OpenML
FOREX_eurrub-day-High

FOREX_eurrub-day-High

active ARFF Publicly available Visibility: public Uploaded 03-06-2019 by Jan van Rijn
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Source: Dukascopy Historical Data Feed https://www.dukascopy.com/swiss/english/marketwatch/historical/ Edited by: Fabian Schut # Data Description This is the historical price data of the FOREX EUR/RUB from Dukascopy. One instance (row) is one candlestick of one day. The whole dataset has the data range from 1-1-2018 to 13-12-2018 and does not include the weekends, since the FOREX is not traded in the weekend. The timezone of the feature Timestamp is Europe/Amsterdam. The class attribute is the direction of the mean of the High_Bid and the High_Ask of the following day, relative to the High_Bid and High_Ask mean of the current minute. This means the class attribute is True when the mean High price is going up the following day, and the class attribute is False when the mean High price is going down (or stays the same) the following day. Note that this is a hypothetical task, meant for scientific purposes only. Realistic trade strategies can only be applied to predictions on 'Close'-attributes (also available). # Attributes `Timestamp`: The time of the current data point (Europe/Amsterdam) `Bid_Open`: The bid price at the start of this time interval `Bid_High`: The highest bid price during this time interval `Bid_Low`: The lowest bid price during this time interval `Bid_Close`: The bid price at the end of this time interval `Bid_Volume`: The number of times the Bid Price changed within this time interval `Ask_Open`: The ask price at the start of this time interval `Ask_High`: The highest ask price during this time interval `Ask_Low`: The lowest ask price during this time interval `Ask_Close`: The ask price at the end of this time interval `Ask_Volume`: The number of times the Ask Price changed within this time interval `Class`: Whether the average price will go up during the next interval

12 features

Class (target)nominal2 unique values
0 missing
Timestampdate1832 unique values
0 missing
Bid_Opennumeric1821 unique values
0 missing
Bid_Highnumeric1806 unique values
0 missing
Bid_Lownumeric1804 unique values
0 missing
Bid_Closenumeric1786 unique values
0 missing
Bid_Volumenumeric1769 unique values
0 missing
Ask_Opennumeric1823 unique values
0 missing
Ask_Highnumeric1802 unique values
0 missing
Ask_Lownumeric1807 unique values
0 missing
Ask_Closenumeric1789 unique values
0 missing
Ask_Volumenumeric1768 unique values
0 missing

19 properties

1832
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
11
Number of numeric attributes.
1
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
91.67
Percentage of numeric attributes.
51.53
Percentage of instances belonging to the most frequent class.
8.33
Percentage of nominal attributes.
944
Number of instances belonging to the most frequent class.
48.47
Percentage of instances belonging to the least frequent class.
888
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
8.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.56
Average class difference between consecutive instances.
0
Percentage of missing values.

10 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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