In today’s high-speed electronic markets, the role of the trader is only becoming more challenging. After all, it’s the trader who is ultimately responsible for the overall trade lifecycle; analysing market conditions, selecting the best strategy for a particular order, monitoring the speed and quality of the executions as they come in, and making appropriate adjustments as conditions change throughout the day. Unfortunately, strategy selection and optimization still remain largely a manual process with traditional execution management systems.
The more orders a trader has to handle, the more difficult it becomes to effectively monitor the factors impacting each one. Factor in the sheer variety of algorithmic strategies made available by the sell-side and the high degree of configurability that allows them to be tailored to so many different market conditions, and it’s easy to see how a trader can become overwhelmed. What’s really required today is a new type of thinking EMS that can bring measurable efficiencies and workflow automation to the trading desk, using artificial intelligence to give traders and portfolio managers the highest level of real-time color and TCA on their orders.
Following conversations with our clients, it’s become apparent that significant value can be delivered by intelligently automating this process.
Over time, it seems clear that the buy-side is going to start moving out of their traditional comfort zone in terms of the way they manage their algorithmic executions, because it’s becoming increasingly apparent that real money is being left on the table every month through non-optimal strategy selection. Adopting tools and technologies to automate and optimise this process is a trend which has already started and which is poised to grow steadily in the years to come.
As a result, we’ve spent the past few years working to develop a solution that uses quantitative predictive analytics to optimise algorithm selection and monitoring. At its core is a 45-factor model that diagnoses a trade at inception and generates an execution profile. As the day progresses, the model is constantly re-factored against the initial hypothesis and adjustments are made automatically to maximise alpha capture while minimising impact costs, adverse selection, information leakage and the impact of high-frequency trading.
A key facet to the technology is its transparency; we allow the user to see the decision-making process and the factors driving the decision for any trade at any given point in time. In this sense, it acts as a decision support tool for the trader. It gives them information and color on the key factors impacting the execution of their trades in real time, providing them the confidence and support they need to ensure their selections are appropriate.
Automating the desk
