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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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BMLL Adds Kalshi Data as Prediction Markets Move Deeper into Institutional Workflows

BMLL, the market data and analytics provider, has added historical prediction market data from Kalshi to its global market data platform, giving quantitative and systematic investment firms a standardised dataset for incorporating event probabilities into macro research, backtesting and trading models. Under the partnership announced today, Kalshi’s historical order book will be normalised into the...

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AI in Capital Markets Summit London

Now in its 3rd year, the AI in Capital Markets Summit returns with a focus on the practicalities of onboarding AI enterprise wide for business value creation. Whilst AI offers huge potential to revolutionise capital markets operations many are struggling to move beyond pilot phase to generate substantial value from AI.

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...