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Search for historical Forex tick data and one name appears again and again:
Dukascopy.
There is a good reason.
Dukascopy provides public historical market-data tools covering Forex and other instruments, including tick-level history.
For researchers and algorithmic traders, that makes it an important source.
But there are two mistakes worth avoiding:
- assuming that raw data is automatically ready for every platform;
- assuming that one high-quality FX feed is identical to every broker feed.
Let's separate the useful facts from the assumptions.
Dukascopy provides tick-level historical data
Dukascopy's Historical Data Export allows historical market data to be retrieved across multiple instruments and timeframes, including tick-by-tick data.
Its JForex environment also provides programmatic access to historical ticks.
Historical tick objects expose information such as Bid and Ask prices and timestamped history.
That makes the data useful for:
- quantitative research,
- strategy testing,
- price-path analysis,
- custom platform imports,
- market-data engineering.
JForex itself uses tick history for detailed testing
Dukascopy's historical testing documentation describes an all-ticks mode that processes historical marketplace ticks sequentially.
As with MetaTrader, this is the higher-fidelity option compared with testing from candle interpolation.
That tells us something important:
tick-level history is not just a downloadable file format.
It is a first-class input into Dukascopy's own historical testing environment.
Time requires careful handling
JForex allows its displayed platform time zone to be configured.
Its documentation also discusses different day-start conventions and shows how GMT versus EET can change higher-timeframe candle construction.
This is an important reminder:
price history and candle time boundaries are related, but they are not the same thing.
When external tick data are moved into another platform — particularly a broker-specific MetaTrader environment — timestamp interpretation has to be explicit.
A raw feed should not simply inherit an arbitrary broker clock.
Dukascopy data is not “your broker's data”
This point matters.
The FX market is decentralized.
Dukascopy represents its own market-data environment.
Your MT5 broker may use:
- different liquidity sources,
- different aggregation,
- different spreads,
- different server time,
- different sessions,
- different symbol specifications.
Therefore the goal of using Dukascopy data should not be:
“Make my historical ticks identical to Broker X.”
That may be impossible.
A better goal is:
Use a detailed, well-understood historical source and transform it transparently into the testing environment you need.
This is where normalization becomes important
Before using external tick data for MT5, ask:
- What time convention is the source using?
- Does the destination broker change UTC offset seasonally?
- Which Bid/Ask fields are available?
- What MT5 symbol specification should be used?
- Are trading sessions correct?
- Has the data been checked for gaps and malformed periods?
Those questions are separate from downloading.
The TickArk approach
TickArk treats source acquisition, data verification and destination formatting as different stages.
For MT5 Broker-Aware Import, the destination broker symbol supplies the target environment.
The historical data remain external historical data.
TickArk then prepares them around the target symbol and verifies time alignment, including checks around DST-change dates.
That distinction is deliberate.
We do not need to pretend all Forex feeds are identical in order to make external historical data extremely useful.
Is Dukascopy “the truth”?
No single phrase causes more confusion in OTC market data.
Dukascopy can be a valuable historical source.
Another broker can also have valid data.
Those datasets can differ.
The relevant questions are:
- Where did the data come from?
- Is the history complete?
- What does each field mean?
- How was time handled?
- How was it transformed?
- Is the resulting environment suitable for the experiment?
Once those questions are answered, historical data becomes much more than a giant CSV file.
It becomes a research asset.
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