Python for Finance: Analyze Big Financial Data

p2 = plt.plot(gbm.time_grid, paths_2[:, : 10 ], ‘r-.’)
plt.grid(True)
l1 = plt.legend([p1[ 0 ], p2[ 0 ]],
[‘low volatility’, ‘high volatility’], loc= 2 )
plt.gca().add_artist(l1)
plt.xticks(rotation= 30 )

Figure 16-1. Simulated paths from GBM simulation class