|
| 1 | +import matplotlib.pyplot as plt |
| 2 | +from Investar import Analyzer |
| 3 | + |
| 4 | +mk = Analyzer.MarketDB() |
| 5 | +df = mk.get_daily_price('삼성전자', '1998-04-27', '2018-04-27') |
| 6 | +""" |
| 7 | +>>> df |
| 8 | + code date open ... volume MA20 MA200 |
| 9 | +date ... |
| 10 | +1998-04-27 005930 1998-04-27 66800 ... 187010 NaN NaN |
| 11 | +1998-04-28 005930 1998-04-28 65000 ... 174220 NaN NaN |
| 12 | +1998-04-29 005930 1998-04-29 66900 ... 238910 NaN NaN |
| 13 | +1998-04-30 005930 1998-04-30 70500 ... 616240 NaN NaN |
| 14 | +1998-05-02 005930 1998-05-02 72000 ... 236600 NaN NaN |
| 15 | +... ... ... ... ... ... ... ... |
| 16 | +2018-04-23 005930 2018-04-23 2550000 ... 232380 2478450.0 2513175.0 |
| 17 | +2018-04-24 005930 2018-04-24 2592000 ... 315406 2479650.0 2513805.0 |
| 18 | +2018-04-25 005930 2018-04-25 2461000 ... 332292 2483900.0 2514520.0 |
| 19 | +2018-04-26 005930 2018-04-26 2521000 ... 360931 2491650.0 2515750.0 |
| 20 | +2018-04-27 005930 2018-04-27 2669000 ... 606216 2501100.0 2517250.0 |
| 21 | +
|
| 22 | +[4967 rows x 10 columns] |
| 23 | +""" |
| 24 | +df['MA20'] = df['close'].rolling(window=20).mean() |
| 25 | +df['MA200'] = df['close'].rolling(window=200).mean() |
| 26 | + |
| 27 | +plt.figure(figsize=(9, 7)) |
| 28 | +plt.plot(df.index, df['close'], color='cyan', label='Close') |
| 29 | +plt.plot(df.index, df['MA20'], 'm--', label='MA20') |
| 30 | +plt.plot(df.index, df['MA200'], 'r--', label='MA200') |
| 31 | +plt.legend(loc='best') |
| 32 | +plt.title('Samsung Electronics') |
| 33 | +plt.grid(color='gray', linestyle='--') |
| 34 | +plt.yticks([65300, 500000, 1000000, 1500000, 2000000, 2500000, 2650000]) |
| 35 | +plt.xticks(['1998-04-27', '2002-04-27', '2006-04-27', '2010-04-27', '2014-04-27', '2018-04-27']) |
| 36 | +plt.show() |
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