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Arithmetic Brownian Motion#
Simulate and visualise paths
# Author: Dialid Santiago <d.santiago@outlook.com>
# License: MIT
# Description: Simulate and visualise an Arithmetic Brownian Motion
from aleatory.processes import BrownianMotion
from aleatory.styles import qp_style
qp_style() # Use quant-pastel-style
p = BrownianMotion(drift=1.0, scale=2.0)
fig = p.draw(n=100, N=200, figsize=(12, 7))
fig.show()
![Arithmetic Brownian Motion, Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal](../_images/sphx_glr_plot_arithmetic_brownian_motion_001.png)
p = BrownianMotion(drift=-1.0, scale=0.5)
fig = p.draw(n=100, N=200, figsize=(12, 7))
fig.show()
![Arithmetic Brownian Motion, Monte Carlo Simulated Paths $\{{X_t, t \in [t_0, T]\}}$, $X_T$ Marginal](../_images/sphx_glr_plot_arithmetic_brownian_motion_002.png)
fig = p.plot(n=100, N=10, figsize=(12, 7))
fig.show()
Total running time of the script: (0 minutes 11.539 seconds)