=link=: Dukascopy Historical Data

For major currency pairs, the data history is particularly deep, generally available , providing nearly two decades of market history for rigorous strategy testing.

: Develop and test your trading logic on the processed data. For tick data backtesting in Python, you might use a library like backtrader with a custom data feed, or build a simple event-driven simulation loop yourself.

Backtesting is the fundamental process of applying a set of trading rules to historical data to see how a strategy would have performed. Dukascopy's free, high-resolution tick data allows for exceptionally realistic backtests. dukascopy historical data

While Dukascopy data is excellent, traders should keep a few technical nuances in mind:

: Offers data down to the individual tick, showing Bid/Ask prices and respective volumes. For major currency pairs, the data history is

and volatility for a specific pair. Let me know what you'd like to do next!

Instead of aggregated M1 (one-minute) bars, you get every single price change (tick) including bid, ask, and volume details. Backtesting is the fundamental process of applying a

If you have already downloaded your Dukascopy data, I can help you: Format the data for use in Python (Pandas) or MetaTrader. Run a basic backtest using the raw tick data.

When decompressed, each individual tick consists of a 20-byte binary struct containing five 32-bit big-endian integers:

Ensure your processing scripts check whether an asset is quoted in 5 decimals (e.g., EUR/USD at 1.08502) or 3 decimals (e.g., USD/JPY at 155.23) to avoid severe scaling errors in your profit/loss calculations.