Hedge funds expertise applied to AI training

Turn financial research workflows into
ready-to-use datasets, long-horizon training tasks, and reproducible evaluations

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  • Expert-authored tasks
  • Reproducible evaluation
  • Verifiable rewards
  • Composable environments
|THE CHALLENGE|

Real Financial Research Is Hard to Capture — and Evaluate.

Model teams need realistic, long-horizon tasks,
expert research trajectories, and reliable ways to assess results.

Creating them takes deep investment expertise and
tools to verify the work.

|MISSION|

Cat Pi Turns Years of Hedge Fund Experience into AI Model Capability.

Built by the team behind a leading quantitative hedge fund with a 14-year history, we transform real investment research into training tasks, expert-reviewed evaluations, and interactive learning environments. Our mission is to mold AI into professionals in the field of investment.

Connected financial research, AI training, and evaluation workflows
|SOLUTIONS|

From expert-built tasks to scalable training
environments.

Post-training tasks & data

Expert-built financial tasks and data, from core skills to complex workflows.

Model evaluation & diagnostics

Reproducible evaluations to measure financial reasoning and pinpoint model weaknesses.

Financial RL environments

Proprietary backtesting, optimization and market simulation systems turned into RL environments.

|MULTI-ASSET · MULTI-STRATEGY|

From investment strategies to RL training.

We turn real-world strategy research into financial AI training tasks—from signal discovery
and portfolio construction to backtesting and risk analysis.

|CORE TEAM|

A multidisciplinary team across finance, AI and systems engineering

Cat Pi was incubated by a leading quantitative hedge fund with a 14-year history. We develop training data, evaluation platform, and reinforcement learning environments for financial AI.

Our core team includes graduates of MIT, Tsinghua, USC, and Columbia, with expertise in quantitative and fundamental research, AI, systems engineering, and data science.

Tsinghua
Columbia
USC
Boston University
MIT

Bring Investment and Trading Expertise
to Your Model Training.

Explore sample research tasks, expert-reviewed reference outputs, and evaluation criteria grounded in real investment workflows.