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Experiments — every one counted in the deflation
Most edges don't survive. Penrose finds the few that do.
An independent, power-aware referee for quantitative trading claims. It reconstructs a strategy, deflates it for the size of the whole search, tests it out-of-sample against a locked holdout, and reports what survives — kill, underpowered, or supported. It finds no alpha of its own; it measures how little holds up, honestly.
A black hole traps nearly all the matter and light that crosses its horizon — gone, unrecoverable. Yet Roger Penrose showed there is a way for energy to escape a spinning black hole, carrying real information back out with it.
Quant research has the same shape. Nearly every backtested edge gets pulled into the void — selection bias, overfitting, look-ahead, decay — and dies there. Penrose is the test almost nothing survives. The rare claim that escapes the gauntlet has earned the energy it carries out. We named the engine after the escape — even though the void is most of the work.
Most backtests are stories the data tells about itself — selection bias, overfitting, and look-ahead dressed up as alpha. Penrose assumes an edge is noise until it survives deflation, out-of-sample testing, and a single-use locked holdout. When it can't tell, it says so — underpowered, not dead. The point isn't to be right; it's to not fool yourself — in the open, where anyone can check the work.
Backtesters ask "can I find an edge?" Penrose asks the harder question: "after counting the whole search and trying hard to break it, is there credible evidence left?" It referees a claim — from a paper, a generator like RD-Agent or Qlib, a notebook, or yourself — not just your own strategy.
Tools like Qlib generate factors and backtest them well — but none of them count the size of the search, deflate for it, or lock a holdout. Penrose is the layer that does.
Penrose is a research release — an independent, power-aware falsification referee for quantitative trading claims. Clone the repo, install, and point it at your experiments.
Open source, Apache-2.0 — contributions welcome: add a data adapter, a strategy module, or a new calibration null. Issues and PRs on GitHub.
An independent, power-aware referee for quant claims. Test a strategy before you trust it — and find out how little holds up.