A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016

A GUIDED TOUR OF MACHINE LEARNING FOR TRADERS

TUCKER BALCH, PH.D. PROFESSOR, GEORGIA TECH CO-FOUNDER AND CTO, LUCENA RESEARCH

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.


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.


ABOUT MY COURSE


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


TALK OVERVIEW •  Machine Learning: Big Picture •  Decision Trees: Classification •  Decision Trees: Regression •  Decision Trees Example: Sentiment-based strategy •  kNN: Classification •  kNN: Regression •  Reinforcement Learning


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


HOW TO BUILD A PREDICTIVE MODEL Factors (X1, X2, … XN) Predict outcome: Y


HOW TO BUILD A PREDICTIVE MODEL Factors (X1, X2, … XN) Predict outcome: Y Classification: One of several outcomes Regression: Numerical outcome


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


WHO SHOULD I VOTE FOR?


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


PREDICT VOTING BEHAVIOR Model: Decision Tree


PREDICT STOCK BEHAVIOR Model: Decision Tree


TREES ALSO WORK FOR REGRESSION Model: Decision Tree


LOTS OF TREES = FOREST


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


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


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


BACKTEST OF LONG SCAN

Backtest simulation performance from QuantDesk® – Past performance is no guarantee of future results. In-sample training period: 2011.


BACKTEST OF SHORT SCAN



BACKTEST OF LONG & SHORT COMBINED



FORWARD TESTING SINCE NOV 2015

Forward testing performance – Past performance is no guarantee of future results. In-sample training period: 2011.


K NEAREST NEIGHBOR •  Solves the same problem as decision trees •  Train: Save data •  Query: Find k nearest neighbors, vote or take mean


K NEAREST NEIGHBOR


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


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


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


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


REVIEW •  Decision Trees

•  Classification •  Regression

•  kNN •  Classification •  Regression

•  Reinforcement learning •  Finds a policy


THANK YOU To learn about my company: •  www.lucenaresearch.com

To learn about my course: •  Google “Balch Udacity”


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.


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.

A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016

Economy & Finance

Transcript of A Guided Tour of Machine Learning for Traders by Tucker Balch at QuantCon 2016