Artificial Intelligence - LTSM for Financial Modeling - Part II

As Part II of my foray into Stock Portfolio Optimization, I decided to implement Python code that I found in a blog: https://www.datacamp.com/tutorial/lstm-python-stock-market

This blog was written by a Data Scientist, Thuresan Ganegedar . And he spent some time on this. I was super excited after I read the blog, to go in and compile the code and run it myself. The first thing I did, was get myself an API Key so that I could fetch data for a stock symbol.

But - I had issue after issue in running this code. 

First, the code was sufficiently outdated that many libraries, especially Keras, had "moved on" and completely deprecated certain libraries, calls, etc. I spent considerable time picking through the code, doing web searches, and trying to "just get the code to run". 

Second, the code was using hard-coded array values for the number of data points for the stock symbol, and because of this, I ran into null exceptions because the number of data points I had in my stock symbol fetch was over 2K less than the example had. To get around this, I wound up going with 4000 training data points and 600 testing data points. Later, I enhanced the code to dynamically set the training and testing sizes based on how many data points came down in a csv, on a percentage basis.

I also decreased the window size and the batch size as well.

In the end, I was able to produce nice graphs that matched the ones in his blog for the Predictions based on Simple Average and Exponential Moving Average. But these graphs are based on algorithms, not a neural AI network - in other words, they aren't "true AI".

The AI portions of the code did run to completion, but the graphs that came out from implementing the AI, did not look correct. The price predictions were FAR FAR lower than the actuals, as well as the ones from the Simple and Exponential Moving Average graphs.

I may come back and take another look at this, but it was a good exercise, and this exercise made me realize that I need to read and learn more about AI - and what is going on "under the hood".