Dukascopy Historical | Data

Swiss-based Dukascopy Bank is renowned not just for its ECN (Electronic Communication Network) brokerage services but specifically for its , often accessed via the Dukascopy JForex platform . Whether you are a quantitative hedge fund manager or a retail trader learning Python, understanding how to harvest and utilize this data is a game-changer.

Use these cautiously. They hit Dukascopy’s servers hard. Be respectful with your request rates to avoid IP bans. Part 4: The Elephant in the Room – Gaps and Quality Control Let’s be honest. No retail data feed is perfect, and Dukascopy Historical Data has specific quirks you must clean before backtesting. The Weekend Gap Issue Dukascopy's feed includes "bid/ask" spreads. Over weekends, the market is closed. However, sometimes you will see strange "Monday open" spikes that aren't real. Always filter your data by removing weekends (Friday 5 PM EST to Sunday 5 PM EST). The "Holiday" Low Volume On Christmas or New Year’s Eve, spreads blow out to 50 pips. If your backtesting script doesn't account for spread widening, it will show false losses or false profits. Data Splicing If you download EURUSD from 2003, note that the liquidity providers changed in 2008 and 2015 (Swiss National Bank event). The quality of ticks in 2004 is lower than in 2024. You may need to splice data from different sources. dukascopy historical data

In the world of algorithmic trading, backtesting, and quantitative analysis, the quality of your output is directly proportional to the quality of your input. If your historical price data is full of gaps, errors, or "bad ticks," your trading strategy is built on a foundation of sand. Swiss-based Dukascopy Bank is renowned not just for

Python pseudo-logic: dukascopy.connect() -> request_ticks("EURUSD", start_date, end_date) -> save_to_parquet() They hit Dukascopy’s servers hard

This article is a deep dive into everything you need to know about Dukascopy Historical Data: what it is, how to get it, its quality, limitations, and how to use it for professional backtesting. Before we discuss how to get the data, we must understand why it is valuable. There are three primary sources of historical Forex data: Banks (Interbank), Brokers (Retail), and Aggregators (Dukascopy/TrueFX).