quantOS· Engine
Workflow · AlphaEngine CLI

Seal studies on your machine.

Anyone can install AlphaEngine free in Python. Firm pods mint organization keys in Connect to push and pull sealed studies across the team.

CLI reference

Six Commands

A simple local loop.

Your proprietary simulator code and raw return series stay on your machine.

  1. 01

    ae init

    Initialize a local research project on your laptop.

  2. 02

    ae data add

    Register a local dataset before running a sweep.

  3. 03

    ae idea new --kill-if

    Record the hypothesis and pre-commit what would kill it.

  4. 04

    ae validate

    Run the parameter sweep and count every trial.

  5. 05

    ae seal & ae verify

    Write and verify a canonical SHA-256 study seal offline.

  6. 06

    ae push / ae pull

    Sync sealed studies to your firm workspace using an ae_live_ key.

alphaengine demo
Northfield ResearchSample data
Local Python CLIverdict: likely_noise
$ alphaengine demo
verdict    likely_noise

A crossover on a random walk has no edge, and the verdict says so
rather than flattering it. That is this working, not failing.
9 parameter trials countedOffline execution
Verdict. When a strategy has no edge, AlphaEngine says so rather than flattering a random walk.
ae seal
Northfield ResearchSample data
$ ae validate
trials     counted on ledger
verdict    promoted

$ ae seal && ae verify
state      Sealed · integrity verified
Seal. Seal a validated study locally and verify its fingerprint at any time.
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