{"id":30801,"date":"2024-10-28T06:03:57","date_gmt":"2024-10-28T06:03:57","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30801"},"modified":"2024-11-26T06:47:22","modified_gmt":"2024-11-26T06:47:22","slug":"%ed%8c%8c%ec%9d%b4%ec%8d%ac-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%ea%b0%9c%eb%b0%9c-matplotlib%eb%a5%bc-%ec%9d%b4%ec%9a%a9%ed%95%9c-%eb%8d%b0%ec%9d%b4%ed%84%b0-%ec%8b%9c%ea%b0%81%ed%99%94","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30801\/","title":{"rendered":"\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, matplotlib\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc2dc\uac01\ud654"},"content":{"rendered":"

<\/p>\n

\n \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub294 \ub9ce\uc740 \uc815\ubcf4\ub97c \ud55c \ub208\uc5d0 \ud30c\uc545\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.
\n \uc774 \uae00\uc5d0\uc11c\ub294 matplotlib<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub098 \ubcc0\ub3d9\uc131\uc774 \uc788\ub294 \uc790\uc0b0\uc758 \uac00\uaca9\uc744 \uc2dc\uac01\ud654\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n

1. \ub370\uc774\ud130 \uc2dc\uac01\ud654\uc758 \uc911\uc694\uc131<\/h2>\n

\n \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub294 \uc8fc\uc2dd \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \uc774\ud574\ud558\uace0 \uc608\uce21\ud558\uae30 \uc704\ud574 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n

1.1. \uc2dc\uc7a5\uc758 \ud2b8\ub80c\ub4dc \ubd84\uc11d<\/h3>\n

\n \uc2dc\uac01\ud654\ub41c \ub370\uc774\ud130\ub294 \uc2dc\uc7a5\uc758 \ud2b8\ub80c\ub4dc, \ubcc0\ub3d9\uc131, \ud328\ud134 \ub4f1\uc744 \uc27d\uac8c \uc2dd\ubcc4\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4.
\n \uc608\ub97c \ub4e4\uc5b4, \uac00\uaca9 \ucc28\ud2b8\ub294 \ud2b9\uc815 \uae30\uac04 \ub3d9\uc548\uc758 \uc8fc\uac00 \uc0c1\uc2b9 \ubc0f \ud558\ub77d\uc744 \ube60\ub974\uac8c \ud30c\uc545\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n <\/p>\n

1.2. \uc758\uc0ac \uacb0\uc815 \uc9c0\uc6d0<\/h3>\n

\n \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9b4 \ub54c \uc2dc\uac01\ud654\ub41c \uc815\ubcf4\ub97c \ud1b5\ud574 \ubcf4\ub2e4 \ub098\uc740 \ud310\ub2e8\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2dc\uc810\uc744 \uc27d\uac8c \ud30c\uc545\ud560 \uc218 \uc788\uc5b4 \uc190\uc2e4\uc744 \uc904\uc774\uace0 \uc774\uc775\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\ub294 \uae30\ud68c\ub97c \ucc3d\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n

2. matplotlib \uc18c\uac1c<\/h2>\n

\nmatplotlib<\/code>\ub294 \ud30c\uc774\uc36c\uc5d0\uc11c \uac00\uc7a5 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4.
\n \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uc2dc\uac01\ud654\ubfd0\ub9cc \uc544\ub2c8\ub77c \uac04\ub2e8\ud55c \uadf8\ub798\ud504\ub3c4 \uc27d\uac8c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc720\ud615\uc758 \ud50c\ub86f\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

2.1. \uc124\uce58 \ubc29\ubc95<\/h3>\n

\nmatplotlib<\/code>\ub294 pip\uc744 \ud1b5\ud574 \uac04\ub2e8\ud788 \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \uba85\ub839\uc5b4\ub85c \uc124\uce58\ud569\ub2c8\ub2e4:\n<\/p>\n

pip install matplotlib<\/code><\/pre>\n
3. \ub370\uc774\ud130 \uc900\ube44\ud558\uae30<\/h2>\n
\n    \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.
\n    \uc608\ub97c \ub4e4\uc5b4 pandas<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub300\ub7c9\uc758 \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n    \uc544\ub798\ub294 \uc608\uc2dc \ucf54\ub4dc\uc785\ub2c8\ub2e4:\n<\/p>\n
import pandas as pd\n\n# Yahoo Finance\uc5d0\uc11c \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \uc608\uc81c\ndata = pd.read_csv('https:\/\/query1.finance.yahoo.com\/v7\/finance\/download\/AAPL?period1=0&period2=9999999999&interval=1d&events=history')\ndata['Date'] = pd.to_datetime(data['Date'])\ndata.set_index('Date', inplace=True)\n<\/code><\/pre>\n
4. \uc8fc\uac00 \uc2dc\uac01\ud654\ud558\uae30<\/h2>\n
\n    \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud558\ub294 \uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \ubc29\ubc95\uc740 \uc2dc\uacc4\uc5f4 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4.
\n    \uc544\ub798\uc758 \ucf54\ub4dc\ub294 matplotlib<\/code>\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n<\/p>\n
import matplotlib.pyplot as plt\n\n# \uc8fc\uc2dd \uac00\uaca9 \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(data['Close'], label='Close Price', color='blue')\nplt.title('AAPL Closing Prices')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
4.1. \uadf8\ub798\ud504\uc5d0 \ubcf4\uc870 \uc9c0\ud45c \ucd94\uac00\ud558\uae30<\/h3>\n
\n    \uc8fc\uac00 \uadf8\ub798\ud504\uc5d0 \uc774\ub3d9 \ud3c9\uade0\uc120 \uac19\uc740 \ubcf4\uc870 \uc9c0\ud45c\ub97c \ucd94\uac00\ud558\uc5ec \ubcf4\ub2e4 \uc2ec\uce35\uc801\uc778 \ubd84\uc11d\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
# \uc774\ub3d9 \ud3c9\uade0\uc120 \ucd94\uac00\ndata['SMA_20'] = data['Close'].rolling(window=20).mean()\n\nplt.figure(figsize=(12, 6))\nplt.plot(data['Close'], label='Close Price', color='blue')\nplt.plot(data['SMA_20'], label='20-Day SMA', color='orange')\nplt.title('AAPL Closing Prices with 20-Day SMA')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
5. \ub2e4\uc591\ud55c \uc2dc\uac01\ud654 \uae30\ubc95<\/h2>\n
\nmatplotlib<\/code>\uc744 \ud65c\uc6a9\ud55c \ub2e4\uc591\ud55c \uc2dc\uac01\ud654 \uae30\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4.\n<\/p>\n
5.1. \ud788\uc2a4\ud1a0\uadf8\ub7a8<\/h3>\n
\n    \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc740 \uc8fc\uac00\uc758 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \uc544\ub798 \ucf54\ub4dc\ub294 \uc8fc\uac00 \uc218\uc775\ub960\uc758 \ubd84\ud3ec\ub97c \ud45c\uc2dc\ud558\ub294 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.\n<\/p>\n
returns = data['Close'].pct_change()\nplt.figure(figsize=(12, 6))\nplt.hist(returns.dropna(), bins=50, alpha=0.7, color='blue')\nplt.title('Histogram of Returns')\nplt.xlabel('Returns')\nplt.ylabel('Frequency')\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
5.2. \uc0b0\uc810\ub3c4<\/h3>\n
\n    \uc0b0\uc810\ub3c4\ub294 \ub450 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uac00\uc640 \uac70\ub798\ub7c9\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
plt.figure(figsize=(12, 6))\nplt.scatter(data['Volume'], data['Close'], alpha=0.5, color='purple')\nplt.title('Volume vs Close Price')\nplt.xlabel('Volume')\nplt.ylabel('Close Price')\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
6. \uacb0\uacfc \ud65c\uc6a9\ud558\uae30<\/h2>\n
\n    \uc704\uc5d0\uc11c \uc0dd\uc131\ud55c \uc2dc\uac01\ud654 \uacb0\uacfc\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc5d0 \ud544\uc218\uc801\uc778 \ucc38\uace0 \uc790\ub8cc\uac00 \ub429\ub2c8\ub2e4.
\n    \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud2b9\uc815 \uc870\uac74\uc744 \ub9cc\uc871\ud558\ub294 \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
def trading_signal(data):\n    signals = []\n    for i in range(len(data)):\n        if data['Close'][i] > data['SMA_20'][i]:\n            signals.append('Buy')\n        else:\n            signals.append('Sell')\n    data['Signal'] = signals\n\ntrading_signal(data)\n\n# \uc2e0\ud638\ub97c \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(data['Close'], label='Close Price', color='blue')\nplt.plot(data['SMA_20'], label='20-Day SMA', color='orange')\nplt.scatter(data.index, data[data['Signal'] == 'Buy']['Close'], marker='^', color='green', label='Buy Signal', alpha=1)\nplt.scatter(data.index, data[data['Signal'] == 'Sell']['Close'], marker='v', color='red', label='Sell Signal', alpha=1)\nplt.title('Trading Signals')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.grid()\nplt.show()\n<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\n    \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc758 matplotlib<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4.
\n    \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uc774\ub97c \ud1b5\ud574 \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n    \uc2e4\uc81c \ub370\uc774\ud130\ub85c \uc2e4\uc2b5\ud574\ubcf4\uace0, \uc790\uc2e0\ub9cc\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud574 \ubcf4\uc138\uc694.\n<\/p>\n
\uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
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\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub294 \ub9ce\uc740 \uc815\ubcf4\ub97c \ud55c \ub208\uc5d0 \ud30c\uc545\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc8fc\ub294 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 matplotlib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub098 \ubcc0\ub3d9\uc131\uc774 \uc788\ub294 \uc790\uc0b0\uc758 \uac00\uaca9\uc744 \uc2dc\uac01\ud654\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \ub370\uc774\ud130 \uc2dc\uac01\ud654\uc758 \uc911\uc694\uc131 \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub294 \uc8fc\uc2dd \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \uc774\ud574\ud558\uace0 \uc608\uce21\ud558\uae30 \uc704\ud574 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. 1.1. \uc2dc\uc7a5\uc758 … \ub354 \ubcf4\uae30 “\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, matplotlib\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc2dc\uac01\ud654”<\/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-30801","post","type-post","status-publish","format-standard","hentry","category-37"],"yoast_head":"\n\ud30c\uc774\uc36c \uc790\ub3d9\ub9e4\ub9e4 \uac1c\ubc1c, matplotlib\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc2dc\uac01\ud654 - 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