The Fintech House, Lisbon

Recorded Webinar: Navigating the Build vs Buy Dilemma: Cloud Strategies for Accelerating Quantitative Research

For many quantitative trading firms and asset managers, building a self-provisioned historical market data environment remains one of the most time-consuming and resource-intensive steps in establishing a new research capability. Sourcing data, normalising symbologies, handling corporate actions and maintaining infrastructure can take months and absorb significant budget before a single model is tested. At the...

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12 Leading Vendors Operationalising AI & ML with Robust Data Pipelines

The transition of artificial intelligence and machine learning (ML) models from experimental sandboxes to production environments remains a persistent operational friction point. While quantitative researchers and data scientists can often demonstrate alpha in isolated backtesting environments, the institutionalisation of these models requires a level of data pipeline robustness, latency control and regulatory auditability that research...

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Data Management Summit New York City

Now in its 15th year the Data Management Summit NYC brings together the North American data management community to explore how data strategy is evolving to drive business outcomes and speed to market in changing times.

Regulatory Data Handbook 2019/2020 – Seventh Edition

Welcome to A-Team Group’s best read handbook, the Regulatory Data Handbook, which is now in its seventh edition and continues to grow in terms of the number of regulations covered, the detail of each regulation and the impact that all the rules and regulations will have on data and data management at your institution. This...