Data
FOREX_eurhuf-hour-High

FOREX_eurhuf-hour-High

active ARFF Publicly available Visibility: public Uploaded 03-06-2019 by Jan van Rijn
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  • finance forex forex_high forex_hour study_219
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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/HUF from Dukascopy. One instance (row) is one candlestick of one hour. 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 hour, 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 hour, and the class attribute is False when the mean High price is going down (or stays the same) the following hour. 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
Timestampdate43825 unique values
0 missing
Bid_Opennumeric18108 unique values
0 missing
Bid_Highnumeric18677 unique values
0 missing
Bid_Lownumeric17797 unique values
0 missing
Bid_Closenumeric17635 unique values
0 missing
Bid_Volumenumeric26040 unique values
0 missing
Ask_Opennumeric18297 unique values
0 missing
Ask_Highnumeric17635 unique values
0 missing
Ask_Lownumeric18785 unique values
0 missing
Ask_Closenumeric17835 unique values
0 missing
Ask_Volumenumeric26076 unique values
0 missing

19 properties

43825
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.
1
Number of binary attributes.
8.33
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.58
Average class difference between consecutive instances.
91.67
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
8.33
Percentage of nominal attributes.
61.36
Percentage of instances belonging to the most frequent class.
26892
Number of instances belonging to the most frequent class.
38.64
Percentage of instances belonging to the least frequent class.
16933
Number of instances belonging to the least frequent class.

10 tasks

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