Prime Time for Big Data
Firms spend too much time processing information and not enough time analyzing it.
Blending tick data with semi-structured or unstructured data is a major pain point for capital markets as systematic and automated traders seek ways to model, simulate, and implement event-driven investment strategies using complex event processing.
Through a partnership between Selerity and Lime Brokerage, a low-latency trading system provider, trading firms can incorporate low-latency, market-moving event data into high-frequency trading strategies, along with a back-testing platform, as well as connect to major liquidity venues through a single API.
“The companies have partnered to deliver Selerity’s company, macroeconomic and energy event data via the LimeTrader strategy development platform,” Jeff Otten, executive vice president of business development at Selerity, told Markets Media. “Traders can leverage event data via a single API to back test, simulate and build algorithmic trading strategies.”
LimeTrader’s cross-platform feed handler reduces the development and integration work necessary to blend a news feed with market data and order entry API, facilitating seamless transition between back-testing a news-driven strategy and trading it live.
Selerity and LimeTrader provide clients with a single API and an end-to-end solution for testing and incorporating event data into their trading models.
The setup enables LimeTrader users to easily simulate news trades, matching up news events to liquidity visible in their market data feeds at the time the news arrives, before they risk trading with a production account.
“Working with multiple API’s of event data and various exchange data feeds can be complicated, each require requiring custom development due to varying formats and delivery protocols,” said Otten. “These development resources can be more efficiently allocated to trading strategy development by using a platform like LimeTrader.”
The term “Big Data” has largely been associated with loosely-structured content, originating from web search companies and social media.
“Market data is messy,” said Louis Lovas, director of solutions at OneMarektData. “For the financial industry, the need for reliability and accuracy is what distinguishes social Big Data from financial Big Data.”
Elementized news feeds, such as those provided by Dow Jones and others, deliver ultra-low-latency economic data and corporate news in a precisely tagged XML format that can be simply parsed and embedded directly into quantitative analysis models and algorithmic trading programs.
This allows traders, quants and strategists to make better trade decisions, cut execution times to mere microseconds and perform deeper, historical trend analysis.
“In finance, data accuracy is vital to determining outcomes,” Lovas said. “Asset prices cannot be inaccurate or missing and they must be adjusted for any corporate actions such as stock splits.”
For the algorithmic world, direct exchange feeds are becoming more desirable for their depth of book and lower latency along with greater use of real-time analytics.
“Firms today spend too much time processing data and not enough time analyzing it,” Lovas said. “When you’re looking for alpha, you don’t want to be sending time processing data, you want to analyze it.”
LimeTrader’s strategy development platform is written in native multi-threaded C++ to efficiently route data from feed handlers to the strategies that need it, and then place orders through a simulator or through a connection to a live execution venue.
“LimeTrader has been built and optimized for low latency trading” Otten said, “a key value proposition for Selerity and many event-based trading strategies.”
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