{"id":29325,"date":"2024-10-27T16:17:54","date_gmt":"2024-10-27T16:17:54","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29325"},"modified":"2024-11-26T06:53:04","modified_gmt":"2024-11-26T06:53:04","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-%ec%a7%80%eb%8f%84%ed%95%99%ec%8a%b5-%ec%98%88","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29325\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc9c0\ub3c4\ud559\uc2b5 \uc608\uc81c\uc5d0 \uc758\ud55c \ud559\uc2b5"},"content":{"rendered":"

<\/p>\n

\uc624\ub298\ub0a0 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub370\uc774\ud130\uc758 \uc591\uacfc \uc811\uadfc \uac00\ub2a5\uc131\uc758 \uc99d\uac00\ub85c \uc778\ud574 \ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc774 \uc810\uc810 \ub354 \ubcf4\ud3b8\ud654\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \ube44\uc815\ud615 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\uace0, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \ud569\ub9ac\uc801\uc778 \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ud070 \ub3c4\uc6c0\uc744 \uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ub97c \uc18c\uac1c\ud558\uace0, \uc9c0\ub3c4\ud559\uc2b5\uc744 \ud65c\uc6a9\ud55c \uc2e4\uc81c \uc608\uc81c\ub97c \ud1b5\ud574 \uc774\ub97c \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h2>\n

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

\uba38\uc2e0\ub7ec\ub2dd\uc740 \uacbd\ud5d8(\ub370\uc774\ud130)\uc744 \ud1b5\ud574 \uc790\ub3d9\uc801\uc73c\ub85c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \ud504\ub85c\uadf8\ub798\ubc0d\uacfc\ub294 \ub2ec\ub9ac, \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c\ub294 \uba85\uc2dc\uc801\uc778 \uaddc\uce59\ubcf4\ub2e4\ub294 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ubaa8\ub378\uc774 \uc2a4\uc2a4\ub85c \uaddc\uce59\uc744 \ucc3e\uc544\ub0c5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud575\uc2ec\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \ud559\uc2b5\uc785\ub2c8\ub2e4.<\/p>\n

1.2 \ub525\ub7ec\ub2dd\uc758 \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, \ud2b9\ud788 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud558\ub294 \ub370 \ud0c1\uc6d4\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc5ec\ub7ec \uac1c\uc758 \uc740\ub2c9\uce35\uc744 \uac00\uc9c0\uba70, \uc774\ub97c \ud1b5\ud574 \uace0\ucc28\uc6d0\uc801\uc778 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574<\/h2>\n

2.1 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780?<\/h3>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815\ud55c \uaddc\uce59\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc2e4\ud589\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uac10\uc815\uc744 \ubc30\uc81c\ud55c \uac70\ub798, \uc2e0\uc18d\ud55c \uac70\ub798 \uc2e4\ud589 \ubc0f \ub300\ub7c9\uc758 \uac70\ub798\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uacbd\uc6b0, \uc815\ub7c9\uc801\uc778 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc758\uc0ac \uacb0\uc815\uc744 \ub0b4\ub9ac\uae30 \ub54c\ubb38\uc5d0 \uc2e0\ub8b0\uc131\uc774 \ub192\uc2b5\ub2c8\ub2e4.<\/p>\n

2.2 \uba38\uc2e0\ub7ec\ub2dd\uacfc \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uad00\uacc4<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uacfc\uac70 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ubbf8\ub798\uc758 \uac00\uaca9 \ubcc0\ud654\ub098 \uc218\uc775\uc744 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ud22c\uc790\uc790\ub294 \ub354 \ub098\uc740 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \uae30\uc220\uc801 \ubd84\uc11d\uc774\ub098 \uae30\ubcf8\uc801 \ubd84\uc11d\uc5d0 \ube44\ud574 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc811\uadfc\ubc95\uc740 \ub354 \ub9ce\uc740 \ub370\uc774\ud130 \ucc98\ub9ac \ubc0f \ud328\ud134 \uc778\uc2dd \ub2a5\ub825\uc744 \uac00\uc9d1\ub2c8\ub2e4.<\/p>\n

3. \uc9c0\ub3c4\ud559\uc2b5\uc758 \uac1c\ub150<\/h2>\n

3.1 \uc9c0\ub3c4\ud559\uc2b5\uc774\ub780?<\/h3>\n

\uc9c0\ub3c4\ud559\uc2b5\uc740 \uc785\ub825 \ub370\uc774\ud130\uc640 \uadf8\uc5d0 \ud574\ub2f9\ud558\ub294 \uc815\ub2f5(label)\uc744 \uc774\uc6a9\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ud2b9\uc815 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec, \uc0c8\ub85c\uc6b4 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc608\uce21\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4. \uc9c0\ub3c4\ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc758 \uc608\ub85c\ub294 \ud68c\uadc0 \ubd84\uc11d, \uacb0\uc815 \ud2b8\ub9ac, \uc11c\ud3ec\ud2b8 \ubca1\ud130 \uba38\uc2e0(SVM), \uc2e0\uacbd\ub9dd \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3.2 \ud68c\uadc0 \ubd84\uc11d<\/h3>\n

\ud68c\uadc0 \ubd84\uc11d\uc740 \uc5f0\uc18d\uc801\uc778 \ubaa9\ud45c \ubcc0\uc218\ub97c \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \uc9c0\ub3c4\ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uc2dd\uc758 \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ubb38\uc81c\ub294 \ud68c\uadc0 \ubd84\uc11d\uc73c\ub85c \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0, \ub2e4\uc911 \ud68c\uadc0 \ub4f1\uc758 \uae30\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3.3 \ubd84\ub958 \ubd84\uc11d<\/h3>\n

\ubd84\ub958 \ubd84\uc11d\uc740 \uc8fc\uc5b4\uc9c4 \uc785\ub825 \ub370\uc774\ud130\uac00 \uc5b4\ub5a4 \uc885\ub958\uc5d0 \uc18d\ud558\ub294\uc9c0\ub97c \uc608\uce21\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uac00\uac00 \uc0c1\uc2b9\ud560\uc9c0 \ud558\ub77d\ud560\uc9c0\ub97c \uc608\uce21\ud558\ub294 \ubb38\uc81c\ub294 \ubd84\ub958 \ubb38\uc81c\ub85c \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0, \uc758\uc0ac \uacb0\uc815 \ub098\ubb34, k-\ucd5c\uadfc\uc811 \uc774\uc6c3(KNN) \ub4f1\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

4. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uad6c\ud604 \uc608\uc81c<\/h2>\n

4.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n

\uc2dc\uc791\ud558\uae30 \uc704\ud574 \uc6b0\ub9ac\ub294 \uae08\uc735 \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. Yahoo Finance API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd\uc758 \uc5ed\uc0ac\uc801 \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Python\uc758 Pandas \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ud504\ub808\uc784 \ud615\ud0dc\ub85c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\nimport pandas as pd\nimport yfinance as yf\n\n# \ud2b9\uc815 \uc8fc\uc2dd\uc758 \ub370\uc774\ud130 \ub2e4\uc6b4\ub85c\ub4dc\nticker = 'AAPL'\ndata = yf.download(ticker, start='2015-01-01', end='2022-12-31')\ndata.to_csv('aapl_data.csv')\n    <\/pre>\n
4.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ub2e4\uc6b4\ub85c\ub4dc\ud55c \ub370\uc774\ud130\uc5d0\ub294 \uacb0\uce21\uce58\uac00 \uc788\uc744 \uc218 \uc788\uc73c\ubbc0\ub85c, \uc774\ub97c \ucc98\ub9ac\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uacb0\uce21\uce58\ub97c \uc81c\uac70\ud558\uace0, \ud544\uc694\ud55c \ud2b9\uc131(feature)\ub9cc\uc744 \uc120\ud0dd\ud558\uc5ec \ubaa8\ub378\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata.dropna(inplace=True)\n\n# \ud544\uc694\ud55c \ud2b9\uc131 \uc120\ud0dd (Close \uac00\uaca9)\nfeatures = data[['Open', 'High', 'Low', 'Volume']]\ntarget = data['Close'].shift(-1)  # \ub2e4\uc74c \ub0a0\uc758 \uc885\uac00 \uc608\uce21\nfeatures = features[:-1]  # Shift\ub85c \uc778\ud574 \ub9c8\uc9c0\ub9c9 \ud589 \uc81c\uac70\ntarget = target[:-1]\n    <\/pre>\n
4.3 \ubaa8\ub378 \uc120\ud0dd \ubc0f \ud559\uc2b5<\/h3>\n
\uc5ec\uae30\uc11c\ub294 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest) \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub294 \uc5ec\ub7ec \uac1c\uc758 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uacb0\ud569\ud558\uc5ec \uc608\uce21 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. sklearn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\n\n# \ub370\uc774\ud130 \ub098\ub204\uae30\nX_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel = RandomForestRegressor(n_estimators=100)\nmodel.fit(X_train, y_train)\n    <\/pre>\n
4.4 \uc131\ub2a5 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc740 Mean Absolute Error(MAE)\ub098 R-squared(R\u00b2) \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud574 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud655\uc778\ud558\uace0, \ud544\uc694 \uc2dc \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.metrics import mean_absolute_error, r2_score\n\n# \uc608\uce21\npredictions = model.predict(X_test)\n\n# \uc131\ub2a5 \ud3c9\uac00\nmae = mean_absolute_error(y_test, predictions)\nr2 = r2_score(y_test, predictions)\nprint(f'Mean Absolute Error: {mae}')\nprint(f'R\u00b2 Score: {r2}')\n    <\/pre>\n
4.5 \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ubaa8\ub378\uc774 \uc131\uacf5\uc801\uc73c\ub85c \ud559\uc2b5\ub418\uc5c8\ub2e4\uba74, \uc774\uc81c \uac70\ub798 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud604\uc7ac\uac00\uac00 \ubaa8\ub378\uc758 \uc608\uce21\uac00\ubcf4\ub2e4 \ub192\uc73c\uba74 ‘\ub9e4\ub3c4’, \ub0ae\uc73c\uba74 ‘\ub9e4\uc218’ \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n# \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131\nsignal = []\nfor i in range(len(predictions)):\n    if predictions[i] > X_test.iloc[i]['Close']:\n        signal.append('Sell')\n    else:\n        signal.append('Buy')\n    \ndata['Signal'] = signal\n    <\/pre>\n
5. \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc811\uadfc<\/h2>\n
5.1 LSTM(Long Short-Term Memory) \ubaa8\ub378 \uc18c\uac1c<\/h3>\n
\ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \ubaa8\ub378 \uad6c\ucd95\uc73c\ub85c LSTM \ub124\ud2b8\uc6cc\ud06c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. LSTM\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)\uc758 \ud55c \uc885\ub958\ub85c, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0 \uc801\ud569\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uae08\uc735 \ub370\uc774\ud130\uc640 \uac19\uc740 \uc21c\ucc28\uc801 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 LSTM \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac (\ubc30\uc5f4\ud615\ud0dc\ub85c \ubcc0\ud658)\nX = np.array(features)\ny = np.array(target)\n\n# LSTM \uc785\ub825 \ud615\ud0dc\ub85c \ubcc0\ud658\nX = X.reshape((X.shape[0], X.shape[1], 1))\n\n# LSTM \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1))\n\nmodel.compile(optimizer='adam', loss='mean_squared_error')\nmodel.fit(X, y, epochs=50, batch_size=32)\n    <\/pre>\n
5.3 LSTM \uc131\ub2a5 \ud3c9\uac00 \ubc0f \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
LSTM \ubaa8\ub378 \ub610\ud55c \uc608\uce21 \uacb0\uacfc\ub97c \uae30\ubc18\uc73c\ub85c \uac70\ub798 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\uce21\ub41c \uac00\uaca9\uc774 \ud604\uc7ac \uac00\uaca9\ubcf4\ub2e4 \ub192\uc740 \uacbd\uc6b0 \ub9e4\ub3c4 \uc2e0\ud638\ub97c, \ub0ae\uc740 \uacbd\uc6b0 \ub9e4\uc218 \uc2e0\ud638\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n
6. \uc2e4\uc804 \ud22c\uc790\uc5d0\uc758 \uc801\uc6a9<\/h2>\n
6.1 \ubaa8\ub378\uc758 \uc870\uc815 \ubc0f \uac1c\uc120<\/h3>\n
\ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c \ud22c\uc790\uc5d0 \uc801\uc6a9\ud558\uae30 \uc804\uc5d0, \ub2e4\uc591\ud55c \uc870\uc815 \ubc0f \uac1c\uc120\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ucd94\uac00\uc801\uc778 \ud2b9\uc131\uc744 \ud3ec\ud568\ud558\uac70\ub098, \ub2e4\ub978 \uc54c\uace0\ub9ac\uc998\uacfc\uc758 \uc870\ud569\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.2 \ubc31\ud14c\uc2a4\ud2b8<\/h3>\n
\ubaa8\ub378\uc5d0 \ub300\ud55c \uc2e0\ub8b0\uc131\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ubc31\ud14c\uc2a4\ud305\uc744 \uc9c4\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub294 \uacfc\uac70\uc758 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \uc798 \uc791\ub3d9\ud588\ub294\uc9c0\ub97c \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac80\uc99d\ud558\uace0, \ub9ac\uc2a4\ud06c\ub97c \ucd5c\uc18c\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc758\uc0ac \uacb0\uc815\uc744 \uac00\ub2a5\ud558\uac8c \ud558\uba70, \ud22c\uc790\uc5d0\uc11c\uc758 \uc2b9\ub960\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubaa8\ub4e0 \ud22c\uc790\uc5d0\ub294 \ub9ac\uc2a4\ud06c\uac00 \ub530\ub974\ubbc0\ub85c, \ubaa8\ub378\uc758 \uacfc\uc801\ud569\uc744 \ud53c\ud558\uace0 \uc9c0\uc18d\uc801\uc778 \uac1c\uc120\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub05d\uc73c\ub85c, \uad6c\ud604\ud55c \ubaa8\ub378\uacfc \uc54c\uace0\ub9ac\uc998\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud558\uace0, \uc2dc\uc7a5\uc758 \ubcc0\ud654\uc5d0 \ub9de\ucdb0 \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \uac83\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\uc640 \uc2e4\uc804 \uc608\uc81c\ub97c \ud559\uc2b5\ud558\uc168\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \uc9c0\uc18d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\ub294 \ub370\uc774\ud130 \uae30\ubc18\uc758 \ud2b8\ub808\uc774\ub529 \uc138\uacc4\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \ud22c\uc790\uc790\uac00 \ub418\uc2e4 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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