Smart Automation: How Technology is Redefining the Trading Business

1. Performing a data-driven assessment of which orders to execute via automation
2.  Selecting optimal execution destinations (e.g., brokers, algorithms, dark pools, electronic communication networks)
3.  Defining and distributing orders to those execution destinations

Once these steps are complete, traders will be able to monitor trades in real time and disable the automation if a tail events happens. Setting up an advanced broker algorithm wheel helps funds identify the best performing execution strategies while removing selection bias. This leads to increased execution performance. 

The Future of Automated Trading Systems

As we head into the future, it’s worth reflecting on how trading workflows will change as the structure of automation evolves.

John Adam, Senior Vice President of Trading Solutions at FactSet, also believes automation will become an important driver for advancing the trading workflow. “I think that 2020 is the year we will see automation and data science enter the mainstream as foundational components of the portfolio lifecycle. Specifically, we will see a marked uptick in multi-asset trade automation,” Adam says. “Most exciting of all, the trader can rely on automation and data science to scale their productivity and assure consistent execution for low-touch and no-touch trading.” 

With a sophisticated and customizable automation tool, traders will become unburdened by the mundane tasks that occupy them today. Their expertise can be directed where it is most needed—whether that be on large or illiquid orders or on updating their portfolio managers with market color and real-time analysis. To ensure precision, orders should be routed out based on a single or combination of data points in the automation system. These include value, price, security type, percentage of volume, market impact analysis, and even a user-defined field or calculation. Simple examples include routing or prohibiting trades based on availability, the advice of portfolio managers, ADV or market-capitalization thresholds, bond rating, and an indication of interest. 

Rules-based automation for OTC markets should be integrated with in-house or third-party algorithm trading tools that offer smarter execution strategies for automated price improvement. Decision support features would help traders determine when alternative strategies will cause better execution performance than a single-counterparty request for quotation. The strategies could then be modified to reflect the desired behavior.

As for asset managers and hedge funds, researchers at Greenwich Associates found that investing in artificial intelligence from a third-party provider is more sensible. “There is significant upfront cost and technology risk inherent in building cutting-edge solutions; cost and risk the buy-side would have to pass on to their clients, which is less than ideal. The ability to easily tap into new, fully vetted tools through a third-party provider alleviates this problem considerably.”

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