From the May 01, 2011 issue of Futures Magazine • Subscribe!

Algo trading in the liquidity mirage of high-frequency trading

Today’s world of electronic trading and computerized trade-matching has allowed a proliferation of programmed high-frequency algorithmic traders to enter the arena under the guise of liquidity providers. There is good reason for this trend. Computers have tremendous power, not just in terms of speed of execution, but also the added advantage of processing multiple calculations on the fly that humans simply cannot accomplish with the same speed.

We, as mere mortals, can be pushed back on our heels when going up against these programmed machines, but not all is lost. Many complain about algorithmic, or algo, trading, which some contend has changed how traditional trading patterns form and unwind in the markets. The high-frequency algo trading systems have managed to change the landscape and, for many strategies, confuse the overall picture. (Algorithmic trading encompasses a lot of different types of traders and is not always high-frequency trading [HFT], but HFT nearly always is algo trading).

The average high-frequency algo trading approach has been programmed to recognize pretty much all known trading strategies. Trading systems range from Fibonacci to Gann, to seasonals, to moving averages. More complicated systems trade approaches such as pure auction volume, volume-weighted average price (VWAP), time-weighted average price (TWAP), price destination, implied shortfall and end-of-day. The list is limited only by the imagination.

Many of these systems include a version of pattern recognition, as well. There even are news-driven algo systems that instantly scan government reports for some form of better- or worse-than-consensus results. More complicated systems have correlations, probability analysis and cross-market basis studies, where multiple layers of orders — some long, some short — will re-create synthetically either a basket or even a currency. For example, a long Dax, short S&P, long U.S. bonds, short German bunds equates to a long euro/dollar position.

It was no accident that Fidelity bought Wealth Labs — a build, design and backtester of systems — allegedly because the company wanted to know what others were doing, so that it could take advantage of them. In other words, perhaps Fidelity wanted to assume the opposite side of the systems on the basis that the vast majority of systems are designed to be trend-followers.

Understanding algos

The role of the high-frequency algo trading system, however, is not to trade the trend, but to enter and exit the market as frequently as possible for miniscule individual profits in a range environment. It does this by identifying what have become known as “child orders.” These are large orders that have been chopped down into small, minor orders, partly to hide the order’s footprint and partly to avoid disturbing the market’s existing liquidity and driving price away from intended execution levels.

The controllers of the high-frequency algo trading system can manipulate the degree of aggression in the system. They can be aggressive by hitting bids or lifting offers, or be passive by using resting limit orders, or even neutral by running a long/short book.

They can create iceberg, or submarine, orders where only a part of the order is visible. Once the portion of the order that is shown has filled, it immediately re-loads with additional orders. All this occurs in the blink of an eye, and the system, almost imperceptibly like the speed of a paddle wheel, can accelerate or slow or shift from buying to selling instantly. These algos more likely are attempting to spot other traders’ icebergs and exploit them.

Algos frequently are turned off en masse. This is most noticeable in the forex market when a government report is awaited. In the few seconds just before release, the machines may widen their bid/offer spreads dramatically. Some even seem to switch themselves to a latent state, creating a massive degree of either gap or volatility in the immediate few seconds post release. For example, most experts believe the May 6 flash crash was more likely the result of high-frequency algos leaving the market than pushing it.

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