Ultralpha: Continual Alpha Mining as Research Compute Scales

Mining alpha factors is an iterative research process. In practice, a quant researcher writes a hypothesis as factor code and backtests it, letting each outcome decide the next attempt. Large language model (LLM) agents now conduct autonomous research for days, and a growing number of works have them run this loop, writing and testing factor code to mine new factors. While these methods have proven effective at producing candidates, our systematic analysis finds that their output repeats known factors and decays quickly in future tests. To mitigate these problems, we propose Ultralpha, a closed-loop harness for long-horizon alpha research. It keeps the agent researching in one protected loop with metered feedback and recorded actions, and carries its progress in a persistent research state that deduplicates output and keeps verified evidence. We evaluate Ultralpha on the A-share CSI300 universe with three base models. Ultralpha keeps mining new alpha. Under the same frozen evaluation, after deduplication 32 to 46 of its delivered factors stay positive on the test window, a retention of 95.7% to 97.0% of all delivered factors. Its Top-10 average 24h development IC is 0.0536 to 0.0576, and that average stays positive on the test window in all three blocks. Traded factors keep compounding while baseline curves rise and fade. Further analysis shows strong generalization of the framework to minute-level crypto perpetual markets and alpha mining continuing as research compute scales.