Who are “WHITE HATS?”

The “White Hats” is an allegorical reference to the “good guys” taken from American movie tradition, in which the good cowboys always wore white hats and the bad guys (bandits, rustlers, thugs) wore black hats.
Used to refer to the secret group;

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The Straddle is my favorite trade these days. But more often than not, one needs to adjust to make a good return from it. There are many ways to do this depending on your view, psychology and imagination. Will discuss a few that I use

The Straddle is a delta neutral strategy and it is important to keep deltas in check during the course of the Trade and not let them run away from you. Of course Volatility is the elephant in the room- but not going there as that will require a separate discussion

The first and most common method is to go 'inverted.' That is to say to shift the untested side to generate additional Premium and increase the runway to Breakeven. So if you start with a Straddle at 700 and price goes to 740, you can move the PE up to say 720.

How much should you move the untested side? While again this is a matter of skill, experience and artistry, the rule of thumb is to cut deltas by half. Remember, prices will move and you have to allow sufficient room for them to fluctuate

The second method is to shift the entire Straddle in the direction of the move. So if you have a 700 Straddle and Price goes to 740, you can shift the Straddle to 720. If you time this right, it can generally be done at virtually no cost or minimal cost.

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.