Data is organized on Dukascopy's servers by asset, year, month, day, and hour: [Asset]/[Year]/[Month-1]/[Day]/[Hour]h_ticks.bi5

Tick data is massive. Downloading 10 years of tick data for a single currency pair can easily consume tens of gigabytes of hard drive space.

: Dukascopy (and other major brokers) may update their historical data. This is typically done by adding more ticks to the original data that were previously unavailable, which can change the OHLCV (Open, High, Low, Close, Volume) of a bar. If you are archiving data for a long-term project, be aware that re-downloading a dataset in the future might produce slightly different results. It is recommended to preserve older data if you need to maintain a consistent backtest history.

# 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

So you have 1 million rows of CSV data. Now what? Here is a professional workflow.

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

QuantDataManager by StrategyQuant is a popular, user-friendly desktop application. It features a point-and-click interface to download Dukascopy data, convert it into any timeframe (M1, M5, H1, etc.), and export it directly to formats compatible with MetaTrader, NinjaTrader, or CSV. 2. Tickstory

Dukascopy records data in .

High-quality historical data is the backbone of any successful algorithmic trading strategy. Without precise market data, backtesting results are unreliable, leading to poor live performance and lost capital.

Dukascopy historical data bridges the gap between retail traders and institutional-grade backtesting. Its tick-level accuracy, inclusion of market depth (volume), and zero-cost accessibility make it an invaluable resource for optimizing trading systems. By pairing this raw data with the right extraction tools, you can build, refine, and deploy automated strategies with maximum structural confidence.