Automation can be used to manage trades with very different best execution objectives. Charlie Campbell-Johnston of Tradeweb gives us real world examples of how to use automated trading to minimise market impact, maximise order size fulfilment and integrate these actions into a trading workflow.
This evolution of trading outcomes allows automation to support execution for asset classes with very different liquidity profiles, further supporting the access to automated trading on buy-side trading desks.
Dan Barnes: Welcome to Trader TV – your insights into trading for professional investors. I am Dan Barnes. The use of automation and trading, particularly in fixed income, has increased at a great pace over the last couple of years, allowing buy-side trading desks to use it in a range of different execution strategies.
Joining me today is Charlie Campbell-Johnston, managing director for AiEX and Workflow Relations at Tradeweb. We’re going to be discussing how buy-side firms can best take advantage of automation.
Charlie, welcome back to the show.
Charlie Campbell-Johnston: Thank you, Dan. Thanks for having me.
Dan Barnes: So tell us which execution strategies are being used for automated trading today?
Charlie Campbell-Johnston: If we look back over the 10 or so years where we’ve been automating trades on Tradeweb, I think the primary concern for the majority of liquidity providers has historically been looking at transaction costs for the most part within a best execution policy. So being able to trade at the lowest possible cost for your client.
However, in the last couple of years, what we’ve seen is a little bit of adoption of other variables. So fx looking at minimizing the market impact of trades or indeed achieving a certainty of execution. And if I were to nuance it, the way that execution rules are being defined is really reflecting those execution objectives rather than fitting execution objectives into a certain set of automation rules.
Dan Barnes: What do desks need in terms of resources in order to enable those strategies?
Charlie Campbell-Johnston: Well, I think actually to set up automation is relatively quick and simple, but what we found more and more is the ability to be able to analyze data post-trade and recycle that information to then educate those rules over a period of time has become more and more valuable. Really, the desk needs to be set up to be able to consume that kind of data post-trades, learn from that and then iterate through rules as execution objectives change, depending on market impact fx, or indeed certain underlying fund strategies that may have certain different objectives in mind.
Dan Barnes: That’s really good. Thank you. And then, don’t rules-based automation system put trading on to rails?
Charlie Campbell-Johnston: No, I don’t think that’s a fair analogy. Whatever we can automate reflects what we can do manually on the system. So you could have very, very wide range of execution protocols, counterparty selection options and so forth. And even stuff that is incorporated into automation functions may not be available to someone executing on a GUI. So as long as liquidity taker is fully aware of the options available for themselves, the chances are that they can actually fit their automation strategy within a framework of rules that exist. And this is something that we’re constantly evaluating. So as we get new protocols in or new liquidity sources, they will be incorporated, as a matter of course, into the options available to someone to use an automation straight trading tools fx.
Dan Barnes: If a trading desk needed to minimize market impact using an automated trading strategy, how might that work?
Charlie Campbell-Johnston: If we think about automation, we also need to think about the contents of the trading protocols that are available, and there is a couple that have been launched on Tradeweb in the last few years that really help that. The first would be request for market protocols, so this is the ability to ask for a two-way market without showing direction to the street and then trading at the level that you want to trade at.
The second thing would also be the incorporation of firm prices. So if a liquidity-taking entity has a certain price in mind, at a certain level, the ability to check prices that are firm negates the need for people to spend out an RFQ and spray the market. They can check what is available in a firm basis and say, ‘can we aggress these prices before going out to an RFQ?’ And I think he’s becoming more and more important, as automation is adopted by different profiles of clients who are not looking purely to execute at the best level and reduce that transaction cost.
The third thing is being able to determine the amount of counterparties and the nature of these counterparties to put into competition for a certain trade. I think we’re seeing a wider proliferation of pre-trade data that can inform the client if they’re looking for a certain level. If there are certain counterparties that have inventory and so forth, we can direct them to those people to ensure that they’re not spraying the whole market, giving them intention of what they’re trying to trade. So a combination of all of those things and flexibility between them adds ads a lot to the toolkit of anyone looking to automate trading.
Dan Barnes: So for a desk that wants to optimize or evolve its automation in line with a data and trading strategy, what does it need to develop a flexible pathway to support that?
Charlie Campbell-Johnston: What we found over the last number of years working with clients is that the collaboration between us and trading desks, to understand really what they want to achieve, has been possibly the most fertile ground for us, developing the functionality. So where we have a certain functionality in one asset class, it may not translate perfectly to another one. But knowing those options are available allows us to explore whether that is appropriate and has a demand from the buy-side desk.
Similarly that constant interaction with desks and understanding their trading objectives gets us a very good understanding of what data requirements they have so we can serve to be able to help them iterate through those rules and develop further rules to make sure that what we can offer is in alignment with what their specific needs are.
Dan Barnes: Charlie, that’s been fantastic. Thank you so much.
Charlie Campbell-Johnston: Thank you very much.
Dan Barnes: I’d like to thank Charlie for his insights today, and of course, you for watching. To catch up on our other shows or to subscribe to our newsletter, go to TRADERTV.NET.
