training period: 2011.

23. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
FORWARD TESTING
SINCE NOV 2015
Forward testing performance – Past performance is no guarantee of future results. In-sample training
period: 2011.

24. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
K NEAREST NEIGHBOR
• Solves the same problem as decision trees
• Train: Save data
• Query: Find k nearest neighbors, vote or take mean

25. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
K NEAREST NEIGHBOR

26. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
TRADE OFFS
KNN
• Classification or regression
• Training is fast
• Query is slow
• Requires data normalization
• Susceptible to overfitting
• Larger K
• Ensemble
• Must discover features
• You must map to strategy
Decision Trees
• Classification or regression
• Training is slow
• Query is fast
• No data normalization
• Susceptible to overfitting
• Larger leafsize
• Ensemble (forest)
• Auto feature discovery
• You must map to strategy

27. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
REINFORCEMENT
LEARNING
Solves a different problem:
• Find a policy π that tells us which action a to take in
every situation s.
• a = π(s)
• π*(s) is the optimal policy
Nomenclature
• s: state
• r: reward for last action
• a: action
• T: transition matrix (which state is next)
• π: the policy

28. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
REINFORCEMENT
LEARNING
For trading problem:
• s: factors/features describing a stock’s “situation”
• r: return
• a: buy, sell, do nothing
Algorithms:
• Model-based:
• Policy iteration
• Value iteration
• Model-free
• Q-learning
• Dyna-Q

29. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
REINFORCEMENT
LEARNING
Advantages:
• Maps well to finance problems
• Provides entire strategy including
entry and exit conditions
• Policy accounts for whether to enter
based on probability of success

30. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
REVIEW
• Decision Trees
• Classification
• Regression
• kNN
• Classification
• Regression
• Reinforcement learning
• Finds a policy

31. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
THANK YOU
To learn about my company:
• www.lucenaresearch.com
To learn about my course:
• Google “Balch Udacity”

32. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
OVERFITTING
Description: An overfit model is one that models in-sample
data very well. It predicts the data so well that it is likely
modeling noise.

33. A Guided Tour of Machine Learning for Traders Tucker Balch, Ph.D.
OVERFITTING
Description: An overfit model is one that models in-sample
data very well. It predicts the data so well that it is likely
modeling noise.
As the degrees of freedom of the model increase, overfitting
occurs when in-sample prediction error decreases and out-
of-sample prediction error increases.