{"id":28769,"date":"2024-10-27T16:14:58","date_gmt":"2024-10-27T16:14:58","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=28769"},"modified":"2024-11-26T06:57:57","modified_gmt":"2024-11-26T06:57:57","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-yfinance-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%a5%bc","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/28769\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, yfinance  \ub370\uc774\ud130\ub97c \uc57c\ud6c4! \ud30c\uc774\ub0b8\uc2a4\ub85c\ubd80\ud130 \uc2a4\ud06c\ub808\uc774\ud551"},"content":{"rendered":"<p><body><\/p>\n<p>\ud604\ub300\uc758 \uae08\uc735 \uc2dc\uc7a5\uc740 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc758\uc0ac \uacb0\uc815\uc5d0 \uc810\uc810 \ub354 \uc758\uc874\ud558\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uace0 \ucd5c\uc801\ud654\ud558\ub294\ub370 \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 <strong>yfinance<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc57c\ud6c4 \ud30c\uc774\ub0b8\uc2a4\ub85c\ubd80\ud130 \uae08\uc735 \ub370\uc774\ud130\ub97c \uc2a4\ud06c\ub808\uc774\ud551\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uacfc\uc815\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\uc758 \uc911\uc694\uc131<\/h2>\n<p>\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd \uac70\ub798, \uc635\uc158 \uac70\ub798 \ubc0f \uae30\ud0c0 \uae08\uc735 \uc0c1\ud488\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \ub2e4\uc74c\uacfc \uac19\uc740 \uc811\uadfc \ubc29\uc2dd\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc9c0\ub3c4 \ud559\uc2b5<\/strong>: \uacfc\uac70 \ub370\uc774\ud130\uc640 \uac00\uaca9 \ubcc0\ub3d9\uc744 \ud559\uc2b5\ud558\uc5ec \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ube44\uc9c0\ub3c4 \ud559\uc2b5<\/strong>: \ub370\uc774\ud130\ub97c \uad70\uc9d1\ud654\ud558\uac70\ub098 \ud328\ud134\uc744 \ubc1c\uacac\ud558\uc5ec \uc7a0\uc7ac\uc801\uc778 \uac70\ub798 \uae30\ud68c\ub97c \ud0d0\uc0c9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uac15\ud654 \ud559\uc2b5<\/strong>: \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ubcf4\uc0c1\uc744 \ud1b5\ud574 \uc804\ub7b5\uc744 \ucd5c\uc801\ud654\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. yfinance \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58 \ubc0f \uae30\ubcf8 \uc0ac\uc6a9\ubc95<\/h2>\n<p><code>yfinance<\/code>\ub294 \ud30c\uc774\uc36c\uc5d0\uc11c Yahoo Finance\uc758 \ub370\uc774\ud130\uc5d0 \uc27d\uac8c \uc811\uadfc\ud560 \uc218 \uc788\ub3c4\ub85d \ud574\uc8fc\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \ubc30\ub2f9\uae08 \ubc0f \uae30\ud0c0 \uc7ac\ubb34 \ub370\uc774\ud130\ub97c \uc190\uc27d\uac8c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>2.1 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n<pre><code>pip install yfinance<\/code><\/pre>\n<h3>2.2 \uae30\ubcf8 \ub370\uc774\ud130 \uac00\uc838\uc624\uae30<\/h3>\n<p>\uc774\uc81c yfinance\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae08\uc735 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \uae30\ubcf8\uc801\uc778 \ucf54\ub4dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>import yfinance as yf\n\n# \ud2f0\ucee4 \uc2ec\ubcfc\uc5d0 \ub530\ub77c \uc8fc\uc2dd \ub370\uc774\ud130 \ub2e4\uc6b4\ub85c\ub4dc\nticker = 'AAPL'\ndata = yf.download(ticker, start='2020-01-01', end='2023-01-01')\nprint(data.head())<\/code><\/pre>\n<h3>2.3 \ub370\uc774\ud130 \uc124\uba85<\/h3>\n<p>\uc704\uc758 \ucf54\ub4dc\ub294 Apple Inc. (AAPL)\uc758 2020\ub144 1\uc6d4 1\uc77c\ubd80\ud130 2023\ub144 1\uc6d4 1\uc77c\uae4c\uc9c0\uc758 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5f4\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>Open<\/strong>: \ucd08\uae30 \uac00\uaca9<\/li>\n<li><strong>High<\/strong>: \ucd5c\uace0 \uac00\uaca9<\/li>\n<li><strong>Low<\/strong>: \ucd5c\uc800 \uac00\uaca9<\/li>\n<li><strong>Close<\/strong>: \uc885\uac00<\/li>\n<li><strong>Adj Close<\/strong>: \uc870\uc815 \uc885\uac00<\/li>\n<li><strong>Volume<\/strong>: \uac70\ub798\ub7c9<\/li>\n<\/ul>\n<h2>3. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n<p>\ub370\uc774\ud130\ub97c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \ud22c\uc785\ud558\uae30 \uc804\uc5d0, \ud544\uc218\uc801\uc778 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \ud544\uc694\ud55c \uc5ec\ub7ec \ub2e8\uacc4\uc785\ub2c8\ub2e4:<\/p>\n<h3>3.1 \uacb0\uce21\uce58 \ucc98\ub9ac<\/h3>\n<p>\uacb0\uce21\uce58\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc800\ud558 \uc2dc\ud0ac \uc218 \uc788\uc73c\ubbc0\ub85c \uba3c\uc800 \uacb0\uce21\uce58\ub97c \ud655\uc778\ud558\uace0 \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<pre><code># \uacb0\uce21\uce58 \ud655\uc778\nprint(data.isnull().sum())\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata = data.dropna()<\/code><\/pre>\n<h3>3.2 \ud53c\ucc98 \uc0dd\uc131<\/h3>\n<p>\uac00\uaca9 \uc608\uce21\uc744 \uc704\ud55c \ucd94\uac00\uc801\uc778 \ud53c\ucc98\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ub3d9 \ud3c9\uade0\uc120\uc774\ub098 \ubcc0\ub3d9\uc131 \ub4f1\uc758 \uae30\uc220\uc801 \uc9c0\ud45c\ub97c \ud3ec\ud568\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>data['SMA_20'] = data['Close'].rolling(window=20).mean()\ndata['SMA_50'] = data['Close'].rolling(window=50).mean()<\/code><\/pre>\n<h3>3.3 \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8 \ubd84\ud560<\/h3>\n<p>\ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 80:20 \ube44\uc728\ub85c \ub098\ub204\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>from sklearn.model_selection import train_test_split\n\n# \ud2b9\uc131\uacfc \ub808\uc774\ube14 \uc815\uc758\nX = data[['SMA_20', 'SMA_50']]\ny = data['Close']\n\n# \ud6c8\ub828\uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8\uc138\ud2b8\ub85c \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n<h2>4. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uc120\ud0dd\uacfc \ud6c8\ub828<\/h2>\n<p>\uc774\uc81c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\uc815\ud558\uace0 \ud6c8\ub828\uc2dc\ud0ac \ucc28\ub840\uc785\ub2c8\ub2e4. \uc5ec\ub7ec \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\ub4e4\uc774 \uc788\uc73c\uba70, \uadf8 \uc911\uc5d0\uc11c <strong>\uc120\ud615 \ud68c\uadc0<\/strong> \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>4.1 \ubaa8\ub378 \uc120\ud0dd: \uc120\ud615 \ud68c\uadc0<\/h3>\n<pre><code>from sklearn.linear_model import LinearRegression\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = LinearRegression()\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(X_train, y_train)<\/code><\/pre>\n<h3>4.2 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n<p>\ud6c8\ub828\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc608\uce21\uac12\uc744 \ud655\uc778\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>from sklearn.metrics import mean_squared_error\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# MSE \uacc4\uc0b0\nmse = mean_squared_error(y_test, y_pred)\nprint(f'Mean Squared Error: {mse:.2f}')  # \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28 \ucd9c\ub825<\/code><\/pre>\n<h2>5. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n<p>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud560 \uc218 \uc788\uc5b4, \ud544\uc694\ud55c \uacbd\uc6b0 \ud2b9\ud788 \uc720\uc6a9\ud569\ub2c8\ub2e4. Keras\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5.1 Keras \uc124\uce58<\/h3>\n<pre><code>pip install tensorflow<\/code><\/pre>\n<h3>5.2 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc124\uacc4<\/h3>\n<p>\ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP) \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>from tensorflow import keras\nfrom tensorflow.keras import layers\n\n# \ubaa8\ub378 \uc815\uc758\nmodel = keras.Sequential([\n    layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n    layers.Dense(64, activation='relu'),\n    layers.Dense(1)\n])\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.1)<\/code><\/pre>\n<h2>6. \uacb0\uacfc \ubd84\uc11d \ubc0f \uc2dc\uac01\ud654<\/h2>\n<p>\ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ubd84\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. matplotlib \ub610\ub294 seaborn\uc744 \ud1b5\ud574 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>6.1 \uc608\uce21 \uac12\uacfc \uc2e4\uc81c \uac12\uc744 \ube44\uad50\ud55c \uc2dc\uac01\ud654<\/h3>\n<pre><code>import matplotlib.pyplot as plt\n\n# \uc2e4\uc81c \uac12\uacfc \uc608\uce21 \uac12 \uc2dc\uac01\ud654\nplt.figure(figsize=(14,7))\nplt.plot(y_test.index, y_test, color='blue', label='Actual Price')\nplt.plot(y_test.index, y_pred, color='red', label='Predicted Price')\nplt.title('Stock Price Prediction')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()<\/code><\/pre>\n<h2>7. \uacb0\ub860 \ubc0f \ud5a5\ud6c4 \ubc29\ud5a5<\/h2>\n<p>\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 <strong>yfinance<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc7ac\ubb34 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc744 \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc73c\uba70, \uc9c0\uc18d\uc801\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \ubaa8\ub378\uc744 \uc5c5\ub370\uc774\ud2b8\ud568\uc73c\ub85c\uc368 \ub354\uc6b1 \ud5a5\uc0c1\ub41c \uc131\ub2a5\uc744 \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>7.1 \ud559\uc2b5 \uacfc\uc81c<\/h3>\n<ul>\n<li>\ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc801\uc6a9\ud574 \ubcf4\uc138\uc694 (\uc608: \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, SVM \ub4f1).<\/li>\n<li>\ub2e4\uc591\ud55c \ud53c\ucc98\ub97c \ucd94\uac00\ud574 \ubaa8\ub378 \uc131\ub2a5\uc744 \ube44\uad50\ud574 \ubcf4\uc138\uc694.<\/li>\n<li>\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \ud574 \ubcf4\uc138\uc694.<\/li>\n<\/ul>\n<h3>7.2 \ucc38\uace0 \uc790\ub8cc<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.yahoofinanceapi.com\/\" target=\"_blank\" rel=\"noopener\">Yahoo Finance API<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/\" target=\"_blank\" rel=\"noopener\">Scikit-learn Documentation<\/a><\/li>\n<li><a href=\"https:\/\/keras.io\/\" target=\"_blank\" rel=\"noopener\">Keras Documentation<\/a><\/li>\n<\/ul>\n<p>\uc774\uc81c \uc5ec\ub7ec\ubd84\ub3c4 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ub97c \uc774\ud574\ud558\uace0, yfinance\ub97c \ud1b5\ud574 \ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uba70 \uc2e4\uc2b5\ud560 \uc900\ube44\uac00 \ub418\uc168\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub098\uc544\uac00 \ub2e4\uc591\ud55c \uace0\uae09 \uae30\ubc95\ub4e4\uc744 \ud0d0\uad6c\ud574\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud604\ub300\uc758 \uae08\uc735 \uc2dc\uc7a5\uc740 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc758\uc0ac \uacb0\uc815\uc5d0 \uc810\uc810 \ub354 \uc758\uc874\ud558\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uace0 \ucd5c\uc801\ud654\ud558\ub294\ub370 \ud601\uc2e0\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 yfinance \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec 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\uc2a4\ud06c\ub808\uc774\ud551&#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-28769","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, yfinance \ub370\uc774\ud130\ub97c \uc57c\ud6c4! \ud30c\uc774\ub0b8\uc2a4\ub85c\ubd80\ud130 \uc2a4\ud06c\ub808\uc774\ud551 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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