Market Data

Normalize and summarize market data for: $ARGUMENTS

Overview

Implementation status: code-backed
Local entry script: <bundle-root>/market-data/run.py
Primary purpose: expose the canonical one-symbol market-data pipeline without adding decision, thesis, or execution claims
Workflow stages: stage 2 Data Collection & Quality Assurance plus stage 3 Data Cleaning & Normalization, with stage-4 feature-base preparation
Local executor guarantee: schema normalization, required-column validation, date sorting, duplicate-date collapse, numeric coercion, indicator enrichment, and optional export

The user wants to inspect whether a symbol’s raw price data is usable for research or backtesting.
The user wants an enriched export that downstream skills or external workflows can reuse.
The user wants to verify date coverage, source, row count, and the basic feature base before analysis.
The caller needs a disciplined preprocessing step before market-analyze, stock-data, market-screen, or backtest-evaluator.