Add training workflow, datasets, and runbook

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+Chapter 28: Mathematical Applications 477 
+upward potential was 65/s points, while the downward potential is about 5¾ points. 
+This difference is due to the use of the lognormal distribution. 
+Step 8: Using the Black-Scholes model, one could estimate that the XYZ 
+January 40 call would be worth about 11/s if XYZ were at 35¼ in 90 days. 
+Step 9: The risk potential in the January 40 call would be: 
+. 4- l1/s 27/s Percent nsk = --- = - 72% 4 4 
+Step 10: The reward/risk ratio is merely the percentage reward divided by the per­
+centage risk: 
+Reward/risk ratio = l03% = 1.43 72% 
+Step 11: This analysis would be repeated for all XYZ options, and then for all other 
+optionable stocks. The less aggressive call purchases would be ranked by their 
+reward/risk ratios, with higher ratios representing more attractive purchases. More 
+aggressive purchases would be ranked by the potential rewards only (step 5). 
+This completes the call buying example. Before leaving this section, it should be 
+noted that the assumption of ranking the purchases after one full standard deviation 
+movement by the underlying stock is probably excessive. A more moderate assump­
+tion would be that the stock might be able to move . 7 standard deviation. There is 
+about a 25% expected chance that a stock could move up at least . 7 standard devia­
+tion at the end of a fixed time period. 
+PRICING A PUT OPTION 
+Theoretical models for pricing put options have been derived; that is, ones that are 
+separate from call pricing models. Black and Scholes presented such a model in their 
+original paper. However, as has been demonstrated, there is a relationship between 
+put and call prices in the listed option market due to the conversion and reversal 
+strategies. 
+One could use the ba,sic call pricing rrwdel for the purpose of predicting put 
+prices if he a,ssumes that arbitrageurs will efficiently influence the market via con­
+versions. Theoreticians will argue that such a method of pricing puts assumes that the 
+arbitrage process is always present and works efficiently, and that this is not true. The 
+"conversion efficiency" assumption could be a serious fault if one were trying to 
+determine the exact overpriced or underpriced nature of the put option. However, if 
+one is merely comparing various put strategies under constant assumptions, the arbi­
+trage model for pricing puts works quite well.