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With regard to quantitative research, we choose to use AI/ML only where there is a clear case for adding value. There are certainly cases when AI/MLpowered models give the best results. However, in multiple cases traditional statistical models still represent an overall better solution. Clients’ key considerations and when to involve them? The electronic trading business is highly competitive. In my view, state of the art technology and sophisticated quantitative research enable differentiated product. Execution quality, customization, access to liquidity and system stability are differentiators of a competitive low-touch offering.

“With modular approach, software components don’t have to change much to meet new requirements and different modules can be combined to create a new strategy, rather than designing from scratch each time the buy-side trader identifies a new trading pattern.” Traders demand better performance and predictability of execution results, all without sacrificing the ability to source liquidity, transparency and control of their executions. With the launch of our Precision Algo Platform, we offer our clients a fully customizable algo suite optimized to each client’s trading DNA.


It is critical to engage clients actively at every stage of the process to ensure transparency and understanding of the full capabilities of the product, and to cater for clients’ current and future requirements. Underlying algos, such as VWAP / TWAP / POV, are the building blocks for “algo of algos”. As our Precision Algo Platform is modular and flexible, the majority of customizations are easy to achieve without having to directly alter the code base. Extended into other asset classes The flexibility incorporated in a modular algo framework should enable the next generation of algos to be adapted to new asset classes and market structures without a full rebuild. Most of the code base, for example, the Allocation and Order Optimization framework, the Macro-Trader (Scheduler) and the Micro-Trader (analytics-driven, quantitative model-based order placement logic) are common components of any electronic trading algorithm and can be shared across different asset classes. The essential differences between asset classes are market data and their microstructure characteristics. Specialized quantitative models may need to be developed and calibrated to deliver better performing algorithms. Unique aggregators are also required to deliver a unified and normalized interface to both market data and market access. As we roll out our next generation, cross-asset electronic trading product, we hope to help shape the industry standard for algo development. Global Trading Q4, 2018 Issue #68  

Fixed Income TCA: A Competitive Differentiator Global Trading Q4, 2018 Issue #68  

Fixed Income TCA: A Competitive Differentiator