Datasets:

Henrik404
/

dukascopy-ticks

timestamp timestamp[ms, tz=UTC]date 2016-01-03 22:00:01 2023-09-29 20:59:55	ask int32 63.3k 81.4k	bid int32 63.3k 81.4k	ask_volume float32 0.1 2.15k	bid_volume float32 0.1 2.15k
2016-01-03T22:00:01.868000	72,897	72,836	0.37	0.75
2016-01-03T22:00:18.127000	72,898	72,836	0.37	0.75
2016-01-03T22:00:26.407000	72,898	72,831	0.37	0.37
2016-01-03T22:00:28.637000	72,898	72,831	0.75	0.75
2016-01-03T22:00:28.687000	72,893	72,826	0.37	0.37
2016-01-03T22:00:30.394000	72,888	72,823	0.37	0.37
2016-01-03T22:00:38.054000	72,889	72,823	0.37	0.37
2016-01-03T22:00:38.584000	72,890	72,823	0.75	0.75
2016-01-03T22:00:39.335000	72,881	72,831	0.37	0.37
2016-01-03T22:01:18.103000	72,880	72,832	0.37	0.37
2016-01-03T22:01:58.051000	72,881	72,831	0.75	0.75
2016-01-03T22:02:18.076000	72,882	72,832	0.75	0.37
2016-01-03T22:02:30.103000	72,882	72,842	0.37	0.37
2016-01-03T22:02:36.435000	72,880	72,842	0.75	0.37
2016-01-03T22:03:03.142000	72,880	72,832	0.75	1.12
2016-01-03T22:03:15.483000	72,876	72,832	0.37	0.75
2016-01-03T22:03:21.944000	72,876	72,830	0.37	0.1
2016-01-03T22:03:25.062000	72,876	72,847	0.37	0.37
2016-01-03T22:03:38.073000	72,878	72,847	0.75	0.37
2016-01-03T22:03:57.996000	72,877	72,847	0.37	0.37
2016-01-03T22:03:58.101000	72,877	72,832	0.37	0.75
2016-01-03T22:04:00.800000	72,879	72,832	0.37	0.75
2016-01-03T22:04:00.951000	72,880	72,843	0.75	0.37
2016-01-03T22:04:01.002000	72,895	72,843	0.37	0.37
2016-01-03T22:04:04.827000	72,893	72,843	1.12	1.37
2016-01-03T22:04:05.337000	72,890	72,843	1.12	1.37
2016-01-03T22:04:13.965000	72,888	72,858	1	1
2016-01-03T22:04:54.048000	72,889	72,851	1.82	1.37
2016-01-03T22:04:54.099000	72,889	72,851	3.07	1
2016-01-03T22:05:01.271000	72,886	72,851	1.12	1
2016-01-03T22:05:06.276000	72,886	72,851	1.12	1.37
2016-01-03T22:05:06.327000	72,886	72,851	1.12	1.75
2016-01-03T22:05:11.134000	72,884	72,851	1.75	1.12
2016-01-03T22:05:11.715000	72,882	72,851	1.75	1
2016-01-03T22:05:14.463000	72,883	72,851	1.75	1
2016-01-03T22:05:15.023000	72,877	72,851	1	1
2016-01-03T22:05:15.791000	72,882	72,851	1.75	1
2016-01-03T22:05:33.951000	72,881	72,852	1.75	1
2016-01-03T22:05:35.734000	72,881	72,852	1	1
2016-01-03T22:05:35.785000	72,882	72,852	1.75	1
2016-01-03T22:05:51.655000	72,888	72,852	2.25	1
2016-01-03T22:05:55.612000	72,881	72,852	1.5	1
2016-01-03T22:05:55.764000	72,886	72,852	1.75	1.12
2016-01-03T22:06:05.447000	72,885	72,852	1.75	1.5
2016-01-03T22:06:05.498000	72,883	72,853	1	1
2016-01-03T22:06:05.553000	72,884	72,848	1.75	1.5
2016-01-03T22:06:05.603000	72,878	72,848	1	1.5
2016-01-03T22:06:05.828000	72,884	72,848	1.37	1.5
2016-01-03T22:06:05.878000	72,884	72,848	1	1.75
2016-01-03T22:06:05.980000	72,884	72,848	1.75	1.75
2016-01-03T22:06:06.352000	72,883	72,848	1.12	1.75
2016-01-03T22:06:12.765000	72,883	72,848	1.12	1.75
2016-01-03T22:06:15.067000	72,884	72,848	1.75	1.75
2016-01-03T22:06:15.652000	72,884	72,848	1.75	1.37
2016-01-03T22:06:15.703000	72,876	72,848	1	1.12
2016-01-03T22:06:15.856000	72,884	72,848	1.75	1.75
2016-01-03T22:06:15.907000	72,884	72,848	1.75	1.75
2016-01-03T22:06:28.292000	72,884	72,850	1.87	1.37
2016-01-03T22:06:28.343000	72,884	72,850	1.87	1
2016-01-03T22:06:28.545000	72,882	72,848	1.75	1
2016-01-03T22:06:45.565000	72,877	72,847	1	1.12
2016-01-03T22:06:47.939000	72,878	72,847	1.12	1.12
2016-01-03T22:06:52.180000	72,885	72,847	1.95	1.12
2016-01-03T22:06:56.066000	72,878	72,847	1.12	1.12
2016-01-03T22:06:57.252000	72,878	72,847	1.12	1.12
2016-01-03T22:06:58.454000	72,878	72,847	1.12	1.12
2016-01-03T22:07:05.194000	72,884	72,847	1.12	1.12
2016-01-03T22:07:10.073000	72,878	72,847	1.12	1.12
2016-01-03T22:07:11.077000	72,878	72,847	1.12	1.12
2016-01-03T22:07:15.083000	72,881	72,847	1.12	1.12
2016-01-03T22:07:18.508000	72,878	72,847	1.37	1.37
2016-01-03T22:07:35.621000	72,878	72,848	1.12	1.12
2016-01-03T22:07:36.131000	72,878	72,848	1.12	1.12
2016-01-03T22:07:37.316000	72,878	72,848	1.12	1.12
2016-01-03T22:07:38.177000	72,883	72,848	1.75	1.12
2016-01-03T22:07:55.755000	72,877	72,847	1.12	2.25
2016-01-03T22:07:56.143000	72,877	72,847	1.12	2.25
2016-01-03T22:07:57.768000	72,877	72,847	1.12	2.25
2016-01-03T22:07:59.155000	72,883	72,847	1.12	2.25
2016-01-03T22:08:41.194000	72,882	72,849	1.87	1
2016-01-03T22:08:41.621000	72,876	72,849	1	1.37
2016-01-03T22:08:41.824000	72,879	72,849	1.87	1.12
2016-01-03T22:08:45.749000	72,878	72,849	1.12	1.12
2016-01-03T22:08:51.838000	72,877	72,849	1.12	1.12
2016-01-03T22:08:52.141000	72,879	72,851	1.12	1.12
2016-01-03T22:08:52.192000	72,879	72,851	1.12	1.12
2016-01-03T22:08:52.243000	72,879	72,849	1.5	1.5
2016-01-03T22:08:52.344000	72,879	72,849	1.75	1.5
2016-01-03T22:08:54.306000	72,879	72,849	1.75	1.5
2016-01-03T22:08:55.855000	72,880	72,851	1.87	1
2016-01-03T22:08:55.911000	72,878	72,851	1.5	1.75
2016-01-03T22:08:56.214000	72,878	72,852	1	1
2016-01-03T22:08:59.731000	72,885	72,853	1.75	1
2016-01-03T22:08:59.832000	72,885	72,860	1.75	1
2016-01-03T22:08:59.934000	72,885	72,860	1	1
2016-01-03T22:09:00.186000	72,885	72,856	1.5	1.37
2016-01-03T22:09:00.692000	72,885	72,857	1	1
2016-01-03T22:09:00.844000	72,885	72,858	1	1.5
2016-01-03T22:09:01.147000	72,880	72,849	1	1.75
2016-01-03T22:09:01.249000	72,880	72,849	1.75	1.5

End of preview. Expand in Data Studio

Dukascopy Tick Data

Tick-level bid/ask quotes from Dukascopy's historical data feed.

from datasets import load_dataset

ds = load_dataset("Henrik404/dukascopy-ticks", "EURUSD", split="train")

Schema

column	type	notes
`timestamp`	`timestamp[ms, tz=UTC]`
`ask`	`int32`	raw, scaled by `point`
`bid`	`int32`	raw, scaled by `point`
`ask_volume`	`float32`	millions of base currency
`bid_volume`	`float32`	millions of base currency

price = raw / point, per-symbol values in points.csv.

Downloads last month: 136