Automated High Frequency Retail Trading

By Gutmann, Michael | Futures (Cedar Falls, IA), October 2008 | Go to article overview

Automated High Frequency Retail Trading


Gutmann, Michael, Futures (Cedar Falls, IA)


The combination of electronic markets, complex algorithms and computers has led to a revolution in trade automation. Lower brokerage fees and affordable data and software packages have extended that revolution to retail traders.

The growth in the electronic trading of stocks, futures and options and the accompanying increase in volume, lower commissions and speed of order execution have created market data networks that support high frequency trading by die retail customer, provided the trader has adequate computing and network bandwidth. In this regard, the latest generation of PCs and high-speed Internet broadband access complete the necessary technical infrastructure for high frequency retail trading. The individual trader is able to keep pace with the sophisticated institutional trading desks while maintaining the flexibility inherent in independent, small-scale trading decisions.

Desktop trading platforms now offer the individual trader an abundance of tools and historical data for developing, backtesting and executing automated strategies. Without the need to trade at volumes of large commercial funds, the individual has the opportunity to profit from the confluence of new trading technology and electronic markets at high trade frequencies.

High frequency automated trading comes with a set of hurdles that must be overcome. Managing data feeds at tick granularities and finding practical means of backtesting high frequency strategies are two of the most challenging obstacles.

TRADE AUTOMATION

Trade strategy algorithm development, backtest and execution are now widespread. Discretionary trading is increasingly being replaced with trade strategies that can be backtested to determine efficacy and executed automatically or semi-automatically with a goal of reducing human emotion from the trade equation. Swing-trade algorithms can be programmed, backtested and tuned by the individual trader and then executed against the market with little real-time oversight.

"Double your pleasure" (right) shows an implementation of a well-known pairs strategy trading the Nasdaq 100 vs. the DIA exchange-traded fund. Backtesting with appropriately tuned inputs shows a 30% annualized rate of return trading one lots with an initial $5,000 margin. Swing-trade systems can be programmed, tested and executed relatively easily using the latest desktop technologies. Trades may be manually placed from program generated signals or, for the more sophisticated, entirely automated in trade strategy code.

Backtesting swing-trade automation has become relatively straightforward. The developer looks to minimize trade strategy input parameters, backtest convincing histories (1, 3, 5, 10+ year durations) and measure key outputs: win-loss percentages, maximum number of sequential losers, maximum drawdown, RINA scores, etc. Once the trader has become comfortable with back-test results, a trade strategy can be brought online.

Success with swing-trade automation typically leads the developer to investigate higher frequency intraday strategies to take advantage of the inherent benefits such as no overnight positions and low margin requirements. However, moving to high frequency automated trading, which may be based on tick level data, introduces new automation challenges.

HIGH FREQUENCY AUTOMATION

When considering high frequency trade automation, today's trader has a wide variety of direct-access PCbased trading platforms to choose from, such as TradeStation, eSignal and Interactive Brokers. These trading desktops provide real-time data, down to the tick, sophisticated charting and indicator tools and typically a means of automating trade strategies, including the ability to run strategy automation against the market. Strategy development and execution may be accomplished from an entirely self-contained programming environment and/or by integrating more traditional programming into the platform; for example, integrating Microsoft. …

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