What have been the recent developments to UBS FX algo offering?
Mike Bichan: We’ve seen a significant increase in the adoption of algorithmic execution from our clients this year. Our volumes have risen significantly, almost doubling year-on-year. At the same time, we’ve expanded our range of algos and the size of global sales team to meet client demand.
Christian Gressel: There has been a big increase in the size of the team that is dedicated to algos at UBS, partly in order to ensure a clear separation between our principal business and our FX algorithm offering This is why we have built out the algo sales trading and quant development team, put it behind a barrier and tried to make it very clear and distinctive in terms of the execution service we offer our clients.
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On the product side, we’ve also introduced what is often referred to as direct market access (DMA), including four electronic communication networks (ECNs), the primary markets (EBS and Reuters) and access to CME to our algo offering.
We really tried to keep the offering simple. We didn’t want to go down the route of giving clients too many different choices and having a confusing offering, but instead have a core five algos that provide the tools and execution capabilities our clients need.
What have clients been asking for and how has this impacted the direction you have taken with the suite of algos?
MB: We’ve historically been very strong in algos aimed at low market impact, but we needed to respond over the past year to client demand for more urgent executions. This is by delivering access to external liquidity, and in the past, the low market impact was typically aimed at UBS liquidity executions and then relying on the high internalisation rate at UBS so that only a small portion of the flow will be hedged in the market. Certainly the attitudes of our clients have changed and they want to access the external market in a fast fashion, so that’s one of the big responses we’ve had.
CG: If you look at our algo offering historically, you’ll see that we have leveraged the strength of our equities algo offering, which is already proven with our clients. Initially, we took that and made it fit for purpose for the FX markets, but we have had to adapt it after adding external venues and liquidity in order to make sure our algos understand the FX market structure correctly. The culmination of that development was our latest algo, known as UBS ORCA-Direct, which is a much more urgent algo that really starts to leverage all of the technology including our next generation smart order router (ORCA), that we have built out for these venues.
What differentiates the UBS algo capabilities compared to the competition?
MB: We now have a wide range of client strategies and so we can cater to whatever the client’s execution objectives are. Our newest offering provides our clients with basically the same tools as our UBS spot traders, meaning they can access broader market liquidity in a more urgent fashion. We initially developed the smart order router which allows our UBS spot traders to access external liquidity over the course of the year. Over time, the positive impact to UBS became very clear and when we felt it was robust enough that we then decided to offer it to our clients.
CG: In the beginning when we set out to develop ORCA, the overarching aim was to reduce cost of trading for our principle trading. We carried out some research into the microstructure of the market, and we found that there are three main points that drive the performance of execution, not necessarily the performance of an algo, but the performance of a smart order router.
Firstly, the liquidity that we are connected to is a simple fact of choosing venues that we want to be connected to, and that’s really about finding the right mix of eight or nine venues where connection means you truly have all of the liquidity you need without starting to access the same liquidity via multiple access points. The second point is around fill probabilities and what is known in the FX market as ‘Last Look’ and dealing with it correctly. We are able to measure and understand the fill probabilities on a currency pair on each of the different venues, and when we assess a price, we are able to make sure that we understand it correctly in terms of the chances of executing at that price, whilst comparing it with a slightly different price on a different venue.
Finally, we looked at latency. Low latency market makers typically have a competitive advantage from building out a network that is extremely fast and allows them to use information quicker than others. The way we are dealing with that is not necessarily by ensuring we have the fastest network, but by being smart about it and avoid being picked off. We have co-located gateways with the servers of each of our connected venues, so for example in London there’s EBS and in Chicago with the CME where we trade futures. Instead of dealing with minimising time difference for reaching each of those servers, we send out our order instructions to our gateways with a timer so that they get released to the market at exactly the same time.
How is artificial intelligence and advancements in technology influencing the FX business and product design?
MB: With technology, it’s all about learning and assessing over time what the right decision is with regards to execution. In terms of execution costs for us, we take into account rejects and the impact of resubmission of child orders. Clearly if one of the child orders from an algo has been rejected, we then need to put that back in the market with, what one would expect, a negative outcome. We continuously measure fill rates and the cost of resubmissions to build the data set on which our smart order router bases execution decisions. It’s important to understand that we may not necessarily hit the best price on the screen, but at prices which are probability-weighted and can get the best outcome for the order as a whole.
CG: Probability of execution and fill rates are updated continuously. They evolve and might change depending on the time of day or currency pairs, depending on how our machine learning and technology infrastructure allows us to assess those fill ratios. The key part here is that it is not static, the liquidity on various venues will change over time and we are able to pick that up reasonably quickly by measuring how the fills are performing. It comes down to a confidence of being filled compared to the best single price on the screen which you may not be able to secure.
How are client expectations regarding algo liquidity management evolving?
MB: If you go back some years and pre-algo, our clients originally wanted to know a price where they could get a trade done. Over time, clients increasingly took on some of the market time risk, as in giving up the certainty of a fill, in the expectation that it would improve the execution outcomes.
Equally now, clients not only want to engage with liquidity over time, they want to access it in a fast way. The evolution is executing on the external venues either slowly over time to try and have minimal market impact, or for immediate fills. So, our smart order router is directing orders to venues based on the best possible outcome as Christian has discussed. We want to hit prices that are real and we can access that liquidity, as opposed to having some kind of semblance of prices on screen, but not being able to hit them. Our clients truly want to understand what is adaptive liquidity and how to access it. From our smart order router concept, over time and at different times in the day, we can direct child orders to different venues based on the confidence of getting filled.
CG: Clients are increasingly trying to get a better understanding on how liquidity is in the market on a day or on a particular point in the day. It’s more about the interaction, moving away from what is your price, to deciphering the activity and depth of the market today and giving valuable feedback around that. Conversations are often around whether clients should be executing large orders over an extended period or as quickly as possible, and we can have those conversations based on the data that we are collecting on our side and based on the knowledge and estimates of how our algos execute.
Another important aspect is being able to show the order to clients in detail and that’s where transaction cost analysis (TCA) comes in to play. Everything we have discussed today wouldn’t stand up against a critical client if we can’t then show them the numbers depicting what we have done with the order. We are able to do that using our TCA, especially if clients are using our smart order router. We can show our clients at which venues they have executed and the fill ratio and at what price, but also in the event that we skipped a venue that possibly had a better price, we can show them that the data proves it gave the better outcome.