Reinforcement learning environments
We build market-derived RL environments to teach agents applied ML and long-horizon planning under adversarial noise. Static synthetic benchmarks are unrealistic and saturate quickly, whereas markets are non-saturating and self-improving.
THESIS
Static worlds produce static intelligence
Quant is the hardest, yet solveable data science task.
We programmatically generate quant research tasks inside environments built from real market data. Agents use professional tools—and build their own in Bash—to make trading decisions and develop profitable strategies.
Markets do not saturate: successful trading makes them more efficient, while edges decay and regimes shift. That makes our environments a continuously harder benchmark for improving models.
HORIZON
A decision is not a moment
Trading decisions affect more than one future step.
Trading successfully means planning ahead multiple steps and assess trade-offs between short and longterm gains
T+00Choose
Act under partial information.
The model sees an incomplete state and commits before the full consequences are observable.
T+18HCompound
The decision becomes part of the environment.
Exposure, opportunity cost and every action not taken reshape the path that follows.
T+53HRevalue
The objective moves.
A decision can remain locally correct while becoming globally expensive as conditions drift.
T+96HAdapt
The policy that worked has expired.
Success belongs to the model that recognizes the new regime before yesterday’s behavior becomes consensus.
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