LSTM for Market Forecasting: A Python deep learning guide

By Alec Stovari and Machine L

(Updated: Code font size increased + python file provided with paperbacks)
Imagine having the ability to peer into the future, to anticipate market shifts with confidence, and to navigate the ups and downs of stock prices like a seasoned trader. With the power of Long Short-Term Memory (LSTM) neural networks, and this book - you will do just that. While it was a model I had initially created for a different use, it suits very well to predicting stocks, crypto, and in fact, any other single feature prediction requirements.
I have provided exact code snippets that you will be able to run, along with a bunch of explanations.
(Edition 1 has functions to filter stocks by highest volatility, draw automatic diagonal converging SR lines, and the complete code and explanation to the LSTM model)
(Edition 2 has a separate chapter for results, and an extra function to filter out stocks that are on their lowest price in 100 days to minimize risk)
While I have aimed to keep the tech stuff to the minimum, there are quite a few good applications and good coding standards you will find within.

• Format: Paperback
• Language: English
• ISBN: B0CDNFGS8S
• ISBN13: 9798856190273
• Release Date: August 2023
• Publisher: Independently Published
• Length: 62 Pages
• Weight: 0.21 lbs.
• Dimensions: 0.1" x 6.0" x 9.0"