Why a Single Liquidity Score Can Mislead: A Better Data Modeling Approach

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Data scientist Johan Wirakarsa, Ph.D., argues that collapsing liquidity into a single score obscures the distinct mechanisms it is meant to measure, such as market depth, funding access, and cash coverage. Writing for DEV Community, he demonstrates a Python data model using standard library dataclasses that keeps each liquidity dimension separate and explicitly typed. The approach prevents unrelated metrics from silently offsetting one another through normalization or averaging. Wirakarsa recommends that any composite indicator be calculated in a separate, versioned function while preserving the underlying inputs, units, and assumptions. He concludes that liquidity is best treated as context rather than a verdict, with interpretation deferred until the consuming application has a clearly defined question.

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How ESP32-S3 Voice Satellites Fail: The Full Audio Pipeline Explained

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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