Data
FOREX_eurtry-hour-Close

FOREX_eurtry-hour-Close

active ARFF Publicly available Visibility: public Uploaded 04-06-2019 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • finance forex forex_close forex_hour Human Activities Machine Learning
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/TRY 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_Opennumeric37558 unique values
0 missing
Bid_Highnumeric37569 unique values
0 missing
Bid_Lownumeric37662 unique values
0 missing
Bid_Closenumeric37518 unique values
0 missing
Bid_Volumenumeric38197 unique values
0 missing
Ask_Opennumeric37622 unique values
0 missing
Ask_Highnumeric37589 unique values
0 missing
Ask_Lownumeric37462 unique values
0 missing
Ask_Closenumeric37458 unique values
0 missing
Ask_Volumenumeric38095 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.
50.64
Percentage of instances belonging to the most frequent class.
8.33
Percentage of nominal attributes.
22193
Number of instances belonging to the most frequent class.
49.36
Percentage of instances belonging to the least frequent class.
21632
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.
0.48
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