Set the export timezone to match your backtesting platform (typically UTC or New York GMT+2/GMT+3 with DST). Step 3: Export to Your Trading Architecture
A single year of tick data for a volatile pair like EUR/USD can easily exceed several gigabytes. Ensure you have ample SSD storage space and sufficient RAM for processing.
In a market where milliseconds and pips define the margin between profit and loss, you cannot afford to guess. Utilizing feeds gives you a window into the past with surgical clarity. Whether you are a manual trader looking to analyze historical price action or a developer refining an HFT algorithm, this data provides the foundation for data-driven confidence.
Dukascopy historical data is not exclusive in content, but it remains a uniquely valuable resource in the retail trading sector. It provides institutional-grade granularity (tick data) free of charge. dukascopy historical data exclusive
: The data is pulled directly from the Swiss Foreign Exchange Marketplace (SWFX). This pool aggregates liquidity from over 20 major banks, providing a highly accurate representation of global market depth. Technical Specifications of the Dataset
Since they are an ECN (Electronic Communication Network), the spreads and price action reflect the actual interbank market. Massive History:
: Includes both bid and ask prices, which is essential for accurate spread and slippage simulation during backtesting. Format Flexibility : Data can be downloaded in formats, making it compatible with major platforms like MetaTrader 4/5 Microsoft Excel Dukascopy Bank SA How to Access and Use the Data Dukascopy Historical Data Feed Tool : Access this free tool on the Dukascopy website Set the export timezone to match your backtesting
To optimize storage, Dukascopy compresses tick data into custom .bi5 files using LZMA compression. Each file represents exactly of trading activity. The file contains a sequence of binary structs.
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Major digital assets paired against fiat currencies. In a market where milliseconds and pips define
: Offers tick-by-tick quotes including both bid and ask prices, essential for accurate spread modeling.
Data is organized chronologically by asset and stored in binary formats using the following folder structure: [Asset] / [Year] / [Month] / [Day] / [Hour]_ticks.bi5 The .bi5 Compression Format
Because Dukascopy’s native tools can be cumbersome, a community of developers has created tools to fetch this data programmatically.
What do you use? (MT4, MT5, cTrader, or Python?) What specific asset class are you planning to test?