Intro:

In my exploration of the world of big data and I became curious about tick data. Unfortunately, market data is almost always behind a paywall or de-sampled to the point of uselessness. After discovering the Dukascopy API, I knew I wanted to make this data available for all in a more accessible format. Over the course of a few months, I downloaded, cleaned, parsed, and compressed over a decade of Forex tick data on 37 currency pairs and commodities. Today I am happy to finally release the final result of my work to the DataHoarder community!

Download Links:

Warning: I have rented a seedbox for the next 3 months from seedbox.io but I have been having some issues. If you have any issues with the torrent please leave a comment. Also, PLEASE SEED when you are done. This is quite a large data set and I can only push so much data on my own.
Torrent File: https://drive.google.com/file/d/18ymZWeFLJK7FggK_iiWZ-TxgWIVdJVvv/view?usp=sharingCompanion Blog Post: https://www.driftinginrecursion.com/post/dukascopy_opensource_data/

Stats Overview:

Totals	Quantities
Total Files	463
Total Line Count	8,495,770,706
Total Data Points	33,983,082,824
Total Decompressed Size	501 GB
Total Compressed Size	61 GB

About the Data:

The data was collected from https://www.dukascopy.com/ via a public API that allows for the download of tick data on the hour level. These files come in the form of a .bi5 file. The data starts as early as 2004 all the way to 2019.
These files were decompressed, then merged into yearly CSV’s named in the following convention. “AUDCHF_tick_UTC+0_00_2011.csv” or ‘Pair_Resolution_Timezone_Year.csv’
These CSV’s are split into 3 categories “Majors”, “Crosses”, “Commodities”.
Majors, Crosses, and Commodities have had their timestamps modified so that they are in the official UTC ISO standard. This was originally done for a Postgresql database that quickly became obsolesced. Any files that have been modified are appended with a “-Parse”. These timestamps have been modified in the following format.
Millisecond timestamps to UTC +00:00 time [2017.01.01 22:37:08.014] -- [2017-01-01T22:37:08.014+00:00]
https://preview.redd.it/x6g277skfiu51.png?width=1399&format=png&auto=webp&s=35cd6735c1826424580919ac3377612377a3107c

User Resources:

For those looking to use this data in a live context or update it frequently, I have included a number of tools for both Windows and Linux that will be useful.

Windows

The ~/dukascopy/resources/windows folder contains a third party tool written in java that can download and convert Dukascopy’s .bi5 files. I have also included the latest zstd binaries from Zstandard Github page.

Linux

Linux is my daily driver in 99% of cases, so I have developed all my scraping tools using Linux only tools. In the ~/dukascopy/resources/linux folder you will find a number of shell script and pyhton3 files that I used to collect this data. There are quite a few files in this directory but I will cover the core ones below.

download-day.py:

This file is used to download a single symbol for a single day and then convert and merge all 24 .bi5 files into a single CSV.

download-year.py

This file is used to download a single symbol for a full year and then convert and merge all .bi5 files into a single CSV.

dukascopy.py

This file contains all the core logic for downloading and converting data from dukascopy.

utc-timestamp-convert.py

This tad slow but works well enough. It requires the pandas project and parses timestamps into the UTC ISO standard. This is useful for those looking to maintain the format of new files with the those in this repo, or those looking to use this in a SQL database.

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