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Machine Learning and Quantitative Finance

June 15, 2017

Eric Hamer, CTO Quantiacs

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The 1st Marketplace For Trading Algorithms A Pioneer Algo Trading Training Institute

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Association

Quantiacs and QuantInsti™ have teamed up to accelerate transformation of quantitative finance and algorithmic

trading education. The partnership will combine QuantInsti’s expertise in professional quant training and

algorithmic trading education programs with Quantiacs’ open-source technology platform and marketplace to help

further democratize the hedge fund industry.

QuantInsti™ will begin offering training sessions within their executive training curriculum to allow students to gain

practical skills using Quantiacs’ open-source tools and data, Quantiacs’ domain experts will be joining QuantInsti’s

faculty team for its Executive Program in Algorithmic Trading (EPAT™)

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About the Speaker

Eric Hamer is a serial entrepreneur with degrees in Physics and Computer Science. Eric’s experience includes

Machine Learning, Cloud Computing, and Python programming. Before joining Quantiacs, Eric was the founding

CTO at NetInformer, a mobile media company whose customers included Major League Baseball, the NCAA, and

Verizon Wireless. Prior to NetInformer, Eric worked at Keynote Systems where he invented their patented

Transactive Perspective which measured, and monitored, the performance of the Internet.

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Eric HamerChief Technology Officer – Quantiacs

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About QuantiacsFC2016

• World’s first crowdsourced hedge fund

• Quants code algorithms, we connect it to capital, the quant profits

• Frequent competitions allow quants to win investment capital

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Getting StartedFC2016

https://www.quantiacs.com/GetStarted

• Downloadable desktop toolkits in Matlab and Python

• Python and Matlab sample strategies

• End of day futures data from Jan 1, 1990

• Macroeconomic indicators

• Online platform for daily evaluation

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Python Toolkit Input FC2016

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Python Toolkit Output

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Evaluating ResultsFC2016

• Positive performance with low volatility is most desired

• Sharpe and Sortino ratios indicate risk adjusted returns

• Strategies with a lot of churn tend not to perform well

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Machine LearningFC2016

• Very hot topic in Quantitative finance

• Eighty-five percent of trades are computer generated

• Matlab and Python provide support for ML

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ML TechniquesFC2016

• Regression: predicting continuous values

• Classification: identifying an object’s category

• Clustering: grouping similar items

Sscikit learn – https://www.scikit-learn.org

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Neural Networks (NN)FC2016

• Very popular in AI/ML

• NN perceptron analogous to a biological neuron

• Layered architecture

• Run times can be lengthy with a traditional CPU

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Machine Learning Process

• Specify the problem statement

• Identify which type of ML the problem represents

• Classification

• Prediction

• Regression

• Encode the data used by the algorithm

• Everything must be numeric

• Divide the data into training data and test data

• Use the training data to teach the algorithm

• Apply the trained algorithm on the test data

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ML Example

• Use the toolkit to load historical data

• Create training data and test data sets from the historical data

• Format data as required by the ML package

• Create the ML engine

• Use the ML engine to “fit” the training data

• Use the ML engine to “predict” the test data

• Display and review results

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Predict the Mini S&P 500 Futures (ES)

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ML EngineFC2016

• Keras neural networks API

• Sequential model is used to create the neural network

• Uses a single layer neural network

https://keras.io

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ML Prediction for ES FC2016

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ML Prediction for ES ReturnsFC2016

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Results Analysis

• Predictions for ES closely matched the actual data

• Predictions for return data were not as good

• Neither set had the same magnitude as the actual data

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Combining toolkit with MLFC2016

• Predict ES returns for “future” twelve months

• Create a neural network based on sequential model

• Use previous two years of data to train the model

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Predicting the S&P MiniFC2016

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AnalysisFC2016

• Results are poor and could not be used to trade

• Algorithm did not use High, Low, Close, Volume or OI

• Consider smoothing and/or categorizing the data

• Moving Average

• Momentum

• Relative Strength

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Quant Strategy ResultsFC2016

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ML OptimizationsFC2016

• Gradient Descent

• Boosting

• Bootstrap aggregating

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ML TipsFC2016

• Simple strategies tend to perform better

• Consider using multiple prediction techniques to reach a consensus

• Replace raw data with features

• Patience and creativity may be heavily rewarded

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ML PitfallsFC2016

• Overfitting may lead to poor results with live data

• Make sure your data is clean with valid missing data

• Random variables may lead to non-deterministic output

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SummaryFC2016

• Moving forward ML and AI will be key tools in forecasting financial markets

• Current tools simplify the task of developing ML based algorithms

• Used properly ML can be used to generated positive trading strategies

Github Resources:

• Sample Python trading strategies - https://github.com/Quantiacs/quantiacs-python

• Source code (simpleKeras.py file) - https://github.com/Quantiacs/quantiacs-python/tree/master/sampleSystems

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Visit us at quantiacs.com

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Additional Information

https://www.quantiacs.com/Data/Reading_List.pdf

https://www.quantiacs.com/For-Quants/GetStarted/Quant-Tutorials/Videos.aspx

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Machine Learning in EPAT™ & Quantra™FC2016

The Executive Programme in Algorithmic Trading at QuantInsti is designed for professionals looking to grow in the field, or planning to start

their careers in Algorithmic and Quantitative trading. The EPAT™ programme includes dedicated sessions on Machine Learning.

The following aspects of Machine Learning are covered under EPAT™

Linear Regression, Logistic Regression, GAM / LDA and Touch Upon Wavelets, Trees, Ensemble Methods, Neural Nets, SVM, Deep Learning,

Feature Selection, Potential Pitfalls along with trading strategy examples and implementable codes

Quantiacs’ domain experts will be joining QuantInsti’s faculty team and as a part of the curriculum they will cover Machine Learning session

using Quantiacs platform.

Self Paced Course - Trading with Machine Learning: Regression on Quantra™

• Learn to trade using machine learning in a step by step way

• Implement regression technique using machine learning using Python while doing lots of guided hands-on coding

• Learn how to interpret predictions and use them to generate trade signals

• Understand and resolve bias and variance related issues to optimize your strategy

• Get downloadable strategy codes and lifetime access to the course contents

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Over 10000 professionals from 75+ countries have benefited from QuantInsti’s educational initiatives. If you want to be a successful Algorithmic Trader, enroll for EPAT™ now!

For more information write to us at:

contact@quantinsti.com

or Call us on

+91-22-6169-1400 / +91-9920-44-88-77

Next Batch Starts from July 29, 2017!

Register now and avail 15% early bird discount

(Offer Till 20th June’17)

To sign-up for the self paced course ‘Trading with Machine Learning: Regression on Quantra™Use coupon code MLWEB20 (applicable till 17th June, midnight GMT) to avail 20% off on the course

For more information visit: www.quantra.quantinsti.com

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Questions?