Available timeframes range from:
The data is completely free to download, even if you do not hold a live trading account with them.
Every tick contains distinct Bid and Ask prices, allowing you to simulate real-world spread widening.
LZMA algorithm compresses the data to save bandwidth. dukascopy historical data
There are three primary ways to retrieve historical data from Dukascopy: 1. The Web Portal (Manual Download) For quick analysis, use the Dukascopy Historical Data Feed : Single-day downloads or small datasets. : The web portal typically limits tick data downloads to one day at a time 2. JForex Platform (Built-in Manager)
: Offers data down to the individual tick, showing Bid/Ask prices and respective volumes.
| | Key Features | | :--- | :--- | | TickVault | High-performance downloading; resume capabilities for large datasets; automatic gap detection; concurrent downloading; proxy rotation for distributed retrieval; SQLite metadata tracking; converts data to pandas DataFrames for analysis. | | dukascopy-python | Official-sounding package for downloading historical data; supports both static historical fetch() and live live_fetch() for streaming data; outputs DataFrames for both tick and OHLC data. | | duka_dl | A fast and simple command-line tool designed to consolidate many daily files into a single clean CSV or Parquet file, ready for analysis. | Available timeframes range from: The data is completely
You do not need to write a complex web scraper from scratch to access this data. Several free, open-source, and commercial tools exist to handle the downloading and conversion process automatically. 1. QuantDataManager (QDM)
If you are building a machine learning model to predict the next 1-minute move, Dukascopy is arguably the best free source of training data available to the public.
Includes the exact timestamp (to the millisecond), bid price, ask price, bid volume, and ask volume. There are three primary ways to retrieve historical
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Tick data is massive. A single year of EUR/USD tick data can exceed several gigabytes. For long-term trend analysis, it is often more efficient to use M1 or M5 data unless you are developing a high-frequency trading (HFT) scalping strategy.
