About ShoalFlow

ShoalFlow began in 2019 at the intersection of two disciplines: quantitative finance and large-scale data engineering. Co-founders Rowan Hale and Dr. Ananya Rao had spent years watching trading firms struggle with the same data challenge — markets were generating exponentially more data, but the tools to process, analyze, and act on that data had not kept pace. Rowan, a data engineer who had built streaming pipelines at a Silicon Valley tech giant, and Ananya, a machine learning researcher with a background in market microstructure, believed they could close the gap.

Their first product was a streaming analytics engine that could ingest, normalize, and enrich tick-level market data from dozens of venues simultaneously, applying configurable transformations and publishing derived signals with end-to-end latency under 50 milliseconds. Early adopters — mostly systematic hedge funds — were drawn to the platform's ability to blend traditional market data with alternative datasets like satellite imagery, shipping manifests, and social sentiment in a single analytical framework.

Today ShoalFlow employs 95 people across offices in San Francisco, New York, and Singapore. Our platform processes over 12 billion events per day for 180 institutional clients. We have expanded from pure analytics into predictive modeling, offering pre-trained ML models for volatility forecasting, liquidity estimation, and event-driven signal generation — all accessible through a unified API and an interactive notebook environment that quant researchers love.

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