Opening Cross: Predicting the Futures: The Multi-Asset Movement

Gazing into my crystal ball, I see something afoot in the world of commodities data: vendors expanding their products with new datasets aimed at appealing to new types of users-among them, those trading multiple asset classes.

In last week's issue, Chicago-based Barchart told us how it is developing a Professional-level workstation with trading capabilities and more sophisticated analytics to compete against larger rivals. This week, we reveal how the vendor is migrating data from its CRB Commodity Yearbook to an online database that will allow professionals trading stocks, commodities, ETFs, and commodity fund managers to query the vendor's supply and demand data in a more interactive manner to support real-time trading decisions.

Meanwhile, Telvent DTN has added a cash data module to its ProphetX commodities terminal, as well as fundamental data on agricultural crops and data on trading activity by commercial agri-firms and speculators, to provide those trading commodities at grain elevators and co-ops with more insight into market conditions - and is looking to expand its new geographical analytics to other energy and agricultural commodity asset classes, such as livestock and fuel pipeline data, which the vendor currently provides in quote sheets but would now be able to make available via interactive mapping tools.

At the same time, Morningstar's energy and commodities data subsidiary Logical Information Machines is opening up its data to a wider audience by making it easier to access, and is looking to become a "shop window" for other vendors' content in addition to its own, increasing the range of data clients can source via the platform.

I believe these moves all reflect - even if they don't contribute directly to - a broader trend underway in financial markets that is affecting the way traders in other asset classes do business: by mounting an assault on multiple fronts, with access to data across different asset classes, and in some cases the need to trade across different assets to achieve desired returns. For example, why wouldn't someone trading commodities or commodity futures also be interested in the stock price of companies that are big traders or consumers of those same commodities?

Similarly, in the world of automated trading, a single strategy is no longer enough. Firms are now running multiple algos concurrently as part of broader strategies in the search for alpha.
"Application architectures in the algo trading space are changing. An algorithm is the tip of a spear, which needs to have multiple tips - that is to say, each individual algo is part of a more complex series of algorithms," says Tervela chief technology officer Barry Thompson. "More and more, the firms using our technology are using it to trade multi-asset class strategies-few are just trading a single asset class."

Of course, you can't trade multiple asset classes without data on multiple asset classes, and the need to handle increased volumes and a broader range of data may well drive firms to deploy technologies such as those from Tervela and Solace Systems.

But joined-up trading needs joined-up data - either consolidated by end-user firms from best-of-breed sources, or from a single vendor with access to multiple datasets (another reason that commodity data vendors are expanding their services?) under one roof. Perhaps this is part of the reasoning behind eSignal's rebranding last week under the umbrella of parent Interactive Data - that while eSignal has strong product brands, the company becomes stronger overall by eliminating the perception of any separation between the two.

Thus, it isn't hard to imagine that vendors of niche datasets must either be the very best at what they do, or look for partners with complementary data.