Python finanzas para economistas, Esquemas y mapas conceptuales de Finanzas

import math

import numpy as np

import scipy.stats as scs

import statsmodels.api as sm

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from google.colab import drive

drive.mount('/content/drive')

(data / data.iloc[0]*100).plot(figsize=(10,6))

rets = np.log(data / data.shift(1))

rets.hist(bins=50,figsize=(10,8))

for pin range (2500):

weights = np.random.random(noa)

weights /= np.sum(weights)

prets.append(port_ret(weights))

pvols.append(port_vol(weights))