{"id":28973,"date":"2024-10-27T16:16:09","date_gmt":"2024-10-27T16:16:09","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=28973"},"modified":"2024-11-26T06:55:41","modified_gmt":"2024-11-26T06:55:41","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%eb%a7%a4%ed%81%ac%eb%a1%9c-%eb%8d%b0%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/28973\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ub9e4\ud06c\ub85c \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc911\uc694\uc131\uc774 \uc99d\uac00\ud558\uba74\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc774 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc740 \ub9e4\ud06c\ub85c \ub370\uc774\ud130\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc694\uc778\ub4e4\uc744 \uae30\ubc18\uc73c\ub85c \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ubd84\uc11d\uc744 \ud1b5\ud574 \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370\uc5d0 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud1b5\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uae30\ubcf8 \uac1c\ub150, \ub370\uc774\ud130 \ucc98\ub9ac, \ubaa8\ub378 \ud6c8\ub828, \ud3c9\uac00 \ubc0f \uc2e4\uc81c \uac70\ub798\uc5d0\uc758 \uc801\uc6a9 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ucd08 \uc774\ud574<\/h2>\n

1.1 \uba38\uc2e0\ub7ec\ub2dd \uc815\uc758<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \ucef4\ud4e8\ud130\uac00 \uba85\uc2dc\uc801\uc73c\ub85c \ud504\ub85c\uadf8\ub798\ubc0d\ub418\uc9c0 \uc54a\uace0\ub3c4 \ud559\uc2b5\ud574\uc11c \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \uc54c\uace0\ub9ac\uc998\uacfc \uae30\uc220\uc744 \uc5f0\uad6c\ud558\ub294 \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub9e4\uc6b0 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\ub294 \ub370 \ucd08\uc810\uc744 \ub450\uace0 \uc788\uc73c\uba70, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub3c4 \uac00\uaca9 \uc608\uce21, \uc704\ud5d8 \uad00\ub9ac \ubc0f \uac70\ub798 \uc804\ub7b5 \ucd5c\uc801\ud654 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0 \uc801\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

1.2 \ub525\ub7ec\ub2dd \uc815\uc758<\/h3>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uac04 \ub450\ub1cc\uc758 \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \ubaa8\ubc29\ud558\uc5ec \ub370\uc774\ud130\uc758 \uace0\ucc28\uc6d0 \ud45c\ud604\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc8fc\ub85c \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud558\ub294 \ub370 \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc774\ub098 \ud328\ud134 \uc778\uc2dd\uacfc \uac19\uc740 \ubb38\uc81c\uc5d0\uc11c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ub9e4\uc6b0 \uc720\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \ub9e4\ud06c\ub85c \ub370\uc774\ud130\uc640 \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0<\/h2>\n

2.1 \ub9e4\ud06c\ub85c \ub370\uc774\ud130\ub780?<\/h3>\n

\ub9e4\ud06c\ub85c \ub370\uc774\ud130\ub294 \uad6d\uac00 \uacbd\uc81c \uc804\uccb4\uc758 \uc131\uacfc\ub97c \ub098\ud0c0\ub0b4\ub294 \ub370\uc774\ud130\ub85c, GDP, \uc2e4\uc5c5\ub960, \uc18c\ube44\uc790 \ubb3c\uac00\uc9c0\uc218(CPI), \ud1b5\ud654\ub7c9 \ubc0f \uae08\ub9ac\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uc9c0\ud45c\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub9e4\ud06c\ub85c \uacbd\uc81c \uc9c0\ud45c\ub294 \uc8fc\uc2dd \uc2dc\uc7a5\uc758 \ub3d9\ud5a5 \ubc0f \uac00\uaca9 \ubcc0\ud654\uc5d0 \uc911\ub300\ud55c \uc601\ud5a5\uc744 \ubbf8\uce58\uae30 \ub54c\ubb38\uc5d0, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n

2.2 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc640 \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0<\/h3>\n

\uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub780 \uc2dc\uac04\uc5d0 \ub530\ub77c \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub85c \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \ud658\uc728 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0 \ubd84\uc11d\uc740 \uc5ec\ub7ec \uac1c\uc758 \uc2dc\uacc4\uc5f4 \ubcc0\uc218\uac00 \uc11c\ub85c \uc5b4\ub5bb\uac8c \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0\ub97c \ubd84\uc11d\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\ud560 \uc218 \uc788\ub294 \uc911\uc694\ud55c \ub3c4\uad6c\uac00 \ub429\ub2c8\ub2e4.<\/p>\n

3. \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h2>\n

3.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n

\ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0 \ubd84\uc11d\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \uae08\uc735 \ub370\uc774\ud130 \uc81c\uacf5\uc5c5\uccb4\ub85c\ubd80\ud130 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. API, CSV \ud30c\uc77c \ub610\ub294 \ub370\uc774\ud130\ubca0\uc774\uc2a4\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ud30c\uc774\uc36c\uc758 pandas \ubc0f yfinance \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n

import pandas as pd\nimport yfinance as yf\n\n# \ud2b9\uc815 \uc8fc\uc2dd\uc758 \ub370\uc774\ud130 \uc218\uc9d1\nticker = 'AAPL'\ndata = yf.download(ticker, start='2020-01-01', end='2023-01-01')\nprint(data.head())<\/code><\/pre>\n
3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uacb0\uce21\uac12 \ucc98\ub9ac, \uc774\uc0c1\uce58 \uc81c\uac70, \ub370\uc774\ud130 \uc815\uaddc\ud654 \ubc0f \ud2b9\uc9d5 \uc0dd\uc131 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4. \uc774\ub7f0 \uc804\ucc98\ub9ac \ub2e8\uacc4\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
data = data.dropna()  # \uacb0\uce21\uac12 \uc81c\uac70\ndata['Return'] = data['Close'].pct_change()  # \uc218\uc775\ub960 \uc0dd\uc131\ndata = data.dropna()  # \ub2e4\uc2dc \uacb0\uce21\uac12 \uc81c\uac70<\/code><\/pre>\n
4. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
4.1 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378<\/h3>\n
\uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uc911 \ud558\ub098\uc778 \uc120\ud615 \ud68c\uadc0\ub294 \uc885\uc18d \ubcc0\uc218\uc640 \ud558\ub098 \uc774\uc0c1\uc758 \ub3c5\ub9bd \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0\uc5d0\uc11c\ub294 \uc5ec\ub7ec \ub3c5\ub9bd \ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc885\uc18d \ubcc0\uc218(\uc608: \uc8fc\uc2dd \uac00\uaca9)\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\nX = data[['feature1', 'feature2']]  # \ub3c5\ub9bd \ubcc0\uc218\ny = data['Return']  # \uc885\uc18d \ubcc0\uc218\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n
4.2 LSTM \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\uc7a5\uae30 \ub2e8\uae30 \uae30\uc5b5 \ubaa8\ub378(LSTM)\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc778 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 \uc7a5\uae30\uac04\uc758 \uc758\uc874\uc131\uc744 \uc720\uc9c0\ud560 \uc218 \uc788\uc5b4 \uc2dc\uac04\uc758 \ud750\ub984\uc5d0 \ub530\ub77c \ubcc0\ud558\ub294 \ub370\uc774\ud130\uc758 \ud2b9\uc9d5\uc744 \uc798 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\n\nX = np.array(X)  # \ub370\uc774\ud130 \ud615\uc2dd \ubcc0\ud658\ny = np.array(y)\n\nX = X.reshape((X.shape[0], X.shape[1], 1))  # LSTM \uc785\ub825 \ud615\uc2dd \uc7ac\uc870\uc815\n\nmodel = Sequential()\nmodel.add(LSTM(50, activation='relu', input_shape=(X.shape[1], 1)))\nmodel.add(Dense(1))\nmodel.compile(optimizer='adam', loss='mse')\n\nmodel.fit(X, y, epochs=200, verbose=0)<\/code><\/pre>\n
5. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
5.1 \ud3c9\uac00 \uc9c0\ud45c<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5 \ud3c9\uac00\ub97c \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\ub85c \uc0ac\uc6a9\ub418\ub294 \uc9c0\ud45c\ub294 RMSE(\ud3c9\uade0 \uc81c\uacf1\uadfc \uc624\ucc28), MAE(\ud3c9\uade0 \uc808\ub300 \uc624\ucc28) \ubc0f R\u00b2(\uacb0\uc815 \uacc4\uc218) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc9c0\ud45c\uc758 \uc758\ubbf8\uc640 \uc0ac\uc6a9\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00 \uc608\uc2dc<\/h3>\n
from sklearn.metrics import mean_squared_error, r2_score\n\ny_pred = model.predict(X_test)\nrmse = np.sqrt(mean_squared_error(y_test, y_pred))\nr2 = r2_score(y_test, y_pred)\n\nprint(f'RMSE: {rmse}, R\u00b2: {r2}')<\/code><\/pre>\n
6. \uc2e4\uc81c \uac70\ub798 \uc804\ub7b5 \uad6c\ud604<\/h2>\n
6.1 \ud2b8\ub808\uc774\ub529 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\ud55c \uc218\uc775\ub960\uc744 \uae30\ubc18\uc73c\ub85c \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub9e4\uc218 \uc2e0\ud638\ub294 \uc608\uce21\ub41c \uc218\uc775\ub960\uc774 \uc591\uc218\uc77c \ub54c, \ub9e4\ub3c4 \uc2e0\ud638\ub294 \uc74c\uc218\uc77c \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/p>\n
data['Signal'] = 0\ndata.loc[data['Return'] > 0, 'Signal'] = 1  # \ub9e4\uc218 \uc2e0\ud638\ndata.loc[data['Return'] < 0, 'Signal'] = -1  # \ub9e4\ub3c4 \uc2e0\ud638<\/code><\/pre>\n
6.2 \ud3ec\uc9c0\uc158 \uad00\ub9ac<\/h3>\n
\ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0\uc11c\ub294 \ud3ec\uc9c0\uc158 \uad00\ub9ac\uac00 \uc911\uc694\ud569\ub2c8\ub2e4. \uc704\ud5d8 \uad00\ub9ac\uc640 \uc790\ubcf8 \ubc30\ubd84 \ub4f1\uc758 \uc804\ub7b5\uc744 \ud1b5\ud574 \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud558\uace0 \uc774\uc775\uc744 \uadf9\ub300\ud654\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
6.3 \ubc31\ud14c\uc2a4\ud2b8<\/h3>\n
\uad6c\ucd95\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \uacfc\uac70 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud574 \ud14c\uc2a4\ud2b8\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uc744 \uac80\uc99d\ud558\uace0, \uc870\uc815\uc774 \ud544\uc694\ud55c \ubd80\ubd84\uc744 \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
initial_capital = 10000\ndata['Position'] = data['Signal'].shift(1)  # \uc774\uc804 \uc2e0\ud638 \uae30\ubc18 \ud3ec\uc9c0\uc158 \uc124\uc815\ndata['Portfolio_Value'] = initial_capital + (data['Position'] * data['Return']).cumsum()\ndata['Portfolio_Value'].plot(title='Portfolio Performance')<\/code><\/pre>\n
7. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub9e4\ud06c\ub85c \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ub2e4\ubcc0\ub7c9 \uc2dc\uacc4\uc5f4 \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, \uc774\ub97c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \ud6c8\ub828, \uc608\uce21, \ud3c9\uac00 \ubc0f \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131\uc5d0 \uc774\ub974\ub294 \uc804 \uacfc\uc815\uc744 \uacbd\ud5d8\ud568\uc73c\ub85c\uc368, \uc54c\uace0\ub9ac\uc998 \uae30\ubc18\uc758 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5 \uc218\ub9bd\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub192\uc77c \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ub354 \ubc1c\uc804\ub41c \ubaa8\ub378\uacfc \ubc29\ubc95\ub860\uc5d0 \ub300\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \uc5f0\uad6c\ud558\uace0 \uc2e4\uc2b5\ud558\uc5ec, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc131\uacfc\ub97c \uadf9\ub300\ud654\ud560 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
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