Improved Moving Average?
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When @quantfblog started following me on Twitter, I was delighted to discover their papers
backed by a nice and improving website http://www.quantf.com. I just could not resist the opportunity to port their improved moving average idea to R and run some additional tests. The entire process was extremely pleasant due to the authors’ willingness to test, comment, and suggest throughout the implementation process. Thanks so much to them for all their help.
In this process, I AM NOT OFFERING INVESTMENT ADVICE. THIS IS SIMPLY A TEST/ILLUSTRATION. PURSUING THESE CONCEPTS WILL MOST LIKELY LOSE SIGNIFICANT AMOUNTS (ALL) OF YOUR MONEY.
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| From TimelyPortfolio |
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| From TimelyPortfolio |
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| From TimelyPortfolio |
For fun, I thought it would be interesting to compare the “Improved Moving Average” to a Mebane Faber style 10-month moving average system.
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| From TimelyPortfolio |
While I enjoyed the testing, I am still not entirely sure if the “improved moving average” is significantly improved, but it certainly might fit someone’s utility curve better than the standard moving average. More than anything, this process has proven to me a couple of things:
1) the beauty of open-source and collaboration. The authors were incredibly generous and helpful as I worked through this process. To even better demonstrate the power of open-source, I will use ttrTests to do additional testing and then the bt examples from http://systematicinvestor.wordpress.com in future posts.
2) how even a simple moving average can become incredibly complex. Making one very slight change markedly changes the results.
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