In our second part of the LSTM stock prediction using Tensorflow. Now that we have our deep learning model with LSTM recurrent layer set up we focus on the times series part of our project and dive deep into the seasonal decomposition of our MACD, RSI, Fast Stochastic, and pct_change indicators. The goal here is that we can just use the last 20 days but for each indicator that create a hundred features. As common with time series stock market prediction, we are limited on the number of rows. So we can't use so many features and we use statsmodel seasonal decompose function from the statsmodel.tsa.seasonal library. This allows use to study the autocorrelation in only the seasonal component and extract which of the past days have a correlation. This allows us to choose only a few features instead of so many. read more...
The standard Perl distribution comes with a debugger, although it's really just another Perl program, perl5db.pl. Since it is just a program, I can use it as the basis for writing my own debuggers to suit my needs, or I can use the interface perl5db.pl provides to configure its actions. That's just the beginning, though. read more...
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