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Trading Desk Optimisation Technology

BTON is an independent technology company utilising machine learning to improve outcomes for all trading desks.

By using large, collaborative datasets BTON powers intelligent decision making for the traders of today and tomorrow

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Achieve cost effective scale by automating trading workflows

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Best-Execution Leveraging Data, Automation and A.I.

Quant analysis & machine learning resulting in improved routing decisions

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More time for traders to retain alpha on larger trades and more esoteric instruments

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Collaborative Intelligence - Best-ex compliance for your trading desk

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BTON financial

BTON : data collaboration, machine learning driven

algowheel / routing logic.

BTON leverages machine learning and the power of the cloud to deliver a unique technology solution. Anonymised community data improves statistical decision making and unshackles participants from the confines of their own limited data sets

What is BTON?

Intelligent broker routing logic using data science and machine learning.

Automated workflow using AI that facilitates data collaboration by customers in order to have a statistically relevant data set.

The collective intelligence gained by our customers enables a positive feedback loop as every single trade should get incrementally better results leading to superior returns.

The optimal solution for a desk seeking the ability to scale volume.

Currently no other market solution uses a collaborative data set and quant analysis on broker performance metrics.

Best-Execution Leveraging Data, Automation and A.I.


We are technologists, entrepreneurs and practitioners dedicated to improving execution outcomes across the industry

Adam Mingos

Held senior positions at 4 major global financial institutions in both investment banking and asset management. engaged with governments, regulators, major exchanges and financial institutions to manage complex risk and develop and improve trading processes using technology. workS closely with key stakeholders to provide innovative solutions across capital markets in a compliant and scalable manner. focus on emerging technology solutions for the expanding fintech platform ecosystem and the interface between humans and technology.

Daniel Shepherd

Over 20 years cross asset class trading experience in London, Hong Kong and Tokyo. Has previously built and sold a low latency infrastructure FinTech company. Formerly head of sales/marketing for a London Stock Exchange MiFID II solution

Emmett Cawley

Senior quant trader, wrote trading algos at Nomura, Credit Suisse and Millennium. BSc Hons Oxford University, PhD Liverpool University and former CERN physicist - the European Organization for Nuclear Research.

Ray Tierney

Formerly CEO of Bloomberg Tradebook and Global Head of Equity Trading at Morgan Stanley Investment Management. Ray chairs the National Organization of Investment Professionals fostering fair and open markets for all participants

Paul Birch

Serial angel investor in B2B Fintech businesses. Co-founder of social network Bebo, which launched in 2005 and was later sold to AOL for $850m

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Best-Execution Leveraging Data, Automation and A.I.


"At AP2, we are trying to be in the forefront when it comes to investment solutions. Therefore, I think the platform solution BTON offers in terms of automation based on machine learning suits us very well. In finance, the integration of data and technology is inevitable and undeniable. It will drive the industry forward and provide a competitive advantage for those who embrace it"

Mathias Eriksson, Senior Trader AP2

More than SEK 400 billion ($38bln) under management in virtually every asset class and all parts of the world, Andra AP-fonden (AP2) is one of northern Europe’s largest pension funds. A secure and stable manager of assets, the Fund is tasked with an important assignment by the Swedish Government – maximizing the long-term return on Swedish pension assets.

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Best-Execution Leveraging Data, AI and Automation

Contact Us


181 Queen Victoria St, London EC4V 4EG

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+44 (0) 203 897 2239

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