What Are Gamma & Gamma Exposure - GEX Full Course Ep.2 transcript

Hello. Hello everyone. Welcome back to deep charts. Let's see for live. Let us know if you can see and hear everything clearly. Is the audio okay? Deep gamma will be available soon for all the deep charts user. I think in maximum a few days uh it should be available. Let's see if everything connects. Seems like we're live. Give a last check in the chat, guys. Okay, awesome. We are indeed live. So, welcome again into this deep gamma boot camp where you will learn everything that you need to know around the basics of gamma exposure and why it matters for futures traders but even CFD traders, anyone really who trades predominantly the stock market like ES the E-IN S&P 500 futures or its related CFDs. uh the NASDAQ futures or its related CFDs and pretty much anyone who trades indices should know what gam exposure is and why it affects market movements.

And this boot camp is exactly for that because in our pursuit of becoming professional traders and traders who can win in the markets, having access to the highest quality of information possible is crucial. And it doesn't mean adding a bunch of extra indicators on your chart. It means having all the information you need, not on lagging indicators that statistically could maybe work. Sometimes yes, sometimes not. We're specifically talking about understanding the flows of the market.

We're mostly orderful traders here in the chat. So, we want to know why these flows might happen, right? And they do not happen just for fundamental reasons or because Trump just tweeted that he's going to kill everybody or because he's going to start printing money all of a sudden. Not just because of that, but also because of some technical reasons. And some of these technical flows are these options dealers hedging flows and the effect the interaction of stock prices either compressing volatility mostly by the way compressing volatility and augmenting volatility in the situations that we will see today especially.

So what we're going to learn to today and what we're going through into this boot camp is first the basics of options and yesterday and if you haven't watched yesterday's live session you have to watch it uh or if you're watching this video later uh in the playlist there should be a first video before this one that you need to watch in order to be able to follow up and understand what we're going to talk about today.

And in the [clears throat] second episode, we're going to get deeper into uh some things that we could not explain yesterday because we've already put a lot of meat on the fire as we say in Italy. Uh we've already talked about a lot of topics that needs to be processed for the beginners of uh options. But today we're going to get deeper into some other elements of the option pricing model and what gamma is and how GAM exposure actually generates some sort of regimes statistically in the action of today's market.

So if you're excited guys, leave a two in the chat and we're going to see the gam exposure on deep charts again. uh we're finalizing a couple things on the database side because we receive we finalized the receiving of uh a lot of CBOE data. So uh to today we're going to see mostly examples and screenshots and together with Fabio tomorrow we're going to see a little more action because tomorrow is or the next video if you're watching later is the session where we're going to get deep into how to use it, how to combine it with orderflow and option flow all together.

And at the end of this week on Friday we have the last appointment which is the fourth video of this series or fifth video of this series with Rodulfo which is the engineer behind uh the deep gamma suite inside of deep charts. So it's going to be awesome. I hope you guys are excited and we can start sharing the screen and getting into what we're going to learn tonight. So yesterday we've learned what an option is, the difference between calls [snorts] and puts.

We've understand that options [clears throat] contract are a little bit like insurance contracts where you have an expiration which is an important part of the pricing. You have the price of the underlying asset that is an important part of the pricing. We can buy at the money calls, in the money calls, out of the money calls or we can buy or sell in the money puts at the money puts out of the money puts. Right? So this thing is called moneyiness.

Then [clears throat] in the pricing of each option and call being the ones that give you the right to buy, puts being the one that gives you the right to sell. The pricing of these option contracts are determined mainly by the price of the underlying asset through gamma and delta, the time to expiration through theta, and the implied volatility measured by Vega. And we've also learned the payoff charts of options. We've seen that [clears throat] if you buy a call, you basically pay for a premium.

You pay a premium to get the right to buy at 500. And your option price as price of the underlying asset moves up, your profit and loss, the the value of your option increases. And how fast this increases is delta. If you sell a call instead, you cash in the premium and you could potentially lose indefinitely and you cash in and then you start losing money. That's selling the call. Buying the put instead, it means you buy the right to sell.

So when price falls, the underlying price falls, you make money. So it's basically taking a directional bet that price is going to go down and selling the put means taking the other side of the trade. So cashing in that premium potentially losing a lot of money. But as we will see there's an advantage to this and we've understood the Greeks. We've understood that the delta because let's build another long call. We've understood that [clears throat] the way price moves up and down or the [clears throat] profit and loss moves up and down is initially linear, right?

But only at expiration. And in the meantime, a lot of things can happen. You know, the fire could start as we say in the insurance language. So, as you can see, there's this curve that goes up gradually. So, from not changing at all, it changes slowly. start going up until it reaches linearity. Right? So [clears throat] the speed at which this line goes up, the tangent of this line is what we call delta. How fast price how fast let's say the profit and loss of our option goes.

And the option premium goes at a change of $1 of the underlying asset. And gamma as we've seen is how much does delta change for a $1 move in the underlying asset. Okay, so this is what we've covered in the last live session. Now I want to get back to buying the call and selling the call. Buying the put and selling the put. Why should one buy or sell in the first place and the concept of standard deviation and option structures.

So [clears throat] let's make an example with this the the good old buy call. Let's put it like yesterday on Apple. Let's say we buy a 30 days to expiration Apple call at the money. Okay. So as you can see there is something in the background here that we define as the standard deviation. Right? This is a Gaussian curve. You've probably seen it before. Uh this is a Gaussian curve. Let's understand what this is because this is crucial because it's a very important part of the pricing model of options.

So when we measure in anything that we can measure really u a sample of data most of things we can measure will fall under what we call a standard deviation right so if you for example let's draw this a little bit better if for example we're measuring the IQ of a population, right? There's going to be [clears throat] a certain amount of people having a low IQ below 80, some people having average IQ and few people having a super high IQ.

Right? Most of things that we can measure in nature in such samples fall within what we call a standard deviation which is around 68% of the data 68.9% of the sample. So yeah you can let it free. So most of the samples will fall under the normality range. This is why we call also the normal distribution of events. Right? So around 70%, let's round it up to 70%. You've probably seen this in the volume profile as well, right? and around 30% so we could say 16% here and 16% here will be the outliers and someone yesterday asked me what is a tail event and a tail is this part over here in the distribution we define a tail because it looks like a tail you know they're the tails literally the tails of the curve where very few events tend to happen.

There's very few people that are going to be extremely uh low IQ for example 60 and there's going to be very few people genius level IQ with 145 right these are the tail events events that happen in very few cases and we know this with the volume profile right you've also seen that the volume profile what it does it creates the standard deviation of volume so most of the volume 70% % of the volume is traded within the normal range of distribution.

And anything that happens outside is statistically speaking an anomaly, a tail event that most of the time tends to revert back to the mean. Okay? And the mean being the middle. Okay? That's the mean. When we see when you see no mean reversion strategies, it means that it reverts back to the mean and reverts back where it usually stays. For example, in a consolidative environments, anything that happens outside can be considered a tail event.

And for example, we call this failed auctions, right? Failed auctions. So in the volume profile, you probably already heard us talk about this, right? But in the matter of options, we consider the standard deviation that you see in the background here. That's the part of how these options are priced. We consider this to be the implied volatility or let's say the average returns. So for example, let's say the price of the our Apple call is 305, right?

Well, if we look at historical returns, so we average out how much price has moved on average, what the volatility was historically, the so-called historical volatility or realized volatility. And we see that most of the time within 31 days it stayed between this range. So between around 330 and 280. and 330. Okay. So, plus minus 25 points more or less. So we what what the option market is telling us by how these options are priced is that we will likely stay within this range around 67 69% to 70% of the time.

Okay, there might be a few events, right? extremely high volatile events that don't happen very often that happen to be very low likelihood of happening and of course the probability distribution there will be less. So if 60% or as we said 70% of the events will happen inside of this range then here we're going to have around 16% chances and here we're going to have around 16 right and if you see here when I move my mouse around we have this percentage probability here see 26 and 74 from here it's 50/50 Right?

There's 50% chances statistically speaking that price will stay down or up from here, right? Slightly in the money or slightly out of the money. That's 50% chances. The more we move here, you see how it goes. 16% chances of staying that much in the money and 60% 16% chances of staying that much out of the money. Okay. As you can see, there's a little skew. It's not really it's not really um it's not really the same. There's a little more belly here, right?

There's a skew in the probabilities that it will go higher. So, it's more probable that will go higher rather than lower. This is what we call the skew, okay, in the option prices in the volatility. Uh that what's that's what's create the the so-called volatility mile. Maybe we'll go deep into it in in some of the next sessions. So this is what you need to understand in terms of the distribution of probabilities. Now back to our long call, right?

Some of you guys asked, well, why why should I even sell, right? Why should I even sell? It seems like I lose indefinitely, right? And I can lose potentially a limited amount of money and my profit is capped. That makes no sense, bro. Well, yeah, but what you didn't see is this part over here, because this part over here makes the game fair. So, if you have 50% chances of expiring slightly out of the money and 50% of slightly in the money, that's great.

You would say I have 50 50% chance of going to profit. But consider that for this range, you're not in profit yet. You're still losing because you have to pay back the premium you paid $1,000, right? So here, you're still losing money. So technically, you're in profit from here on. And in fact, as you can see, if I go here with the probability distribution, it's around 66% chances of losing money and 35% chances of making money.

H, that's much different. And if I sell this option instead, it's exactly the opposite. So, you will see that I have exactly the opposite. 66% chances of closing my trade in profit because I still cashed in, right? I have to lose $1,000. Like, it has to move 10 points before I actually lose money. And I have exactly 34% chances of losing. So, I may have a on average lower risk-to-reward ratio on my trades because I can lose way more than I potentially make, but at the same time, my win rate technically is better.

So there's an advantage in selling options, right? There's an advantage in selling options because of how these options are priced. So this is why it makes sense to do either, right? You have it's always you you always have a balanced in in everything in financial markets. There's always a risk and there was always an opportunity. Okay. So both buying options and selling options has its risks and has its opportunities.

Now as I was saying because of how these options are priced they price in the implied volatility in this $1,000. There's implied in this price what the market expects the volatility of today to be. So, let's say we go with SPX. Let's try with an SPX option that maybe has a lesser skew. And this is going to be more and more looking like let's do a put. Let's see a put. Switch to put. Okay. Yeah, it's around the same now. the chances of profit are now 64%.

Right now, another thing you need to know is that options can be bought and sold together and there's the so-called option structures. Okay? You cannot just, you know, sell a call and that's it. Right? As we said, you can if the price of S&P now is around 7,700, we can sell this put slightly in the money, right? Or slightly out of the money, right? Look at what happens when I take this put and I sell it out of the money.

Look at what happens to my chance of profit. 85% chances of profit. Why? Because the probability distribution says that I have around 85% chances based on historical data and based on the normal distribution of events that we will fall above these prices rather than below. And a lot of strategies that traders and investors use are all about selling options, selling out of the money options. When you see all of these volumes in uppers and lower strikes, it's all out of the money options.

Buying and selling. Now if I what what another thing I can do right is I can buy or sell in this case a put and I can also sell a call right and let's say the volatility now it's 10%. You will tend to see that with this type of structure where I I'm selling both a call and a put at the same time. And we call this a straddle, right? And in this case, I'm earning money from a put and I'm earning money from a call selling them at the money and I'm only making profit if price stays within this range.

So if price stays within one standard deviation, exactly one standard deviation as you see because of how options are priced, a straddle corresponds to the expected move, right? I will earn money only if price stays here. And as you can see, the break even line is exactly where the standard deviation is. I have exactly 69% chances of being in profit if the implied volatility is around 10%. If the volatility increases though, of course, as you can see, the probability distribution will change.

So the implied volatility is the standard deviation of whatever market expects today's volatility will be like. But normally an options will be priced so that if you sell both a call and a put at the same strike at the money this straddle will look something like this will fall under the exact standard deviation. This is this happens most of the times you know sometimes it's a little bit uh higher if the volatility is higher if volatility is lower it's going to be slightly inside and but this is the concept right within the prices within the premium that I'm cashing in at the moment it's already priced in the fact that most of the time price will stay within this range around 70% of the time okay and of course implied volatility can make this you know vary Okay.

And this is just one option structure, right? There's multiple option structure. This is a short straddle because I'm selling a put and a call that the same strike. But what if I sell them out of the money? I sell them slightly away. Well, in this case, I'm just betting on the fact that price will stay within this range. I will earn way less money. As you can see, because out of the money options are cheaper, right? But I will have an 86% chances of profit.

This is a straddle, a strangle, sorry. Another thing that I can do is, for example, I can buy a put and I can sell a put at slightly different strikes. Why doesn't it add by put. Okay. Yeah. And I can for example buy a put and financing it with a put that I'm selling. And basically what this does, it creates this structure where I lose for example maximum 300 $3,000 but I can earn $2,000 with a 53% chances of profit.

This is what we call a spread, right? And there's so many guys, so many of these structures that you can build. You can buy uh iron condor. You can sell an iron condor. Let's see if we can find it. Or a butterfly, for example. You can buy put ratio back spreads and broken wings. And there's so many combinations of buying calls, buying puts, buying calls, buying puts because you're not just buying and selling futures or or CFDs or betting on one direction.

You're buying insuranceances on different strikes at different expirations. And you can be much more creative with how you express your edge, yourformational advantage. So a lot of the times and this is going to be useful to to understand how these options work and how these option structures work. You're going to see in the gam exposure of market makers these huge positions and you'll be able to understand what they are and I'm going to show you some example later.

But is everything clear until now? So what we've learned until now is that we can have multiple structures of options. We can buy sell options at different strikes. We can build very complex structures and the standard deviation right is where we expect most of the sessions to stay within. And the options are priced in such a way that within the price of the option we have this implied volatility. Is everything clear?

Let's see the chat. You mentioned when you sell a call, you could put a stop loss so you don't lose it definitely. But then who buys that from you? a market maker typically feels like magic being able to make money while price is absolutely yeah it's it's awesome it's absolutely awesome like you can just bet on price staying there right it's uh it's something you cannot do with other with other strategies okay now by now you should have a deeper understanding of how the option is priced how the probability of an event happening is shaping the price of an option and how uh option structures can be used to express different advantages okay differentformational advantages and you should understand that for example if I let's say buy let's Say if I sell a call.

Uh one second. I sell a call and I sell a put. This is called a straddle. And if I zoom in, I can see that I'm betting on price staying within a range within a normal range. And we could understand also that when I'm selling options, right, I'm technically long del long theta, right? So time decay slowly cooks my profit and Vega is against me because I'm selling volatility. If volatility goes high then I lose more money, right?

And how about delta and gamma? Well, selling options means I am short gamma, right? Gamma affects my price negatively. And because of because of this thing because of this weird thing, let's get back to a long call. Because of this thing of options that they expire and there's implied volatility involved, blah blah blah, you have this curve, right? So now back to back to what option market makers do right in every market there's market makers and market takers and especially in the option market makers are around 50% of the volume.

What does this mean really? Let's try to understand this. So when you buy or sell, let's say you want to buy market, you guys should pretty much all know that you have the bid, right? Where you have all the buy limits, right? at lower prices. At the upper prices, you have sell limits, right? You guys should remember this if you're order flow traders. So, you have sell limits above the price and buy limits above the price. >> [snorts] >> And what this means and what these are are the ask and the bid.

You guys all know this, right? You know what I'm talking about here. Write three in the chat if you know what I'm writing down. And typically the best bid and the best ask are the prices at where you can buy or sell market. Let's say we're on the NASDAQ and this is 22 500.25 and a little bit lower the tick below is 50. Sorry actually 0 0. Awesome. So who typically sells to you is a market maker most of the time. Okay.

So if you buy market, you're going to basically accept to pay 22500.25 to buy now, right? Because you're an aggressive participant. You buy market. You're an aggressive market participant. Market makers instead are passive. You remember all this. If you sell markets, you basically choose to accept whatever price is made by a market maker by the closest guy that is wanting to sell sorry to buy to you if you sell market.

And what the market makers do in this case is they earn the bid ask spread. This is what market makers do in every single market. So they earn the spread that traders pay in order to trade. And the goal that the market makers have is only this. They only want to earn the spread. So they will sell [snorts] here and they will buy back here. And price most of the times does this right? Stays there. Tuck, tuck, tuck, tuck, tuck, tuck, tuck, tuck.

So it goes up and down, up and down, up and down. Bid ask, bid ask, bid ask, bid ask. In a normal finished auction, you see this happening, right? So market making firms, the only business model they have is providing the bid and the ask to traders to earn the spread. And this is a hugely hugely profitable business. Okay, but this is what happens in the NASDAQ, right? And let's say that price starts going up up up up and doesn't tick down.

Well, they'll start losing money. They don't want this to happen. Market makers prefer sideways market in directional markets like NASDAQ or you know stock markets that are directional. So in case they start selling too much, they have to unload that exposure because they're selling here, selling here, selling here, and like they're mediating at a loss. They're constantly losing money, right? So they need to hedge themselves maybe by rebinding later or, you know, neutralizing their exposure.

Okay? Because their only reason is not the the the business model of market makers is not to earn from markets going up or down. It's earning the spread. That's it. That's all markets are market makers are doing. They're providing liquidity to the market. Okay. So market makers provide liquidity to traders and making it easier for them to trade and aggressive participants or you know the market participants will basically pay the spread pay a slightly higher price.

Okay. Now this is an example on the NASDAQ. Then in options is slightly different because I don't know if you guys have ever traded options but when you see an option chain this is how it looks like. Let's see if we can find an option chain. Let's use this one over here. This is an option chain and this is basically the book of options. [snorts] And you know how we have the bid, one bid and one ask in the NASDAQ. Here we have a bid and an ask for the at the money strike.

We have a bid and an ask slightly below. Another bid and ask. Another bid and ask. Another bid and ask for the calls. So every strike has its own bid and its own ask. Every strike of every call of every expiration. Okay, this is just one expiration. Here we have on the puts one bid, one ask, another bid, another ask, another bid price, another ask price, bid ask, bid ask. Every single strike of every put of every expiration has its own bid and ask.

So imagine it's imagine the books of options just like this times every expiration and times every strike. So there's like not one book of the mini NASDAQ. There's like one book for the 40 45, one book for this, one book for that, one book for that. There's like multiple books running at the same times every single day. So it's much more complex. The the mathematical formulas used by options market makers are very very uh let's say detailed and complicated.

So this is how it looks like and what they do is still the same. For example, let's just take into consideration these. They might on the bid offer to buy at 4740. This is the premium, right? And they might sell at 4910. So, how much money are they earning here? Well, around $2 minus 30. It's around $1.70, right? So they they're earning $1.17 per contract, which actually results in times 100. So $170 every time someone trades.

Okay? So it's a massively profitable business. Every time someone buys or sells or buys or sells or buys or sells, they make $170 per contract. It's a hugely profitable business. What is the risk? The risk is if you start losing money in the position you just took against any trader. So market makers also here will buy and sell and will provide liquidity. So market makers will provide liquidity and earn the spread and that's what they do.

But what they do not want is for price to start moving around, right? Because then if for example, you know, Fabio to today buys 100 calls on S&P and he's long, right? Well, the market maker will be short because he's exactly the counterpart. And of course he's short at a slightly better price, right? Because he made the price. The market makers make the price. So, but now he's short 100 calls. Let's see how that looks like.

Minus 100. Look at that. Well, every time price goes a little bit lower now, he start losing money. losing more and more and more and more, right? And here it's not he the market maker here doesn't lose a lot of money, but here he does. And here, as we said, he loses around 50. Here it loses around 100 because of delta. Here it loses zero. So what the market makers will have to do in case price moves down one point is he will have to sell 50 times S&P in this case and there's no S&P right because SPX is just the weighted average of all the 500 stock in the S&P 500.

So he will either have to sell in this case actually to buy to offset this position. He will have to buy 50 either spy or ES contracts or maybe he will do it also diversified in the single stocks of the S&P 500. Maybe he will, you know, hedge on the NASDAQ or he will hedge on the single stocks of the magnific Magnificent 7 or he will find ways to hedge this position. And this is the whole thing about gam exposure because of the multiplying factor of options as we discussed in the first webinar.

If this is the notional value of futures, if this is the notional value of the stock market, if this is the notional value of ETFs, ETPs and blah blah blah in terms of daily volume, the volume traded in options is this. So being the fact that options market makers are half around 53% of the transactions that's a ton of volume that they need to hedge a lot because zero DTE options are so traded as of today mostly by retails by the way that these market makers have to hedge and this hedging activity has an impact an impact that has been proven by multiple scientific studies and one of them was the one that we've also shown in the first presentation which is this which is the one from squeeze metrics.

Okay. So we we'll get into that how that works right now. But first let's understand how they actually hedge, right? Because when they're short 100 calls of S&P, let's um let's do this simple. Let's do it with Apple stocks and let's try with one Apple stock not 100 just to make the calculations very simple. Okay. So because of this curve that the profits and loss of the market makers will make he will not hedge his position linearly because here every point he has to hedge by buying a 100 stocks.

So here to offset this loss he will have to buy the same amount or the amount of stocks that will cause the same profit and loss and here is 100. So here he will buy 100 shares per point around here in this area he will buy to offset this loss because price is going up. he's short a call and he's losing money as price goes down here instead he will have to buy 50 shares to hedge this exposure right so the profit and loss of that will go something like this and he has to do it dynamically first 50 then 60 then 70 then 80 then 90 then 100 that's why we call this dynamic delta hedging because of this curve Because of this convexity, they will need to hedge this dynamically.

So they will engage in dynamic delta hedging. That's why the first type of thing you see on option flow software is the DEX or the delta exposure. We call it GAM exposure because delta changes, right? And so we call it gam exposure because of the convexity because of the fact that we we do not want to see just how delt how the price changes but also how delta changes. So that's why we also call it gamma exposure. It's the same principle but calculated differently. calculated based on gamma because gamma takes into consideration not only how the price of the option changes but also how delta changes not only how fast it goes but also the acceleration.

So because they will have to hedge a lot of these contracts because as we said there's a there's a 100x multiplier then even one call sold by a guy will result in market makers hedging a lot of contracts especially if it starts going out of the tails. So a lot of the flows that will come inside of the markets will be because of this delta hedging from market makers. This is the main idea and squeeze metrics asks themselves this question in where the only data we had was the end of day open interest.

So every day we would see how many trades were open or closed within that day. And based on the open interest, what they would assume is that market makers took all the other sides of the trade. So market makers were always the counterpart and they assumed that they were always buying puts and selling calls. sorry that they were selling puts because investors typically buy them and typically buying calls. So they came up with this idea, right?

So they calculated the open interest. They multiplied by the gamma times 100 and they would calculate the notional volume in terms of billions of dollars of their gamma exposure of their net gamma exposure. Not based on each strike yet, just the overall gamma exposure they have. And they would notice that if they were exposed negatively to gamma, the days would be more volatile. If they were long gamma, the those days will tend to be less volatile.

So they found a at least a correlation, but also you would say a causation. So why does this matter? Because as we've seen when they're short gamma so they're selling option so short gamma it means they are either short call or put same thing what do they need to do well price is rising I'm losing money so I need to buy the stock the underlying stock here so that starts is making money and it offsets the loss and I'm neutral.

I'm at break even. That's all. That's it. That's what it is. So, if price rises, I have to buy. Okay, got it. So, if price rises, I keep buying onto it. This will increase the volatility to the upside. So in a short gamma scenario where they've shorted a call, if price goes up, they buy into price rising and this will exacerbate the current level of volatility and it will exacerbate just a little bit here. 50 50 shares per point here.

A little bit more here. way more here. [snorts] Way more here. We sell, we buy the out of everything. So that's when the squeeze, the gamma squeeze happens because that's where there's no convexity anymore, but the the the the growth of the loss is linear. Now, let's say we bought a put and these market makers have sold us a put. Well, if price goes up, they don't really have to hedge much. If price goes down, well, they have to start hedging.

They will start hedging. How? Well, the price is going down and I'm losing money. So, if I sell the underlying stock, the profit and loss of that will be something like this, right? Linearly up. But it can't I cannot do it linearly, right? I have to do it a little bit and then gradually up with a curve dynamically. So, I will sell when price falls 50 shares here. I will sell 60 per point. Here I will sell 100. So as price falls me as a market maker I sell.

So I sell the dip. And so if he's a short a put and price falls, he will sell into every time price falls exacerbating the volatility to the downside. So, they're going to buy the rip and sell the dip. So in a short gamma scenario because of what they need to do here, well, if they're short gamma and it's a very volatile day and maybe the fire starts, look at what happens. If implied volatility hikes by any chance, and this is the so-called van exposure, they will have to hedge even more.

And then what? When time passes, as time passes, they're going to have to hedge more and more at the money. And especially in the last few days, in the last few hours at the money, they will have to hedge a lot. So, first they will have to hedge a little bit and then more and more and more and more and more, right? So, there's also a time component and a volatility component, which are the charm and the vanna, but we'll get deep into that as soon as we release the functions.

So, this is if they're short gamma. Let's see if they're long gamma. So, if they just bought and just bought long calls and puts because we sold it to them. Well, how does it look like? Well, if price, let's say I bought a put and price falls, well, I'm making money. I'm not going to necessarily hedge that, right? But if price falls from here, I'll have to sell first 50, then a little bit less, 30, then a little bit less, 10, up to the point where I'm delta neutral, right?

Because if price goes below, I'm not losing more money at this point. I don't have to, let's say, um, I don't have to sell anymore, right? So in this long gamma scenario in this uh scenario where they lose money if price fall they will have to do it dynamically not faster but slower right and if it would go up of course what they would need to do is they would neutralize that exposure. I'm not sure why they would do that.

And this is all the debate comes is the the the whole reason why then um some critics of the gam exposure idea um they they they basically say hey you don't know how they edge. Yeah you really do not know how they hedge if they use the ES if they use the NASDAQ but overall there will be an effect in the stock market. That's the principle and these studies has proven it. And when they're long gamma instead, because of this, they will do the exact opposite.

So they will sell if price rises to hedge their exposure and they will buy if price falls. Right? So they will buy the dip and sell the rip. And they will contribute to the compression of volatility. So this effect was just theorized but then they proved it in 2017. this works. But then something happened in 2017. In 2017, zero DTE options started being the majority of the volume. And as we were looking at end of day open interest, well, zero DTE options, yeah, by the end of the day, they're already expired.

So this is let's say not so predictive anymore [snorts] because most of the volume now it's not on plus DTE it's zero DTE most of the volume so can we see the exposure of market makers and predict their hedging flows within the day. Well, it turns out that the CBOE decided to make this data available to the public for a mere $28,000 a month. And if you pay them $20,000 a month, they will tell you in real time, almost real time, on a minute-by-minute basis, what's the exact trades taken by either customers, mostly retails, brokers and dealers, maybe also they are hedging themselves or professional customers. or market makers that based on CBOE data themselves are 50% of the volume in zero DTE SPS XPX options unfortunately they do it only with a clear like you don't have to assume anymore right because the naive Gex was assuming that market makers were always the counterpart and they were always selling puts and always buying calls that's not always true but still it had a predictive value right but now the CBOE itself tells us this I don't know if you I don't know if you understand this we know for a fact if market makers are buying or selling now options contracts we know there's no need to guess anymore no need to be naive anymore or do some naive assumptions that still could work by the Okay.

But at Deep Charts, what we've done is to take this data. We did the investment, guys. And because this costs so much, $28,000 a month, guys, 12* 3, it's 36. It's $360,000. a year, right? Is it? Yes. It's a lot of money. So that's why a lot of platforms, gam exposure platforms that use this typically charge every users around $300 a month to $400 a month for this data. on the launch we're charging $94. Okay, just just as a reminder.

Okay, so we can know the exact exposure of market makers. And the cool thing is that we don't know we don't just you know know the overall gam exposure of market makers. We know every single level. Okay. And this is our gam exposure heat map. We know where are the short gamma levels and where are the long gamma levels. So what do these mean? Well, technically it means that we can know if they will either exacerbate volatility or compress volatility because here they will, as we said, buy the dip and sell the rip and compress volatility in these areas.

We expect them to do the opposite by the RIP and sell the dip contributing to expanding volatility. So you can assess with the highest quality of data possible where they are likely to intervene and this is the first big use case for gam exposure. These are the areas where price will typically maybe bounce or consolidate. These are the areas where it may accelerate instead given all other factors. Okay, we understand that the hedging activity of market makers in the futures market is not 100% of the volume 100% of the time, but we know that in these areas they will interact likely in that way and they will not do it all at once right away. they will also do it more and more through time especially as a lot of scientific studies have proven in the last one hour in the last let's say 60 to 30 minutes of the session it's where they do a where they can it can happen that they do even more of this hedging because of the fact that in zero DTE the delta decay for example on an X on an SPX option because of the quadratic fall of theta.

As you can see at now we are 1.2 days. Let's do the zero DTE. Yeah, 4 hours. The more you get closer, the faster the more you get closer to expiration, the faster theta goes at the money. So we can basically anticipate what the hedging flows of market makers will look like on a session by session basis. And this is just one of the use cases what we've covered until now. It's just the gam exposure. But it's not the only thing we have on deep charts.

We also have other things you know we can we can also we can also see the tra big trades in options from big market participants or you know we can have the gam exposure we can have it for zero DTE which is mostly market makers against retails or we can have it also for 1DT 2DT 10 DTS 90Ds right where there's more long-term traders And what you can do for example here if you see that someone some market maker bought an option here and the same maybe sold an option here right what does it mean?

Well it means that someone else has done the opposite right they probably bought here and sold here. So someone else has done the opposite and this is huge volume, right? This is probably a big institutional player that has sold an option here and then bought the option here, right? Because it's the mirror, right? Market maker took this position. It means that someone else, very big one, took this position. Let's see what this position could be.

Someone bought and sold an option. A little bit out of the money. How does this look like? Look at how this looks like. It looks like someone has just bet on the fact that price will stay up above here by taking a small risk. H interesting. So someone is betting that price will not go below here or maybe this is a huge protection structure from a huge player and you can start also thinking in terms of option flow not just market maker hedging delta because that's just one dimension right option flow can give you so many insights the more you get into options the more you understand also these structures that you can way deeper insights into how markets could move today.

And this is the beauty of GAM exposure. How is is everything clear for now guys? Let's get back to full screen. Is everything clear for now? If you have any questions, let me know here. Yes, these exactly are put walls and call walls by the way. >> [snorts] >> We love you guys. Thank you, Brian. How can you tell that we're going to break the major call volume or go higher? Well, that's why you need order flow as well.

And that's going to be the topic for tomorrow. Look for a new major call that doesn't exist yet. It's not the majority anyway. Yeah, you need to contextualize this with volume analysis. That's why it's important to have both. Your brain is not braining. [laughter] Yeah, for for those who are the first time in options, I understand this can be a lot to process. I understand that. I strongly advise you to rewatch this course multiple times. re-watch this course at least five times before because it has to get in.

Okay, awesome. So guys, Deep Gamma will be available for you guys in the next few days. We're finalizing the plugging in the electricity and downloading all the data that you guys will need so that you can have a seamless experience inside of deep gamma. So in the first lessons we learned the basics of options. Today we understood the basics of gamma exposure. Why is it important? And tomorrow with Fabio finally we will get deep into how to contextualize this together with orderflow to get better insights, better entries and better vision of what today's volatility could be like.

If there's some levels of option that could be attractive for price or that could be rejecting price. the put walls, the call walls, the delta, and a lot of other really important and crucial pieces of information that will give you the entire puzzle of the day's action, order flow, and volatility. That's it for today, guys. Re-watch again this video and the previous one a lot of times, at least once more before tomorrow because we're getting deep into the practical stuff.

That's it for today, guys. Thank you for staying here.