The engine

The machine that does the killing.

Market microstructure goes in. Features become hypotheses, hypotheses get a criterion locked in writing, and then the tests run. Almost nothing survives. That funnel, run at this volume, is the whole edge: it is far cheaper to kill a bad idea than to trade it.

The kill engine
173 test files
A pipeline diagram: market data flows through features, hypotheses, and pre-registered tests to verdicts, backed by 2,248 automated tests. 27 strategies were published and killed; 0 survived to a bot you can buy.
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lines of Python
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automated tests behind the verdicts
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strategies published and killed
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survived to a bot you can buy

Counts are real and re-derived from the repository. The flow is an illustration of the pipeline, not a measured throughput. Nothing here uses private data.

Market data

Sub-second order-book and trade data from Hyperliquid, collected continuously. The raw material.

Features

The signals we derive from it: funding, basis, volatility, flow. Candidates, not conclusions.

Hypotheses

Each becomes a written prediction with a pass/fail line, locked before we look at the result.

Tests

The criterion runs against fresh data. Over 2,000 automated tests guard the machinery itself.

Verdicts

Almost everything is killed. What survives is rare, small, and only works at institutional scale.

Why it matters

A bot you can buy never shows this. The funnel is the proof the work was actually done.