The data is sourced from the , which aggregates quotes from multiple banks and financial institutions, ensuring real-market depth.
: Over 1,000 instruments, including Forex (majors/minors), Commodities (Gold, Silver, Oil), Indices, Bonds, Stocks, ETFs, and Cryptocurrencies. dukascopy+historical+data
Dukascopy uses an Electronic Communication Network (ECN) model. Their historical data is not a single bank's feed but an aggregated stream of liquidity from multiple Tier-1 banks. This makes the data more realistic than single-source feeds. The data is sourced from the , which
The most popular tool is (available on GitHub). Using a simple Python script, you can replicate a decade of history in minutes: Their historical data is not a single bank's
The easiest way for most traders is using Dukascopy’s proprietary platform, JForex. Open any chart in JForex. Right-click and select "Export Data." Choose your timeframe (from Tick to Monthly). Select the date range and CSV format. 2. Automated Tools (TickStory and QuantDataManager)
To understand Dukascopy’s role, one must first recognize a structural gap in the financial data market. Professional-grade historical tick data from major exchanges or interbank sources—such as Reuters, Bloomberg, or exchanges like CME—is prohibitively expensive for most individual traders and small funds. Licenses can cost tens of thousands of dollars annually, creating a significant barrier to entry. Dukascopy, through its JForex platform and public API, inadvertently bridged this gap. By offering free, downloadable historical tick and minute bar data to anyone who registers for a demo account, Dukascopy democratized access to a previously gated resource. This strategic move, likely intended to drive platform adoption, instead spawned an entire ecosystem of third-party downloaders, conversion scripts, and backtesting libraries (e.g., Python’s dukascopy module, R scripts, and MetaTrader converters).