Retail data often ignores spread fluctuations. Dukascopy includes explicit bid and ask quotes for every tick, allowing traders to simulate realistic execution costs.
| Feature | Yahoo / Alpha Vantage | Dukascopy (exclusive) | |---------|------------------------|------------------------| | Tick data | ❌ | ✅ | | Bid/Ask separated | ❌ | ✅ | | Spread calculation | ❌ (mid only) | ✅ | | Tick volume (bid/ask) | ❌ | ✅ | | Raw 1-minute OHLC | ❌ (often 1min from 5sec) | ✅ (true) | | Max history (major FX) | 5-10 years | 20+ years | | Free for all | ✅ (delayed/limited) | ❌ (tick data public, but parsing needed) |
What (Forex, Indices, Commodities) are you targeting?
| Instrument | Symbol in Dukascopy | |------------|----------------------| | EUR/USD | EURUSD | | Gold (spot) | XAUUSD | | Bitcoin | BTCUSD | | US 30 Index | US30 | | Apple stock | AAPL (limited history) | dukascopy historical data exclusive
Many brokers offer historical data, but it is often heavily filtered, compressed into M1 (one-minute) bars, or plagued by missing bars and artificial price spikes. Dukascopy stands apart by offering true, institutional-grade data. True Tick-by-Tick Precision
Global benchmarks including the S&P 500, NASDAQ, DAX, and FTSE.
Beyond standard timeframes, users can access price-based periods like Renko, Kagi, Line Break, and Point & Figure bars. Retail data often ignores spread fluctuations
Dukascopy archives all historical data files in . It does not adjust for Daylight Saving Time (DST). If your trading strategies or indicators rely on specific local session opens (e.g., the New York opening bell at 9:30 AM EST), you must programmatically shift the timestamps in your database to account for regional DST changes. Volume Representation
Spreads are dynamic, variable, and widen drastically during news events. Because Dukascopy provides simultaneous Bid and Ask streams, your backtesting software can simulate real-world variable spreads rather than relying on a fixed, unrealistic spread value. Slippage and Execution Modeling
I can provide specific, step-by-step instructions to get your environment configured. Share public link While CSV files are highly readable
To get started with this exclusive data, follow this technical workflow:
High-quality historical data is the backbone of successful algorithmic trading, backtesting, and market analysis. Among retail and institutional traders alike, has earned a legendary reputation for providing some of the most precise, tick-by-tick historical data available.
While CSV files are highly readable, they are highly inefficient for tick storage. Convert your extracted data into or HDF5 formats. These formats offer high-ratio compression and allow blazing-fast read/write speeds within Python pandas . Standardize Timezones
The volume metrics reflect the internal liquidity available within the SWFX ECN marketplace. While this serves as an excellent proxy for broader global interbank liquidity, it does not represent total global trading volume, as the forex market is entirely decentralized. Summary Checklist for Quants