The primary purposes of trading via algorithms, as mentioned in our previous article, are to reduce trading costs, provide a potentially higher degree of anonymity, a lower degree of risk, and allow for increased volumes to be executed. Within this framework, a number of different trading strategies can be employed. Let’s look further at these different types.

Logical Participation Strategies- These methods attempt to minimize costs by breaking up trades into smaller pieces and pushing them through the market based on specified rules. They derive their name from the fact that once implemented, they will participate in the market based upon the rules in their program. Furthermore, they are by far the most popular type in use by the buy-side. The simplest and most popular of these are VWAP (volume-weighted average price) and TWAP (time-weighted average price) algorithms. VWAP strategies use historical volumes to impute projected volumes for the day. Based upon this data, their trading patterns attempts to mirror the probable volume of the time period in which the transactions occur. By trading more during times of increased volume, (particularly during the morning and late afternoon), it implicitly attempts to reduce market impact. A TWAP strategy attempts to evenly trade the volume of a security over a predetermined amount of time. A similar strategy, called Percent of Volume, and also employed as an option by most sell-side sources, attempts to consistently trade within a specified band of volume as an overall percentage throughout the order’s history. A more sophisticated brand of logical participation involves the concept of Implementation Shortfall, which places a greater emphasis on implicit costs such as opportunity costs and delay costs, rather than simply focusing on market impact. Also labeled Arrival Price by a number of sell-side platforms, this strategy weights the above costs and creates an automated program that often weights the volume of trades at the beginning of the day, where the arrival price is computed. The logic behind this is rather straightforward- a manager makes his/her decision to trade a security based upon a price. Opportunity cost in terms of receiving that prices increases on inversely with time. The longer it takes to fully execute a trade, the more difficult it is to achieve this arrival price, based on factors such as volatility and the presence of new market information.

Opportunistic Participation/Specialized Strategies- These strategies involve themselves in the market when a predetermined criteria is met. They often seek out liquidity by posting limit orders in an effort to minimize trading costs (pegging/discretion strategies). Furthermore, pairs trading can be classified as another common strategy. In an effort to take advantage of perceived arbitrage opportunities, one can pre-specify price points in which to buy and sell simultaneous long-short pairs. Index arbitrage strategies, the process of profiting from the difference in the price of an index versus the prices of the individual stocks within that basket of securities, is another specialized strategy that can be implemented via algorithms.

Most brokers offer access to the same strategies mentioned above. Furthermore, their prices are often comparable. The pertinent question to be asked when considering which broker’s algorithm to use is, what kind of market access do the algorithms have? Can one send their trades through the algorithms to a host of exchanges, ECN’s, and DMA’s? As the next step to increased liquidity, sell-side firms have been configuring their algorithms to tap into dark pools. This seems to be the next logical step in minimizing trading costs.