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Texas Hold'em: Algorithmic Trading


for Members

Texas Hold'em: Algorithmic Trading

Thursday, April 16, 2009

The New York Academy of Sciences

Presented By

Presented by the Quantitative Finance Discussion Group and Moody's Corporate Foundation


Success in Texas Hold'em requires understanding and acting on the odds of the cards. Similarly, trading strategies require understanding and reacting to changes in the market. The use of algorithms to optimize execution of these strategies has surged in popularity in recent years. This meeting will feature experts discussing recent advances in algorithmic trading.

Quantitative Finance is part of a new initiative launched in physical sciences and engineering. Given the Academy's new location at 7 World Trade Center and the number of scientists who work in finance, the goal of this symposium is to build community by creating collaborations and developing professional networks between the science and finance communities.


Ian Domowitz (Investment Technology Group)
Lee Maclin (Courant Institute of Mathematical Sciences & Founding Partner, Pragma Financial Systems)


Myles Thompson


Cul de Sacs and Highways: An Optical Tour of Dark Pool Trading Performance
Ian Domowitz, Investment Technology Group

Algorithmic trading is the driver behind the explosion in 'dark pool liquidity.' We analyze execution performance of selected dark pools and algorithmic strategies qualifying as "liquidity aggregators," using transaction costs as a metric. Examination of the characteristics of dark pool execution activity is primarily based on a sample of 12.6 million orders entered during 2007, covering point-in-time matches, continuous crossing, and algorithmic liquidity aggregation. These data are supplemented by an additional 8.2 million orders, executed through diverse means and venues. The results suggest that dark pool execution is beneficial, but that fragmentation of dark pool liquidity enabled through algorithmic trading has neither increased the ability to trade in large size, nor reduced transaction costs relative to direct access to a periodic crossing mechanism. Transaction costs increase with duration, as orders are exposed to dark pools by algorithmic means, motivating the conjecture that movement from one dark pool to another in the execution of a single order engenders information leakage.

Algorithmic Trading and Portfolio Management

Lee Maclin, Courant Institute of Mathematical Sciences, Founding Partner of Pragma Financial Systems

The last ten years of academic and industry research have given algorithmic trading a more complete theoretical framework. The problem has emerged of integrating this framework into the portfolio management process. Many institutions are unprepared for the transition from alchemy to chemistry, and are struggling to understand the intuitions behind the new science and the changes it requires. A framework will be presented for thinking about the algorithmic trading problem from a portfolio management perspective. The implications of using this framework in the current environment will be discussed.

Reception to Follow.

Presented by