Dukascopy provides high-quality, that is widely considered an industry "gold standard" for backtesting and strategy development. This data is distinct because it comes from a single, transparent Swiss FX Marketplace (SWFX) price feed, ensuring that all clients receive the same quotes regardless of account size. Key Features and "Exclusive" Aspects
Are you looking to integrate this data into a specific like MetaTrader or a custom Python script? dukascopy historical data exclusive
df['spread_pips'] = df['spread'] * 10000 # for EURUSD print("Avg spread (ticks):", df['spread_pips'].mean()) print("Spread std dev:", df['spread_pips'].std()) df['spread_pips'] = df['spread'] * 10000 # for EURUSD
For data scientists, repositories like the theorycraft-trading/dukascopy GitHub project allow users to download and stream historical tick and bar data directly into Python or R environments. Tips for Working with High-Volume Tick Data Handle Weekend Gaps Cleansely
Dukascopy data natively records timestamps in . When aligning your historical data with external indicators, economic calendars, or alternative datasets, explicitly convert your local times to UTC to eliminate daylight savings misalignment. Handle Weekend Gaps Cleansely