import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from Practice.Investar import Analyzer
mk = Analyzer.MarketDB()
stocks = ['삼성전자', 'SK하이닉스', '현대자동차', 'NAVER']
df = pd.DataFrame()
for s in stocks:
df[s] = mk.get_daily_price(s, '2016-01-04', '2018-04-27')['close']
daily_ret = df.pct_change()
annual_ret = daily_ret.mean() * 252
daily_cov = daily_ret.cov()
annual_cov = daily_cov * 252
port_ret = []
port_risk = []
port_weights = []
sharpe_ratio = []
for _ in range(20000):
weights = np.random.random(len(stocks))
weights /= np.sum(weights)
returns = np.dot(weights, annual_ret)
risk = np.sqrt(np.dot(weights.T, np.dot(annual_cov, weights)))
port_ret.append(returns)
port_risk.append(risk)
port_weights.append(weights)
def __init__(self):
"""생성자: KRX 종목코드(codes)를 구하기 위한 MarkgetDB 객체 생성"""
self.mk = Analyzer.MarketDB()