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January 3, 2006

Algorithmic Trading Systems and Solutions - Q & A - Cont'd

By Editorial Staff

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Carl Carrie, JP Morgan:

We believe reverse engineering of algorithms is happening in the marketplace. Our process for minimizing the risks of this include algorithmic randomization of venue, size, wait time, limit prices and micro-order types. In general, algorithms should avoid pegging between the NBBO predictably and instead use non-posting order types and venues appropriately. We avoid trying to be predictably reactive (e.g. too large quotes, narrow spreads) and use other stealth techniques to confuse arbitrageurs and minimize the profit potential of algorithmic gamers.

Frank Brown, EdgeTrade:

Without question, reverse engineering is taking place both upstairs and on the floor. More egregious, is the reverse engineering carried out by the brokers with proprietary trading desks to whom buy-side firms are sending their algorithmic orders. EdgeTrade's independent agency model makes reverse engineering by us, the broker, a non-issue. We also go to great lengths to design algorithms that blend their trading into the overall market activity, reduce the footprint our clients' trades leave in the marketplace, confound attempts at reverse engineering and minimize market impact and slippage.

Q: How important is pre-trade analysis when using algorithms? Do you see that importance of pre-trade analysis growing? Describe your pre-trade analysis tool and how it gives investors an edge?

Richard Johnson, Miletus Trading

As a quantitative brokerage firm, we believe pre-trade is a very important tool for traders, particularly when it comes to determining appropriate trading strategy. However, it is important not to look at pre-trade in isolation from other components of the trading process: pre-trade only offers a static snapshot based on historical data, and the recommended optimal trading strategy may no longer be the most appropriate given changing market conditions.

At Miletus, our market impact model is integrated into our algorithms and our Strategy Interface. Thus, when users determine their desired aggressiveness and trading horizon using our Trade Impact Estimator, they can input these parameters directly into our algorithms. This means the algorithm can intelligently adapt to changing market conditions as opposed to doggedly following an outdated pre-trade strategy.

We are definitely seeing increased demand for pre-trade analytics, especially from portfolio managers who are using our tool in the portfolio construction process.

David Liles, Bernstein:

Pre-trade analysis tools help the algorithmic trader by setting reasonable expectations regarding trading costs, identifying outliers, and assisting in the choice of an appropriate strategy. Where pre-trade analysis truly shines is when it is used in conjunction with portfolio-level algorithms. Bernstein's pre-trade analysis offers clients detailed estimates of trading costs and confidence intervals for multiple implementation scenarios including our PortFall algorithm, trading each stock independently using Price Capture (our single-stock IS strategy), and also using a VWAP-based strategy. We assist clients in handling outliers by highlighting stocks that may be ill-suited for automated strategies due to their individual trading characteristics. In a portfolio setting, we also alert clients to the potential effect that removing a single name for manual handling will have on the overall risk of the portfolio.

Derek Morris, BNY Brokerage: