Dukascopy Historical Data Updated 【PREMIUM · 2025】

For developers using Python or C#, the JForex API allows scripted downloading. You can write a loop to pull 20 years of EURUSD ticks automatically.

Do you need assistance setting up a to automate your downloads?

# Conceptual example of a Python workflow for downloading Dukascopy data # 1. Target the URL structure: https://dukascopy.comSymbol/Year/Month/Day/Hourh_ticks.bi5 # 2. Download the compressed .bi5 file. # 3. Decompress using LZMA decompression. # 4. Unpack binary structs into readable timestamp, ask, bid, ask_volume, bid_volume formats. Use code with caution. Limitations and Things to Keep in Mind dukascopy historical data

Dukascopy stores its historical data in custom binary files ( .bi5 ) compressed on their public servers. The data is organized hierarchically by year, month, day, and hour.

Dukascopy data is provided in . When importing this into a trading platform, you must ensure your platform’s offset matches the data, or your sessions (like the New York Open) will be misaligned. Volume Discrepancies For developers using Python or C#, the JForex

Calculate the actual spread: (Ask Price - Bid Price) / Pip Size . If your strategy trades on limit orders, you care about the Bid. If market orders, you care about the Ask.

Dukascopy Bank provides some of the most comprehensive free historical data for retail traders, covering . The data is prized by the algorithmic trading community for its high resolution and extended history. 📊 Data Specifications # Conceptual example of a Python workflow for

Accurate historical data is the backbone of any successful algorithmic trading strategy. In the Forex market, finding high-quality, tick-level data without paying a premium subscription fee is a notoriously difficult challenge.

The liquidity available at the Bid price (measured in millions).