\n \uc8fc\uc2dd \uc2dc\uc7a5\uc740 \ubcf5\uc7a1\ud558\uace0 \ubcc0\ud654\ubb34\uc30d\ud55c \ud658\uacbd\uc785\ub2c8\ub2e4. \uc774 \ud658\uacbd\uc5d0\uc11c \uc131\uacf5\ud558\ub824\uba74 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uc815\ud655\ud55c \uc608\uce21\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc0c8\ub85c\uc6b4 \uacbd\uc9c0\ub97c \uc5f4\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95, \uadf8\ub9ac\uace0 \ud310\ub2e4\uc2a4\uc640 \ub118\ud30c\uc774\ub97c \uc0ac\uc6a9\ud55c \ud329\ud130 \uacf5\ud559\uc758 \uae30\ucd08\ub97c \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780 \uc815\ud574\uc9c4 \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc218\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc740 \uc0ac\ub78c\uc758 \uac10\uc815\uc774\ub098 \uc8fc\uad00\uc774 \uac1c\uc785\ub418\uc9c0 \uc54a\uc73c\uba70, \ud2b9\uc815\ud55c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ub2e4\uc591\ud55c \ubc29\uc2dd \uc911\uc5d0\uc11c\ub3c4 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc811\uadfc\ubc95\uc774 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubc0f \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \ub9d0\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc774 \ub450 \uae30\uc220\uc740 \uc8fc\uc2dd \uc2dc\uc7a5 \ub370\uc774\ud130 \uc608\uce21\uc5d0 \uc788\uc5b4 \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uc558\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \ub525\ub7ec\ub2dd\uc740 \ub2e4\uce35\uc758 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud070 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \ud2b9\uc9d5\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ud2b9\ud788 \uc774\ubbf8\uc9c0, \ud14d\uc2a4\ud2b8, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\ub85c\ub294 CNN, RNN, LSTM \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \ud329\ud130 \uacf5\ud559\uc740 \uc790\uc0b0\uc758 \ubbf8\ub798 \uc218\uc775\ub960\uc744 \uacb0\uc815\ud558\ub294 \ub2e4\uc591\ud55c \uc778\uc790(factor)\ub97c \ubd84\uc11d\ud558\uace0 \ud65c\uc6a9\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \uc8fc\uc2dd \uc2dc\uc7a5\uc5d0\uc11c \uc720\uc6a9\ud55c \ud328\ud134\uc744 \ubc1c\uacac\ud558\uace0 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ud329\ud130\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \uac00\uaca9, \uac70\ub798\ub7c9, \uc7ac\ubb34 \uc9c0\ud45c \ub4f1\uc73c\ub85c \uc81c\uc791\ub429\ub2c8\ub2e4.\n <\/p>\n
\n \uc8fc\uc2dd \uc2dc\uc7a5 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0\ub294 \ud310\ub2e4\uc2a4(Pandas)\uc640 \ub118\ud30c\uc774(Numpy)\uac00 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ud310\ub2e4\uc2a4\ub294 \ub370\uc774\ud130 \uc870\uc791\uacfc \ubd84\uc11d\uc744 \uc704\ud55c \ud30c\uc774\uc36c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc774\uba70, \ub118\ud30c\uc774\ub294 \uace0\uc131\ub2a5 \uc218\uce58 \uacc4\uc0b0\uc744 \uc704\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \n pip install pandas\n <\/code>\n <\/pre>\n\n \ud310\ub2e4\uc2a4\uc758 \uc8fc\uc694 \ub370\uc774\ud130 \uad6c\uc870\ub294 \ub370\uc774\ud130\ud504\ub808\uc784(DataFrame)\uc774\uba70, \uc774\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc190\uc27d\uac8c \ubd84\uc11d\ud558\uace0 \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \uc0dd\uc131\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \nimport pandas as pd\n\n# \ub370\uc774\ud130\ud504\ub808\uc784 \uc0dd\uc131\ndata = {'\uc885\ubaa9': ['A', 'B', 'C'], '\uac00\uaca9': [100, 200, 300]}\ndf = pd.DataFrame(data)\nprint(df)\n <\/code>\n <\/pre>\n4.2 \ub118\ud30c\uc774 \uc124\uce58 \ubc0f \uae30\ubcf8 \uc0ac\uc6a9\ubc95<\/h3>\n\n \n pip install numpy\n <\/code>\n <\/pre>\n\n \ub118\ud30c\uc774\ub294 \ubc30\uc5f4\uc744 \ud6a8\uc728\uc801\uc73c\ub85c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \uc218\uce58 \uacc4\uc0b0\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc544\ub798\ub294 \ub118\ud30c\uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc30\uc5f4\uc744 \uc0dd\uc131\ud558\uace0 \uae30\ubcf8 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \nimport numpy as np\n\n# \ub118\ud30c\uc774 \ubc30\uc5f4 \uc0dd\uc131\narr = np.array([1, 2, 3, 4, 5])\nprint(arr.mean())\n <\/code>\n <\/pre>\n5. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95\ud558\uae30<\/h2>\n
\n \uc8fc\uc2dd \uc2dc\uc7a5 \uc608\uce21\uc744 \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uacfc\uc815\uc740 \ub370\uc774\ud130 \uc900\ube44, \ubaa8\ub378 \uc120\ud0dd, \ud559\uc2b5, \ud3c9\uac00\uc758 \ub2e8\uacc4\ub85c \ub098\ub204\uc5b4\uc9d1\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ud310\ub2e4\uc2a4\uc640 \ub118\ud30c\uc774\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uac00\uacf5\ud558\uace0, Scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
5.1 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
\n \uc8fc\uc2dd \ub370\uc774\ud130\ub294 Yahoo Finance, Alpha Vantage \ub4f1 \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uc744 \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ud310\ub2e4\uc2a4\ub97c \uc774\uc6a9\ud558\uc5ec CSV \ud30c\uc77c\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \ndf = pd.read_csv('stock_data.csv')\n <\/code>\n <\/pre>\n\n \ub370\uc774\ud130 \uc218\uc9d1 \ud6c4\uc5d0\ub294 \uacb0\uce21\uce58 \ucc98\ub9ac, \uc774\uc0c1\uce58 \uc81c\uac70 \ub4f1\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac \ub2e8\uacc4\uc5d0\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndf.fillna(method='ffill', inplace=True)\n\n# \uc774\uc0c1\uce58 \uc81c\uac70\ndf = df[df['\uac00\uaca9'] < 1000]\n <\/code>\n <\/pre>\n5.2 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uc120\ud0dd\ud558\uae30<\/h3>\n
\n \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ud6c4, \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc5d0\ub294 \ud68c\uadc0 \ubaa8\ub378, \ubd84\ub958 \ubaa8\ub378 \ub4f1 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 \uacb0\uc815 \ud2b8\ub9ac, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, \uc11c\ud3ec\ud2b8 \ubca1\ud130 \uba38\uc2e0(SVM) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
5.3 \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00<\/h3>\n
\n \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \ud3c9\uac00\ud558\uae30 \uc704\ud574 Scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uace0, \uc5ec\ub7ec \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud569\ub2c8\ub2e4.\n <\/p>\n
\n \nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\n\nX = df[['\ud2b9\uc9d51', '\ud2b9\uc9d52']] # \ud2b9\uc9d5 \ubcc0\uc218\ny = df['\ubaa9\ud45c\ubcc0\uc218'] # \ud0c0\uac9f \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 = RandomForestRegressor()\nmodel.fit(X_train, y_train)\npredictions = model.predict(X_test)\n\n# \ubaa8\ub378 \ud3c9\uac00\nmse = mean_squared_error(y_test, predictions)\nprint(f'MSE: {mse}')\n <\/code>\n <\/pre>\n6. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95\ud558\uae30<\/h2>\n
\n \ub525\ub7ec\ub2dd \ubc29\ubc95\uc744 \ud1b5\ud574 \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Keras \ub610\ub294 TensorFlow \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2ec\uce35 \uc2e0\uacbd\ub9dd\uc744 \uc27d\uac8c \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub3c4 \ub370\uc774\ud130 \uc900\ube44\uc640 \ubaa8\ub378 \uad6c\uc131\uc758 \ub2e8\uacc4\ub97c \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4.\n <\/p>\n
6.1 Keras \uc124\uce58 \ubc0f \ubaa8\ub378 \uad6c\ucd95<\/h3>\n\n \n pip install keras\n <\/code>\n <\/pre>\n\n \uc2e0\uacbd\ub9dd\uc744 \uad6c\uc131\ud558\uae30 \uc704\ud574 Keras\uc758 Sequential \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4.\n <\/p>\n
\n \nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\nmodel = Sequential()\nmodel.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(1, activation='linear'))\n\nmodel.compile(optimizer='adam', loss='mean_squared_error')\nmodel.fit(X_train, y_train, epochs=100, batch_size=32)\n <\/code>\n <\/pre>\n6.2 \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc608\uce21<\/h3>\n
\n \ub525\ub7ec\ub2dd \ubaa8\ub378 \ub610\ud55c \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc131\ub2a5\uc744 \uce21\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\uce21 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ub2e4\uc591\ud55c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc138\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \nloss = model.evaluate(X_test, y_test)\npredictions = model.predict(X_test)\nprint(f'Loss: {loss}')\n <\/code>\n <\/pre>\n7. \uc804\ub7b5 \uc2dc\ubbac\ub808\uc774\uc158 \ubc0f \uacb0\uacfc \ubd84\uc11d<\/h2>\n
\n \ucd5c\uc885\uc801\uc73c\ub85c \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \ubc14\ud0d5\uc73c\ub85c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc2dc\ubbac\ub808\uc774\uc158\ud558\uace0, \uadf8 \uacb0\uacfc\ub97c \ubd84\uc11d\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uc131\uacfc \uc9c0\ud45c\ub97c \uc218\uce58\ud654\ud558\uc5ec \ucd5c\uc801\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \ucc3e\uc544\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
7.1 \uc131\uacfc \uc9c0\ud45c<\/h3>\n\n- \uc0e4\ud504 \ube44\uc728(Sharpe Ratio): \uc704\ud5d8 \ub300\ube44 \uc218\uc775\ub960\uc744 \uce21\uc815\ud569\ub2c8\ub2e4.<\/li>\n
- Max Drawdown: \ucd5c\ub300 \uc190\uc2e4\uc744 \ucd94\uc801\ud569\ub2c8\ub2e4.<\/li>\n
- \uac70\ub798 \ube48\ub3c4: \ub9e4\ub9e4 \ube48\ub3c4\ub97c \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
7.2 \ubc31\ud14c\uc2a4\ud2b8 \uad6c\ud604<\/h3>\n
\n \uacfc\uac70 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \ud655\uc778\ud558\ub294 \uacfc\uc815\uc744 \ubc31\ud14c\uc2a4\ud2b8\ub77c\uace0 \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ub9e4\ub9e4 \uc804\ub7b5\uc774 \uc720\ud6a8\ud55c\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \n# \uac04\ub2e8\ud55c \ubc31\ud14c\uc2a4\ud2b8 \uad6c\uc870 \uc608\uc81c\ninitial_balance = 1000000\nbalance = initial_balance\n\nfor price in predictions:\n if price > threshold: # \ub9e4\uc218 \uc870\uac74\n balance -= price\n else: # \ub9e4\ub3c4 \uc870\uac74\n balance += price\n\nprint(f'Final Balance: {balance}')\n <\/code>\n <\/pre>\n\uacb0\ub860<\/h2>\n
\n \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc55e\uc73c\ub85c\uc758 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub354\uc6b1 \uc911\uc694\ud574\uc9c8 \uac83\uc785\ub2c8\ub2e4. \ud310\ub2e4\uc2a4\uc640 \ub118\ud30c\uc774\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \ubd84\uc11d \ubc29\ubc95\uc744 \uc2b5\ub4dd\ud558\uace0, \uc774\uc5d0 \uae30\ubc18\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uba74 \uc131\uacf5\uc801\uc778 \ud22c\uc790\uc5d0 \ud55c \ubc1c \ub354 \ub2e4\uac00\uc124 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub97c \ud1b5\ud574 \uc5bb\uc740 \uc9c0\uc2dd\uc744 \ubc14\ud0d5\uc73c\ub85c \uc790\uc2e0\ub9cc\uc758 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uad6c\ucd95\ud558\uace0 \uac80\uc99d\ud558\ub294 \uc990\uac70\uc6c0\uc744 \ub204\ub9ac\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n\n- Python for Data Analysis by Wes McKinney<\/li>\n
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aur\u00e9lien G\u00e9ron<\/li>\n
- Deep Learning for Finance by Jannes Klaas<\/li>\n<\/ul>\n