Z-Score Trading Strategy

Using R-programming, for the purpose of illustration, I am customising a mean reversion trading strategy which involves the use of  z-score:

  • z-score >2.25: overbought (“sell signals”)
  • z-score<-2.25: oversold (“buy signals”)

High-level visualization of the proposed trading strategy (using OKA Corporation as an example):

Slide1

There may be a number of trading signals that have been generated based on the above set of rules. However, the number of trading actions (i.e actual buy or sell transaction) may be far less if it is assumed that one does not buy what one already owns, as well as one does not sell what one does not have. Moving forward, there is a need to (i) back-test this strategy; (ii) benchmark against other trading strategies; and (iii) optimization of this trading strategy.

DISCLAIMER: THIS IS A PERSONAL BLOG AND SHALL NOT BE RELIED IN WHATSOEVER MANNER BY ANYONE. ALL ARTICLES CONTAINED IN THIS SITE ARE STRICTLY FOR INFORMATION AND ILLUSTRATIVE PURPOSES ONLY AND DOES NOT PURPORT TO SHOW ACTUAL RESULTS. IT IS NOT, AND SHOULD NOT BE REGARDED AS INVESTMENT ADVICE OR AS A RECOMMENDATION REGARDING ANY PARTICULAR SECURITY OR COURSE OF ACTION. SOURCES USED IN THIS SITE HAVE NOT BEEN INDEPENDENTLY VERIFIED FOR ACCURACY, COMPLETENESS AND TIMELINESS. YOU SHOULD SEEK INDEPENDENT AND PROFESSIONAL INVESTMENT ADVICE IN REGARD TO YOUR INVESTMENT DECISIONS. THE AUTHOR MAY HOLD POSITIONS IN THE SECURITIES MENTIONED IN THE ARTICLES

Author: Ken Utau

Markets Observer + Food Lover

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