ITG (NYSE:ITG), a leading independent broker and financial technology provider, today announced enhancements to its dark aggregation algorithm—Dark.
Capturing 82% of all available midpoint block volume in the U.S. equity market, ITG’s dark aggregator provides broad access to natural liquidity using an unbiased routing optimization across all major dark pools, and uses advanced segmentation strategies to find quality fills that minimize information leakage. Built to help institutions trade large blocks of stock, Dark had client order sizes in the third quarter of 2017 that were 5.9% of average daily volume—64% higher than the industry average for dark aggregators.1
Available in North America, Dark now offers an enhanced workflow that provides traders with a new one-click block mode and control over order aggressiveness, order type usage, and minimum-fill thresholds with an intuitive urgency parameter. Traders have full transparency into real-time child order placement via Triton EMS or ITG’s web-based Prism tool.
“One of the most important issues facing global investors is the transaction cost created by unnecessary intermediation in our fragmented marketplace,” said Ben Polidore, Head of Algorithmic Trading Products at ITG. “Our goal with Dark is to consolidate the agency block market with an efficient and unbiased product designed to link buyside traders wherever they are.”
Also commenting on the rollout, ITG’s Global Head of Product, Brian Pomraning, said, “The enhanced Dark algorithm demonstrates our commitment to investing in best-in-class execution, liquidity, analytics and workflow technology products to help our clients improve investment performance and increase operational efficiency.”
Measuring Information Leakage
Information leakage in the trading process is a major concern for traders. In an ITG poll earlier this year, 37% of buy-side traders attributed more than half of their trading costs to information leakage. Dark focuses on interacting with quality sources of liquidity by continuously monitoring and limiting information leakage through large scale micro-experiments which inform the algorithm’s routing logic.