Stock-Price-Prediction-LSTM
Unverified ML strategy on Indices by NourozR. BotFinder score 18 out of 100.
OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network
Source: github
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Stock-Price-Prediction-LSTM
Stock Price Prediction of Apple Inc. Using Recurrent Neural Network OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network Dataset: The dataset is taken from yahoo finace's website in CSV format. The dataset consists of Open, High, Low and Closing Prices of Apple Inc. stocks from 3rd january 2011 to 13th August 2017 - total 1664 rows. Price Indicator: Stock traders mainly use three indicators for prediction: OHLC average (average of Open, High, Low and Closing Prices), HLC average (average of High, Low and Closing Prices) and Closing price, In this project, OHLC average has been used. Data Pre-processing: After converting the dataset into OHLC average, it becomes one column data. This has been converted into two column time series data, 1st column consisting stock price of time t, and second column of time t+1. All values have been normalized between 0 and 1. Model: Two sequential LSTM layers have been stacked together and one dense layer is used to build the RNN model using Keras deep learning library. Since this is a regression task, 'linear' activation has been
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