Dukascopy Historical Data //top\\

Access history for dozens of Forex majors, minors, and exotics, alongside major stock indices, commodities (Gold, Silver, Crude Oil), and even cryptocurrencies.

Dukascopy's historical data covers a wide range of financial instruments and can be retrieved at various levels of granularity:

Data is available in multiple timeframes, from tick-by-tick (every price change) to monthly candles.

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I can provide targeted steps to help configure your workflow.

To save file space, prices in the raw binary files are stored as integers. For example, a EUR/USD price of 1.12345 is stored as 112345 . You must divide Forex pairs by 100,000 (or 1,000 for JPY pairs) during parsing to restore the proper decimal placement. Methods for Downloading Dukascopy Historical Data

You can retrieve data through three primary methods as of April 2026: Web-Based Feed Dukascopy Historical Data Feed Select your instrument, date range, and desired timeframe. Access history for dozens of Forex majors, minors,

Some users complain that Dukascopy historical data looks "noisy" or "choppy" compared to MetaTrader demo data. This is actually a feature, not a bug.

Dukascopy provides several official methods to access its historical data, each suited to different use cases and technical expertise.

How does Dukascopy stack up against the competition? The table below provides a high-level comparison. This link or copies made by others cannot be deleted

Loop through the target dates and hours to construct the URL string pointing to Dukascopy’s data repository. Download: Use the requests library to fetch the .bi5 files.

While primarily famous for Forex, Dukascopy’s data repository spans multiple asset classes, including:

If you are building a custom algorithmic trading system in Python using libraries like Backtrader or Pandas, you can download the data programmatically. Below is a conceptual workflow of how a Python script handles the download:

I can provide the exact or code snippets you need to get started.