CQG unveils AI/ML Trading Toolkit for predicting futures market moves

Success in Live Trading Environment Confirms Internal Test Results
PRNewswireMarch 12, 20244 min

CQG, a leading global provider of high-performance technology solutions for market makers, traders, brokers, commercial hedgers and exchanges, announced completion of internal testing and proof-of-concept using live data on what the firm believes to be a first-of-its kind artificial intelligence (AI) predictive model for traders. Following extensive machine learning (ML) training in a back-testing environment, the firm just started applying the technology to live data, with an extremely high level of predictive success in anticipating futures market moves. CQG made the announcement on the first full day of FIA Boca, the International Futures Industry Conference.

Based on the firm’s deep experience in analytics, mathematics and market intelligence, the new ML initiative aims to offer retail traders and buy-side firms, including proprietary trading firms and hedge funds, unprecedented tools for identifying new trading and analytics opportunities, guiding trading strategies, and managing their positions. CQG has been exploring the field of AI for the past year in the context of solving for its clients’ challenges, testing the technology in a state-of-the-art multi-platform lab. Last week, for the first time, the company tested its next-generation machine learning toolkit in a live trading environment and achieved 80% predictive accuracy – matching the results attained in the back-testing environment.

CQG CEO Ryan Moroney said: “In early 2023, we decided we wanted to do something different in machine learning and AI that leveraged our unique position in the market, building off our comprehensive database of historical trade data and analytics in a way that could help our clients and prospects analyze, predict and trade markets through a new lens. We built a lab, and Kevin Darby – our Vice President of Execution Technologies – has done an extraordinary job of turning that effort into an exciting reality with results that have significantly surpassed our expectations.”

Darby said: “We first had to solve multiple real-world challenges, such as storing and curating terabytes of historical market data while retaining the ability to make decisions in microseconds in real-time environments. We built bridges between the current ML infrastructure, based on the Python language, and the reliance of the financial industry infrastructure on C++. We also needed to recast the traditional ML training pipeline to optimize for generative time series prediction to estimate conditional probability distributions in a mathematically satisfying and stable way.”

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