The following code snippet will plot the data we downloaded and processed:

def show_plot(key="", show=True): 
    fig = plt.figure() 
    fig.set_figwidth(20) 
    fig.set_figheight(15) 
    for code in codes: 
        index = code.split("/")[1] 
        if key and len(key) > 0: 
            label = "{}_{}".format(index, key) 
        else: 
            label = index 
        _ = plt.plot(closings[label], label=label) 
 
    _ = plt.legend(loc='upper right') 
    if show: 
        plt.show() 
 
show = True 
show_plot("", show=show) 
show_plot("scaled", show=show) 
show_plot("log_return", show=show) 

The original market data to close values. As you can see here, the value for WAWEL is a couple of magnitudes larger than the other markets:

The closing values for WAWEL visually reduced the trends in data for the other market values. We will scale this data so we can see it better. Take a look at the following screenshot:

The scaled market values help us visualize the trends better. Now, let's see how the log_return looks:

The log returns the markets' closing values