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Enhancing Statistical Significance of Backtests by Dr. Ernest Chan

Insufficient historical data is a major hurdle in building a trading model free from data snooping bias. Dr. Ernest Chan's talk from QuantCon 2017 discusses several techniques, some borrowed from machine learning, that can alleviate overfitting and enhance the statistical significance of a backtest.

Dr. Ernest Chan will be joining us again at QuantCon 2018, Quantopian's fourth annual algorithmic trading and quantitative finance conference, in NYC April 27th-28th.

Enhancing Statistical Significance of Backtests by Dr. Ernest Chan from QuantCon NYC 2017

Ernie Chan shared about his time at QuantCon NYC 2017 saying, "I have been surprised by the sophistication of the talks at QuantCon year after year. This is the only conference where I have to delve into the papers cited in the talks for months afterwards!" 

Tickets are now on Sale for QuantCon NYC 2018

QuantCon 2018 will feature numerous expert speakers and focus on how data science and machine learning can help improve your trading strategies.  The two-day conference consists of workshops, talks, tutorials, and networking with top individuals in the quantitative investment space.

Confirmed 2018 speakers include:

  • Dr. Kathryn Kaminski, Visiting Scientist, MIT Laboratory for Financial Engineering
  • Dr. Stephen Malinak, Chief Data and Analytics Officer at TruValue Labs
  • Andreas Clenow, Chief Investment Officer at ACIES Asset Management
  • Dr. Jess StauthInvestment Team Managing Director at Quantopian

To see our full lineup, visit: www.quantcon.com.

 

 

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