\ucd5c\uadfc \uba87 \ub144 \uc0ac\uc774\uc5d0 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ud65c\uc6a9\uc774 \uae09\uaca9\ud788 \uc99d\uac00\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \uac70\ub798\ub294 \uc774\uc81c \ub2e8\uc21c\ud55c \uae30\uc220\uc801 \ubd84\uc11d\uc744 \ub118\uc5b4\uc11c \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ud328\ud134\uc744 \uc2dd\ubcc4\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \uc801\uc6a9\ud558\ub294 \ud615\ud0dc\ub85c \uc9c4\ud654\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \ud65c\uc6a9\ud55c \ud329\ud130 \uacb0\ud569 \uae30\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc138\ubc00\ud55c \uac70\ub798 \uc804\ub7b5\uc744 \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc73c\ub85c \uad6c\ud604\ud558\uc5ec \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc218\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uba70, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \uc801\uc6a9\ub418\uc5b4 \ub354 \ub098\uc740 \uc608\uce21\uacfc \uc758\uc0ac\uacb0\uc815\uc744 \uc774\ub04c\uc5b4\ub0c5\ub2c8\ub2e4.<\/p>\n
\uacfc\uac70\uc5d0\ub294 \uac70\ub798\uc790\ub4e4\uc774 \uc9c1\uc811 \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub838\uc9c0\ub9cc, \ub370\uc774\ud130\uc758 \uc591\uc774 \ubc29\ub300\ud574\uc9d0\uc5d0 \ub530\ub77c \uc54c\uace0\ub9ac\uc998 \uac70\ub798\uac00 \ub300\ub450\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc8fc\uc2dd, \uc678\ud658, \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uac70\ub798 \uc54c\uace0\ub9ac\uc998\uc774 \ub9ce\uc740 \uc131\uacfc\ub97c \uc62c\ub9ac\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud574 \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uad6c\uc870\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \uc5ec\ub7ec \uac1c\uc758 \uce35(layer)\uc73c\ub85c \uad6c\uc131\ub41c \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud569\ub2c8\ub2e4. \uac01 \uce35\uc740 \ub178\ub4dc(node)\ub85c \uad6c\uc131\ub418\uba70, \uac70\uccd0\uac00\ub294 \ub370\uc774\ud130\uc758 \ud2b9\uc131\uc744 \ubcc0\ud615\ud558\uc5ec \ucd5c\uc885 \ucd9c\ub825 \uacb0\uacfc\ub97c \ub3c4\ucd9c\ud569\ub2c8\ub2e4.<\/p>\n
\uc131\uacf5\uc801\uc778 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \uc774\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \uae08\uc735 \ub370\uc774\ud130 \uc678\uc5d0\ub3c4 \ub274\uc2a4, \uc18c\uc15c \ubbf8\ub514\uc5b4 \ub370\uc774\ud130, \uacbd\uc81c \uc9c0\ud45c \ub4f1 \uc5ec\ub7ec \uc694\uc18c\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\ud329\ud130 \uacb0\ud569\uc740 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\uc5d0\uc11c \ub3c4\ucd9c\ud55c \uc5ec\ub7ec \uc9c0\ud45c(factor)\ub97c \ud1b5\ud569\ud558\uc5ec \uac70\ub798 \uc804\ub7b5\uc744 \ud5a5\uc0c1 \uc2dc\ud0a4\uae30 \uc704\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4. \uac01 \ud329\ud130\ub294 \uc2dc\uc7a5\uc758 \ud2b9\uc815 \ubd80\ubd84\uc744 \uc124\uba85\ud558\uba70, \uc774\ub4e4\uc744 \uc870\ud569\ud558\uc5ec \ub354 \uacac\uace0\ud55c \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c \uc2e4\uc81c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc801\ud569\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc120\ud0dd\ud558\uace0, \ud559\uc2b5\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \ucd5c\uc801\ud654\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \ub370\uc774\ud130\ub294 \ubc18\ub4dc\uc2dc \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uacb0\uce21\uce58 \ucc98\ub9ac, \uc774\uc0c1\uce58 \uc81c\uac70, \uc815\uaddc\ud654 \ub4f1 \ub2e4\uc591\ud55c \ubc29\ubc95\uc73c\ub85c \ub370\uc774\ud130\ub97c \uc815\uc81c\ud569\ub2c8\ub2e4.<\/p>\n
import pandas as pd\ndata = pd.read_csv('financial_data.csv')\ndata.fillna(method='ffill', inplace=True)\ndata = (data - data.mean()) \/ data.std()\n<\/code><\/pre>\n4.2 \ubaa8\ub378 \uc120\ud0dd\uacfc \ud559\uc2b5<\/h3>\n
\ubaa8\ub378\uc744 \uc120\ud0dd\ud55c \ud6c4, \ud559\uc2b5 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud574 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uacfc \uad50\ucc28 \uac80\uc99d\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\n\nX = data.drop('target', axis=1)\ny = data['target']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\nmodel = RandomForestClassifier()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n5. \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131<\/h2>\n
\ud6a8\uacfc\uc801\uc778 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc740 \ub2e8\uc77c \uc790\uc0b0\uc5d0 \uad6d\ud55c\ub418\uc9c0 \uc54a\uace0, \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. \uac01 \ud329\ud130\uac00 \uc5b4\ub5bb\uac8c \uc0c1\ud638\uc791\uc6a9\ud558\ub294\uc9c0\ub97c \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654 \uae30\ubc95<\/h3>\n
\uc704\ud5d8\uacfc \uc218\uc775\uc758 \uade0\ud615\uc744 \ub9de\ucd94\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud3c9\uade0-\ubd84\uc0b0 \ucd5c\uc801\ud654\ub294 \ub300\ud45c\uc801\uc778 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
from scipy.optimize import minimize\n\ndef portfolio_variance(weights, cov_matrix):\n return np.dot(weights.T, np.dot(cov_matrix, weights))\n\nconstraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})\nbounds = tuple((0, 1) for asset in range(len(asset_names)))\n\nresult = minimize(portfolio_variance, initial_weights, args=(cov_matrix,),\n method='SLSQP', bounds=bounds, constraints=constraints)\n<\/code><\/pre>\n6. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac80\uc99d<\/h2>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \uac80\uc99d\ud558\ub294 \uacfc\uc815\uc740 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub2e4\uc591\ud55c \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \uc131\ub2a5 \ud3c9\uac00 \uc9c0\ud45c<\/h3>\n\n- \uc815\ud655\ub3c4 (Accuracy)<\/li>\n
- \uc815\ubc00\ub3c4 (Precision)<\/li>\n
- \uc7ac\ud604\uc728 (Recall)<\/li>\n
- F1 Score<\/li>\n
- Sharpe Ratio<\/li>\n<\/ul>\n
from sklearn.metrics import classification_report\n\ny_pred = model.predict(X_test)\nprint(classification_report(y_test, y_pred))\n<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \uc788\uc5b4 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \ud65c\uc6a9\ud55c \ud329\ud130 \uacb0\ud569\uc740 \uc131\uacf5\uc801\uc778 \uac70\ub798 \uc804\ub7b5\uc758 \ud575\uc2ec\uc785\ub2c8\ub2e4. \uc801\uc808\ud55c \ubaa8\ub378\uc758 \uad6c\ucd95, \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131, \uadf8\ub9ac\uace0 \uc131\ub2a5 \ud3c9\uac00\ub97c \ud1b5\ud574\ubcf4\ub2e4 \ub192\uc740 \uc218\uc775\ub960\uc744 \ubaa9\ud45c\ub85c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774 \uae00\uc5d0\uc11c \ub2e4\ub8ec \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \uc2e4\uc81c \uc0ac\ub840\ub97c \uc801\uc6a9\ud574 \ubcf4\uc2dc\uace0, \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub378\uc744 \uac1c\uc120\ud574 \ub098\uac00\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ud5a5\ud6c4 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ubc1c\uc804\uc5d0 \ub530\ub77c \ub354\uc6b1 \uc815\uad50\ud55c \uc804\ub7b5\uc774 \uc694\uad6c\ub420 \uac83\uc774\uba70, \uc774\ub7ec\ud55c \uae30\ubc95\uc744 \uc798 \ud65c\uc6a9\ud558\ub294 \uc0ac\ub78c\uc774 \ub9ce\uc740 \uc131\uacfc\ub97c \uc5bb\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"