Inferring the Aggressor using Options Data
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- Опубліковано 12 чер 2023
- We will be implementing the bulk volume classification algorithm to attempt to discern information from tick by tick trade data. We will be using ThetaData's API which provides both Historical and Real-time Streaming of Options Tick Level Data!
We first explore what algorithms have been used previously to attempt to infer the aggressor (the trader who initiates the trade), which would classify every trade as either a buy or sell initiated trade. In todays world of high frequency and complex execution algorithms that can split orders up into multiple child order and distribute across exchanges, the two papers we discuss argue that these traditional classification algorithms are not so relevant.
Therefor we implement the Bulk Volume Classification algorithm that looks at aggregated trades, and therefore captures market makers response to trade flow over trade periods. We have completed this analysis using only Historical trades data, however in the next video we will implement this algorithm with Real-time Streaming.
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fantastic video, looking forward to the next one absolutely
Yesss a new videooooo !
We missed you
You are top 💜💜
Siqueira et al. calculated the VPIN using real data to compare with the performance of the Tick Rule (TR) and BVC models in classifying assets traded on the Brazilian stock exchange (B3). In conclusion, TR algorithm shows much better performance than BVC when compared to the real data. ("Analysis of the Tick Rule and Bulk Volume Classification Algorithms in the Brazilian Stock Market").
Muito bom o paper!!!
I understand the quotes soukd be fragmented across muktiple venues the synchronization is impossible to quotes to orders. But how about ES futures which have a single exchange?
Hello, why don't you make a video please about forward variance model? ( Bergomi model for ex )
The market priced this in
how would we model "net exposure change" across products and markets? For example, if we detect aggressive buying in asset XYZ (eg, using bulk volume classification) and simultaneously aggressive selling in short-dated call options on XYZ (or even on another asset with high correlation, etc), it could be helpful to know that the net delta exposure in XYZ by aggressors may not be changing much due to observed activity in this period (but obviously this could reflect opinions on volatility pricing, interest rates, liquidity, etc, etc).
Would it be plausible to add a column for "net delta exposure" (where 100 shares = 100 and an option computed for that point-in-time as delta 0.20 = 20) and then sum this as the volume?
Anyway, the history and implementation overview were really well-placed here. another great video!
can you code a garch volatility model in python?
Volatility videos coming very soon
You sure its not hindsight biased?
In my oppion can be for crypto trade bro
Bro u r overcomplicating..... It's far more simpler in real life....
I wonder if u trade at all?
lol what? what exactly is simpler in real life?
This is deep knowledge beyond your skill level. Get a degree in math and you will realize there is much much more at play and far more to be explored using these fundamentals as a basis.
@@NCF80M3agree well said
He's back, time to get back to work, boys