MarketAxess Composite+ Recognized by Industry Experts for its Use of AI
H2O.ai, the open source leader in artificial intelligence (AI) and machine learning (ML), announced that its open source platform, H2O, provides critical machine learning capabilities to MarketAxess, the operator of a leading electronic trading platform for fixed-income securities and the provider of market data and post-trade services for the global fixed-income markets. MarketAxess’ Composite+, powered by H2O open source, delivers greater insight and price discovery in real-time, globally, for over 24,000 corporate bonds. Composite+ has won several awards for its use of AI including the Risk Markets Technology Award for Electronic Trading Support Product of the Year and the Waters Technology American Financial Technology Award for Best Artificial Intelligence Technology Initiative.
“Congratulations to David and the research team at MarketAxess for creating the industry leading algorithmic pricing engine with H2O AI. With H2O’s machine learning algorithms in Composite+, MarketAxess has fully automated corporate bond pricing with better predictions and features”
“H2O is an integral part of Composite+ and provides some of the fundamental machine learning tools and support that make our algorithms run as well as they do,” said David Krein, Global Head of Research at MarketAxess. “The Composite+ pricing engine is helping fulfill our clients’ critical liquidity needs with more accurate and timely pricing data, which we make available within the MarketAxess electronic trading workflow. H2O.ai has been a great partner which has contributed to our recent success.”
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“Congratulations to David and the research team at MarketAxess for creating the industry leading algorithmic pricing engine with H2O AI. With H2O’s machine learning algorithms in Composite+, MarketAxess has fully automated corporate bond pricing with better predictions and features,” said Sri Ambati, CEO and founder at H2O.ai. “Fixed-income markets are ripe for AI transformation and MarketAxess is leading the change bringing speed and transparency to the marketplace. We are thrilled to be a partner in their amazing success and look forward to accelerating the age of AI in Capital markets with faster, cheaper and open platforms.”
MarketAxess Composite+
Composite+ is a leading algorithmic pricing engine for corporate bonds that leverages a range of proprietary and industry data sources, with updates up to every 15 seconds. It combines public data from the FINRA Trade Reporting and Compliance Engine (TRACE) with proprietary data from the MarketAxess trading platform and Trax®. The solution applies artificial intelligence and machine learning to predict accurate two-way reference prices for more than 24,000 bonds globally. When the most proximate signals are missing, the Composite+ algorithm can still learn to triangulate less predictive features successively.
Composite+ is designed to support a variety of trading functions such as pre-trade price discovery, liquidity provision, transaction cost analysis, auto-execution and crossing. The pricing engine is seamlessly incorporated into all aspects of MarketAxess’ trading workflow, including within its all-to-all Open Trading™ marketplace and request for quote (RFQ) inquiry screens. It is also integrated into the company’s BondTicker® and Axess All® web-based data platforms.
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H2O: Open Source AI
H2O is the leading open source, scalable and distributed in-memory AI and machine learning platform. H2O supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is extensively used in industries such as financial services that require the machine learning at scale.
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January 27, 2020 at 05:23PM
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H2O.ai Empowers MarketAxess to Innovate and Inform Trading Strategies - MarTech Series
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