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()