Data
FOREX_eurhuf-hour-Close

FOREX_eurhuf-hour-Close

active ARFF Publicly available Visibility: public Uploaded 03-06-2019 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • Economics finance forex forex_close forex_hour Manufacturing
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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 Close_Bid and the Close_Ask of the following hour, relative to the Close_Bid and Close_Ask mean of the current minute. This means the class attribute is True when the mean Close price is going up the following hour, and the class attribute is False when the mean Close price is going down (or stays the same) the following hour. # 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.
0
Number of attributes divided by the number of instances.
91.67
Percentage of numeric attributes.
60.75
Percentage of instances belonging to the most frequent class.
8.33
Percentage of nominal attributes.
26622
Number of instances belonging to the most frequent class.
39.25
Percentage of instances belonging to the least frequent class.
17203
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.
0.55
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
Define a new task