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().
Este ejercicio forma parte del curso
Machine Learning for Finance in Python
Instrucciones del ejercicio
- Use
plt.plot()to plot thealgo_cash(with label'algo') andspy_cash(with label'SPY'). - Use
plt.legend()to display the legend.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Plot the algo_cash and spy_cash to compare overall returns
plt.plot(____, ____)
plt.plot(spy_cash, label='SPY')
____ # show the legend
plt.show()