sinanw/lstm-stock-price-prediction
This project implements a time series multivariate analysis using RNN/LSTM for stock price predictions. A deep RNN model was created and trained on five years of historical Google stock price data to forecast the stock performance over a two-month period.
This project helps financial analysts and traders forecast future stock prices. It takes historical daily stock data, including opening, closing, high, low, and volume, and uses it to predict stock performance over a future period, showing trends and specific price points. The output is a visualized prediction of how a stock like Google's might perform.
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Use this if you need to analyze historical stock market data to predict future price movements for a single stock.
Not ideal if you need to predict stock prices for an entire portfolio or require real-time, high-frequency trading signals.
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27
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20
Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 11, 2024
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