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().
Cet exercice fait partie du cours
Machine Learning for Finance in Python
Instructions
- Use
plt.plot()to plot thealgo_cash(with label'algo') andspy_cash(with label'SPY'). - Use
plt.legend()to display the legend.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Plot the algo_cash and spy_cash to compare overall returns
plt.plot(____, ____)
plt.plot(spy_cash, label='SPY')
____ # show the legend
plt.show()