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Stock Market Index Data and indicators for Day Trading as a Binary Classification problem.

Renato Bruni1

  • 1Dip. di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Rome, Italy.

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This study models day trading as a binary classification problem. Datasets with stock index values and technical indicators are provided to identify favorable trading days automatically.

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Area of Science:

  • Financial modeling
  • Machine learning applications
  • Time series analysis

Background:

  • Classification algorithms are widely used in various applications.
  • Stock market indices track market trends, but forecasting them is challenging.
  • Technical analysis studies past market data to predict future price movements.

Purpose of the Study:

  • To address the challenge of identifying favorable days for day trading.
  • To model the day trading problem as a binary classification task.
  • To provide datasets for evaluating different classification approaches.

Main Methods:

  • Utilizing a binary classification framework.
  • Developing datasets that include daily stock index values.
  • Incorporating technical indicators and a class label indicating trading favorability.

Main Results:

  • The study proposes a novel approach to automatically identify optimal trading days.
  • The provided datasets enable the testing and comparison of various classification algorithms.
  • This research facilitates the development of automated day trading strategies.

Conclusions:

  • The binary classification model offers a viable method for identifying favorable day trading opportunities.
  • The curated datasets are valuable resources for researchers in quantitative finance and machine learning.
  • This work contributes to the advancement of automated trading systems and financial forecasting.