Quote from syswizard:
I agree...and now wonder if anyone has coded that SOBI method and given it a real world trial ?
I've now have seen the work that has gone into the Penn-Lehman Automated system project managed by Mike Kearns and it's pretty impressive.
Preliminary findings with regards to the effectiveness of market-makers strategies is intriquing. However, some of the test results such as the recent contest won by the two PhD's from the London School of Economics indicate more "reality checks" are needed.....as it's doubtful that winning strategy of manipulating the market would work in the long run.
There's been no apparent activity with that project for the past 8 months or so....I wonder if Lehman has dropped it's sponsorship of that research ?
Lehman's original contract was for 3 years, and that was up by last year, but they have since extended it to put it on maintenance, if you will. The core of the PLAT project was actually the PLAT simulator, which has proved to be very valuable as an simulation market for Lehman to test out their in-house algorithms.
Back to SOBI for a moment, it is almost too simplistic (but a good starting point as food for thought regardless). Since I have simulator of my own, one day I got bored so I wrote a simple SOBI client and tested it against 4-5 days of tick data. The overall result was that the SOBI client got killed, but it did exhibit a few stable, profit producing periods.
The fact that MM agents did well should come as no surprise. Since by definition, MMs would get involved on a lot of volume, and would shave off the spread in 50-60% of the time (70% is a figure quoted in the paper, but actually very close to reality, speaking from experience). So while the "win percentage" of MM agents are low, and the per share profit is very low, it all adds up to decent profits. For instance, in reality, if a system / trader is to be a "market maker" in any product, the old Spear Leeds saying goes that the MM should account for at least *20%* of daily volume.
