It has been nearly three years since my last update on Midas51, but not for lack of activity. The gulf between updates has been due to project work dominating most of the time remaining after my day jobs have gotten their due.
A progress update is warranted by the 32% compound annual growth rate (CAGR) and -34% maximum drawdown that was yielded in simulation by an ensemble learning model discovered in Midas51’s third iteration.
I spent much of last summer reading up on data science, machine learning, statistical learning, and predictive modeling. Interestingly those topics turned out to be highly overlapping in the details although they differed in terminology. In turn I will refer to them collectively as data science.
My motivation for getting up to speed on data science came from my experience with the second iteration of the Midas51. Here I was still utilizing well known financial market technical analysis, price chart pattern techniques, along with some custom techniques that were inspired by these well known techniques. A lot of experiments were done but none had yielded any results that were acceptable to me. The CAGR was to low, the maximum drawdown to deep, and/or the result was most likely overfit and unlikely to be reproducible.
In the sprint of last year I read numerous books on financial market technical analysis and price chart patterns in an effort to ensure I hadn’t missed a crucial technique. I had not done experiments with Elliot Waves (EW) previously, and it appeared to be worthy of investigating further. After paying subscription fees to watch self-proclaimed experts do EW analysis, it was clear there is a subjective aspect to EW analysis that precludes using simple algorithms to automate EW analysis. EW practitioners also have fairly inconsistent track records. These observations raised concerns that either EW analysis doesn’t work consistently or that human analysts are not able to apply it consistently.
This lead to asking the questions:
- Could EW analysis be enhanced by data science sufficiently that it was fully automated and more consistent than when applied manually?
- Could data science be applied to do price chart pattern discovery?
And thus began my journey into data science land. After getting a solid introduction to data science and some inspiration from Mandelbrot’s The Misbehavior of Market, I had some newly formed hypothesis that I believed to be worthy of making a substantial investment in exploring.
So, with this combination of new knowledge and inspiration I left my position at Tableau Software last September and fully immersed myself into the third iteration of Midas51.
And now back to work!