HPC for Quant Trading Firms running backtesting at scale.
Quant research is generating candidate strategies orders of magnitude faster than backtesting infrastructure can evaluate them. AI has further accelerated alpha research from a handful of signals per quarter to hundreds per day. The bottleneck has moved from researcher time to compute throughput.
Today, sweeps that should take minutes take days, breaking the research loop. Researchers cut parameter grids, shorten windows, and down sample data to fit what the cluster can run. Every compromise on your backtest is a place where alpha goes uncaptured.
We remove the compute ceiling, so the dimensionality of the backtest and the velocity of the research iteration are determined by the hypotheses, not the cluster.
Founders
We've built software for a combined 25+ years that serves billions of users and runs on hundreds of thousands of machines.
Every time you ask LinkedIn for a warm intro, you hit three databases we built: Venice, Liquid, and Espresso.
After LinkedIn, we were tech leads of Ray, the open-source compute platform used by xAI, Cursor, Bridgewater, and Two Sigma and many others. Ray has roughly 12 million weekly downloads and is a PyTorch Foundation project.
Product
We're targeting clusters that scale to:
Our goal is to make backtesting strategies simple, fast, and cheap at scale.
Ask
Please reach out if you:
- Run backtesting or other simulations at a hedge fund or an AI native trading firm.
- Can make a warm introduction to someone who is.
- Want to grab a beer and talk about distributed systems or HPC.