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Neuralmarketheader 7c152dac750b1174ae01dbd815403220ff59ca7206c11535b6b221ddbeb93f69


An application that uses neural networks to accurately predict closing stock prices.

Github13 7cc3e10610ad42f7f693d1c16a4a06e5dc17f14745567ba5a679de8e7e259ee3


  • Train a PyBrain recurrent neural network to identify patterns in a stock's daily opening, closing, high, and low prices with the goal of predicting closing prices. Remarkable error percentage of only 0.007%.
  • Develop a GUI in Python using Tkinter, Matplotlib, and Pandas to allow user to input desired stock symbol and to see a graph illustrating it's closing prices and predicted closing price. Threading was used so as to provide an optimal interface experience; running the GUI in the main thread and all other logical and networking jobs in different threads.
  • Used Yahoo Finance API to request stock information in JSON and fed chronologically to RNN.

Possible Improvements

  • Use genetic algorithm to evolve the neural network using low error percentage as an NP optimization solution.
  • Incorporate sentiment analysis as a variable in the training process.