THETRADETECHFX DAILY from the floor
What is the principal consideration for desks when looking at the merging of asset classes ? In multi-asset trading , merging desks can be a viable option if the desks ’ flows are not heavily reliant on specialisation and can benefit from a broader understanding of the market . It is crucial to have sufficient overlap in trading tools , knowledge , and interests for a successful integration . It ’ s also of prime importance that in the process of merging there ’ s still at least one trader who acts as a specialist and single point of contact to ensure accountability .
Based on our experience of merging desks , there are clear advantages in fostering the exchange of ideas and optimising desk staffing , leading to improved overall performance and effectiveness of the merged desk .
When looking to build a talented multi-asset desk , what is the main thing you look for ? Operating a successful multi-asset trading desk demands not only deep subject matter knowledge but also a delicate balance of technical and quantitative expertise . Constantly evolving and innovating trading tools and processes is a necessity to avoid falling behind and mitigate the risks of complexities and errors . Whether it ’ s a principal or agency desk , we place a lot of importance on minimising avoidable errors and maintaining a high standard of performance , with adherence to benchmarks and execution policies .
In the contemporary trading landscape , comprehensive trade analysis and support form an integral part of the trading desk ’ s responsibilities . Any trade is incomplete without a meticulous pre- and post-trade analysis . The advantages of a multi-asset desk become evident through the synergies derived from different asset classes . Some asset classes may outpace others in terms of available data and trading technology , pointing to potential avenues of development for the rest .
How does the multi-asset aspect affect which technologies you employ , and workflow processes you prioritise ? Dealing with multiple assets presents a complex challenge with no one-size-fitsall solution currently available . Two main approaches emerge : building a platform from scratch , though costly and resource-intensive , or leveraging multiple solution providers to combine resources and technologies , which appears more favourable for asset managers facing resource constraints .
At APG , a strong emphasis is placed on adopting forward-compatible technologies and tools to avoid deploying solutions that may become outdated within a few years . Additionally , there is a significant focus on self-development of code and processes atop basic infrastructure . After years of focus on digitisation , we now have a thriving
Building a successful multi asset trading desk
The TRADE sits down with SUNIL PATEL , senior trader at APG Asset Management , to discuss how to succeed in a multi-asset trading environment , exploring technology implementation , data optimisation , and the intricate puzzle of TCA .
community of quants and developers . This community serves as a hub for sharing insights and offers various pathways for collaborative learning .
Regarding process prioritisation , relevant desks have the authority to make judgments unless the decision affects multiple desks . In such cases , a more democratic approach is taken , and every aspect is thoroughly analysed . Ultimately , client needs , and robust policy frameworks steer us in prioritising tasks and ensuring our commitment to excellence .
How can data be optimised / aggregated to make multi-asset trading more efficient ? This is probably the holy grail of a modern multi-asset trading desk . The key lies in prioritising data standardisation , ensuring seamless data collection , mapping , and cleaning processes across diverse asset classes . Integrating data from order management systems ( OMS ) and execution management systems ( EMS ) needs further augmentation with market tick data sourced through various APIs . Integrating ‘ alternative data ’ on top of this adds an interesting dimension , expanding analysis possibilities .
Data usage licenses bring significant challenges , necessitating careful consideration when choosing the right data provider to avoid unexpected and substantial data costs . Similarly , the design of the data architecture poses another hurdle , as data dispersion across different silos can lead to cumbersome interactions , extractions , and potential system unreliability . Ensuring a cohesive and well-organised data architecture becomes crucial to facilitate efficient data handling and minimise any possible disruptions .
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