{"id":242940,"date":"2026-05-18T11:52:40","date_gmt":"2026-05-18T15:52:40","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=242940"},"modified":"2026-05-18T11:56:06","modified_gmt":"2026-05-18T15:56:06","slug":"reading-between-the-trades-understanding-options-flow","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/podcasts\/ibkr-podcasts\/reading-between-the-trades-understanding-options-flow\/","title":{"rendered":"Reading Between the Trades: Understanding Options Flow"},"content":{"rendered":"\n

What do massive option sweeps and unusual volume really reveal and what are traders getting completely wrong? Dmitry Pargamanik of Market Chameleon<\/a> joins the IBKR Podcast to break down how professionals decode options activity, spot hidden institutional behavior and separate real signals from market noise.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

Hey, everyone. This is Jeff Praissman with Interactive Brokers Podcast. It’s my pleasure to welcome back to the podcast studio from Market Chameleon, Dmitry Pargamanik. How are you?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Hey, Jeff. How are you?<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

Oh, love having you come in here. And for our listeners who don’t know, every second Tuesday of the month, Will and Dmitry do a webinar with us, and they come by the podcast studio afterwards to take, I guess, a different dive into the subject. So, what are we gonna talk about today? It was a great webinar, and I’m gonna let you kind of tell our listeners what we’re gonna do this podcast on.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

We’re gonna talk about option trading activity, option trading volume from the perspective of single-leg trades. So when we look at the option trades, you could have single-leg trades like buying a call or put, or you could have a multi-leg option trade that’s entered in as a spread. You could also have a contingent option trade that’s tied to another security like a stock. But we’re gonna talk about just focusing on the single-leg trades and how we could aggregate those trades or even categorize them to get more meaningful insights and analytics.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

Let’s start with a 10,000-foot view. Like, what are option traders actually seeing when they look at the options tape? And are there items that they’re usually misunderstanding?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

When we look at just the regular option tape, we get a few raw data points in there. We get a time and sales feed where the exchanges report a transaction, a trade, with the volume, the trade price, the trade condition, and the time of the trade. We also have another feed that reflects the bids and offers, or the market makers’ bids and offers, the limit orders that are on the book that get displayed.<\/p>\n\n\n\n

So that comes from a different feed. And it’s important to know that these are separate feeds, but oftentimes we do try to connect them together, tie them together, because they’re related by timestamps to give more meaningful insights. But the trade tapes themselves are the raw data, and then from that raw data, we start to make inferences, analytics, derive data, and that’s all added value.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

So, Dmitry, from your perspective, what do traders sometimes think the option order flow is telling them versus what it may really be showing about market behavior?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

I don’t know what everybody out there thinks, but I think a lot of times people turn to order flow as an indication of direction for a stock. And that’s not always the case. People use options for many different reasons. It could be for volatility, it could even be for carry trades, it could be for different spread-type transactions.<\/p>\n\n\n\n

So I think that it’s important to understand that options themselves are tools that could be used for different purposes, different hedging, different strategies, and it’s not necessarily exclusive to directional-type trades.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

There’s a lot of data out there. This is kind of like a common theme that comes up in a lot of our webinars and podcasts. What are the dangers of an option trader just looking at a single trade or even just a handful of trades, and how can that give them a very misleading picture of the intent?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

I guess the intent we never really know because that’s not revealed on the trade tape. And the trade tape doesn’t let us know who the parties are to the trade. It doesn’t reveal this and this person transacted, and we don’t know their analytics or positions, so we don’t know the intent.<\/p>\n\n\n\n

But one of the things we were discussing is how to aggregate trades. And sometimes there’s more insight if you could aggregate related trades. That could be much more revealing than just looking at a single trade because some of these trades are not actually just in isolation and unrelated. So aggregating trades is one of the things that we discussed in our webinar that becomes important, especially where you could enter in a trade and it could be for a big lot, but that doesn’t mean it gets executed in one block. On a trade tape, it could look like many different trades that look like individual trades. But if you look at the timestamps and the way it was executed, it was one trade executed against multiple exchanges or limit orders on the book from different parties.<\/p>\n\n\n\n

So then going back and reconstructing the activity using the available data becomes more insightful of the volume, right? You could then categorize that volume much better.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

Summarize it for our listeners. An institution might do a big trade, but it might get broken up for several different reasons into smaller trades, whether different parties or counterparties are doing the trade or whether they’re just trying to get liquidity. And then what you’re saying is that you really have to aggregate it. It’s really one big trade, even though it might be 50 different pieces, 50 different orders. And then really the options trader needs to kind of make sure they’re looking at the full picture with that trade. What signals can help with that? Obviously time, I would assume, and price.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

And you can’t get it exactly perfect because we don’t know for sure. It’s always an inference, and you’re looking at it making the most logical conclusion. So, for example, you’re looking at the same option transactions, and if you’re seeing a lot of transactions, let’s say I see one lot, five lot, 10 lot, 20 lot, 30 lot, they’re all within milliseconds, and that added up to 1,000, then we could infer, well, this is not a typical way order flow comes in. That looks like one trade swept the book or lifted the limit order book. And even if you go out there and get those limit orders, each one will have its own execution because you have different counterparties. So each transaction with different counterparties gets reported, even though on one side you could have one party sweeping that entire book.<\/p>\n\n\n\n

So that’s one signal, by looking at how it was executed and the timestamps. Another is you can potentially observe some repeated activity throughout the day. Certain times you could see it spaced out where there was trading, but all of a sudden you see some big block spikes going on. And that could also signal some type of more institutional behavior that’s coming in that looks unusual and is scattered throughout the day, so it kind of mixes in with the rest of the volume.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

If I kind of understand you correctly, the traders really should look at the aggregated order flow because it may more closely reflect the institution’s view of the market. It’s not random, in other words. It’s not random that you have these same strike, same price, or maybe not even same price, but similar time, same strike, same expiration, and so forth, and they might be in pieces. But in reality, it might be 1,000 option contracts versus the 75 you’re seeing at a time or whatever.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Right. Exactly.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

How can they tell, though? I mean, is that really the only way to tell between retail, institution, or professional flow \u2014 really just trying to see the overall aggregated volume versus, like, if they see a 150 lot, it could be an individual’s order, but if they see 1,050 lots, there’s a better chance that it’s some sort of professional flow?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Right. And it’s not a perfect science. Now you’re trying to analyze and group the data to get the most logical inference or the most likely explanation because, on the other side, there’s no actual indicator on the tape that says, “Hey, I’m a retailer. Hey, I’m an institution.” We don’t know that on the trade tape for each transaction. So the grouping is what starts to matter. You could have one contract that trades in the last minute, and you could make an inference that’s probably a retail trade because institutions kind of don’t trade that way. And if they do, maybe they put something out there, but that’s not a typical institutional trade. They’re managing much larger portfolios and moving much larger quantities of funds. But at the same time, if we see 1,000 contracts trade for $300,000 or half a million dollars in notional value, that’s not a small retail trader. It’s not a typical size. And even if you have a retail trader out there that has that type of fund, it’s almost an institutional-size trade because you’d have to be approved. Even if you have the funds to do those types of trades, you have to go through an approval process where your broker would have to establish that you know what you’re doing. So even if you came in with a million dollars and opened an account, and you told your broker, “This is the first time I’m trading options,” they probably won’t let you. They’re gonna restrict your account, even if you have the money, because that’s just how the rules work.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

You’d have to be level one, level two, level three, level four certified to do it.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Yeah, exactly. So just by looking at the trade size, you already have a good indication that this person’s already level three, level four, whatever it is. It’s already a professional person with years of experience that they could show, versus a typical retail trader.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

And you hear this phrase a lot, right? “Follow the smart money.” What does that look like? How can that be converted for options data? How can someone follow the quote-unquote “smart money” by looking at this data?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Yeah, I think there’s some misconception where if you see a large trade, that means it’s smart money and you gotta piggyback it. But large trades could have many different purposes.<\/p>\n\n\n\n

A lot of times it could just be hedges, where a large trade goes up because it’s a hedge against an existing position. Other times you could have a large trade that goes up because they were sticking to some kind of methodology or strategy that requires it. Right now I think we have ETFs out there that utilize options, but they’re sticking to a methodology. They’re not buy-writing Tesla because they know something. That’s the strategy they’re sticking to.<\/p>\n\n\n\n

So it could be a large trade, but it’s not because they know something is gonna happen or that they know implied volatility is too high or too low. This is just the strategy and methodology they use. And that exists throughout the market. Other times people do trades based on their own research. So they do have their own research, they trade it, it could work out or not, but they do feel that the probabilities and the markets right now are set up favorably for what their outlook is. But on the other side, there’s a market maker willing to transact, right? So there’s another person on the other side who thinks that they’re getting edge or getting a good deal as well.<\/p>\n\n\n\n

So I think that the point I wanna make is there’s a misconception. If you see a large block or big trade go up, then that’s smart money, that’s an unusual whale, and you gotta piggyback it. But that’s not gonna work because we don’t know the intent, who the other side is, or why they’re doing it. And there are two sides to both transactions.<\/p>\n\n\n\n

To catch anomalies where there might be a real interesting or meaningful signal usually comes in clusters of anomalies, not just a single one. Usually you start seeing different anomalies, different things setting up with divergence in things that usually happen in sync, and large volume, and divergence in pricing and other things that are going on.<\/p>\n\n\n\n

Then you start looking at it like, “Wait a minute, there’s a lot of things here that are starting to move far away from the baselines.” That would maybe be a much stronger signal than just a single trade.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

Got it. Yeah, and kind of going back to what you said, the right trade for me in my portfolio may be the wrong trade for you, right? I might be buying something to hedge my portfolio, which makes perfect sense for me, but makes zero sense for you that does not have that position.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Exactly. And with options, actually, you could have winners on both sides. It’s very possible to have winners on both sides. Somebody buy-writes it and the other side buys the option and trades the volatility, and in the end it made sense for the volatility trader and it made sense for the person who buy-wrote it. It worked out for both of their positions.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

And kind of going back to price, does it matter whether the trade occurred at the bid, the ask, or somewhere in the midpoint? Does that matter for this data?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Yeah, so the other interesting thing is when we look at the trading volume, we start stitching related data together, and one of them is looking at the liquidity of the market at the time of the trade.<\/p>\n\n\n\n

So we’d have to go and look at the quotes that come in from a different feed, but we could put them in sync through the timestamps. By comparing it, we see what the liquidity of the market was at the time. Was the spread wide? Was it narrow? Did the trade occur closer to the offers or closer to the bids? Who was the aggressor in the execution?<\/p>\n\n\n\n

And then there\u2019s the surrounding data. So yes, by taking the relative information and then taking the trade and starting to compare and put all those pieces together, it will definitely give you more insight into that trading activity.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

And another word that always comes up: volatility, especially implied volatility and historical volatility. How should retail traders think about implied volatility when looking at option order flow? Can it help them avoid chasing false signals?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Yeah, so implied volatility is very useful and helpful for looking at relative value and trading behavior.<\/p>\n\n\n\n

The relative value is where the implied volatility is right now. Is it high or low relative to history? Are we on the high end or low end of the trading range? But we also could use implied volatility to track trading behavior. So, for example, we could look at the implied vols for the day, and we can determine that the trading activity is centered around this implied vol. So this is the fair value. The implied vol is 50, and the trading is bouncing around between 48 and 52. So we could say that’s the trading behavior. We could also see if it’s trending. So if we see implied volatility moving up, now we’re looking at trading behavior that is pushing implied volatility up and demand building. The demand for the premium is increasing while the supply is fading. We could also see the velocity or acceleration of it. Is it moving slowly, or is it spiking quickly? And vice versa on the downside.<\/p>\n\n\n\n

So implied volatility is important for many reasons, but the two that we talked about are, first, what is the relative value of this option? Is the premium high or low compared to historical levels? And second, how is the trading activity impacting that premium? Because we could observe it through the implied volatility reference prices.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

This has been great. Do you have any final words for our listeners?<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Yeah, I think that when you start out analyzing markets and understanding and studying markets, it’s important to kind of understand the trade tape, the limitations of the trade tape, and the added value or derived analytics coming from it.<\/p>\n\n\n\n

Jumping to early conclusions is never good, but understanding the trading activity and studying it over time starts to make more sense. So I think that’s the key takeaway.<\/p>\n\n\n\n

Jeff Praissman<\/h3>\n\n\n\n

And for our listeners, you can find more from Will and Dmitry every weekday morning on their YouTube show, I believe from 9:00 to 9:30 Eastern Time, on our website, ibkr.com<\/a>, and also at Market Chameleon. Thanks. Always love having you come in here.<\/p>\n\n\n\n

Dmitry Pargamanik<\/h3>\n\n\n\n

Thanks, Jeff.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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