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stock-market-prediction

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This repository hosts a stock market prediction model for Tesla and Apple using Liquid Neural Networks. It showcases data-driven forecasting techniques, feature engineering, and machine learning to enhance the accuracy of financial predictions.

  • Updated May 18, 2024
  • Python

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 …

  • Updated May 2, 2018
  • Jupyter Notebook

In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.

  • Updated Feb 25, 2024
  • Jupyter Notebook

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