Check the correlations
Before we fit our first machine learning model, let's look at the correlations between features and targets. Ideally we want large (near 1 or -1) correlations between features and targets. Examining correlations can help us tweak features to maximize correlation (for example, altering the timeperiod argument in the talib functions). It can also help us remove features that aren't correlated to the target.
To easily plot a correlation matrix, we can use seaborn's heatmap() function. This takes a correlation matrix as the first argument, and has many other options. Check out the annot option -- this will help us turn on annotations.
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Machine Learning for Finance in Python
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# Plot heatmap of correlation matrix
sns.heatmap(____, annot= ____, annot_kws = {"size": 14})
plt.yticks(rotation=0, size = 14); plt.xticks(rotation=90, size = 14) # fix ticklabel directions and size
plt.tight_layout() # fits plot area to the plot, "tightly"
plt.____ # show the plot