\uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc810\ucc28 \uc911\uc694\ud574\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ube44\ub86f\ud55c \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc774 \uc5b4\ub5bb\uac8c \ud2b8\ub808\uc774\ub529\uc5d0 \uc751\uc6a9\ub420 \uc218 \uc788\ub294\uc9c0, \uadf8\ub9ac\uace0 \uadf8 \uc911\uc5d0\uc11c\ub3c4 \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc5ed\ud560\uacfc \uc911\uc694\uc131\uc744 \uc0c1\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd\uc740 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0, \uc774 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \uad6c\uc870\uc640 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f9\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc9c0\ub3c4\ud559\uc2b5<\/strong>, \ube44\uc9c0\ub3c4\ud559\uc2b5<\/strong>, \uac15\ud654\ud559\uc2b5<\/strong>\uc73c\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4.<\/p>\n \ub525\ub7ec\ub2dd\uc740 \ub2e4\uc218\uc758 \uce35\uc744 \uac00\uc9c4 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc8fc\ub85c \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)<\/strong> \ubc0f \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)<\/strong>\uc774 \ub300\ud45c\uc801\uc785\ub2c8\ub2e4.<\/p>\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc774 \uac70\ub798 \uc804\ub7b5\uc744 \uc790\ub3d9\uc73c\ub85c \uc2e4\ud589\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud2b8\ub808\uc774\ub529 \ubc29\uc2dd\uc740 \ube60\ub978 \uc18d\ub3c4\uc640 \ub192\uc740 \uc815\ud655\uc131\uc73c\ub85c \uc2dc\uc7a5 \uae30\ud68c\ub97c \ud3ec\ucc29\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uc218\uc775\uc131\uc744 \ub192\uc774\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uae30\uacc4\ud559\uc2b5 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ud2b8\ub808\uc774\ub529 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ub370\uc774\ud130\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc8fc\uac00 \ub370\uc774\ud130, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c, \uadf8\ub9ac\uace0 \uae08\uc735 \ub274\uc2a4\uc640 \uac19\uc740 \ube44\uc815\ud615 \ub370\uc774\ud130 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n \uba38\uc2e0\ub7ec\ub2dd\uc758 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998 \uc911\uc5d0\uc11c \uac00\uc7a5 \uc801\ud569\ud55c \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\uc5ec \ud559\uc2b5\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc5d0\ub294 \uc120\ud615 \ud68c\uadc0, \uacb0\uc815 \ud2b8\ub9ac, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, SVM \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ud1b5\uacc4\uc801 \ubc29\ubc95\ub860\uc5d0 \uae30\ubc18\ud558\uc5ec, \uc0ac\uc804 \ud655\ub960(prior)\uacfc \uc99d\uac70 \uae30\ubc18 \ud559\uc2b5\uc744 \ud1b5\ud574 \ud30c\ub77c\ubbf8\ud130\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ub294 \ubd88\ud655\uc2e4\uc131\uc774 \ub192\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n \ubca0\uc774\uc9c0\uc548 \ud68c\uadc0\ubd84\uc11d\uc740 \uc5ec\ub7ec \ubcc0\uc218\ub4e4 \uac04\uc758 \uad00\uacc4\ub97c \uc124\uba85\ud558\ub294 \ub370 \uc720\uc6a9\ud558\uba70, \ud68c\uadc0 \uacc4\uc218\uc5d0 \ub300\ud55c \uc0ac\uc804 \ubd84\ud3ec\ub97c \uc124\uc815\ud558\uace0 \uc774\ub97c \ub370\uc774\ud130\ub85c \uc5c5\ub370\uc774\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n \ubca0\uc774\uc9c0\uc548 \uc811\uadfc\ubc95\uc740 \ub2e4\uc591\ud55c \uae08\uc735 \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654 \ubc0f \ub9ac\uc2a4\ud06c \uad00\ub9ac\uc5d0\ub3c4 \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc790\uc0b0\uc758 \uc218\uc775\ub960\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 \ubca0\uc774\uc9c0\uc548 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \ucd5c\uc801\uc758 \uc790\uc0b0 \ubc30\ubd84\uc744 \uacb0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc790\uc0b0\uc758 \uae30\ub300 \uc218\uc775\ub960\uacfc \ubcc0\ub3d9\uc131\uc744 \uace0\ub824\ud558\uc5ec \ud3ec\ud2b8\ud3f4\ub9ac\uc624\uc758 \ub9ac\uc2a4\ud06c\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n \ubca0\uc774\uc9c0\uc548 \ucd5c\uc801\ud654\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc5d0 \uc720\uc6a9\ud558\uac8c \uc801\uc6a9\ub418\uba70, \uc774\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ubb34\uc791\uc704 \uc0d8\ud50c\ub9c1\ubcf4\ub2e4 \ub354 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac1c\uc694\uc640 \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc911\uc694\uc131\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \ud1b5\ud574 \uae08\uc735 \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0 \uc0c8\ub85c\uc6b4 \uae30\ud68c\ub97c \ud3ec\ucc29\ud560 \uc218 \uc788\uc73c\uba70, \ubca0\uc774\uc9c0\uc548 \uc811\uadfc\ubc95\uc740 \uc774\ub7ec\ud55c \uacfc\uc815\uc5d0\uc11c \ubd88\ud655\uc2e4\uc131\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \uad00\ub9ac\ud558\ub294 \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0 \uc9c1\uc811 \uc801\uc6a9\ud574\ubcf4\uae30\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4.<\/p>\n\n
1.2 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\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\uc758 \uc7a5\uc810<\/h3>\n
\n
2.2 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ub2e8\uc810<\/h3>\n
\n
3. \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5<\/h2>\n
3.1 \ud2b8\ub808\uc774\ub529 \ub370\uc774\ud130\uc758 \uc900\ube44<\/h3>\n
import pandas as pd\n\n# \uc8fc\uac00 \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\n# \uc6d0\ud558\ub294 \ud53c\ucc98 \uc0dd\uc131\ndata['Moving_Average'] = data['Close'].rolling(window=20).mean()<\/code><\/pre>\n3.2 \ubaa8\ub378 \uc120\uc815 \ubc0f \ud559\uc2b5<\/h3>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = data[['Feature1', 'Feature2', 'Moving_Average']]\ny = data['Target']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = RandomForestClassifier()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n4. \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc774\ud574<\/h2>\n
4.1 \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc7a5\uc810<\/h3>\n
\n
4.2 \ubca0\uc774\uc9c0\uc548 \ubaa8\ub378\uc758 \uc801\uc6a9<\/h3>\n
from sklearn.linear_model import BayesianRidge\n\nmodel = BayesianRidge()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n5. \ubca0\uc774\uc9c0\uc548 \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud1b5\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5<\/h2>\n
5.1 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654<\/h3>\n
import numpy as np\n\n# \uc790\uc0b0 \uc218\uc775\ub960 \uc608\uce21\nmu = np.array([expected_return_asset1, expected_return_asset2])\ncov_matrix = np.array([[var_asset1, cov_asset1_asset2], [cov_asset1_asset2, var_asset2]])\n<\/code><\/pre>\n5.2 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h3>\n
from skopt import BayesSearchCV\n\nopt = BayesSearchCV(model, search_space, n_iter=50)\nopt.fit(X_train, y_train)<\/code><\/pre>\n\uacb0\ub860<\/h2>\n