Trading desks can customise their technology thanks to greater data democratisation and desktop interoperability, making trade analysis more effective than ever. This will allow asset managers at every level to tailor their tools to suit their own style of trading, ultimately improving returns for investors.
Carl Carrie, head of trading analytics at LSEG, shows us how traders can get better execution through system connectivity, yielding a more focused picture of data and ultimately more .
The DESK: What is the greatest challenge in representing liquidity on the trading desk?
Carl Carrie: Liquidity is fleeting, often fickle and very fragmented. Liquidity is multi-dimensional liquidity and that means that the application of data science into the trading process has never been more important. Liquidity analytics need to be part of the new trading workflow. As the number of routing choices increases, so does the need ability to score liquidity using analytical and visualisation tools.
TD: How does the EMS help to manage that?
CC: The classifications of execution, order and portfolio management (E/O/PMS) systems is very outdated. Workflows are more complicated and now span the full process, from decision to invest, to optimizing that’s important for the new breed of EMSs to interoperate in a way that can be customised and optimised in different ways for different-type firms, reflecting different behaviours in indexing and alpha generation. Supporting trade execution differences for bonds, futures and portfolios is also important for many firms.
Interoperability with Market Data Containers / Desktops is key to reducing slippage and harvesting opportunities. In today’s markets, there's a lot more spill-over of information between instruments. A corporate bond trader cannot ignore fixed income exchange traded funds (ETFs) or credit default swaps (CDS). In some cases, they have to think about structured products, swings and to-be-announced (TBA) mortgage-backed securities. There are many aspects that might need to be brought in; the market impact of trades, expected variance in related markets; changes in liquidity scores or credit spreads which are often an indicator for price changes in corporate bonds and CDS.
If I'm trading the term structure of Tesla, news, stock, CDSs, spread term structures all matter at different times in different ways. So, market participants need the interoperability and the science to manifest itself in the form of actionable decisions at the point of trade and pre-trade.
TD: What challenges does the trading desk face, to make that occur?
CC: The target is no longer just about optimal execution, it's about optimizing the path from investment through execution, taking decisions and information flows and optimising different things at different times.
In the early 2000s, managing the deluge of information on traders desktop was really about filtering. Today, traders’ best friends are their machines. They live on big data and they're able to help make actionable decisions. When you need to trade, how do you that news, information, all that unstructured content that was in your streaming price feeds, your negotiated RFQs, and your text chats? How do you blend that into a process? That's part of the new nirvana.
TD: How do they overcome those challenges?
CC: Desktop interoperability is very important, in order to be take information from one system, using an API or FDC 3 to transmit information between platforms, but because the in/out (IO part is so big, with so much information, it needs to be transferred in the back end as well. That means APIs are hugely important. If you're limited to REST API’s and don't have Graph QL for example, you're limited in terms of the way the queries can be optimised. That could put you behind your competitors in performance. If you don't have low latency across your APIs, access to raw data and tick data, that's a problem as well. In the current environment, you need interoperability at the desktop and the back-end to bridge together the disparate information flows in a way that doesn’t create I/O clogs on the desktop.
TD: Why is this more important in a volatile market?
CC: The important of timeliness and accuracy; the cost of making a mistake is just so much higher now. You've got volatility at multiple dimensions as well: clearly in price movement, then the movement of volumes which are fragmented across different venues, with information flows becoming more disparate, and liquidity dynamics shifting intraday. It becomes harder for traders to remain aligned to benchmarks. Traders need to weave that information to flow in into the process so when dynamics shift, you've got the right information at the right time, whether it's structured or unstructured, to make the right decision. That is a new, critical dynamic.
TD: How are different buy-side firms approaching this challenge?
CC: There are zones of demarcation between firms; some have incredible intellectual assets and technical capabilities, but there are also opportunities for firms that don't have all of those capabilities. Being able to build sustainable niches is hugely important.
We know firms need the right set of tools to experiment with, and we're embarking upon being more transparent with our customers and our ability to deliver applications. That means applications are often going to come with visible Python code. Our applications will allow customers who may not have access to as much of the raw data as we do, to pick the spots where they want to find alpha, with the right approach to trading that fits their style and capabilities. The new benchmark are for cloud-based Execution and Portfolio management systems to interoperate with desktop applications and APIs with code to wire them together.
Now smaller forms with one or two traders who can code or have access to coders who can write a Python script can add differentiated value with tools that are connected into the ecosystem, the trade flows, the news, and APIs, their risk tools, their optimizers and portfolio construction tools s part of an ergonomic workflow.
TD: Do you see greater democratisation of data both between, and within, firms to enable that?
CC: In the digital asset space, where democratisation of data is at the core of trading, some innovations are spilling over into fixed income particularly on the post trade side. That will shape market structure in years to come.
But trading is often about asymmetric information. Democratisation of information is often in conflict with that, but they can happen together. It really depends on where you seek your competitive advantage.
Generally, we do see appetite for more democratic access to data and our transparency initiative fits with that. Liquidity conditions mean that certain participants have information at the tick level, if they have been market making in corporate bonds, which can be really advantageous for trading desks to analyse. Investment managers with quant teams can run analysis over data – if they have the right tools – that can spot execution patterns unique to them. These are exciting times.
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