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OHLCV Data Quality Checker for Candle Exports

Inspect a candle export before trusting its chart, indicators or backtest. Paste rows in timestamp, open, high, low, close, volume order and enter the expected interval in minutes. The checker reports malformed rows alongside timestamp duplicates, ordering problems, gaps and inconsistent candle ranges. It keeps inspecting after an invalid row so you can see more than the first problem. File selection reads data locally in your browser; no exchange API receives the contents. The example demonstrates the input format rather than a current market dataset.

Explicit assumptionsFormula & methodology includedNo account required

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Worked example — illustrative data
Data rows inspected60
Rows with valid field structure60
Recorded issues0
Distinct valid timestamps60
Check six fields, numeric OHLCV bounds, timezone-aware timestamps, duplicate timestamps, row order and exact expected spacing.

Preserves your input order and reports issues rather than sorting, filling gaps or deleting duplicates. This does not authenticate an exchange source or detect every anomaly. A field-valid row may still have a duplicate or spacing issue.

CSV rowIssue classDetail
0No structural issues detectedDoes not prove accuracy, completeness or that the final candle has closed.

Validate the candle itself

Each row needs a usable timestamp and finite numeric open, high, low, close and volume values. Prices must be positive, and volume cannot be negative. The high must be at least as large as the open, close and low; the low must be no larger than the open or close. A zero-volume candle is different from a missing volume field and can be valid for some datasets. Invalid rows remain part of the diagnostic report rather than silently disappearing before inspection.

Check the time sequence

Duplicate timestamps can cause an indicator to count the same interval twice. Out-of-order rows can create misleading returns and wrong previous-close comparisons. The expected interval tells the checker how far apart adjacent observations should be; a larger separation may indicate missing candles, while a smaller one can reveal mixed frequencies. Review anomalies against the source market's session schedule. A checker cannot decide whether a gap is legitimate without knowing when that instrument actually trades and how its provider labels candle times.

Do not confuse clean syntax with reliable history

Passing these checks establishes internal consistency, not the authenticity of prices or completeness of an exchange export. A provider can revise candles, change volume units or supply data from a different venue without violating OHLC bounds. Keep source, symbol, interval, timezone and download date with each dataset. Do not automatically fill missing candles with invented price movement or remove outliers simply because they are inconvenient. After corrections, rerun validation and compare row counts before feeding the data into indicators or historical strategy calculations.

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