Investor – Stock Forecasting with LSTM

Published by pschatzmann on

In this blog we show how to forecast the closing price of AAPL using a LSTM RRN network. We use the open, closing, high and low rates and the volume of the current day as input in order to predict the subsequent closing price.

This demo has been implemented in Scala using Jupyter with the BeakerX kernel using the following libraries
– Investor
– DL4j

The details can be found in the following Gist.

see also:

  1. Deeplearning4j – Recurrent Neural Networks (RNN)
  2. Deeplearning4j – Iris Classification Example
  3. Solid Modelling for 3D Printing for the JVM in Jupyterlab
  4. H2O Sparkling Water – Distributed Random Forrest
  5. An OpenSCAD Kernel for Jupyter
  6. Random Forrest Classifier in Spark ML
  7. H2O Deployment in Scala, Groovy, Kotlin….
  8. NFS in Docker-Compose
  9. Progressive JavaScript Frameworks with Microcontrollers (Vue.js on ESP32)
  10. WooCommerce und Java REST Webhooks
Categories: Machine LearningQuantitative Trading

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