Sample load test
31,680 EUR/USD M1 rows in public Parquet sample
Download sampleClean Forex Data for BacktestingStop backtesting
Stop backtesting
dirty FX data
Clean forex data for backtesting starts with audit evidence, not a bigger download button. HistoricalFX shows the EUR/USD sample file, Major-8 release QA, known source-observed gaps, and forex audit routes before a trader or developer trusts the result in a strategy, report, or model.
Current QA Proof
8
Major pairs/assets
56
Parquet files
79.0M
Audited rows
0
Structural blockers
The current Major-8 release has 79,042,363 audited rows and51 files with known source-observed gaps. That is the point: clean forex data should show limitations before a backtest depends on it.
Audit-Ready Evidence
What to verify before trusting clean FX data
The page-one promise is not that every historical minute is perfect. The useful promise is that the buyer can test a sample, inspect current release coverage, and see limitations before a backtest or paid audit depends on the file.
Release QA
79,042,363 Major-8 rows across 56 Parquet files
Inspect coverageKnown limitations
51 files carry source-observed gap caveats; 0 structural blockers
Scope an auditSearch Intent Fit
Pick the right clean-data path
The useful next step depends on whether you need a validated HistoricalFX file, an independent forex audit on your own archive, or the exact quality checks behind the cleaning claim.
clean forex data
Use this page when the job is not just downloading prices, but proving timestamp order, duplicate handling, OHLC validity, known gaps, and loader behavior before a backtest.
Test the sampleforex audit
Use the audit path when you already have broker exports, CSV archives, or platform history and need a written defect report before repair, conversion, or strategy work.
Request audit scopeforex data cleaning
Use the methodology path when you need the checks behind the claim: schema, timestamp policy, duplicate rows, bad OHLC values, source gaps, and conversion caveats.
Review checksWhat dirty data does to a backtest
Small data defects compound quickly. A single bad spike, shifted session, or duplicate timestamp can change stops, indicators, fills, and performance reports.
Duplicate timestamps create false repeated bars.
Missing minutes break indicators and strategy warmups.
Bad ticks create fake stop-outs or impossible wins.
Broker/session differences shift candles and distort comparisons.
CSV conversion errors silently change dates or numeric types.
Mixed timeframe sources make M1, H1, and daily files disagree.
The HistoricalFX cleaning layer
01
Normalize
Convert raw archives into one canonical OHLCV schema with consistent timestamp handling and predictable columns.
02
Validate
Check timestamp order, duplicate bars, OHLC relationships, missing files, suspicious outliers, and export readability.
03
Package
Deliver Parquet-first files for Python and modern data tools. CSV and MetaTrader workflows stay scoped separately until matching artifacts are rebuilt and audited.
04
Document
Publish methodology, release manifests, and known limitations so teams can reason about the data before trusting a backtest.
Pick the Right Proof Path
Clean data means evidence before commitment
The useful next step depends on whether you need finished files, a quality check on files you already have, or release evidence before spending time on integration.
Need a clean historical FX dataset?
Start with the current Major-8 coverage proof, then inspect a free sample before choosing a paid package.
Review Historical DataNeed to validate your own files?
Use the starter audit path for duplicate timestamp, OHLC, gap, and backtest-readiness checks.
Scope Data AuditNeed proof before checkout?
Check the release coverage and known-gap caveats before treating any file as research-ready.
See Coverage ProofCompetitive Wedge
Where we outshine bigger data vendors
Large providers win on breadth, API depth, and institutional sourcing. HistoricalFX is built to win the backtesting workflow: transparent coverage, sample-first validation, visible QA checks, and repair decisions that do not invent fake continuity.
APIs are convenient, but still need QA
An API key does not tell you whether a backtest input has duplicate bars, bad candles, timestamp drift, or gaps that matter to your strategy.
Bulk files are useful, but packaging is the work
Large downloads still need schema normalization, timeframe consistency, coverage reporting, and reproducible loader examples before they are research-ready.
Clean claims need visible proof
HistoricalFX is built to show sample files, coverage reports, known limitations, audit findings, and repair notes before asking for larger commitments.
Clean data questions
What makes forex data clean enough for backtesting?
Clean forex data should have a stable schema, UTC timestamp policy, sorted unique bars, valid OHLC values, readable files, and visible source gaps instead of hidden continuity claims.
When should I request a forex audit instead of buying files?
Request a forex audit when you already have broker exports, CSV archives, MetaTrader history, or vendor files and need duplicate, gap, OHLC, timezone, or conversion defects identified before repair work.
Does HistoricalFX hide missing bars?
No. Known source-observed gaps are reported as caveats. Multi-year gaps should be repaired only from real source data, excluded from analysis, or documented before a strategy depends on them.
From cleaned files to data infrastructure
This is the product direction: retail data downloads first, then commercial licenses, release manifests, validation reports, recurring updates, and API access for teams that need market data they can defend.
If your team needs custom forex data cleaning, source comparison, gap detection, or repeatable validation reports, start with a commercial request.
Discuss a data-quality request