def get_simulation(ticker,name):
data=pd.DataFrame()
data[ticker]=wb.DataReader(ticker,data_source='yahoo',start='2020-1-1')['Adj Close']
log_return =np.log(1+data.pct_change())
u=log_return.mean()
var=log_return.var()
drift=u-(0.5*var)
stdev=log_returns.std()
t_intervals=365
iteration =10
daily_returns =np.exp(drift.value+ stdev.values*norm.ppf(np.random.rand(t_intervals,iterations))
S0=data.iloc[-1]
price_list=np.zeros_like(daily_returns)
price_list[0]=S0
for t in range(1,t_intervals):
price_list[t]=price_list[t-1]+daily_returns[t]
plt.figure(figsize=(10,6))
plt.title("1 year Monte Carlo Simulation"+name)
plt.ylabe("Price(P)")
plt.xlab("Time (Day)")
plt.plot(price_list)
plt.show()
get_simulation("MSFT","Microsoft Coperation")
def plot_estimated_deriviative():
def square(x):
return x * x
def derivative(x):
return 2 * x
derivative_estimate = lambda x: difference_quotient(square, x, h=0.00001)
import mathplotlib.pyplot as plot
x = range(-10, 10)
plt.plot(x, map(derivative, x), 'rx')
plt.plot(x, map(derivative_estimate, x), 'b+')
plot.show()
def init():
line.set_data([], [])
return line,
def animate(i):
# We let the particle evolve for 0.1 time units
simulator.evolve(0.01)
X = [p.x for p in simulator.particles]
Y = [p.y for p in simulator.particles]
line.set_data(X, Y)
return line,
# Call the animate function each 10 ms
anim = animation.FuncAnimation(fig, animate,
init_func=init, blit=True,# Efficient animation
interval=10)
plt.show()
import mathplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.spines["left"].set_position("center")
ax.spines["bottom"].set_position("zero")
ax.spines["right"].set_color("none")
ax.spines["top"].set_color("none")
xs = [x for x in range(-10, 11)]
ys = [x**2 for x in xs]
plt.plot(xs, ys)
plt.show()
def test_run():
df = pd.read_csv('data/APPL.csv')
print df['Adj Close']
df[['Adj Close', 'Close']].plot()
plt.show()
def print_img(img): plt.imshow(np.array(img)) plt.show()
def show(self):
"""display the pseudocolor representation of the CA"""
pyplot.show()
