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ORV2016
Machine Learning and Quantitative Finance
June 15, 2017
Eric Hamer, CTO Quantiacs
FC2016
The 1st Marketplace For T...
ORV2016
Association
Quantiacs and QuantInsti™ have teamed up to accelerate transformation of quantitative finance and algo...
ORV2016
About the Speaker
Eric Hamer is a serial entrepreneur with degrees in Physics and Computer Science. Eric’s experie...
ORV2016
About Quantiacs
FC2016
• World’s first crowdsourced hedge fund
• Quants code algorithms, we connect it to capital,...
ORV2016
Getting Started
FC2016
https://www.quantiacs.com/GetStarted
• Downloadable desktop toolkits in Matlab and Python
•...
ORV2016
Python Toolkit Input
FC2016
ORV2016
FC2016
Python Toolkit Output
ORV2016
Evaluating Results
FC2016
• Positive performance with low volatility is most desired
• Sharpe and Sortino ratios i...
ORV2016
Machine Learning
FC2016
• Very hot topic in Quantitative finance
• Eighty-five percent of trades are computer gene...
ORV2016
ML Techniques
FC2016
• Regression: predicting continuous values
• Classification: identifying an object’s category...
ORV2016
Neural Networks (NN)
FC2016
• Very popular in AI/ML
• NN perceptron analogous to a biological neuron
• Layered arc...
ORV2016
Machine Learning Process
• Specify the problem statement
• Identify which type of ML the problem represents
• Clas...
ORV2016
ML Example
• Use the toolkit to load historical data
• Create training data and test data sets from the historical...
ORV2016
ML Engine
FC2016
• Keras neural networks API
• Sequential model is used to create the neural network
• Uses a sing...
ORV2016
ML Prediction for ES
FC2016
ORV2016
ML Prediction for ES Returns
FC2016
ORV2016
Results Analysis
• Predictions for ES closely matched the actual data
• Predictions for return data were not as go...
ORV2016
Combining toolkit with ML
FC2016
• Predict ES returns for “future” twelve months
• Create a neural network based o...
ORV2016
Predicting the S&P Mini
FC2016
ORV2016
Analysis
FC2016
• Results are poor and could not be used to trade
• Algorithm did not use High, Low, Close, Volume...
ORV2016
Quant Strategy Results
FC2016
ORV2016
ML Optimizations
FC2016
• Gradient Descent
• Boosting
• Bootstrap aggregating
ORV2016
ML Tips
FC2016
• Simple strategies tend to perform better
• Consider using multiple prediction techniques to reach...
ORV2016
ML Pitfalls
FC2016
• Overfitting may lead to poor results with live data
• Make sure your data is clean with valid...
ORV2016
Summary
FC2016
• Moving forward ML and AI will be key tools in forecasting financial markets
• Current tools simpl...
ORV2016
Visit us at quantiacs.com
FC2016
Additional Information
https://www.quantiacs.com/Data/Reading_List.pdf
https://ww...
ORV2016
Machine Learning in EPAT™ & Quantra™
FC2016
The Executive Programme in Algorithmic Trading at QuantInsti is design...
ORV2016
FC2016
Over 10000 professionals from 75+ countries have benefited from QuantInsti’s educational initiatives.
If yo...
ORV2016
FC2016
Questions?
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Introduction to machine learning for quantitative finance webinar ppt

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This exciting webinar on Machine Learning will take you through the basics of machine learning, it will cover the cool features of the Quantiacs toolkit, and illustrate how to create and test machine learning strategies using Quantiacs.
The webinar covers:
- An Overview of Machine Learning
- The Machine Learning Process
- Features of Quantiacs toolkit
- Applying Machine Learning to Futures Data Using Quantiacs
- Discussing Results
- Machine Learning Tips and Pitfalls

Learn more about our EPAT™ course here: https://www.quantinsti.com/epat/

Most Useful links
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
Like us on Facebook: https://www.facebook.com/quantinsti/
Follow us on Twitter: https://twitter.com/QuantInsti

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

Access the webinar recording here: https://youtu.be/V1vMkd8jFy4

Know more about EPAT™ by QuantInsti™ at http://www.quantinsti.com/epat/

Published in: Education
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Introduction to machine learning for quantitative finance webinar ppt

  1. 1. ORV2016 Machine Learning and Quantitative Finance June 15, 2017 Eric Hamer, CTO Quantiacs FC2016 The 1st Marketplace For Trading Algorithms A Pioneer Algo Trading Training Institute
  2. 2. ORV2016 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™) FC2016
  3. 3. ORV2016 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. FC2016 Eric Hamer Chief Technology Officer – Quantiacs
  4. 4. ORV2016 About Quantiacs FC2016 • 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
  5. 5. ORV2016 Getting Started FC2016 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
  6. 6. ORV2016 Python Toolkit Input FC2016
  7. 7. ORV2016 FC2016 Python Toolkit Output
  8. 8. ORV2016 Evaluating Results FC2016 • 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
  9. 9. ORV2016 Machine Learning FC2016 • Very hot topic in Quantitative finance • Eighty-five percent of trades are computer generated • Matlab and Python provide support for ML
  10. 10. ORV2016 ML Techniques FC2016 • Regression: predicting continuous values • Classification: identifying an object’s category • Clustering: grouping similar items Sscikit learn – https://www.scikit-learn.org
  11. 11. ORV2016 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
  12. 12. ORV2016 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 FC2016
  13. 13. ORV2016 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 FC2016 Predict the Mini S&P 500 Futures (ES)
  14. 14. ORV2016 ML Engine FC2016 • Keras neural networks API • Sequential model is used to create the neural network • Uses a single layer neural network https://keras.io
  15. 15. ORV2016 ML Prediction for ES FC2016
  16. 16. ORV2016 ML Prediction for ES Returns FC2016
  17. 17. ORV2016 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 FC2016
  18. 18. ORV2016 Combining toolkit with ML FC2016 • 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
  19. 19. ORV2016 Predicting the S&P Mini FC2016
  20. 20. ORV2016 Analysis FC2016 • 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
  21. 21. ORV2016 Quant Strategy Results FC2016
  22. 22. ORV2016 ML Optimizations FC2016 • Gradient Descent • Boosting • Bootstrap aggregating
  23. 23. ORV2016 ML Tips FC2016 • 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
  24. 24. ORV2016 ML Pitfalls FC2016 • 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
  25. 25. ORV2016 Summary FC2016 • 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
  26. 26. ORV2016 Visit us at quantiacs.com FC2016 Additional Information https://www.quantiacs.com/Data/Reading_List.pdf https://www.quantiacs.com/For-Quants/GetStarted/Quant-Tutorials/Videos.aspx
  27. 27. ORV2016 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
  28. 28. ORV2016 FC2016 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
  29. 29. ORV2016 FC2016 Questions?

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