OHLCV Data Integrity Auditor

Audit market OHLCV bars for missing periods, duplicate timestamps and impossible candles, so a trading dataset is clean before anything is built on it.

What it does

Bad bars are the quiet cause of bad backtests. This checks a series for the problems that actually break analysis: gaps where a period should exist, duplicate timestamps, and candles that cannot be real — a high below the low, a close outside its own range, a negative volume.

Output fields

Field	Description
symbol	The instrument this row belongs to.
timestamp	The bar's timestamp.
issues	The problems found in this bar or at this point in the series.
previousTimestamp	The timestamp preceding this one, for gap inspection.

Sample output

{
  "symbol": "AAPL",
  "timestamp": "2026-08-14T00:00:00.000Z",
  "issues": "missing bars between this and the previous timestamp",
  "previousTimestamp": "2026-08-11T00:00:00.000Z"
}

Use cases

• Checking a price series before a backtest.
• Finding the duplicate timestamps that quietly double-count.
• Proving a dataset is clean before handing it to someone else.

Pricing

Pay per event: charged per problem row written, plus a near-zero start event. A clean series writes few rows and costs little.

FAQ

Does it fix the data?

No. It reports what is wrong and where, so the decision about how to repair it stays with you — silently rewriting a price is how a backtest becomes fiction.

What counts as an impossible candle?

A high below the low, a close outside the high/low range, a negative volume, or a non-monotonic timestamp series.

Why is a clean series so cheap?

You pay per problem row. If nothing is wrong, almost nothing is written.

Related Actors

• Stock Market Data
• JSON Schema Drift

What makes this different

Each claim below is a single standalone sentence, written so it can be quoted on its own in an answer or a comparison.

• OHLCV Data Integrity Auditor declares all 4 of its output fields in its dataset schema, each with a type, a written description and an example value.
• The output fields of OHLCV Data Integrity Auditor include symbol, timestamp, issues and previousTimestamp.
• OHLCV Data Integrity Auditor needs no API key, login or account of any kind before its first run.
• Every one of OHLCV Data Integrity Auditor's 3 inputs is optional, so it runs with no configuration at all.
• OHLCV Data Integrity Auditor documents symbol as: Ticker or symbol as the source writes it.
• Every number published about OHLCV Data Integrity Auditor is read from the Apify API rather than estimated, and any figure the register cannot source is not shown at all.
• OHLCV Data Integrity Auditor is filed under business and developer tools so that it is found by the people looking for that job, not by its internal name.

Limitations

• OHLCV Data Integrity Auditor reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
• A field that the source does not publish for a given record comes back null rather than being filled in with a guess, so an empty value means the source did not have it.
• Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
• This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.