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Folks interested in HFT might like this - HFT firm Virtu has a nearly flawless daily trading record over the past 3.4 years: http://www.businessinsider.com/vir

by svedlin 12y ago

Folks interested in HFT might like this - HFT firm Virtu has a nearly flawless daily trading record over the past 3.4 years: http://www.businessinsider.com/virtu-hft-only-one-losing-day-2014-3 http://www.businessinsider.com/virtu-hft-only-one-losing-day... Prospectus: https://www.sec.gov/Archives/edgar/data/1592386/000104746914002070/a2218589zs-1.htm https://www.sec.gov/Archives/edgar/data/1592386/000104746914... Looking at the S&P 500, it was positive for 55.8% of the trading days between 2010 and 2013: http://www.crestmontresearch.com/docs/Stock-Yo-Yo.pdf http://www.crestmontresearch.com/docs/Stock-Yo-Yo.pdf BofA had 10 trading day losses in 2013: http://blogs.marketwatch.com/thetell/2014/02/28/goldmans-trading-revenue-more-daily-losses-fewer-outsize-gains/ http://blogs.marketwatch.com/thetell/2014/02/28/goldmans-tra... US equity HFT revenues are around $1.3 billion, making it a pretty small niche compared to wider market activity: "TABB Group estimates that US equity HFT revenues have declined from approximately $7.2 billion in 2009 to about $1.3 billion in 2014."

NkVczPkybiXICG 12y ago
Law of large numbers. This is not surprising. EDIT: Law of large numbers. Not central limit theorem. I blame alcohol.
- asdfologist 12y ago
  Um, what? If anything, applying the theorem in this case would say that profits should be normally distributed, whereas it's fairly common for market makers to have perfect/near-perfect trading records.
  - NkVczPkybiXICG 12y ago
    Sorry, that was vague. My point was that market participants who make many trades will rapidly converge on their mean return, with little fluctuation. If you sample a normal distribution 10M times with a mean of 0.01%, you will very rarely see a negative sum of the samples.
    - asdfologist 12y ago
      The theorem says nothing of the sort. By your argument, if I flip a coin 10M times, where heads is 1 and tails is -1, and get a mean of 0, then I will very rarely see tails. You're confusing mean and variance.
      - NkVczPkybiXICG 12y ago
        Sorry. I said some pretty silly things. See the new version of the posts. Hopefully it's a bit more coherent now.
        asdfologist 12y ago
        Ok, I think your main idea was that since they've achieved consistently positive returns, then they're likely to keep it up. Even if that were true, the 800lb gorilla question is how they've managed to achieve such consistency in the first place.
        consz 12y ago
        They have slightly positive return per trade, and they make millions of trades per day. Their PnL is the sum of their trades -- the expected values grows like O(n), while the volatility grows like O(sqrt(n)), so for n = several million, the probability their PnL will be negative is extraordinarily low.

by svedlin 12y ago

NkVczPkybiXICG 12y ago

Law of large numbers. This is not surprising. EDIT: Law of large numbers. Not central limit theorem. I blame alcohol.

asdfologist 12y ago
Um, what? If anything, applying the theorem in this case would say that profits should be normally distributed, whereas it's fairly common for market makers to have perfect/near-perfect trading records.
- NkVczPkybiXICG 12y ago
  Sorry, that was vague. My point was that market participants who make many trades will rapidly converge on their mean return, with little fluctuation. If you sample a normal distribution 10M times with a mean of 0.01%, you will very rarely see a negative sum of the samples.
  - asdfologist 12y ago
    The theorem says nothing of the sort. By your argument, if I flip a coin 10M times, where heads is 1 and tails is -1, and get a mean of 0, then I will very rarely see tails. You're confusing mean and variance.
    - NkVczPkybiXICG 12y ago
      Sorry. I said some pretty silly things. See the new version of the posts. Hopefully it's a bit more coherent now.
      - asdfologist 12y ago
        Ok, I think your main idea was that since they've achieved consistently positive returns, then they're likely to keep it up. Even if that were true, the 800lb gorilla question is how they've managed to achieve such consistency in the first place.
        consz 12y ago
        They have slightly positive return per trade, and they make millions of trades per day. Their PnL is the sum of their trades -- the expected values grows like O(n), while the volatility grows like O(sqrt(n)), so for n = several million, the probability their PnL will be negative is extraordinarily low.