Personalizing Trading with Deep Learning on Snowflake

Paradigm, a leading institutional liquidity network for cryptocurrency options trading, faced challenges with notification overload and missed opportunities due to a lack of personalized ranking in their Requests for Quotes (RFQs). To address this, they collaborated with Predibase to develop a deep learning-based recommendation system integrated with their Snowflake Data Cloud. Utilizing Predibase's ML infrastructure, Paradigm built predictive models that score maker-trade combinations, enabling real-time personalized notifications to market makers. This system improved trading volume by ensuring relevant market makers are alerted to crucial RFQs. Predibase's platform allowed Paradigm to efficiently develop, deploy, and maintain these models, significantly reducing the time and cost of production. The successful implementation not only enhanced trader engagement but also paved the way for Paradigm to explore additional machine learning applications, such as intelligent order book seeding and large language model fine-tuning, showcasing the adaptability of Predibase's technology.

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