yfinance<\/strong>: Yahoo Finance API\ub97c \ud1b5\ud574 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc27d\uac8c \uac00\uc838\uc624\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac.<\/li>\n<\/ul>\n\ub2e4\uc74c\uacfc \uac19\uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install pandas numpy matplotlib yfinance<\/code><\/pre>\n3. \ub370\uc774\ud130 \uc218\uc9d1\ud558\uae30<\/h2>\n
\uc774\uc81c \uc8fc\uc2dd \ub370\uc774\ud130 \uc218\uc9d1\uc744 \uc2dc\uc791\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 yfinance<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec Yahoo Finance\ub85c\ubd80\ud130 \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud560 \uac83\uc785\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc81c\uc5d0\uc11c\ub294 \uc560\ud50c(Apple)\uc758 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\nimport yfinance as yf\nimport pandas as pd\n\n# \uc560\ud50c \uc8fc\uc2dd \ub370\uc774\ud130 \uc218\uc9d1\nticker = 'AAPL'\ndata = yf.download(ticker, start='2020-01-01', end='2023-01-01', interval='1d')\n\n# Dataframe \ud655\uc778\nprint(data.head())\n<\/code><\/pre>\n\uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 2020\ub144 1\uc6d4 1\uc77c\ubd80\ud130 2023\ub144 1\uc6d4 1\uc77c\uae4c\uc9c0\uc758 \uc560\ud50c \uc8fc\uc2dd \ub370\uc774\ud130\uac00 \ub2e4\uc6b4\ub85c\ub4dc\ub418\uc5b4 DataFrame\uc73c\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4. data.head()<\/code>\ub97c \ud1b5\ud574 \uac00\uc7a5 \ucc98\uc74c 5\uac1c\uc758 \ud589\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uc885\uc885 \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uacb0\uce21\uce58 \ucc98\ub9ac, \ub370\uc774\ud130 \ud0c0\uc785 \ubcc0\ud658, \ud2b9\uc815 \uc5f4\ub9cc \uc120\ud0dd\ud558\ub294 \ub4f1\uc758 \uacfc\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c \uc804\ucc98\ub9ac \uc608\uc81c\uc785\ub2c8\ub2e4:<\/p>\n
# \uacb0\uce21\uce58 \ud655\uc778\nprint(data.isnull().sum())\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata = data.dropna()\n\n# 'Close' \uac00\uaca9 \uc5f4\ub9cc \uc120\ud0dd\nclose_prices = data[['Close']]\nprint(close_prices.head())\n<\/code><\/pre>\n5. \ub370\uc774\ud130 \ubd84\uc11d<\/h2>\n
\uc774\uc81c \uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uac04\ub2e8\ud55c \ubd84\uc11d\uc744 \uc218\ud589\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ub3d9 \ud3c9\uade0\uc120\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
# 20\uc77c \uc774\ub3d9 \ud3c9\uade0\uc120\nclose_prices['20_MA'] = close_prices['Close'].rolling(window=20).mean()\n\n# \ub3d9\uc2dc\uc5d0 \ub370\uc774\ud130 \uc2dc\uac01\ud654\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(14,7))\nplt.plot(close_prices['Close'], label='\uc560\ud50c \uc8fc\uc2dd \uac00\uaca9', color='blue')\nplt.plot(close_prices['20_MA'], label='20\uc77c \uc774\ub3d9 \ud3c9\uade0', color='red')\nplt.title('\uc560\ud50c \uc8fc\uc2dd \uac00\uaca9 \ubc0f 20\uc77c \uc774\ub3d9 \ud3c9\uade0')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n6. \ub370\uc774\ud130 \uc800\uc7a5<\/h2>\n
\uac00\uacf5\ub41c \ub370\uc774\ud130\ub97c CSV \ud30c\uc77c\ub85c \uc800\uc7a5\ud558\uace0 \uc2f6\ub2e4\uba74, pandas\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud788 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
# \ub370\uc774\ud130 \uc800\uc7a5\nclose_prices.to_csv('aapl_close_prices.csv')\n<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc744 \ud65c\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub97c DataFrame \ud615\uc2dd\uc73c\ub85c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. yfinance<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uba74 \uc190\uc27d\uac8c \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc73c\uba70, pandas\ub97c \ud1b5\ud574 \ubd84\uc11d \ubc0f \uc804\ucc98\ub9ac \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ucd08\ub97c \ubc14\ud0d5\uc73c\ub85c \uc5ec\ub7ec\ubd84\ub9cc\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \ud55c \ubc1c \ub354 \ub098\uc544\uac08 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n\ud5a5\ud6c4 \uae00\uc5d0\uc11c\ub294 \uc2e4\uc2dc\uac04 \ub370\uc774\ud130 \uc218\uc9d1\uacfc \uc54c\ub9bc \uc2dc\uc2a4\ud15c \uad6c\uc131 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uae30\ubcf8\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub354\uc6b1 \ubc1c\uc804\ub41c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<\/article>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc8fc\uc2dd \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \uac83\uc740 \uc790\uc0b0 \uad00\ub9ac\uc640 \ud22c\uc790\uc5d0\uc11c \ud070 \ud61c\ud0dd\uc744 \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 Python\uc740 \uadf8 \uc720\uc5f0\uc131\uacfc \ub2e4\uc591\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ub355\ubd84\uc5d0 \ub9ce\uc740 \uac1c\ubc1c\uc790\uc640 \ud2b8\ub808\uc774\ub354\ub4e4\uc774 \uc120\ud638\ud558\ub294 \uc5b8\uc5b4\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub97c Dataframe \ud615\uc2dd\uc73c\ub85c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ud30c\uc774\uc36c\uc758 pandas \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uac00\uacf5\ud558\uace0 \ubd84\uc11d\ud558\ub294 \uae30\ubcf8\uc801\uc778 \ubc29\ubc95\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. 1. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uac1c\uc694 \uc790\ub3d9\ub9e4\ub9e4 … \ub354 \ubcf4\uae30 “\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, Dataframe \ud615\uc2dd\uc73c\ub85c \uc8fc\uc2dd \ub370\uc774\ud130 \ubc1b\uae30”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[37],"tags":[],"class_list":["post-30783","post","type-post","status-publish","format-standard","hentry","category-37"],"yoast_head":"\n\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, Dataframe \ud615\uc2dd\uc73c\ub85c \uc8fc\uc2dd \ub370\uc774\ud130 \ubc1b\uae30 - 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