Custom Data Quality Audit

Find out whether your
forex data is usable

If you already have historical FX files, we can scope a quality audit: gaps, duplicate timestamps, impossible bars, source-backed repair options, and what it would take to make the dataset backtest-ready.

If you are still comparing methodology rather than requesting a custom review, start with the forex data quality proof page for the sample-load, Major-8 QA, known-gap, and release-coverage path.

Request Audit ScopeBuy $299 Starter AuditView Sample ReportReview MethodologyTry Our Sample

Audit checks

  • ✓Duplicate and non-monotonic timestamps.
  • ✓Missing M1 intervals by symbol and date range.
  • ✓Impossible OHLC bars and malformed rows.
  • ✓Weekend/session artifacts and suspicious quiet periods.
  • ✓Outlier candles that can create fake stops, fills, or returns.
  • ✓Export readiness for Parquet, pandas, DuckDB, and downstream conversion.

What we can quote

The point is not to sell a generic upload button. The point is to identify the concrete data problem and quote the smallest useful validation or repair job.

Dataset Audit

From $299

For traders or developers who already have files and need a defensible quality report before using them in research.

  • ✓Schema inspection
  • ✓Gap and duplicate report
  • ✓OHLC validation
  • ✓Backtest-readiness notes
Buy Starter Audit

Source-Backed Repair

Quoted

For teams that need missing ranges repaired from source-observed data instead of interpolation or fake continuity.

  • ✓Symbol/date scoping
  • ✓Source-backed backfill plan
  • ✓Repair manifest
  • ✓Before/after coverage report
Scope This

Recurring Refresh

Quoted

For teams that want daily or weekly updates, validation reports, and repeatable delivery after the initial cleanup.

  • ✓Refresh cadence planning
  • ✓QA gates
  • ✓Release manifests
  • ✓R2/API delivery planning
Scope This

Built from internal QA

The audit product is not theoretical

The same checks behind this service were run against our local all-pairs v3 candidate: schema loads, duplicate timestamp checks, invalid OHLC checks, known-gap reporting, and file-level review gates. That gives buyers a concrete audit workflow instead of a vague upload promise.

74
Symbols audited in local candidate
518
Parquet files checked
418.9M
Rows loaded through QA
0
Structural blockers after candidate QA

See the deliverable before you buy

The $299 starter audit produces a practical report: score, file-level checks, duplicate timestamp counts, invalid OHLC rows, gap samples, and recommended repair actions. The sample report uses a deliberately flawed EURUSD M1 fixture so the issues are visible.

Open Sample Audit Report

Sample Report Flags

Duplicate timestamps

Out-of-order timestamps

Invalid OHLC rows

Unexpected M1 gaps

Non-positive prices

Large outlier candles

Starter Audit

What happens after purchase

The starter audit is designed to remove uncertainty fast. You are not buying a vague upload portal. You are buying a practical quality pass that tells you whether your forex dataset is trustworthy, repairable, or too risky to use.

Buy $299 Starter Audit

Buying for a research team, client deliverable, or internal data product? Review the commercial license and release-proof path before sending procurement requirements.

01

Buy the starter audit

The $299 starter audit buys a fixed first-pass review instead of an open-ended consulting call.

02

Send scope, not raw files

Start with symbol, timeframe, date range, file format, source, and the problem you are trying to solve. We only request files after handling expectations are clear.

03

Receive a usable-data report

We check schema, duplicates, timestamp order, invalid OHLC rows, M1 gap samples, outlier candles, and backtest-readiness risk.

04

Decide the next move

You receive the report, a repair quote if source-backed repair is realistic, or a clear decline if the dataset is outside scope.

Credit toward repair work

If the audit turns into a quoted source-backed repair or recurring refresh workflow, the $299 starter audit is credited toward that follow-on work.

What to send first

Do not send raw files in the first message. Start with scope. That gives us enough information to determine whether a fixed audit, repair job, or recurring workflow makes sense.

Symbols or markets included.

Date range and timeframe.

Current file format and approximate size.

Source, if known.

Where the data will be used: Python, MT4/MT5, app, API, report, or internal research.

Specific concern: gaps, duplicates, bad ticks, conversion, coverage, or recurring updates.

Scope an audit

Send the minimum details needed to quote validation, repair, or recurring refresh work.

Email audit scope

Do not upload raw files here. We will ask for files only after scope and handling expectations are clear.

Audit questions

Do I need to upload my dataset first?+
No. Start with a scoping request that describes the symbols, date range, format, and problem. We only request files after the scope and handling expectations are clear.
Do you fill gaps with interpolation?+
No. Repair work is scoped around source-observed data. If a range cannot be sourced, it is reported as a known limitation rather than fabricated.
Who is this for?+
This is for traders, developers, educators, and small teams that already have forex files but do not trust the quality enough to run or publish results.
Can this become a recurring update service?+
Yes. The first audit identifies the current state. Recurring refreshes can be scoped after the source, delivery format, and QA gates are defined.