Monday, January 7, 2013

What is the header chart?

The chart I've chosen to use as my blog header represents daily calories burned from around 9 and a half months of readings. By explaining the various filters I've applied to this data, we will begin to see the cool things that can be inferred about our activity levels, things that wouldn't always be readily apparent without the aid of technical analysis.

First of all, the squiggly red line represents a simple moving average of my weekly (7 day) average calories burned. Plotting a moving average allows us to remove a lot of the volatility in the day-to-day readings. If you track personal data, one of the first things you may have noticed is that the values can vary dramatically from day to day. Averaging a series of numbers over time gives us an idea of a typical reading and plotting this value moving through time tells us the direction (i.e. trend) in which that reading is moving. The use of 7 days is a natural choice since so many of our behaviors are attuned with weekly cycles. The result is a clear picture of the overall direction of (in my case) personal activity. This type of analysis can also be very useful in tracking weight since upside volatility can be very discouraging with dieters.

One thing I found interesting about this particular chart was the fact that my spikes below the weekly average (i.e. rest days) tended to rise and fall with my overall activity, when one might expect all rest days to be the same regardless of how active you were the day before. This anomaly is seen in my example, when the weekly average rises to its highest point on the chart (just under 3500), you see a series of higher high spikes but also higher low spikes. This tells me that not all rest days are created the same! As my overall activity levels are rising, I am increasingly less likely to 100% veg out on the couch, even on complete rest days.

The weekly moving average and raw calorie intake data points both oscillate around the black/red linear regression line on the chart as well, which is a mathematically generated "center trend line". The result is a line the splits the data in half and can therefore quantify basic trend direction by looking at the angle/slope. In this case, the slope is down since it moves from higher left to lower right as the chart moves through time. This was to be expected as I lost 25 lbs during the course of these readings! As I moved closer and closer to my target weight, my focus on pure calorie burning diminished.

We've only scratched the surface in terms of what we can determine from a chart like this. Cycle analysis can tell us our typical exercise or behavior intervals. Measuring data extremes vs. average values (or the relationship of 2+ averages) tells us when to expect cyclical behavioral reversals and precisely what level of activity is needed to change broad physical trends. At the very least, this chart tells me that even if I lost my tracker, 2500 calories is break-even for food consumption on even my least active days (save for Christmas at the in-laws....the lowest dip to the far right of the chart!!)

Friday, January 4, 2013

Hello world!

There are many insights that can be gained by applying methods of financial market technical analysis to self-quantified data. This type of analysis promotes increased awareness of behavioral trends and cycles, and does so for anything that the user wishes to track. From calories burned to cups of coffee consumed, charting self quantified data and applying measurements of trend, momentum, and volatility, among other measurements, will quickly become an indispensable resource for those with precise exercise related goals and those who wish to understand more about themselves and their behaviors. As for me, my name is Tucker Sferro. I am a professional commodity trader who has been using technical analysis to navigate the financial markets for over a decade now. I hold the CMT (Chartered Market Technician) license, and aim to use these skills to bring a better understanding of the nature of self quantified data.