Add training workflow, datasets, and runbook

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+806 Part VI: Measuring and 1iading Vo/atillty 
+at any time during the life of the probability study, usually the life of an option. It 
+turns out that there are a couple of ways to approach this problem. One is with a 
+Monte Carlo analysis, whereby one lets a computer run a large number of random­
+ly-generated scenarios (say, 100,000 or so) and counts the number of times the tar­
+get price is hit. A Monte Carlo analysis is a completely valid way of estimating the 
+probability of an event, but it is a somewhat complicated approach. 
+In reality, there is a way to create a single formula that can estimate the "ever" 
+probability, although it is not any easy task either. In the following discussion, I am 
+borrowing liberally from correspondence with Dr. Stewart Mayhew, Professor of 
+Mathematics at the University of Georgia. For proprietary reasons, the exact formu­
+la is not given here, but the following description should be sufficient for a mathe­
+matics or statistics major to encode it. If one is not interested in implementing the 
+actual formula, the calculation can be obtained through programs sold by McMillan 
+Analysis Corp. at www.optionstrategist.com. 
+This discussion is quite technical, so readers not interested in the description of 
+the mathematics can skip the next paragraph and instead move ahead to the next sec­
+tion on Monte Carlo studies. 
+These are the steps necessary in determining the formula for the "ever" proba­
+bility of a stock hitting an upside target at any time during its life. First, make the 
+assumption that stock prices behave randomly, and perform at the risk-free rate, r. 
+Mathematicians call random behavior "Brownian motion." There are a number of 
+formulae available in statistics books regarding Brownian motion. If one is to esti­
+mate the probability of reaching a maximum (upside target) point, what is needed is 
+the known formula for the cumulative density function (CDP) for a running maxi­
+mum of a Brownian motion. In that formula, it is necessary to use the lognormal 
+function to describe the upside target. Thus, instead of using the actual target price 
+in the CDF formula, one substitutes ln( qlp ), where q is the target price and p is the 
+current stock price. 
+The "ever" probability calculator provides much more useful information to a 
+trader of options. Not only does a naked option seller have a much more realistic esti­
+mate of the probability that he's going to have to make an adjustment during the life 
+of an option, but the option buyer can find the information useful as well. For exam­
+ple, if one is buying an option at a price of 10, say, then he could use the "ever" prob­
+ability calculator to estimate the chances of the stock trading 10 points above the 
+striking price at any time during the life of the option. That is, what are the chances 
+that the option is going to at least break even? The option buyer can, cf course, deter­
+mine other things too, such as the probability that the option doubles in price ( or 
+reaches some other return on investment, such as he might deem appropriate for his 
+analysis).