Dukascopy Historical Data [portable]

🔹 – Data includes actual traded spreads and volume from their liquidity pool, not synthetic approximations.

Includes both Bid and Ask prices , which is critical for calculating accurate spreads and slippage in backtesting. How to Access and Download the Data

Forex trading closes on Friday evening and opens on Sunday night. Ensure your backtesting engine cleanly cuts out weekend gaps so indicators do not produce skewed calculations due to holiday or weekend data dead zones. dukascopy historical data

These contain bar data (M1, M5, H1, etc.) and go into the history/[your-broker-server] folder.

History for major pairs generally extends back to 2003–2006 . 🔹 – Data includes actual traded spreads and

High-quality datasets for Forex, commodities, and indices are available for free through their Historical Data Feed tool .

# 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 Ensure your backtesting engine cleanly cuts out weekend

Which (forex, indices, crypto) are you targeting?

Another widely used software with a free tier. It automates the entire pipeline of downloading Dukascopy data, launching MT4 with launcher patches, and ensuring 99.9% backtesting quality.

Because JForex is so slow, many developers write scripts to scrape the data directly from Dukascopy's servers using their public API links.

For developers, several Node.js and Python command-line interfaces exist on GitHub to scrape and parse the .bi5 files directly via terminal. Method 2: Python Scripting for Custom Quant Pipelines