Training machines to trade: a deep-learning application to technical analysis
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Training machines to trade: a deep-learning application to technical analysis

6/29/2017
When: Thursday, June 29th, 2017
From 12:00 PM until 1:30 PM
Where: Golden Gate University, Room 5210
536 Mission St
San Francisco, California  94105
United States
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Training Machines to Trade:
A Deep-Learning Application to Technical Analysis 

 

Please join Vincent Deluard, CFA, and Fernando Vidal as they provide a summary of the traditional rationale and methods of technical analysis and argue that machines are likely to outperform humans at pattern recognition. They will give a broad overview of how neural networks work, and how image-recognition algorithms can be applied to identify stock market patterns.  The session will conclude with an assessment of the out-of-sample performance of these trading strategies

Learning Objectives:
- Fundamentals of technical analysis
- Introduction to neural networks and deep learning
- Properly measuring the performance of market indictors
- Out-of-sample versus in-sample analysis
- Market-timing tools


Vincent Deluard, CFA
Global Macro Strategist
INTL FCStone


Vincent is the global macro strategist for INTL FCStone, where he authors weekly commentary on asset allocation. Prior to joining INTL FCStone, Vincent served as Europe strategist for Ned Davis Research where he created the firm’s Europe product. Before that, Vincent was executive vice president for TrimTabs Investment, where he headed the firm’s quantitative research. Vincent is frequently quoted in the Financial Times, WSJ, and Bloomberg and speaks at CFA and Institutional Investors events around the world.  He also teaches at the CFA Society of San Francisco. In November 2013, Vincent was awarded the Padraic Fallon Editorial Prize for his work on the European debt crisis. Vincent completed a dual master’s degree at Sciences-Po Paris (Cum Laude) and Columbia University.


 


Fernando Vidal
Data Scientist
Sauce Labs

Sauce Labs is a fast growing Bay Area software startup where Fernando focuses on predictive analytics and developing time series prediction models. Prior to this, he spent 7 years working as a quantitative research analyst at the independent investment research firm Ned Davis Research. During this time he specialized in building models for equity selection and asset allocation and wrote publications about using quantitative methods to gain market insights. Fernando is a graduate of the Georgia Institute of Technology where he received his Masters in Computer Science, specializing in Machine Learning. 

 

 

CFASF Members: $25

Nonmembers: $40

 

  

Continuing Education:
This event qualifies for 1.5 hours of continuing education credit for CFA Charterholders

 

Cancellation Policy
Please contact tracy@cfa-sf.org with at least 24 hours notice to receive a full refund.