Best Day Of The Week To Trade

About the Author Kevin Davey

Kevin Davey is a professional trader and a top performing systems developer. Kevin is the author of “Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading” (Wiley Trading, 2014.) . He generated triple digit annual returns 148 percent, 107 percent, and 112 percent in three consecutive World Cup of Futures Trading Championships® using algorithmic trading systems.His web site, www.kjtradingsystems.com, provides trading mentoring, trading signals, and free trading videos and articles. He writes extensively in industry publications such as Futures Magazine and Active Trader and was featured as a “Market Master” in the book The Universal Principles of Successful Trading by Brent Penfold (Wiley, 2010). Active in social media, Kevin has over 15,000 Twitter followers. An aerospace engineer and MBA by background, he has been an independent trader for over 20 years. Kevin continues to trade full time and develop algorithmic trading strategies.

  • I sometimes fudge a little. Say I’ve come up with a theory, I’ll then design and optimize over a subset of data. For example, 2000-2009. Then I’ll see how it performs in the out of sample set, 2010-present. I may then have an idea that I think may improve the system. I do NOT test on the entire 2000-present data. I go back and implement the change and optimize on the original set of data. This change must improve results in both the in-sample and OOS sets of data for me to even consider it.

    Not strictly kosher. While I’m not optimizing over the entire data set, I’m still allowing some selection bias in. But I can’t just erase the good idea from my brain! And really, anyone who has looked at a chart more than once is going to have selection bias from historical OOS data. One has to just eventually take a chance with small positions to see if it’s really valid.

  • Ed Carp says:

    All I can say is, take a look at Duane Davis’ famous book for an explanation: https://www.amazon.com/Never-Thursday-Little-Known-Make-Break/dp/B000V8V4KG/ref=sr_1_sc_1

    • Hi Ed –

      Thanks for the comment!

      That book came out a while ago. Do the trades still hold up since the book release?

      THANKS

  • MARCO says:

    I think you are definitely right Kevin. If one optimize with day of week or months, must find a “fundamental” reason to support that seasonality edge. Otherwise, it’s only data mining!

    Marco Simioni
    https://nightlypatterns.wordpress.com/

  • Mark says:

    I think optimization is a good and necessary thing provided it is then applied logically in terms of searching the parameter space. When you don’t isolate variables and optimize then you have no idea if good results are fluke or more likely robust. Fluke would be a spike on a profit graph (or whatever your desired outcome variable is) in the midst of mediocre results. “More likely robust” would be a high plateau region of profit. So in this case, you’d want to plot net profit vs. day of the week.

    I think with day of the week you could make a case for insufficient granularity but this is how I conceptualize the overall approach.

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