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Technical Analysis

Turnkey Trading Systems


Important Information and System Requirements:

  1. All code provided is in Amibroker Formula Language (AFL) for Amibroker v5.0 of higher.
  2. All code is sold 'as is' and a working knowledge of Amibroker is suggested.
  3. Purchase includes Operating Code, Monte Carlo Simulation Code and Instructions.
  4. No refund is available on this product after the code has been provided.
  5. All purchasers will be required to sign a Sale Agreement that conatins a non-disclosure clause.

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Survivorship Bias

chart guyLast weeks Double 7's Strategy article proved to be a very popular topic - thanks for all the feedback. In the article I stated, "...we'll use historical constituents to remove survivorship bias" and this has created a few questions, namely what the heck does it mean?

According to Wikipedia, "Survivorship Bias is the logical error of concentrating on the people or things that "survived" some process and inadvertently overlooking those that did not because of their lack of visibility. This can lead to false conclusions..."

When it comes to the stockmarket, investing and systematic trading, Survivorship Bias is not only prolific, but can greatly exaggerate reality going forward. The following chart shows the rise and fall of Regis Resources (RRL), a current member of the ASX-100.

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The Oops! Pattern - In Sample / Out of Sample

Percentage manThe terms In Sample and Out of Sample refer to a technique for backtesting trading strategies where there is some kind of optimisation or data mining taking place, i.e. finding the ideal parameters to trade with. It's common to use two sets of data, however, I was always taught to use three. Here's how it works...

  1. Take a data set, let's use 2000 through 2015.
  2. Divide that set into three, so we'll use 2000-2005, 2005-2010 and 2010-2015
  3. Next, take the middle set 2005-2010, which is known as the In-Sample data, and do all testing, data mining and wanted optimisation on it.
  4. Then take the parameters and rules from that and apply it to the other two sets of data, which are both considered the Out of Sample data.

If the results appear consistent across the entire data series then you may be on to something.

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