CNNpred: CNN-based stock market prediction using a diverse set of variables
Donated on 12/25/2019
This dataset contains several daily features of S&P 500, NASDAQ Composite, Dow Jones Industrial Average, RUSSELL 2000, and NYSE Composite from 2010 to 2017.
Dataset Characteristics
Sequential, Time-Series
Subject Area
Computer Science
Associated Tasks
Classification, Regression
Feature Type
Real
# Instances
1985
# Features
84
Dataset Information
Additional Information
It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of 'CNNpred: CNN-based stock market prediction using a diverse set of variables'.
Has Missing Values?
Yes
Dataset Files
| File | Size |
|---|---|
| Processed_NASDAQ.csv | 1.4 MB |
| Processed_DJI.csv | 1.4 MB |
| Processed_NYSE.csv | 1.4 MB |
| Processed_S&P.csv | 1.4 MB |
| Processed_RUSSELL.csv | 1.4 MB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset cnnpred_cnn_based_stock_market_prediction_using_a_diverse_set_of_variables = fetch_ucirepo(id=554) # data (as pandas dataframes) X = cnnpred_cnn_based_stock_market_prediction_using_a_diverse_set_of_variables.data.features y = cnnpred_cnn_based_stock_market_prediction_using_a_diverse_set_of_variables.data.targets # metadata print(cnnpred_cnn_based_stock_market_prediction_using_a_diverse_set_of_variables.metadata) # variable information print(cnnpred_cnn_based_stock_market_prediction_using_a_diverse_set_of_variables.variables)
CNNpred: CNN-based stock market prediction using a diverse set of variables [Dataset]. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C55P70.
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.