Documentation

What Kurzharr is, what a “Cleared” verdict actually claims, and who the platform is built for.

Build your first strategy, run it once, and find your way around the result it produces.

The named checks every run passes through — the order they run in, and the specific failure each one is there to catch.

How to read the verdict and the checks beneath it — and why running the same idea again raises the bar it has to clear.

A scalping strategy that clears seven of eight checks at the midpoint — and inverts the moment the fill is placed where a real order executes.

Every run is recorded permanently as a trial. What that means for the significance bar, and why refining costs something.

The strategy spec — universe, indicators, entry and exit conditions, geometry — and what the DSL will and will not let you express.

The prompt that teaches an AI you choose this platform's strategy language — plus a worked example, a sample report, and what we do and do not check about what comes back.

The built-in indicator families, grouped by what they do, and how a stateful indicator stands in for sequential logic the DSL forbids.

The path for your own Python indicator, coming later (unlimited on Research, capped on Pro): what the contract requires, what the admission pipeline checks, and what it cannot check.

What a run executes against, and how registering a strategy for forward confirmation fixes it to a date that a parameter search cannot reach.

The standalone tool: drop in an entry rule and a stop/target ratio, and see whether the entries beat the chance baseline the geometry implies.

What Free, the Audit pass and Pro each include, what Research will add later — and why the tier changes which checks execute, not just how many audits you get.

Plain-language definitions for the statistical terms the scorecard and these docs use.