와이콤비네이터 S26, LLM 퀀트 트레이딩 학습 환경 공개

Skip to content 0 1 About 0 2 Why Markets 0 3 Team 0 4 Blog 0 5 News MARKET MICROSTRUCTURE POLICY ROLLOUT / 096H MARKET STATE POLICY πθ(a|s) DELAYED REWARD 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. Join waitlist ↗ Read the thesis ↓ NONSTATIONARY BY DESIGN NO RESET / NO SATURATION MARKET-DERIVED / RESEARCH-FOCUSED 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+00 T+96H T+00 Choose Act under partial information. The model sees an incomplete state and commits before the full consequences are observable. T+18H Compound The decision becomes part of the environment. Exposure, opportunity cost and every action not taken reshape the path that follows. T+53H Revalue The objective moves. A decision can remain locally correct while becoming globally expensive as conditions drift. T+96H Adapt The policy that worked has expired. Success belongs to the model that recognizes the new regime before yesterday’s behavior becomes consensus.