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stop wasting your time with GEX and DEX - they are literally scams. they sound really cool and make intuitive sense until you actually look at the underlying assumptions and then the data surrounding them. i have because i was really interested in them myself.
the purpose of this post is to prevent you from thinking there's some magic or edge in gex and wasting your time that could be spent on more fruitful work.
if you're interested in it generally, i totally get it dig in - i did. it genuinely is interesting. but interest shouldn't be mistaken with practical.
i have zero dog in the fight outside of being a practitioner myself and continually looking for edge, things that work, and dont. what you see below are outputs of my own research process for myself, this is how i evaluate things like this to see if they are viable in my arsenal. i keep a massive record repository so i can track things over time, cross reference, etc.
you will find citations throughout. my process is to scour all existing research first, then explore in my own dataset to confirm or deny the prevailing findings.
there is one conditional use that is actually viable and it has nothing to do with direction guessing but sizing protocols.
lets start:
[GEX \(this is within SPX\) does about nothing](https://preview.redd.it/fslcvogqc8lh1.png?width=596&format=png&auto=webp&s=e289a9a4d249d2213b599b491281c2e600e06eb6)
below is the assumed behavior:
https://preview.redd.it/wjefp41wc8lh1.png?width=579&format=png&auto=webp&s=189945a0d4f64a2b19e7ca96c601474b30ebb708
if we think about the inputs to GEX and where they come from, there's a bit of an issue:
1. OI - this is solid and comes from the chain, no interpretation. does have a one day lag
2. Strike, Expiry, Multiplier - all standardized, solid - no issue
3. Gamma - this is the output of a model. this itself has way more variance that retail traders understand on average. the design, inputs, etc to the model also hinge on a volatility assumption - which is even more debatable.
4. The sign - this is a complete assumption. its measurable only with capacity tagged trade records which zero of the GEX services have. we have no idea who bought or sold what, we are guessing here. It presumes that people are ALWAYS selling calls and ALWAYS buying puts - which simply is not accurate.
5. Dealer behavior - a complete assumption. account level data literally shows us most operators dont hedge this way for a lot of reasons which i'll explain.
6. Counter party - this entire model assumes that the dealer is on the other side of every trade - not a bad assumption but again, not accurate.
of these, some are directly observable which are completely fine. most aren't and most are imperfect inferences.
example, quote rule tagging classifies CBOE options trades correctly 83% of the time. index spreads and combination trades (15% of the sample i looked at) are misclassified literally 50% of the time.
we have no idea which counter party is a dealer, what dealer net inventory is (we look at one ticker in a vacuum which is the complete opposite of how they look at their books. most of the time, they have a massive basket of things all over the place).
we also have no idea what their offsetting positions might be in futures or other instruments.
onto the vol modeling, below im showing you the gamma for a synthetic SPX chain simply to show how massive of a change differences in input volatility are.
https://preview.redd.it/pifbm44zc8lh1.png?width=595&format=png&auto=webp&s=19c7141eb3492c2a5b3c67b8231d98c4ae9d292f
on the retail side, we CONSUME implied vol - we derive what the market is pricing in by isolating it from the quoted prices. dealers are the ones who need to make the quote - they are the ones actually forecasting volatility and turning it into what we see as implied.
onto the lag issue this becomes a huge issue in 0DTEs where you cannot see OI. you cannot infer anything from volume if it's opening or closing unless volume is greater than OI.
https://preview.redd.it/5sqv2591d8lh1.png?width=599&format=png&auto=webp&s=997b1f4c1f6534a568bcfee11d4fb4373094823c
to the research this is from Hu, Kirilova, Muravyev, and Ryu (2023) and Kospi from another paper.
https://preview.redd.it/kfive0t2d8lh1.png?width=666&format=png&auto=webp&s=42cb144210ca122da38c2063c0ef9821b9fff3fa
the overwhelming majority of market makers, aren't even hedging the way GEX and DEX would need them to for the metrics to mean anything. they often hedge elsewhere, discretely, have a tilt to their book, and biggest the counter party is often not a dealer book. it's vol funds, dispersion desks, systematic writers, etc.
so we have a series of errors that stack onto one another. this leads to an insanely unstable foundation.
[much of the data used for the research i looked at isn't available to any of us](https://preview.redd.it/e7nwcwx4d8lh1.png?width=780&format=png&auto=webp&s=fc701ae6ab6c549678b471893b71e6d467313259)
to observed effects funny enough, dealer gamma is often POSITIVE vs negative
https://preview.redd.it/22b5nhx7d8lh1.png?width=625&format=png&auto=webp&s=899dcc4abb9f7c89ee1d3afe26c86ffe8306e259
consumer option trading follows a completely different path than what the system requires
https://preview.redd.it/vug4sep8d8lh1.png?width=648&format=png&auto=webp&s=59ba3fa8ffe40d58464143ccc81234c583df3860
below is research from my own data:
https://preview.redd.it/xhaq1mncd8lh1.png?width=616&format=png&auto=webp&s=aa3b7267e17085b8817b005f45e3696d841c7d8d
https://preview.redd.it/pqoqrczdd8lh1.png?width=596&format=png&auto=webp&s=68966b27d7315090d28a1d9ad47abbcef91ca248
https://preview.redd.it/9qwc7mdfd8lh1.png?width=665&format=png&auto=webp&s=9b1cc51f343f7dadcd983ba3b0f7d72623bd00d7
stay frosty out there