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In [1]:

    
import numpy as np
import pandas as pd

from pandas_datareader import data

import matplotlib
import matplotlib.pyplot as plt

%matplotlib inline



In [2]:

    
# 삼성전자
# http://finance.yahoo.com/quote/005930.KS?p=005930.KS

df = data.get_data_yahoo(
    "005930.KS",
    start="20160101",
    end="20161231",
)



In [3]:

    
df.head()



In [4]:

    
ax = df["Adj Close"].plot(label=True)
ax.set_title("삼성전자 주가 변화 (2016)")









    Out[4]:





<matplotlib.text.Text at 0x1108cd5f8>



In [5]:

    
# 이동평균선 ( 5일선, 20일선, 60일선 )

df["MA5"] = df["Adj Close"].rolling(window=5).mean()
df["MA20"] = df["Adj Close"].rolling(window=20).mean()
df["MA60"] = df["Adj Close"].rolling(window=60).mean()

ax = df[["MA5", "MA20", "MA60", "Adj Close"]].plot()
ax.set_xlim("20161201", "20161231")
ax.set_ylim(1500000, 1900000)
ax.set_title("삼성전자 차트 - 이동평균선")









    Out[5]:





<matplotlib.text.Text at 0x110a122b0>

	Open	High	Low	Close	Volume	Adj Close
Date
2016-01-01	1260000.0	1260000.0	1260000.0	1260000.0	0	1239852.43
2016-01-04	1260000.0	1260000.0	1205000.0	1205000.0	306900	1185731.88
2016-01-05	1202000.0	1218000.0	1186000.0	1208000.0	216000	1188683.91
2016-01-06	1208000.0	1208000.0	1168000.0	1175000.0	366700	1156211.59
2016-01-07	1166000.0	1183000.0	1151000.0	1163000.0	282300	1144403.47