Add training workflow, datasets, and runbook

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+Chapter 38: The Distribution of Stock Prices 801 
+back over the last 1,000 trading days for XYZ. A 100-day historical volatility can be 
+computed, using 100 consecutive trading days of data, for 901 of those days (begin­
+ning with the 100th day and continuing through the l,000th day, which is presumably 
+the current trading day). Admittedly, these are not completely unique time periods; 
+there would only be ten non-overlapping (independent) consecutive 100-day periods 
+in 1,000 days of data. However, let's assume that the 901 periods are used. One can 
+then arrive at a distribution of 100-day historical volatilities. Suppose it looks some­
+thing like this: 
+Percentile 100-Day Historical 
+oth 34% 
+10th 37% 
+20th 43% 
+30th 45% 
+40th 46% 
+50th 48% 
+60th 51% 
+70th 58% 
+aoth 67% 
+90th 75% 
+1 ooth 81% 
+In other words, the 901 historical volatilities (100 days in each) are sorted and then 
+the percentiles are determined. The above table is just a snapshot of where the per­
+centiles lie. The range of those 901 volatilities is from 34% on the low side to 81 % on 
+the high side. Notice also that there is a very flat grouping from about the 20th per­
+centile to the 60th percentile: The 100-day historical volatility was between 43% and 
+51 % over that entire range. The median of the above figures is 48% - the 100-day 
+volatility at the 50th percentile. 
+Referring to the early part of this example, the current 100-day historical is 
+80%, a very high reading in comparison to what the measures were over the past 
+1,000 days, and certainly much higher than the median of 48%. 
+One could perform similar analyses on the 1,000 days of historical data to deter­
+mine where the 10-day, 20-day, and 50-day historical volatilities were over that time. 
+Those, too, could be sorted and arranged in percentile format, using the 50% per­
+centile (median) as a good estimate of volatility. After such computations, the trader 
+might then have this information: