Our TradeTech key takeaway: automating workflows in trading on financial markets has entered a new phase, driven by Big Data analytics.
Automating workflows in trading on financial markets has entered a new phase, driven by Big Data analytics. That’s the key takeaway for us from the panel discussions, meetings with buy-side and sell-side clients, and from touring the stands at the equities-focused Trade Tech conference in Paris earlier this month.
Equity markets are more advanced in terms of automation than markets for other securities, so what we found particularly striking at Trade Tech is how the industry is examining where improvements can be delivered to the entire lifecycle of a trade. This was a marked shift away from the narrower focus on trade execution that has characterised the initial phase of the automation trend.
Big Data analytics at the pre-trade and post-trade stages are clearly critical as the industry charts a course to achieving the ultimate in terms of best execution.
While operational and cost efficiencies are naturally important benefits delivered by insightful Transaction Cost Analysis, Big Data analytics can provide significantly more actionable intelligence leveraging experience learned from the past and calculate in real-time various execution metrics and bespoke indicators. That’s before we start considering the application to predictive models and machine learning.
It's no wonder that industry respondents to the latest Refinitiv-Coalition Greenwich survey identified data science and analysis as the major skills that will be required for the next generation of traders.
As automation removes the impact of human error or natural biases, taking best execution to the next level means addressing a number of key technology challenges for Big Data analytics to unlock the full potential across the trade lifecycle.
The first is to have a scalable technology stack capable of handling the ever-larger data sets that are the key to achieving the desired outputs. This means the ability to process multiple sources of real-time data, market inputs, trade and static data and external data sets. The greater the inputs, the better the outputs, as the saying goes.
Secondly, buy- and sell-side users need tools which allow them to interact freely with the data sets, with as much data as needed or the level of detail they wish to explore. This means the ability to slice and dice data on demand and to populate dashboards at each stage in the trade lifecycle. At Opensee we’re strong believers in this democratisation of data and analytics for internal and external business users.
Aside from all these functionalities our data analytics platform provides an additional layer of tools, notably UDF (User Defined Function) using Python. This allows users to apply their own complex calculations to their data sets. This additional level of sophistication enables buy-side users in particular to feed their own machine learning models, helping accelerate and automate trade execution.
At Trade Tech this year our partner Kepler Cheuvreux, the leading independent European brokerage, was demonstrating at its stand a new post-trade analytics platform for clients which leverages Opensee’s technology to enhance trading-related execution quality management and to provide detailed client reporting.
The pleasure of meeting in-person at an industry event was matched by Trade Tech delegates being very much aligned with our vision for Big Data analytics – in helping power best trade execution. Our conversations with the buy-side and sell-side at the conference turned to how to deliver Big Data analytics through a software-as-a-service (SaaS) model. As we look ahead, that is something for exploring in another blog post.
Christophe Rivoire has written an excellent blog post that details some of the requirements involved in empowering financial institutions with SaaS models.