\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud1b5\ud574 \ud601\uc2e0\uc774 \uc77c\uc5b4\ub098\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\ub3d9\ud654\ub41c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc740 \ub354 \uc774\uc0c1 \ub2e8\uc21c\ud55c \uc54c\uace0\ub9ac\uc998\uc774\ub098 \uaddc\uce59\uc5d0 \uc758\uc874\ud558\uc9c0 \uc54a\uace0, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ub354 \uc815\uad50\ud55c \uc758\uc0ac \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc124\uacc4\uc640 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc8fc\uc694 \ubaa9\ud45c\ub294 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798\uc758 \uacb0\uacfc\ub97c \uc608\uce21\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc740 \uac00\uaca9 \uc608\uce21, \ub9ac\uc2a4\ud06c \uad00\ub9ac, \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654 \ub4f1 \ub2e4\uc591\ud55c \uc751\uc6a9 \ubd84\uc57c\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \ub370\uc774\ud130\uc5d0\uc11c \uace0\ucc28\uc6d0\uc801\uc778 \ud328\ud134\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP), \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \ubd84\uc11d \ub4f1\uc5d0\uc11c \uac15\ub825\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc758 \uacbd\uc6b0, \uac00\uaca9 \ubcc0\ud654\uc758 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \uac00\uaca9 \ud328\ud134\uc744 \uc2dd\ubcc4\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
\uc804\ud1b5\uc801\uc778 \ud2b8\ub808\uc774\ub529 \ubc29\uc2dd\uc740 \uc8fc\ub85c \uacbd\ud5d8\uacfc \uc9c1\uad00\uc5d0 \uc758\uc874\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub7ec\ud55c \ubc29\ubc95\uc740 \uc8fc\uad00\uc801\uc778 \uc694\uc18c\uac00 \ub9ce\uc544 \uc77c\uad00\ub41c \uacb0\uacfc\ub97c \ubcf4\uc7a5\ud558\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ubc18\uba74, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uba85\ud655\ud55c \uaddc\uce59\uacfc \ub370\uc774\ud130 \uae30\ubc18\uc758 \ubaa8\ub378\uc5d0 \uc758\ud574 \uacb0\uc815\ub418\uc5b4 \ubcf4\ub2e4 \uc77c\uad00\ub41c \uc131\uacfc\ub97c \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc778\uac04\uc758 \uac10\uc815\uc801 \uc694\uc18c\ub97c \ubc30\uc81c\ud558\uc5ec \ub354 \ud6a8\uc728\uc801\uc778 \uac70\ub798 \uc2e4\ud589\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
\uc55e\uc11c \uc5b8\uae09\ud55c \ubc14\uc640 \uac19\uc774, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \ub300\ud55c \uc131\ub2a5\uc740 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c, \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \uc120\ud0dd\ud558\uace0, \uc801\uc808\ud55c \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uce58\ub294 \uac83\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.<\/p>\n
\nimport pandas as pd\n\n# \uac00\uaca9 \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('market_data.csv')\n\n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata.fillna(method='bfill', inplace=True)\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\ndata['price'] = (data['price'] - data['price'].mean()) \/ data['price'].std()\n <\/code><\/pre>\n3.2 \ubaa8\ub378 \uc120\ud0dd<\/h3>\n
\ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc801\uc808\ud55c \uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc778 \ud68c\uadc0 \ubaa8\ub378 \ub610\ub294 \uc758\uc0ac \uacb0\uc815 \ud2b8\ub9ac\uc640 \uac19\uc740 \uc804\ud1b5\uc801\uc778 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ubd80\ud130, RNN(\uc7ac\uadc0\uc2e0\uacbd\ub9dd)\uc774\ub098 LSTM(\uc7a5\uae30\ub2e8\uae30\uae30\uc5b5)\uacfc \uac19\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\uae4c\uc9c0 \ub2e4\uc591\ud55c \uc120\ud0dd\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\ubaa8\ub378 \ud559\uc2b5 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \uac80\uc99d \uc138\ud2b8\ub85c \ub098\ub204\uace0, \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ucf1c\uc57c \ud569\ub2c8\ub2e4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654\ub294 \uc774 \ub2e8\uacc4\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc758 \uc774\ud574<\/h2>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ubaa8\ub378 \ud559\uc2b5 \uacfc\uc815\uc5d0\uc11c \uc0ac\uc804\uc5d0 \uc124\uc815\ud574\uc57c \ud558\ub294 \ubcc0\uc218\ub97c \ub9d0\ud569\ub2c8\ub2e4. \uc62c\ubc14\ub978 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc758 \uc870\uc815\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2e0\uacbd\ub9dd\uc758 \uce35 \uc218, \ud559\uc2b5\ub960, \ubc30\uce58 \ud06c\uae30 \ub4f1\uc774 \uc774\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.<\/p>\n
4.1 \uc8fc\uc694 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130<\/h3>\n\n- \ud559\uc2b5\ub960(Learning Rate)<\/strong>: \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \uc18d\ub3c4\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4. \ub108\ubb34 \ud06c\uba74 \ubc1c\uc0b0\ud560 \uc218 \uc788\uc73c\uba70, \ub108\ubb34 \uc791\uc73c\uba74 \ud559\uc2b5 \uc18d\ub3c4\uac00 \ub290\ub824\uc9d1\ub2c8\ub2e4.<\/li>\n
- \ubc30\uce58 \ud06c\uae30(Batch Size)<\/strong>: \ubbf8\ub2c8 \ubc30\uce58 \ud559\uc2b5 \uc2dc \ud55c \ubc88\uc5d0 \ucc98\ub9ac\ud558\ub294 \uc0d8\ud50c\uc758 \uc218\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \ud070 \ubc30\uce58 \ud06c\uae30\ub294 \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub192\uc774\uc9c0\ub9cc, \uba54\ubaa8\ub9ac \uc0ac\uc6a9\ub7c9\ub3c4 \uc99d\uac00\ud569\ub2c8\ub2e4.<\/li>\n
- \uc5d0\ud3ec\ud06c \uc218(Epochs)<\/strong>: \uc804\uccb4 \ub370\uc774\ud130\uc14b\uc744 \uba87 \ubc88 \ubc18\ubcf5\ud558\uc5ec \ud559\uc2b5\ud560\uc9c0\ub97c \uacb0\uc815\ud569\ub2c8\ub2e4. \ub108\ubb34 \ub9ce\uc73c\uba74 \uacfc\uc801\ud569(overfitting)\uc744 \uc720\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870(Neural Network Architecture)<\/strong>: \ub124\ud2b8\uc6cc\ud06c\uc758 \uce35 \uc218 \ubc0f \uac01 \uce35\uc758 \ub178\ub4dc \uc218 \ub4f1 \uad6c\uc870\uc801\uc778 \uc694\uc18c\ub4e4\uc744 \uacb0\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
5. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \uae30\ubc95<\/h2>\n5.1 \uadf8\ub9ac\ub4dc \ud0d0\uc0c9(Grid Search)<\/h3>\n
\uadf8\ub9ac\ub4dc \ud0d0\uc0c9\uc740 \uba87 \uac00\uc9c0 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc758 \uac12\uc744 \ubbf8\ub9ac \uc815\ud574\ub193\uace0, \uc870\ud569\uc744 \ubaa8\ub450 \uc2dc\ub3c4\ud574\ubcf4\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uac04\ub2e8\ud558\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc9c0\ub9cc, \uacbd\uc6b0\uc758 \uc218\uac00 \ub9ce\uc544\uc9c0\uba74 \uacc4\uc0b0 \uc2dc\uac04\uc774 \ub9ce\uc774 \uc18c\uc694\ub429\ub2c8\ub2e4.<\/p>\n
5.2 \ub79c\ub364 \ud0d0\uc0c9(Random Search)<\/h3>\n
\ub79c\ub364 \ud0d0\uc0c9\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uacf5\uac04\uc5d0\uc11c \ubb34\uc791\uc704\ub85c \uac12\ub4e4\uc744 \uc120\ud0dd\ud558\uc5ec \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uadf8\ub9ac\ub4dc \ud0d0\uc0c9\ubcf4\ub2e4 \ub354 \ube60\ub974\uace0 \ud6a8\uc728\uc801\uc73c\ub85c \ucd5c\uc801\ud654\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.3 \ubca0\uc774\uc988 \ucd5c\uc801\ud654(Bayesian Optimization)<\/h3>\n
\ubca0\uc774\uc988 \ucd5c\uc801\ud654\ub294 \uc774\uc804 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \uacb0\uacfc\ub97c \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc74c \uc81c\uc548\ud560 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uac12\uc744 \uc608\uce21\ud558\ub294 \uace0\uae09 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc740 \ud6a8\uc728\uc131\uc774 \ub192\uc544 \uc801\uc740 \ud3c9\uac00\ub85c\ub3c4 \ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.4 \uad50\ucc28 \uac80\uc99d(Cross Validation)<\/h3>\n
\ubaa8\ub378 \uc131\ub2a5\uc744 \uc815\ud655\ud558\uac8c \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uad50\ucc28 \uac80\uc99d \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc5ec\ub7ec \ubd80\ubd84\uc73c\ub85c \ub098\ub204\uace0, \uac01\uac01\uc758 \ubd80\ubd84\uc5d0\uc11c \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc77c\ubc18\ud654 \uc131\ub2a5\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \uc608\uc81c<\/h2>\n
\uc544\ub798\uc758 \uc608\uc2dc\ub294 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uadf8\ub9ac\ub4dc \ud0d0\uc0c9\uc744 \uc774\uc6a9\ud574 \ud29c\ub2dd\ud558\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import GridSearchCV\n\n# \ubaa8\ub378 \ubc0f \ud30c\ub77c\ubbf8\ud130 \uc815\uc758\nmodel = RandomForestRegressor()\nparam_grid = {\n 'n_estimators': [100, 200, 300],\n 'max_depth': [None, 10, 20],\n 'min_samples_split': [2, 5, 10]\n}\n\n# \uadf8\ub9ac\ub4dc \ud0d0\uc0c9\ngrid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\n# \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130 \ucd9c\ub825\nprint(grid_search.best_params_)\n <\/code><\/pre>\n7. \uacb0\uacfc \ubd84\uc11d \ubc0f \uc131\ub2a5 \uc9c0\ud45c<\/h2>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uc131\ub2a5 \uc9c0\ud45c\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd \uac70\ub798\uc5d0\uc11c\ub294 \uc8fc\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4:<\/p>\n
\n- \uc815\ud655\ub3c4(Accuracy)<\/strong>: \uc804\uccb4 \uc608\uce21 \uc911\uc5d0\uc11c \uc62c\ubc14\ub974\uac8c \uc608\uce21\ud55c \ube44\uc728\uc785\ub2c8\ub2e4.<\/li>\n
- F1 \uc810\uc218(F1 Score)<\/strong>: \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc758 \uc870\ud654 \ud3c9\uade0\uc73c\ub85c, \ubd88\uade0\ud615 \ub370\uc774\ud130\uc14b\uc5d0\uc11c\ub3c4 \uc720\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n
- \uc218\uc775\ub960(Return)<\/strong>: \ud22c\uc790\uc5d0 \ub300\ud55c \uc218\uc775 \ube44\uc728\uc785\ub2c8\ub2e4.<\/li>\n
- \uc0e4\ud504 \ube44\uc728(Sharpe Ratio)<\/strong>: \uc704\ud5d8 \ub300\ube44 \uc218\uc775\uc744 \ud3c9\uac00\ud558\ub294 \uc9c0\ud45c\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n
7.1 \uc0e4\ud504 \ube44\uc728 \uacc4\uc0b0<\/h3>\n
\uc544\ub798\uc640 \uac19\uc774 \uc0e4\ud504 \ube44\uc728\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\nimport numpy as np\n\ndef sharpe_ratio(returns, risk_free_rate=0.01):\n excess_returns = returns - risk_free_rate\n return np.mean(excess_returns) \/ np.std(excess_returns)\n\nreturns = np.random.normal(0.01, 0.02, 100) # \uc608\uc81c \uc218\uc775\ub960\nprint(\"Sharpe Ratio:\", sharpe_ratio(returns))\n <\/code><\/pre>\n8. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130\uc758 \ud798\uc744 \uc0b4\ub9ac\ub294 \uac15\ub825\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc8fc\uc758\ud560 \uc810\uc740, \ubaa8\ub378\uc758 \uc131\ub2a5\uc740 \ub370\uc774\ud130\uc640 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815\uc5d0 \ud06c\uac8c \uc758\uc874\ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \ucca0\uc800\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc640 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \uacfc\uc815\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 \ubaa8\ub378\uc744 \ucc3e\uc544\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\uc55e\uc73c\ub85c\ub3c4 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc5d0 \ub9de\ucdb0, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc0c8\ub85c\uc6b4 \uae30\ubc95\ub4e4\uc774 \ub4f1\uc7a5\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubcc0\ud654\uc5d0 \ubc1c\ub9de\ucd94\uc5b4, \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\uc640 \uacf5\ubd80\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n