const getHistoricalRates = require('dukascopy-node'); (async () => const data = await getHistoricalRates( instrument: 'btcusd', dates: from: new Date('2019-01-13'), to: new Date('2019-01-14') , timeframe: 'tick', format: 'json' ); console.log(data); )();
Accurate historical data is the backbone of any successful algorithmic trading strategy. Without high-quality price data, backtesting results are unreliable, leading to a phenomenon known as "overfitting" or "curve-fitting." When it comes to free, high-resolution tick data for Forex, commodities, and indices, is widely considered the industry gold standard.
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.
Artificial price generation can skip micro-spikes that would trigger your stop loss in reality. dukascopy historical data
Many brokers provide historical data, but it is often riddled with gaps, artificial price spikes, or limited to specific timeframes. Dukascopy remains a premier choice for several distinct reasons:
Utilize data for EURUSD, USDJPY, GBPUSD, and others to test correlation-based strategies. How to Access Dukascopy Historical Data
For traders, quantitative analysts, and financial researchers, the availability of high-quality historical market data is the cornerstone of effective strategy development. Without reliable data, even the most sophisticated algorithm is built on a fragile foundation. Dukascopy Bank, a regulated Swiss brokerage, has become a leading source for this data, primarily because it provides free access to high-resolution tick-level data for forex, commodities, indices, and cryptocurrencies. The quality of ticks in 2004 is lower than in 2024
Dukascopy's historical data covers a wide range of financial instruments and can be retrieved at various levels of granularity:
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).
| | Key Features | | :--- | :--- | | TickVault | High-performance downloading; resume capabilities for large datasets; automatic gap detection; concurrent downloading; proxy rotation for distributed retrieval; SQLite metadata tracking; converts data to pandas DataFrames for analysis. | | dukascopy-python | Official-sounding package for downloading historical data; supports both static historical fetch() and live live_fetch() for streaming data; outputs DataFrames for both tick and OHLC data. | | duka_dl | A fast and simple command-line tool designed to consolidate many daily files into a single clean CSV or Parquet file, ready for analysis. | Many brokers provide historical data, but it is
Export the data directly as MT4 .fxt (for strategy tester bars) and .hst (for chart history) files.
Data analysts can easily parse compiled CSV data into Pandas DataFrames. A standard Dukascopy CSV output maps perfectly to columns like Timestamp , Bid , Ask , BidVolume , and AskVolume , which can be fed straight into institutional-grade backtesting frameworks.
This is where enters the conversation. Dukascopy Bank (Switzerland) has established itself not just as a reputable ECN broker, but as the industry gold standard for granular, free, reliable tick-by-tick and minute-by-minute historical forex data.
Files are stored inside a strict URL folder hierarchy: Year/Month/Day/Hour_ticks.bi5 .