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