Add training workflow, datasets, and runbook

training_data/curated/text/40344aaf160391b165b7a0c3f7fc75d99403efdb880cd92f88b7c240b986134b.txt

@@ -0,0 +1,38 @@
+808 Part VI: Measuring and Trading Volatility 
+of them resulted in the stock being unchanged. Also, only about 2,500 or them, or 
+1110th of one percent, resulted in a move of-4.0 standard deviations or more. Those 
+percentages, along with all of the others, would be built into the computer, so that 
+the total distribution accounts for 100% of all possible stock movements. 
+Then, we tell the computer to allow a stock to move randomly in accordance 
+with whatever volatility the user has input. So, there would be a fairly large proba­
+bility that it wouldn't move very far on a given day, and a very small probability that 
+it would move three or more standard deviations. Of course, with the fat tail distri­
+bution, there would be a larger probability of a movement of three or more standard 
+deviations than there would be with the regular lognormal distribution. The Monte 
+Carlo simulation progresses through the given number of trading days, moving the 
+stock cumulatively as time passes. If the stock hits the break-even price, that partic­
+ular simulation can be terminated and the next one begun. At the end of all the tri­
+als (100,000 perhaps), the number in which the upside target was touched is divided 
+by the total number of trials to give the probability estimate. 
+Is it really worth all this extra trouble to evaluate these more complicated prob­
+ability distributions? It seems so. Consider the following example: 
+Example: Suppose that a trader is considering selling naked puts on XYZ stock, 
+which is currently trading at a price of 80. He wants to sell the November 60 puts, 
+which expire in two months. Although XYZ is a fairly volatile stock, he feels that he 
+wouldn't mind owning it if it were put to him. However, he would like to see the puts 
+expire worthless. Suppose the following information is available to him via the vari­
+ous probability calculators: 
+Simple "end point" probability of XYZ < 60 at expiration: 10% 
+Probability that XYZ ever trades < 60 (using the lognormal distribution) 20% 
+Probability that XYZ ever trades < 60 (using the fat tail distribution): 22% 
+If the chances of the put never needing attention were truly only 10%, this trader 
+would probably sell the puts naked and feel quite comfortable that he had a trade 
+that he wouldn't have to worry too much about later on. However, if the true proba­
+bility that the put will need attention is 22%, then he might not take the trade. Many 
+naked option sellers try to sell options that have only probabilities of 15% or less of 
+potentially becoming troublesome. 
+Hence, the choice of which probability calculation he uses can make a differ­
+ence in whether or not a trade is established. 
+Other strategies lend themselves quite well to probability analysis as well. 
+Credit spreaders - sellers of out-of-the-money put spreads - usually attempt to quan­
+tify the probability of having to take defensive action. Any action to adjust or remove