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Stock value prediction using Multiple Linear Regression

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YHack 2018 -- Yale University -- Goldman Sachs Marquee API Challange


About MacMarquee

MacMarque aims to eliminate the barriers of entry for stock investment by providing information and advise for all S&P500 companies at a fraction of the cost of a stock advisor. Through the use of machine learning, we are able to predict the future value of stock within an accuracy of 80-89%. This information is combined with the correlation values of price level changes and financial statements values to give users tailored advise based on their investment strategy.

Technology

  • Python backend
  • Pandas for Data Mannipulation and Analysis.
  • Scikit-Learn for Machine Learning Model
  • Marquee API from Goldman Sachs
  • HTML,CSS, and JS for Front End

Dashboard View

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30 Day Price Predictions

pv apple tesla_pv

150 Day Price Predictions

aapl_5 tesla_5

Price-Factor Correlation

aapl_cor tesla_cor

What's Next

  • Integrate automated trading features
  • Increase number of variables used in regression
  • Implement tools that will educate users on finace

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Stock value prediction using Multiple Linear Regression

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  • HTML 46.0%
  • JavaScript 40.0%
  • CSS 13.0%
  • Python 1.0%