Guide Outlines Python Steps to Clean Messy Free OHLCV Market Data

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A free or exported OHLCV dataset often contains duplicate timestamps, gaps, mixed timezones and adjustment artifacts that backtesting engines accept without warning. The article recommends normalizing all timestamps to UTC first, then deduplicating on timestamp while favoring the newest revised rows. Gap detection should be judged against the market's actual trading calendar, since crypto trades continuously while equities do not. It also advises running vectorized OHLC and volume sanity checks and treating extreme single-day moves as likely split or adjustment seams to be fixed by re-pulling adjusted history rather than patching rows by hand. The cleaned data is then sorted and saved as Parquet, with the raw data re-downloaded and re-cleaned periodically.

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