{"id":28758,"date":"2024-10-27T16:14:55","date_gmt":"2024-10-27T16:14:55","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=28758"},"modified":"2024-11-26T06:58:05","modified_gmt":"2024-11-26T06:58:05","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-ta-lib%ec%97%90%ec%84%9c-%ea%b5%ac%ed%98%84","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/28758\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, TA-Lib\uc5d0\uc11c \uad6c\ud604\ub41c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c"},"content":{"rendered":"<p><body><\/p>\n<p>\n    \ud22c\uc790 \uc804\ub7b5\uc758 \uc131\uacf5\uc740 \ub9ce\uc740 \uc694\uc18c\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubd84\uc57c\uc5d0\uc11c \ud070 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uc774\ub860\uc744 \uc18c\uac1c\ud558\uace0, TA-Lib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h2>1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574<\/h2>\n<p>\n    \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub9ac \uc124\uc815\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \ubc29\uc2dd\uc744 \ub9d0\ud569\ub2c8\ub2e4. \uc774\ub294 \uc778\uac04\uc758 \uac10\uc815\uc801 \uacb0\uc815\uc744 \ubc30\uc81c\ud558\uace0, \ubcf4\ub2e4 \uc2e0\uc18d\ud558\uace0 \uc815\ud655\ud558\uac8c \uac70\ub798\ub97c \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h3>1.1 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\uc810<\/h3>\n<ul>\n<li><strong>\uc815\ud655\uc131:<\/strong> \uc54c\uace0\ub9ac\uc998\uc740 \uc778\uac04\uc758 \uc2ec\ub9ac\uc801 \uc694\uc18c\ub97c \ubc30\uc81c\ud558\uc5ec \uc624\ucc28\ub97c \uc904\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc18d\ub3c4:<\/strong> \uc218 \ucd08 \uc548\uc5d0 \uac70\ub798\ub97c \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubc31\ud14c\uc2a4\ud305:<\/strong> \uacfc\uac70 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574 \uc804\ub7b5\uc744 \uc2dc\ud5d8\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub2e4\uc591\uc131:<\/strong> \ub2e4\uc591\ud55c \uc790\uc0b0\uc5d0 \ub300\ud55c \uac70\ub798\uac00 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uac1c\ub150 \uc18c\uac1c<\/h2>\n<p>\n    \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130 \ud328\ud134\uc744 \ubd84\uc11d\ud558\uc5ec \uc608\uce21\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h3>2.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\n<p>\n    \uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0 \uc720\ud615\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4.\n<\/p>\n<ul>\n<li><strong>\uac10\ub3c5 \ud559\uc2b5(Supervised Learning):<\/strong> \uc785\ub825\uacfc \ucd9c\ub825 \ub370\uc774\ud130\uac00 \uc8fc\uc5b4\uc84c\uc744 \ub54c, \ud574\ub2f9 \uad00\uacc4\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ube44\uac10\ub3c5 \ud559\uc2b5(Unsupervised Learning):<\/strong> \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\uc5d0\uc11c \uc228\uaca8\uc9c4 \ud328\ud134\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uac15\ud654 \ud559\uc2b5(Reinforcement Learning):<\/strong> \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ubcf4\uc0c1\uc73c\ub85c \uc9c0\ub3c4\ub97c \ubc1b\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>2.2 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\n<p>\n    \ub525\ub7ec\ub2dd\uc758 \ud575\uc2ec\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc785\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uce35 \uad6c\uc870\ub97c \ud1b5\ud574 \uc785\ub825 \ub370\uc774\ud130\uc5d0\uc11c \uc911\uc694\ud55c \ud53c\ucc98\ub97c \uc790\ub3d9\uc73c\ub85c \ucd94\ucd9c\ud569\ub2c8\ub2e4.\n<\/p>\n<h2>3. TA-Lib \uc18c\uac1c<\/h2>\n<p>\n    TA-Lib\ub294 \uae30\uc220\uc801 \ubd84\uc11d\uc744 \uc704\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uc9c0\ud45c\uc640 \ud5e4\ud504\ub2dd \ud328\ud134\uc744 \uc81c\uacf5\ud558\uc5ec \ud2b8\ub808\uc774\ub354\ub4e4\uc774 \uc2dc\uc7a5\uc744 \ubd84\uc11d\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. Python\uc5d0\uc11c TA-Lib\ub97c \uc0ac\uc6a9\ud558\uba74 \uc190\uc27d\uac8c \ub2e4\uc591\ud55c \uae30\uc220\uc801 \uc9c0\ud45c\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h3>3.1 TA-Lib \uc124\uce58<\/h3>\n<pre><code>pip install TA-Lib<\/code><\/pre>\n<h3>3.2 TA-Lib\ub85c \uad6c\ud604\ud558\ub294 \uae30\ubcf8 \uc9c0\ud45c<\/h3>\n<p>\n    TA-Lib\ub294 \uc774\ub3d9\ud3c9\uade0, RSI, MACD \ub4f1 \ub2e4\uc591\ud55c \uae30\uc220\uc801 \uc9c0\ud45c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 TA-Lib\uc758 \uc774\ub3d9\ud3c9\uade0 \uacc4\uc0b0 \uc608\uc81c\uc785\ub2c8\ub2e4.\n<\/p>\n<pre><code>\nimport talib\nimport numpy as np\n\ndata = np.random.randn(100)  # \ub79c\ub364 \ub370\uc774\ud130 \uc0dd\uc131\nmoving_average = talib.SMA(data, timeperiod=10)  # 10\uc77c \uc774\ub3d9\ud3c9\uade0\n<\/code><\/pre>\n<h2>4. \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c\uc758 \uc774\ud574<\/h2>\n<p>\n    \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c(Common Factor Alpha)\ub294 \uc5ec\ub7ec \uc790\uc0b0\uc758 \uac00\uaca9 \ubcc0\ud654\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \ud2b9\uc815 \uc694\uc778\ub4e4\ub85c\ubd80\ud130 \uc0dd\uc131\ub418\ub294 \ucd08\uacfc \uc218\uc775\uc785\ub2c8\ub2e4. \uc774\ub294 \uc2dc\uc7a5\uc758 \uc5b4\ub5a4 \uc694\uc778\uc774 \uc790\uc0b0 \uc218\uc775\ub960\uc5d0 \uc601\ud5a5\uc744 \uc8fc\ub294\uc9c0\ub97c \ud30c\uc545\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n<\/p>\n<h3>4.1 \uc54c\ud30c \uc0dd\uc131\uc758 \uae30\ucd08<\/h3>\n<p>\n    \uc54c\ud30c \uc0dd\uc131\uc740 \ub2e4\uc591\ud55c \ubc29\ubc95\uc73c\ub85c \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uae30\uc220\uc801 \ubd84\uc11d, \uae30\ucd08\uc801 \ubd84\uc11d, \uadf8\ub9ac\uace0 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud65c\uc6a9\ud55c \uc811\uadfc \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h2>5. \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c \uc0dd\uc131 \uc0ac\ub840<\/h2>\n<p>\n    \uc774\uc81c \ubcf8\uaca9\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c \uc0dd\uc131 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \ud559\uc2b5, \uc608\uce21\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.\n<\/p>\n<h3>5.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n<p>\n    \uc6b0\uc120, \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. Yahoo Finance API\ub098 Alpha Vantage API \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h3>5.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n<p>\n    \uacb0\uce21\uce58 \ucc98\ub9ac, \uc815\uaddc\ud654, \ud2b9\uc131 \uc120\ud0dd \ub4f1\uc758 \ubc29\uc2dd\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. Pandas\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \uc791\uc5c5\uc744 \uc27d\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h3>5.3 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n<p>\n    \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, Gradient Boosting, \uc2ec\uc9c0\uc5b4 LSTM\uacfc \uac19\uc740 \ub525\ub7ec\ub2dd \ubaa8\ub378\ub3c4 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<pre><code>\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\n\n# \uc0d8\ud50c \ub370\uc774\ud130\uc14b \uc0dd\uc131\nX = np.random.rand(1000, 10)  # 10\uac1c\uc758 \uc785\ub825 \ud2b9\uc131\ny = np.random.rand(1000)  # \uc608\uce21\ud560 \uc218\uc775\ub960\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\nmodel = RandomForestRegressor()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n<h3>5.4 \uc608\uce21\uacfc \uacb0\uacfc \ubd84\uc11d<\/h3>\n<p>\n    \ud559\uc2b5 \uc644\ub8cc \ud6c4 \ubaa8\ub378\uc5d0 \uae30\ubc18\ud55c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uacb0\uacfc\ub97c \ubd84\uc11d\ud569\ub2c8\ub2e4. \uc774\ud6c4, \uae30\uc874 \uc804\ub7b5\uacfc \ube44\uad50\ud558\uc5ec \uc131\ub2a5\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<h2>6. \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c \uc0dd\uc131 \uc0ac\ub840<\/h2>\n<p>\n    \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \uc778\uc2dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c, LSTM\uacfc \uac19\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud6a8\uacfc\uc801\uc73c\ub85c \uc54c\ud30c\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n<pre><code>\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\n\n# LSTM \ubaa8\ub378 \uc0dd\uc131\nmodel = Sequential()\nmodel.add(LSTM(50, input_shape=(X_train.shape[1], 1)))\nmodel.add(Dense(1))  # \ucd9c\ub825\uce35\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train.reshape(X_train.shape[0], X_train.shape[1], 1), y_train, epochs=50)\n<\/code><\/pre>\n<h3>6.1 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud3c9\uac00<\/h3>\n<p>\n    \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ud6c8\ub828\uacfc\uc815\uc5d0\uc11c \ub9ce\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud574\uc57c \ud558\uba70, \uacb0\uacfc \ubd84\uc11d \ub610\ud55c \ubcf5\uc7a1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ubaa8\ub378 \ud3c9\uac00 \ud6c4 \ud53c\ub4dc\ubc31\uc744 \ud1b5\ud574 \uc131\ub2a5\uc744 \uac1c\uc120\ud574\uc57c \ud569\ub2c8\ub2e4.\n<\/p>\n<h2>7. \uacb0\ub860<\/h2>\n<p>\n    \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c \uc0dd\uc131\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. TA-Lib\uacfc \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \uacb0\ud569\ud558\uc5ec \ub354 \uc815\uad50\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ubaa8\ub4e0 \ud22c\uc790\uc5d0\ub294 \uc704\ud5d8\uc774 \ub530\ub974\ubbc0\ub85c, \uc2e0\uc911\ud55c \uc811\uadfc\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n<\/p>\n<h2>8. \ucc38\uace0\uc790\ub8cc<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/\">Scikit-learn Documentation<\/a><\/li>\n<li><a href=\"https:\/\/keras.io\/\">Keras Documentation<\/a><\/li>\n<li><a href=\"https:\/\/www.talib.org\/\">TA-Lib Documentation<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud22c\uc790 \uc804\ub7b5\uc758 \uc131\uacf5\uc740 \ub9ce\uc740 \uc694\uc18c\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ucd5c\uadfc \uba87 \ub144\uac04 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubd84\uc57c\uc5d0\uc11c \ud070 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uc774\ub860\uc744 \uc18c\uac1c\ud558\uace0, TA-Lib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c\ub97c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub9ac \uc124\uc815\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \ubc29\uc2dd\uc744 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/28758\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, TA-Lib\uc5d0\uc11c \uad6c\ud604\ub41c \uacf5\ud1b5 \uc694\uc778 \uc54c\ud30c&#8221;<\/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":[29],"tags":[],"class_list":["post-28758","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - 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