WatersTechnology | Bloomberg ups focus on quants, intraday strategies

This article was originally published by WatersTechnology.

The vendor hopes its OHLC Bar data product will woo new audiences among quant traders and analysts, who have previously had to painstakingly build solutions in-house.

Quant firms looking to hire traders and analysts with PhD and MSc qualifications and experience have a new competitor in the search for talent: Bloomberg. The data giant is stepping up its focus on the quant community and is hiring practitioners to help build tailor-made products specifically targeted at quants.

“Our quant pricing products are part of an investment we are making in building end-to-end solutions that power quant research,” says Angana Jacob, head of enterprise research data at Bloomberg in London.

That investment isn’t just in technology, but also in people. Bloomberg’s recruitment website shows 42 open positions for quants specifically—with some roles paying up to almost $300,000 per year—or for individuals to work on products targeted at quants, which the vendor sees as a growing area of demand for data, resulting in the creation of new products as quant strategies evolve.

From a product perspective, the investment includes the launch of the vendor’s Company Financials, Estimates, Pricing, and Point-in-Time Data earlier this year. In addition, Company Revenue Segmentation Data and Company Industry Specific Fundamental Data were also recently rolled out. Jacob says these products provide deeper insights for customers, and they are interconnected to all Bloomberg’s other datasets. They are also accessible via a range of delivery mechanisms, including via the cloud. There’s also the vendor’s BQuant Enterprise platform, which it describes as “a managed sandbox in the cloud.”

Its latest tools, including the vendor’s new Open High Low Close (OHLC) Bar pricing product, have been developed specifically in response to shifts in quant trading.

“As a general trend, clients have been moving to intraday strategies to capture more alpha than was available in low-frequency trading (weekly or monthly),” Jacob says, adding that doing this in-house can involve some heavy lifting.

To move to intraday quant strategies, she says, in the past, customers needed to use sophisticated time-series databases to manage tick history. These databases are expensive and require large amounts of storage. And it’s not plug-and-play, as a significant quant development effort is needed to build and maintain them. “Once customers have a time-series database in-house, they then need to source the tick-by-tick historical data and host it internally, maintain that database on an ongoing basis at the raw tick level, and then build capability to generate OHLC bars and determine what trades to include and exclude in that bar creation.”

OHLC bars provide a simple and concise way to visualize and analyze the vendor’s Tick History historical data product of quote and trading activity, which it launched last year to spot potential trading signals and buying or selling pressure. Users can customize the inputs by quote or trade, time interval to analyze and to include in each bar, and can filter out certain types of indicators or trades to focus on the specific dataset most relevant to their use case.

For example, a systematic strategy for trading momentum or mean reversion in global futures would need intraday pricing and volume information, but wouldn’t need the same level of granularity of tick-by-tick data, so users can choose how much to aggregate into each OHLC bar.

The product is aimed at quantitative analysts and systematic researchers performing strategy modeling and back-testing, fine-tuning algorithms, transaction cost analysis, and compliance and surveillance. It was also developed for traders using intraday strategies that may trade every minute or five minutes, compared to the core target user base for its Tick History service, which would be high-frequency traders and low-latency strategies that need tick-by-tick level data.

“In the past, firms had to build out the capability of aggregating these 10-minute bars from raw ticks themselves, which is complex to get right given the size of the data and the different condition codes across global exchanges,” Jacob says, adding that the OHLC Bar product eliminates that complexity by reducing that heavy lifting into customizable filters and standardized metadata.

“[It] significantly simplifies data engineering and operations, allowing clients to … focus on research and execution, and not the data wrangling, maintenance, and storage. Time to alpha generation and strategy rollout is reduced,” she says.

Rising interest in quant solutions

Tamara Stevens, a product strategy and business development specialist who most recently worked at Intercontinental Exchange, and before that at MarketAxess, Brokertec, and Tradeweb, after starting her career at Bloomberg, also notes a rising interest across the industry in quant-focused solutions, especially among firms that don’t have their own teams of quants to create models in-house. She says the ability for a broader range of firms to adopt quantitative models will ultimately enable wider participation in the markets.

“Some of the largest firms can afford to have their own quant efforts. But there’s a whole sub-sector of participants in the market who don’t have the resources to create something in-house,” Stevens says. “So, to have a boilerplate where you put in the inputs and the ‘smarts’ are generated behind the scenes, you can create a personal model based on what’s important to you—and that makes you far more useful.”

The alternative, she says, is that firms who cannot adopt quantitative tools will fall behind competitors’ investment strategies and economic outlooks.

“These types of tools work alongside their human counterparts, but have the ability to gather, internalize, and organize information faster than any human,” Stevens adds. “The element of what is done with this information is still the human factor. However, not having access to these types of tools and trying to compete with others who do—especially with the vastness and ‘instantaneousness’ of financial markets information—leaves limited scope for viability and success.”

Indeed, Bloomberg has plans for more uses leveraging the 17 years of historical data contained in its Tick History product to create more quant-focused insights.

“Once we launched [Tick History], the next step was to build and roll out OHLC bars to give customers even more control and customization,” Jacob says. “Now that the OHLC Bar product is launched and is being rolled out to clients, we are focused on developing enhancements related to liquidity analytics and order book information.”

About Bloomberg

Bloomberg is a global leader in business and financial information, delivering trusted data, news, and insights that bring transparency, efficiency, and fairness to markets. The company helps connect influential communities across the global financial ecosystem via reliable technology solutions that enable our customers to make more informed decisions and foster better collaboration. For more information, visit Bloomberg.com/company or request a demo.