Data

FOREX_eurusd-minute-Close

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/USD from Dukascopy.
One instance (row) is one candlestick of one minute.
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 minute,
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 minute,
and the class attribute is False when the mean Close price is going down (or stays the same) the following minute.
# 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

Class (target) | nominal | 2 unique values 0 missing | |

Timestamp | date | 375840 unique values 0 missing | |

Bid_Open | numeric | 13035 unique values 0 missing | |

Bid_High | numeric | 13026 unique values 0 missing | |

Bid_Low | numeric | 13006 unique values 0 missing | |

Bid_Close | numeric | 13024 unique values 0 missing | |

Bid_Volume | numeric | 47917 unique values 0 missing | |

Ask_Open | numeric | 13018 unique values 0 missing | |

Ask_High | numeric | 13007 unique values 0 missing | |

Ask_Low | numeric | 13010 unique values 0 missing | |

Ask_Close | numeric | 13009 unique values 0 missing | |

Ask_Volume | numeric | 48116 unique values 0 missing |

Minimal mutual information between the nominal attributes and the target attribute.

-1.2

Third quartile of kurtosis among attributes of the numeric type.

Maximum mutual information between the nominal attributes and the target attribute.

2

The minimal number of distinct values among attributes of the nominal type.

2

The maximum number of distinct values among attributes of the nominal type.

Third quartile of mutual information between the nominal attributes and the target attribute.

0.41

Third quartile of skewness among attributes of the numeric type.

-1.26

First quartile of kurtosis among attributes of the numeric type.

251.43

Third quartile of standard deviation of attributes of the numeric type.

0

Standard deviation of the number of distinct values among attributes of the nominal type.

First quartile of mutual information between the nominal attributes and the target attribute.

0.41

First quartile of skewness among attributes of the numeric type.

Average mutual information between the nominal attributes and the target attribute.

0.04

First quartile of standard deviation of attributes of the numeric type.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

2

Average number of distinct values among the attributes of the nominal type.

-1.26

Second quartile (Median) of kurtosis among attributes of the numeric type.

1.18

Second quartile (Median) of means among attributes of the numeric type.

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.41

Second quartile (Median) of skewness among attributes of the numeric type.

0.04

Second quartile (Median) of standard deviation of attributes of the numeric type.