Quantitative Brokers (QB), a provider of advanced execution algorithms and data-driven analytics for global futures, options and OTC Fixed Income markets, today launched a free online tool that allows institutional traders to track market liquidity and quote size at a level of transparency and ease never before available. Today’s announcement also marks a new direction for QB, who aims to offer an integrative suite of market microstructure analytics soon.
“The Liquidity Tracker provides guidance and visibility across multiple markets,” said Robert Almgren, QB’s Co-Founder and Chief Scientist. “We are very transparent with our clients and strongly believe the wider trading community can benefit from this open-source tool.”
Phase 1 of QB’s Liquidity Tracker initially charts 20+ markets worldwide, analyzes historical and real-time liquidity and quote size activity in each asset class, updates intra-day and serves as a health monitor of the markets. Traders can use the tool to minimize the price impact of their orders. QB Liquidity Tracker is accessible to anyone here.
“QB Liquidity Tracker marks a significant milestone for us since the launch of QB’s Roll Tracker, strengthening our position as a leading algorithmic and analytics provider,” said QB Head of Research, Shankar Narayanan. “Identifying the state of liquidity is a vital step to understanding and utilizing the next frontier in algorithmic execution, which we call Regimes. In 2022, we look forward to expanding QB’s analytics tools to help empower users to optimize their trading decisions.”
QB DRIVING INNOVATION
The Liquidity Tracker is QB’s latest innovation, providing clients with a multi-asset class multi-exchange analysis using real-time liquidity and quote size data.
In recent years, QB has expanded its suite of execution algorithms with the addition of Octane, The Roll, and, most recently, the launch of Striker 2.0. QB’s long-term view is that clients use the Liquidity Tracker to infer and minimize slippage when making trading decisions. QB’s clients have long inquired about robust liquidity reports. The challenge has constantly been the normalization of large data sets and using the same standards across the board when comparing markets. While reporting presents its challenges, QB has been predicting liquidity and quote size change, for a long time, by having its algorithmic strategies built upon these models.