Algorithmic trading accounts for approximately half of all U.S. equity trading volume, according to a report in the New York Times. Unlike traditional trading, these strategies rely on automated computer programs to execute buy and sell orders. These trading programs often execute trades at a very rapid rate, especially in what has become known as high frequency trading.
In this article, we’ll take a closer look at the history of algorithmic trading, how it has grown over the years, strategies employed by these firms, and considerations for traditional traders in the market.
History of Algo Trading
Stock exchanges began transitioning from a traditional auction to computerized transactions in the early 1970s. In the late 1980s and early 1990s, Electronic Communication Networks (“ECNs”) became increasingly popular for traders looking for more efficient access to the markets. These developments set the stage for algorithmic trading that eventually took humans out of the equation.
Algorithms are simply a set of rules that a computer program executes in sequence until a desired end point. In the case of algorithmic trading, the algorithms are simply a series of criteria that must be met to execute a buy or sell order. For example, in an arbitrage scenario, algorithms may compute the difference between an ADR and foreign stock and execute a buy order when profitable.
In 2001, algorithmic trading received a boost when IBM researchers published Agent-Human Interactions in the Continuous Double Auction. The research paper found that simple agent bidding strategies were able to outperform non-expert human subjects by a clear margin, setting the stage for the high-frequency trading algorithms that are widely used today in the financial markets.
The development of low latency proximity hosting and global exchange connectivity in the 2000s helped accelerate the ubiquity of algorithmic trading. By executing trades faster and faster, these faster networks enabled trading algorithms to access and act on information more quickly than human traders. Many algorithmic trading servers are now located near major exchanges and data sources.
Algo Strategies & Limitations
Algorithmic trading is a highly competitive segment of the global financial markets, since the marginal profit per trade is so narrow and the potential for profit is so high. Trading algorithms must be modified over time in order to keep ahead of front-running competition. If an edge is lost, trades can quickly turn negative and losses can pile up, especially if nobody is paying close enough attention.
For example, Knight Capital famously lost $440 million in 45 minutes due to competition from the NYSE’s Retail Liquidity Program. Observers believe that Knight had tried to upgrade its algorithms to work around the NYSE’s new system, but the algorithms were flawed and thousands of bad trades were made. Dozens of stocks were affected by the glitch and faith in the markets was shaken.
The high profit potential of high frequency trading is compelling enough to look past many of the risks, however, with a number of strategies employed to profit.
Index Fund Rebalancing – Index funds must rebalance their portfolios at regular intervals, which presents an opportunity for traders looking to front-run the purchases for a small profit on a large scale. For example, United States Natural Gas Fund (UNG) has become infamous for its predictable rolling trades in Figure 1.
Arbitrage Opportunities – Arbitrage strategies aim to profit from price differences between assets that are typically strongly correlated, including ADRs/foreign stocks, M&A transactions, or simply statistical correlations, as seen in Figure 2. In this case, a trader might take advantage of Exxon Mobil (XOM) and Chevron (CVX) divergence.
Low-Latency Strategies – Faster connections to exchanges enable algorithms to front-run each other, creating an opportunity to profit from the speed of transactions relative to other traders in the market. Often times, these transactions are known as high frequency trading (“HFT”), with an example in Figure 3.
Market Making Trades – Many ECNs pay a fee to organizations willing to make a market in illiquid securities, which creates an opportunity for trading algorithm operators to earn a profit over time. Of course, these programs must be careful to account for the added liquidity risk present in these companies.
As algorithmic trading grows in popularity, the profit potential from many of these operations has significantly diminished. Many high-frequency trading operations are also competing with each other in low-latency trading and areas where profit potential is simply a matter of owning the best technology. As a result, algorithmic trading volumes have started to decline from their peak.
Retail Trading and Considerations
Algorithmic trading has largely increased the efficiency of the financial markets by narrowing the spreads in arbitrage opportunities, increasing liquidity where needed, and ultimately ensuring fast executions. The problems arise when these companies start front-running the market, which ends up digging into traders’ margins and increasing transaction costs and slippage.
Following some basic guidelines can avoid these problems:
- Avoid Market Orders &8211; Traders can use specific limit orders to ensure that their trades are executed exactly at their desired price. In particular, the forex markets are well known for excessive slippage in some cases.
- Careful with Stops &8211; Algorithmic trading can result in periods of high volatility, which can quickly trigger stop-loss orders. Traders that wish to use stop-loss points should be aware of the risks before placing those limits.
- Hedge Your Bets &8211; Algorithmic trading introduces greater volatility into the financial system at times, which means it’s also important to hedge bets using techniques like covered calls or put options in some cases.
Traders looking to become involved with algorithmic trading face intense competition these days, although there are some retail options available. Programs like TradeStation enable traders to devise automated strategies based on technical analysis and price/volume/order dynamics in many markets. Similarly, MetaTrader has become a popular option for similar trading in the foreign exchange markets.
The basic process used to build an algorithmic strategy is:
- Identify a Strategy &8211; Most retail trading algorithms are focused on using various forms of technical analysis rather than low-latency strategies involving the order book. For example, a strategy might be to buy when an equity’s price crosses above its 200-day moving average.
- Backtest the Strategy &8211; Many popular platforms enable traders to backtest their strategy using historical market data. That way, they can see if the strategy would have worked in the past. While past performance doesn’t guarantee future performance, it’s certainly a good starting point.
- Implement the Strategy &8211; Many popular platforms enable traders to upload their algorithms and then trade automatically. In many cases, the process is as simple as flipping a switch from paper trading to real trading.
- Setup Risk Management &8211; Risk management practices are extremely important to limit losses in extraordinary situations. Try to identify all possible scenarios and account for the risks by setting up rules that cut losses short.
- Monitor and Refine &8211; The markets tend to adapt to successful trading strategies, which makes it necessary for traders to constantly refine their systems to avoid trades becoming unprofitable over time.
The Bottom Line
Algorithmic trading gives computers the ability to make buy and sell trades based on sets of rules provided to them. In many cases, these automated trades can help make markets more efficient by minimizing spreads and increasing liquidity. In other cases, algorithmic trading can introduce greater volatility into the financial system by introducing automated instability, as in the case of Knight Capital Group.
Traders should keep these benefits and risks in mind when trading and take precautions to limit their exposure to the issues. For example, limit orders can be used to avoid slippage and other issues, while careful use of stop-loss points can avoid potential losses during times of instability. In the end, these tools can help traders ensure they aren’t being ripped off by automated programs.