One of my readers, let’s call him Francis, sent me an interesting email yesterday. Apparently he had been inspired by Scott’s original post on the use of scatter charts for what I call raw edge discovery (RED), for lack of a sexier term. So he proceeded to spend a significant amount of time on slinging spreadsheets in Excel, which can get quite involved and in my opinion is rather error prone as each extra condition requires the addition of at least one more column. His primary focus thus far had been mean reversion and he is now attempting to apply a similar approach to trending or momentum systems.
A few weeks ago during my trip to Tenerife Scott produced a post on testing mean reversion in the context of parsing for what I personally categorize as ‘Raw Edge Discovery’ (RED). Since I was on vacation I had very little time to contribute to the ensuing discussion but I had been planning to circle back on Scott’s post for two reasons: First I was positively surprised by the high level of interest regarding machine learning and basic system development. Secondly, although being rather comprehensive, I had felt that his post could benefit from a more in-depth explanation of the math behind scatter charts, which of course directly relates to linear regression.