Add training workflow, datasets, and runbook

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+Chapter 36: The Basics of Volatility Trading 731 
+probability models. We need to be able to make volatility estimates in order to deter­
+mine whether or not a strategy might be successful, and to determine whether the 
+current option price is a relatively cheap one or a relatively expensive one. For exam­
+ple, one can't just say, "I think XYZ is going to rise at least 18 points by February expi­
+ration." There needs to be some basis in fact for such a statement and, lacking inside 
+information about what the company might announce between now and February, 
+that basis should be statistics in the form of volatility projections. 
+Historical volatility is, of course, useful as an input to the (Black-Scholes) option 
+model. In fact, the volatility input to any model is crucial because the volatility com­
+ponent is such a major factor in determining the price of an option. Furthermore, 
+historical volatility is useful for more than just estimating option prices. It is neces­
+sary for making stock price projections and calculating distributions, too, as will be 
+shown when those topics are discussed later. Any time one asks the question, "What 
+is the probability of the stock moving from here to there, or of exceeding a particu­
+lar target price?" the answer is heavily dependent on the volatility of the underlying 
+stock (or index or futures). 
+It is obvious from the above example that historical volatility can change dra­
+matically for any particular instrument. Even if one were to stick with just one 
+measure of historical volatility ( the 20-day historical is commonly the most popular 
+measure), it changes with great frequency. Thus, one can never be certain that bas­
+ing option price predictions or stock price distributions on the current historical 
+volatility will yield the "correct" results. Statistical volatility may change as time 
+goes forward, in which case your projections would be incorrect. Thus, it is impor­
+tant to make projections that are on the conservative side. 
+ANOTHER APPROACH: GARCH 
+GARCH stands for Generalized Autoregressive Conditional Heteroskedasticity, 
+which is why it's shortened to GARCH. It is a technique for forecasting volatility that 
+some analysts say produces better projections than using historical volatility alone or 
+implied volatility alone. GARCH was created in the 1980s by specialists in the field of 
+econometrics. It incorporates both historical and implied volatility, plus one can throw 
+in a constant ("fudge factor"). In essence, though, the user of GARCH volatility mod­
+els has to make some predictions or decisions about the weighting of the factors used 
+for the estimate. By its very nature, then, it can be just as vague as the situations 
+described in the previous section. 
+The model can "learn," though, if applied correctly. That is, if one makes a 
+volatility prediction for today (using GARCH, let's say), but it turns out that the actu-