Make multi step predictions on new time series data from a saved LSTM model

I work as a risk manager in a financial firm. I watch, trade and analyze the financial markets in my free time. I came across this interesting article "TIME SERIES PREDICTION USING LSTM DEEP NEURAL NETWORKS" by Jakob Aungiers

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The full code is available on github: [login to view URL]

I was able to run the program, test, train and save the model, however I am unable to use it to make future predictions. I need a script that will do the following:

Load the trained model; read sp500 data in the form of "Open,High,Low,Close,Volume" from a .csv file; feed the input data to the trained model; generate predictions N bars ahead

; inverse normalizing the predicted close prices, inverse normalizing of the input close prices, plot the input close prices and the predicted price vector ( N bars ahead), save the plot image, save the prediction to a .csv file. That's it. I am attaching the full zip file with the github code. Please note that I have replaced the [login to view URL] file as there was a zero divide issue in the original post.

Kemahiran: Python, Statistik, Algoritma, Analisis Statistik

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Tentang Majikan:
( 1 ulasan ) Sofia, Bulgaria

ID Projek: #21102108

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