and volatility for a specific pair. Let me know what you'd like to do next!
This method allows for custom timeframes, such as price-based Renko bars. Developer API & Scripting IHistory Interface : Developers can use the IHistory Javadoc
| | 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. |
Dukascopy's commitment to transparency is reflected in its provision of historical price data. The bank states that "transparency of historical prices is an essential element for strategy development," and it provides a consistent historical data stream for strategy development and back-testing with full access to tick-by-tick quotations. This approach is designed to ensure high reliability for backtesting and to eliminate any possibility of price manipulation by the broker. dukascopy historical data
The data inside is compressed using standard LZMA algorithms. Structure of a Decoded Tick
Available in .CSV (standard spreadsheet) and .HST (MetaTrader format). 🛠️ Retrieval Methods There are three primary ways to access this data: 1. Web Interface (Historical Data Feed)
As a Swiss-regulated bank, Dukascopy’s data is widely trusted in the quantitative community. and volatility for a specific pair
Node.js usage:
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.
Each .bi5 file represents exactly one hour of tick data. Inside the file, the data is compressed using standard LZMA compression. Once decompressed, the file yields a binary stream where each tick is a 20-byte record structured as follows: Description 32-bit Integer Milliseconds elapsed since the start of the hour Ask Price 32-bit Integer Ask price multiplied by 10510 to the fifth power 10210 squared for JPY pairs) Bid Price 32-bit Integer Bid price multiplied by 10510 to the fifth power 10210 squared for JPY pairs) Ask Volume 32-bit Float Liquidity available at the Ask price (in millions) Bid Volume 32-bit Float Liquidity available at the Bid price (in millions) Methods for Downloading Dukascopy Data Developer API & Scripting IHistory Interface : Developers
For analysts and data scientists who prefer Python, a rich ecosystem of open-source libraries has been built around downloading and processing Dukascopy data. These libraries simplify the process of fetching data and loading it directly into , the industry standard for data analysis.
Key features include:
I can provide the exact or code snippets you need to get started.