Data
Dollar-Stock-Prices-and-infos

Dollar-Stock-Prices-and-infos

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Context To build a AI to predict the stock price of the Dollar currency on IBOVESPA I had to make this dataset. All information collected here is from a standard graphic for stock prices. Content The data is organized by prices and infos per minute. Each row contains: date, open price, maximim value, minimum value, close price, volume, financial, negotiations, mme13, mme72, high mean, low mean ,diffMACD, deaMACD, MACDlh, difflh, dealh, target. The target columns is the price 15 minutes in the future. Inspiration This data is here to construct an Artificial Intelligence to predict the price in 15 minutes. We want to buy if the predicted price is above of your goal per trade (mine goal is 8 point per trade) and sell if the prediction is below of your goal.

18 features

21-11-19_09:03string33070 unique values
0 missing
4206.0numeric2220 unique values
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4206.5numeric2124 unique values
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4204.5numeric2123 unique values
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4206.0.1numeric2247 unique values
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5024numeric8016 unique values
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211292030.0numeric33051 unique values
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4201.6numeric8123 unique values
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4202.4numeric8241 unique values
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4202.8numeric8104 unique values
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4201.2numeric8143 unique values
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-0.637numeric11760 unique values
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-0.125numeric6023 unique values
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-0.44numeric8466 unique values
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-0.377numeric8238 unique values
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4200.0numeric2220 unique values
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19 properties

33077
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
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.
17
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
94.44
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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