Thinking

Cluster Analysis for Evaluating Trading Strategies

June 03, 2012 Ben Polidore

In this paper, we introduce a new methodology to empirically identify the primary strategies used by a trader using only post-trade fill data. To do this, we apply a well-established statistical clustering technique called k-means to a sample of “progress charts,” representing the portion of the order completed by each point in the day as a measure of a trade’s aggressiveness. Our methodology identifies the primary strategies used by a trader and determines which strategy the trader used for each order in the sample. Having identified the strategy used for each order, trading cost analysis (TCA) can be done by strategy. We also discuss ways to exploit this technique to characterize trader behavior, assess trader performance, and suggest the appropriate benchmarks for each distinct trading strategy.

Assessing trader performance is challenging because traders often vary their strategies depending on the objectives of each trade. For example, when orders are benchmarked to the open, traders may front-load their trades, perhaps executing a large portion of the trade in the opening auction. For larger, more impactful orders, traders may choose to trade more passively, stretching the order over a longer period
of time. Ideally, trading cost analysis (TCA) should take into account the trader’s underlying strategy. In reality, doing so is challenging because 1) it is often unclear how to characterize the underlying strategies used by the trader and 2) even if the strategies were known, determining which orders apply to which strategy can be difficult if that information is not captured in post-trade databases.

In light of these challenges, one common approach to assessing trader performance is to group trades by algorithm as a proxy for the trader’s underlying strategy. If traders use specific algorithms to meet their objectives (e.g. using Close Algorithms for trades benchmarked to the close, VWAP Algorithms for trades benched to VWAP, etc.), this approach makes sense because the algorithm is the strategy. However, high-touch traders often use algorithms as tactics rather than strategies, switching between different algorithms within a given order. As a result, TCA by algorithm will not yield information about the effectiveness of the trader’s hybrid strategy.

Another commonly used approach to evaluate trader performance is to assess their performance in the context of average aggressiveness. For example, one could look at the average progress chart of a trader to see how passively or aggressively the trader tends to work orders, and assess performance in that context. Such averages may not be meaningful, however, as they aggregate across underlying strategies. For example, Figure 1 shows the aggregate fill progress chart for a single trader. From the graph, it would appear that this trader’s underlying strategy is VWAP. However, in reality, this trader may have used multiple strategies that resemble VWAP in aggregate, even if the trader never actually targeted full-day VWAP on a single order.

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  • Ben Polidore

    Managing Director, Head of Algorithmic Trading