forex data quality
Use this page when the question is whether a historical FX file is clean enough for backtesting, not just whether it can be downloaded.
Run checker →Data Quality LayerCheck forex data quality
Check forex data quality
before the backtest
HistoricalFX turns raw historical price archives into validation-ready datasets for backtesting and Python research. Use this forex data quality page to verify sample-load behavior, release QA, known-gap visibility, and audit routes before a strategy depends on the file.
If you are evaluating whether a dataset is clean enough to use, start with the clean forex data proof path: sample load, release coverage, known-gap caveats, and audit routing before strategy work.
For the practical failure pattern, read why forex backtests fail when data quality is not checked first. It connects this methodology to missing bars, duplicate timestamps, invalid OHLC rows, timezone drift, and source-backed gap caveats.
Backtest risk check
Free diagnostic
Screen for source uncertainty, missing minutes, duplicate timestamps, OHLC errors, and conversion risk.
Run checker →Proof before purchase
EUR/USD sample
Load the public Parquet sample before choosing a paid bundle or requesting a custom audit.
Download sample →8
Verified symbols in current Major-8 release
7
Timeframes generated consistently
79.0M
Rows audited in current Major-8 release
Search intent routes
Pick the right quality proof
Different searches need different evidence. Start with the mechanical quality question, then move to the smallest proof asset that answers it before buying data or requesting repair work.
forex data cleaning
Use the clean-data route for the practical workflow: sample load, release coverage, known-gap caveats, and audit routing before strategy work.
Open clean-data path →historical forex data validation
Inspect the current release coverage before trusting row counts, pair lists, or timeframe assumptions in a paid workflow.
View coverage proof →forex data audit
If you already have broker exports, CSV archives, or platform files, scope a paid audit instead of assuming the defects are repairable.
Scope audit →Manifest-backed proof layer
The business is being built around auditable data releases. The current manifest ties the paid R2 bundle to the local release-quality report, so validation claims can be proven release by release instead of hand-waved.
8
Delivered symbols
56
Delivered Parquet files
79.0M
Delivered rows
0
Files flagged for review
This manifest describes the current uploaded Major-8 paid R2 bundle and the local next-release quality audit. Coverage varies by symbol; use pair-level reports before making strategy assumptions.
Last manifest generated: 2026-07-03T17:24:35.863Z
Validation checks
The goal is to make historical FX data boring: predictable columns, consistent timestamps, reproducible exports, and fewer hidden assumptions in every backtest.
Timestamp continuity
Every symbol and timeframe is checked for missing bars, duplicate timestamps, non-monotonic order, and weekend/session artifacts.
OHLC integrity
Bars are rejected or flagged when high/low/open/close relationships are impossible, malformed, or outside expected market structure.
Bad tick screening
Outlier moves are detected against neighboring bars and higher-timeframe ranges so obvious feed spikes do not poison a backtest.
Timeframe reconciliation
M5, M15, H1, H4, daily, and weekly files are generated from the same normalized minute base to keep timeframes internally consistent.
Format validation
Parquet exports are loaded after generation to verify schema, date parsing, numeric columns, and row counts. CSV and MetaTrader conversion files are verified separately when rebuilt.
Reproducible packaging
Dataset builds are scripted so future refreshes can be regenerated, compared, and audited instead of hand-edited file by file.
Next release QA
What is in candidate review
The uploaded R2 release remains the customer-facing artifact. Separately, the local v3 candidate has been rebuilt and quality-checked so commercial buyers can discuss broader access, refreshes, and custom validation with concrete numbers.
74
Symbols in candidate QA
518
Parquet files checked
418.9M
Rows loaded through QA
0
Structural review blockers
Cleaning pipeline
This is the core product engine. As the business matures, the same pipeline can power subscriptions, commercial licenses, API access, and custom validation reports.
01
Ingest raw minute archives by symbol and month.
02
Normalize timestamps and column names into one canonical OHLCV schema.
03
Remove duplicates and impossible bars.
04
Detect gaps, quiet-session edge cases, weekend bars, and suspicious spikes.
05
Generate derived timeframes from the normalized minute base.
06
Export buyer-ready Parquet packages; rebuild CSV and MetaTrader conversion files only when matching delivery artifacts are ready.
07
Run post-export validation and sample-load checks before release.
Why this can become more than a file store
Raw data is easy to compare on price. Cleaned, documented, reproducible data can become infrastructure. The same validation layer can support one-time downloads, commercial licenses, API subscriptions, and custom data-quality work for teams.
Download sample →Limits and roadmap
The current product focuses on audited OHLCV bar releases. The next defensible layers are expanded historical rebuilds, commercial licensing, refreshed data builds, and API access for repeat users.
support@...Data quality FAQ
Is this just raw free forex data repackaged?
No. The value is the normalization, validation, timeframe generation, packaging, and developer-ready delivery layer. Free sources can be useful, but they usually push the cleaning burden onto the trader or developer.
Why does data cleaning matter for forex backtesting?
Bad timestamps, duplicate bars, missing minutes, feed spikes, and inconsistent timeframes can turn a strategy from profitable to useless or make a broken backtest look attractive.
Do you provide tick data?
The current product is OHLCV bar data from M1 through weekly. Tick-level products and data-quality reports are on the roadmap.
Can teams request custom cleaning or validation?
Yes. The next product layer is commercial licensing and custom data validation for teams that need documented, repeatable datasets.