The Workings of Trading Algos
Understanding the algos
Electronic trading (e-trading) is continuing to rise in popularity, with electronic trading platforms capturing 44% of buyside US equities order flow in 2023, reflecting a 2% increase on 2022. In part, this trend is being driven by the growing use of and dependency on algorithms, with 37% of trading volumes in 2023 being executed through algorithms and/or smart routers across the capital markets industry. Specifically, ‘large’ financial institutions channelled roughly 60% of their trade flow through algorithms in 2023.
Algorithmic trading executes orders using automated and pre-programmed trading instructions to account for variables such as price, volume and timing. Although nothing new, the growing sophistication and influence of trading algorithms means now, more than ever before, firms need to ensure their trading algorithms are optimised. This is only possible by having a thorough understanding of how they operate.
The use of algorithms in trading has several benefits for financial firms. These include cost efficiencies, competitiveness, liquidity seeking, regulatory obligations and trading profitability. In addition, the use of algorithms in trading opens up financial firms to the potential of artificial intelligence, which can help firms to achieve best execution with their trades, while also helping firms stay relevant to the proliferating AI trend.
However, in e-trading, there is more to an algorithm than meets the eye. Trading algorithms can be categorised into execution algorithms and strategy algorithms. Execution algorithms are used to support the execution of trades, whereas algorithms supplemented by quantitative models are referred to as ‘strategy algorithms’ and are used to determine which trades are made. In many ways, the execution algorithm is the business end of an automated trade execution and hence should be optimised by firms looking to achieve best execution. As highlighted below, there is a no one-size fits all approach for how firms should do this: the right execution algorithm for a specific firm being dependent on its needs, market conditions, and even its trading style.
Execution algorithms are typically integrated with smart order routing (SOR) logic. Both work in tandem in executing trade orders. The SOR considers only where the order is being directed and at what price, systematically splitting larger parent orders into smaller child orders based on the available liquidity. The algorithm itself determines what, how and when a specific order will be placed.
In particular, the SOR uses event-based action decisions to achieve a trade execution, that can be viewed across three phases:
Aggressive Round: The aggressive round is used to capture liquidity that is readily available for fresh orders. When a best execution order is received from a client, market data is obtained from all liquidity pools that list that particular security. The smart order router then creates different combinations of “solutions” to execute the available liquidity via one or more aggressive orders. It compares these solutions and chooses the best one based on a set of criterion, including price, liquidity and preferred venue ranking. Once a decision is made on the given solution, the order that constitutes it will be sent to the various markets.
Passive Round: The job of the passive round is to manage the position of the remaining quantity of the parent order. If there is little or no liquidity on the venues within the given limit in the aggressive round, passive orders will be placed on one or more venues or dark pools. The choice of the destination venue(s) is done based on the given parameters, such as order type and time-in-force.
Sweeping Round: This is a brief phase that connects passive to aggressive and back to passive again. The purpose of the sweeping round is to seek out liquidity through the rerouting of the remaining passive quantity. The algorithm continuously scans the various markets for compatible opportunities, and once found, will activate sweeping to capture this liquidity.
The SOR will continue this cycle until the order is executed, terminated or expired. While it is the SOR that does most of the dirty work, it is the type of trade execution that will determine how the SOR will respond.
Three of the most commonly used trade execution algorithms are time-weighted average price (TWAP), volume weighted average price (VWAP) and percent of value (PoV):
TWAP: TWAP algorithms break up trade orders into several equal parts and executes them during the trading day, usually at five-minute intervals. By spreading the order evenly throughout the trading day, TWAP minimises the impact on market prices, reducing the risk of price manipulation and ensuring a ‘fair’ execution. However, one potential issue with a TWAP order is that it doesn’t take into account the fact that the volume traded is often greater at the beginning and end of the trading day, meaning that executions of these child orders are not always seamlessly filled at a given point in time.
VWAP: VWAP algorithms estimate the average volume traded for each five-minute interval and the order based on historical trading information, splitting the order into smaller pieces based on an average weighted volume. One challenge with VWAP is that the historical averages used may not correspond to the activity on that specific day and hence the algorithm may not be executing trades based of entirely accurate, up-to-date information.
POV: POV algorithms address the VWAP issue of relying on historical averages by using actual volume during the trading day. It calculates the smaller blocks based on the percentage of participation in the market. The POV trade execution algorithm also avoids having an excessive impact on market pricing, potentially lowering execution costs.
Generally speaking, financial firms will have to weigh up several factors when deploying trading algorithms, aside from generic cost and regulatory factors. In deciding what algorithm to execute their trades, firms are essentially in a balancing act between managing the market impact and market risk that their algorithmic trade execution can induce. On the one hand, using algorithms that slice up market orders across a number of venues over a certain time period means there may be less of a market impact; the longer the duration of an algorithm, the lower the percentage of volume, therefore causing a lower impact on price. A trader may be more willing to accept the increased time for trade completion if it means a larger order can be optimally executed. According to data from FX Algo News, slower execution styles can deliver better execution than more aggressive algorithmic executions because liquidity is more likely to be sourced. However, a longer duration increases exposure to a potential market move during the execution and therefore heightens market risk and can ultimately increase the likelihood of a poorer execution. In fact, in deciding what algorithm strategy to implement, firms should consider four key aspects including:
Objectives: Includes appetite for risk, and weighing up the prospect of waiting for better prices against seizing better liquidity opportunities. This can be addressed by setting an optimal order schedule so that expected cost and expected risk are optimally balanced subject to the specified investors risk appetite.
Constraints: These can result from the overall investment strategy (e.g. maximum stock) or can be trade specific. The most common constraint includes price limit, which refers to the price at which a trade is no longer attractive.
Context: The feasibility of meeting trade objectives depends on the context of the trading day, which change on a sessional or seasonal basis.
Evaluation Criteria: After a few similar trades, the performance of trade execution versus the objectives and constraints can be evaluated to examine if the framework has been successful.
Additionally, a key question for a financial firm to consider is what they want to get out of a particular algorithm, and the opportunity cost of deploying one execution algorithm over another at different points in time. Again, this will be different on a firm-to-firm basis and a firms’ appetite for risk.
Given the steady rise in popularity and growing dependency on trade execution algorithms, it is vital that financial firms give close consideration to what the best trade execution algorithm might be for them, as they seek to maximise profitability and streamline trade flows.