Plot returns
Lastly, we'll plot the performance of our machine-learning-generated portfolio versus just holding the SPY. We can use this as an evaluation to see if our predictions are doing well or not.
Since we already have algo_cash and spy_cash created, all we need to do is provide them to plt.plot() to display. We'll also set the label for the datasets with legend in plt.plot().
Questo esercizio fa parte del corso
Machine Learning for Finance in Python
Istruzioni dell'esercizio
- Use
plt.plot()to plot thealgo_cash(with label'algo') andspy_cash(with label'SPY'). - Use
plt.legend()to display the legend.
esercizio interattivo pratico
Prova questo esercizio completando questo codice di esempio.
# Plot the algo_cash and spy_cash to compare overall returns
plt.plot(____, ____)
plt.plot(spy_cash, label='SPY')
____ # show the legend
plt.show()