Introduction

In this paper, we analyze which Alternative Trading Systems (ATS) provide the most unique block liquidity using a large, private execution dataset containing conditional orders. We address the challenges of race conditions when utilizing conditional orders and examine which ATSs algorithms choose when they all offer block liquidity in the same stock. Finally, we explore the market share of each ATS in various transaction size buckets, helping to determine the optimal minimum execution quantity. 

Below is a preview of the research paper, showcasing some important sections and the analysis we've performed on a large private dataset of conditional orders. To request access to the full paper, submit the form at the bottom of the page.

Race conditions on conditional orders

While conditional venues mitigate the issue of aggregating fragmented liquidity, they can create race conditions. For example, if Algo A sends a sell order for 20K shares to 5 conditional venues, and Algo B sends a buy order for 20K shares of the same instrument to the same 5 conditional venues, they both will receive simultaneous invitations from all 5 venues. However, the sequence of these invites received could differ for Algo A and Algo B.

Algo A might receive the invites in the sequence 1, 2, 3, 4, and 5, while Algo B receives them in a different sequence (2, 3, 1, 4, and 5). This discrepancy could arise due to differences in latency between Algo A and Algo B to these venues. Depending on the logic, Algo A may respond to venue 1 with a firm-up order, and Algo B may respond to venue 2, resulting in no order being executed though both parties were available and willing to execute. Liquidity-seeking algorithms must be designed optimally to address such race conditions.

BestEx Research utilizes conditional orders across its suite of algorithms, including liquidity-seeking algorithms, schedule-based algorithms, and smart order routers. In an analysis of approximately 140K invitations received from conditional orders, we found that 41% of the time there was a race condition—invitations received from at least two venues simultaneously. All invitations arriving within 50 milliseconds of one another are treated as “simultaneous.” This data provides an opportunity to study which dark pools offer unique liquidity—those venues not part of these clusters of invitations. Additionally, this data offers insights on which venues tend to provide a fill when in a race condition.

Algorithm preferences during race conditions

Every execution algorithm must be prepared to handle race conditions as multiple invitations can be received at the same time. Most broker algorithms firm up on the first invitation they receive, while others may wait a few milliseconds and then firm up to the most preferred venue among the invitations received. Both approaches can lead to missed opportunities if the counterparty firms up at a different venue. The table below shows the % of invitations that have race conditions after the algorithm has resting conditional orders, varied by the size of conditional order.

Click here to request access to the full research paper

Conditional WinMatrix

The results of our algorithm preferences analysis are presented in the WinMatrix below. Here, we evaluate only the largest block crossing networks. For each pairwise combination of venues, the value in the corresponding cell indicates the percentage of times the venue in the row got a fill when the venue in the column didn't. The last column represents the average winning percentage (against all other venues) for the designated row.

Click here to request access to the full research paper

Determining minimum fill size and liquidity loss

One of the dilemmas in optimizing conditional order execution is setting an appropriate minimum fill quantity. Venues generally support conditional orders sent with a minimum quantity, which is used in their matching algorithms to ensure the fills each party receives are equal to or greater than the specified minimum quantity. Traders can also use this minimum quantity to access selective liquidity. Traders generally prefer a larger minimum quantity to reduce information leakage and to minimize the number of small transactions. However, setting the minimum too high can reduce access to available liquidity. 

To evaluate minimum quantity selection, we analyzed the percent of liquidity within each ATS that belongs to each transaction size category. In this analysis, we include all fills regardless of the ATS being unique or part of a cluster of simultaneous invitations. We introduced a new category called “Mega block” by dividing the block transactions into “regular blocks” (those between 5,000 and 25,000 shares) and “Mega blocks” (those greater than 25,000 shares). 

Click here to request access to the full research paper