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")
import sys sys.path.append('/usr/local/anaconda3/lib/python3.6/site-packages') from math import sqrt from numpy import cos ** 2 (x/2), linspace def f(x): return cos**2(x/2) x = linspace(0, 4, 11) print(x) #y = sin(x) y = f(x) print(y) legend[] from mathplotlib import pyplot as plt plt.grid() plt.xlabel("x") plt.ylabel("y") plt.title("Cosinus funkcija un taas atvasinaajumi") plt.plot(x, y, "k") legend.append("$scos**2(x/2)$ funkcija") plt.plot(x, y, "go") legend.append("$cos**2(x/2)$ funkcija(dazhi punkti)")
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()
print(plt._doc_) AttributeError: module 'matplotlib.pyplot' has no attribute '_doc_' >>> print(plt.__doc__) `matplotlib.pyplot` is a state-based interface to matplotlib. It provides a MATLAB-like way of plotting. pyplot is mainly intended for interactive plots and simple cases of programmatic plot generation:: import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 5, 0.1) y = np.sin(x) plt.plot(x, y) The object-oriented API is recommended for more complex plots. >>> ================= RESTART: /home/user/RTR105/Atvasinajums.py ================= Traceback (most recent call last): File "/home/user/RTR105/Atvasinajums.py", line 30, in <module> print(plt.legend._doc_) AttributeError: 'function' object has no attribute '_doc_' >>> ================= RESTART: /home/user/RTR105/Atvasinajums.py ================= Traceback (most recent call last): File "/home/user/RTR105/Atvasinajums.py", line 30, in <module> print(plt.legend._doc_) AttributeError: 'function' object has no attribute '_doc_'
def wald(table): N = np.sum(table) rowsums = [np.sum(x) for x in table] colsums = [np.sum(x) for x in table.T] p1 = table[0][0] / colsums[0] p2 = table[0][1] / colsums[1] p = rowsums[0] / N return (p1 - p2) / np.sqrt(p * (1 - p) * (1.0 / rowsums[0] + 1.0 / rowsums[1])) N, m, n = 14, 2, 2 position = [posi for posi in range(N + m * n - 1)] walds = [] chis = [] stars = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] for x in permutations(balls): i = 0 t = [0, 0, 0, 0] for s in stars: if (s == 0): i += 1 t[i] += 1 table = np.array([[t[0], t[1]], [t[2], t[3]]]) walds.append(wald(table)) chis.append(chisquare(table)) print len(waltl) plt.plot(chis, walds)
================= RESTART: /home/user/RTR105/atvasinajums.py ================= >>> print(plt.__doc__) `matplotlib.pyplot` is a state-based interface to matplotlib. It provides a MATLAB-like way of plotting. pyplot is mainly intended for interactive plots and simple cases of programmatic plot generation:: import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 5, 0.1) y = np.sin(x) plt.plot(x, y) The object-oriented API is recommended for more complex plots. >>> print(plt.plot__doc__) Traceback (most recent call last): File "<pyshell#9>", line 1, in <module> print(plt.plot__doc__) AttributeError: module 'matplotlib.pyplot' has no attribute 'plot__doc__' >>> print(plt.plot.__doc__) Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. *args* is a variable length argument, allowing for multiple *x*, *y* pairs with an optional format string. For example, each of the following is legal::
colsums = [np.sum(x) for x in table.T] n = np.outer(rowsums,colsums)*1.0/N return np.sum( (table - n )**2/n) def wald(table): N = np.sum(table) rowsums = [np.sum(x) for x in table] colsums = [np.sum(x) for x in table.T] p1= table[0][0]/colsums[0] p2=table[0][1]/colsums[1] p= rowsums[0]/N return (p1-p2)/np.sqrt(p*(1-p)*(1.0/rowsums[0]+1.0/rowsums[1])) N,m,n=14,2,2 position = [posi for posi in range(N+m*n-1)] walds = [] chis = [] stars=[0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1] for x in permutations(balls): i = 0 t = [0,0,0,0] for s in stars: if(s ==0): i+=1 t[i]+=1 table = np.array([[t[0],t[1]],[t[2],t[3]]]) walds.append(wald(table)) chis.append(chisquare(table)) print len(waltl) plt.plot(chis,walds)
# our model for the forward pass def forward(x): return x * w # loss function def loss(x, y): y_pred = forward(x) return (y_pred - y) * (y_pred - y) w_list = [] mse_list = [] for w in np.arrange(0.0, 4.1, 0.1): print("w = ", w) l_sum = 0 for x_val, y_val in zip(x_data, y_data): y_pred_val = forward(x_val) l = loss(x_val, y_val) l_sum += l print("\t", x_val, y_val, y_pred_val, 1) print("MSE = ", l_sum / 3) w_list.append(w) mae_list.append(l_sum / 3) plt.plot(w_list, mae_list) plt.ylabel("Loss") plt.xlabel("w") plt.show()
import mathplotlib.pyplot as plt import numpy as np plt.plot([1,2,3,4]) plt.ylabel('some numbers') plt.show data = {'a' : np.arange(50), 'c' : np.random.randint(0,50,50), 'd' : np.random.randn(50), data['b']=data['a']+ 10*np.random.randn(50) data['d']=np.abs(data[d])*100 plt.scatter('a','b',c='c',s='d',data=data) plt.xlabel('entry a') plt.ylabel('entry b') plt.show()