Inferring the Aggressor using Options Data

แชร์
ฝัง
  • เผยแพร่เมื่อ 14 ธ.ค. 2024

ความคิดเห็น • 18

  • @jorgeih
    @jorgeih ปีที่แล้ว +2

    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").

  • @Smarttradingchannel
    @Smarttradingchannel ปีที่แล้ว +2

    Yesss a new videooooo !
    We missed you
    You are top 💜💜

  • @JecJT
    @JecJT ปีที่แล้ว

    fantastic video, looking forward to the next one absolutely

  • @themorleyc
    @themorleyc 9 หลายเดือนก่อน

    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?

  • @xXchakirosXxKiller
    @xXchakirosXxKiller ปีที่แล้ว

    Hello, why don't you make a video please about forward variance model? ( Bergomi model for ex )

  • @rakid123
    @rakid123 5 หลายเดือนก่อน

    would you be able to please do a video on applying this algo to the realtime spx options feed. i’ll also post the request in ur discord group. Thanks!

  • @cole6167
    @cole6167 ปีที่แล้ว +1

    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!

  • @putanginamojepoydizon5936
    @putanginamojepoydizon5936 ปีที่แล้ว

    He's back, time to get back to work, boys

  • @stevenstumpo2748
    @stevenstumpo2748 ปีที่แล้ว

    can you code a garch volatility model in python?

    • @QuantPy
      @QuantPy  ปีที่แล้ว +1

      Volatility videos coming very soon

  • @valueray
    @valueray ปีที่แล้ว

    You sure its not hindsight biased?

  • @sebastianpirzada2170
    @sebastianpirzada2170 ปีที่แล้ว +1

    The market priced this in

  • @telifhakki8745
    @telifhakki8745 ปีที่แล้ว

    In my oppion can be for crypto trade bro

  • @amanchhabra5564
    @amanchhabra5564 ปีที่แล้ว

    Bro u r overcomplicating..... It's far more simpler in real life....
    I wonder if u trade at all?

    • @goodlack9093
      @goodlack9093 ปีที่แล้ว +4

      lol what? what exactly is simpler in real life?

    • @NCF80M3
      @NCF80M3 ปีที่แล้ว +1

      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.

    • @themorleyc
      @themorleyc 9 หลายเดือนก่อน

      ​​@@NCF80M3agree well said