Заболотников_9373_ПР3

ПРИЛОЖЕНИЕ А.

КОД ПРОГРАММЫ (PYTHON)

import pandas as pd import numpy as np from math import sqrt

from cvxopt import matrix from cvxopt.blas import dot from cvxopt.solvers import qp

from matplotlib import pyplot as plt stocks = ['GAZP_210101_211231.csv',

'RASP_210101_211231.csv', 'SBER_210101_211231.csv', 'SNGS_210101_211231.csv', 'YNDX_210101_211231.csv']

variations = []

total_g = [0, 0, 0, 0, 0] total_r = [0, 0, 0, 0, 0] i = 0

for filename in stocks:

df = pd.read_csv(filename)

Dm_i = (df['<CLOSE>'].shift(-1) / df['<CLOSE>'] - 1).dropna() for j in range(253):

total_g[i] += Dm_i.iloc[j] total_g[i] *= 100

total_r[i] = np.var(Dm_i) i += 1

variations.append((df['<CLOSE>'].shift(-1) / df['<CLOSE>'] - 1).dropna().values.reshape(1, -1))

variations = np.concatenate(variations, axis = 0) C = np.cov(variations)

A = matrix([[1.0, 1.0, 1.0, 1.0, 1.0],

[total_g[0], total_g[1], total_g[2], total_g[3], total_g[4]]]) G = matrix([[1.0, 0.0, 0.0, 0.0, 0.0],

[0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0],

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[-1.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, 0.0, 0.0, 0.0], [0.0, 0.0, -1.0, 0.0, 0.0], [0.0, 0.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, -1.0]])

h = matrix([[0.5], [0.5], [0.5], [0.5], [0.5], [0.0], [0.0], [0.0], [0.0], [0.0]])

P = matrix(C)

Ps = np.zeros((5, 5)) j = 0

for k in range(5):

for l in range(5): Ps[k][l] = P[j] j += 1

ttl = matrix([0.0, 0.0, 0.0, 0.0, 0.0], tc = 'd') profits = matrix(0.0, (1, 99))

risks = matrix(0.0, (1, 99)) ports = np.zeros((99, 5)) for i in range(99):

profits[i] = profits[i] + (17 + 0.05 * i) for i in range(99):

b = matrix([[1.0], [profits[i]]])

portfolios = qp(2 * P, ttl, G.T, h.T, A.T, b.T)['x'] for j in range(5):

ports[i][j] = portfolios[j] for m in range(5):

for n in range(5):

prod = Ps[m][n] * portfolios[m] * portfolios[n] risks[i] += prod

for i in range(99):

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print("Портфель: ", ports[i][:]) print("Доходность: ", profits[i])

X = np.zeros(99)

Y = np.zeros(99)

for i in range(99): X[i] = profits[i] Y[i] = risks[i]

plt.figure(figsize = (10, 8)) plt.scatter(X, Y)

plt.show()

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