All historical bar data is formed based on Coordinated Universal Time (UTC) by default, ensuring a consistent global reference point. When you download data in CSV format from the website, you can choose your desired timeframe, ranging from tick-by-tick to monthly.
The keyword "Dukascopy historical data" is searched thousands of times per month because traders realize that MetaTrader’s built-in history is garbage. It is filtered, smoothed, and useless for serious quantitative analysis.
If you code in Java, Dukascopy’s proprietary platform, JForex, offers a robust built-in API. You can write simple scripts within the platform to export historical data directly to your local drive. 4. Python Libraries ( QuantAtRisk or Dukascopy-Converter ) dukascopy historical data
Unlike many brokers who "smooth" their data, Dukascopy provides a true historical price feed directly from their SWFX Swiss FX Marketplace liquidity pool.
Delete all existing .hst files inside the history/[your-server] directory to avoid data corruption. Step 3: Export and Convert All historical bar data is formed based on
Open your downloader of choice, choose your desired currency pairs (e.g., GBP/USD), and set the historical range (e.g., 2018 to 2026).
MT5 natively handles custom tick data much better than MT4. You can export Dukascopy data as a CSV file, open the MT5 symbols manager, create a custom symbol, and import the CSV ticks directly. Python & Backtrader / Zipline It is filtered, smoothed, and useless for serious
: User reports from the Dukascopy support board indicate occasional data corruption or anomalies. For example, one user found that the close price of a previous bar retrieved via the IHistory API was incorrect when queried via onTick() , while the next bar's open price was correct. While such issues are often resolved, they serve as a reminder to always verify critical data points.
Let’s be honest. No retail data feed is perfect, and Dukascopy Historical Data has specific quirks you must clean before backtesting.
: After a successful backtest, test the strategy on a JForex demo account with live (simulated) data. The historical data in the demo is from the live feed, making this a perfect validation environment.