\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uacb0\uc815\uacfc\uc815\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud558\ub294\uc9c0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc778 OLS (Ordinary Least Squares)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc744 \uc704\ud55c \uae30\ucd08\uc801\uc778 \uc811\uadfc \ubc29\uc2dd\uc744 \uc81c\uc2dc\ud560 \uac83\uc785\ub2c8\ub2e4. \ub610\ud55c, Python\uc758 \uc0ac\uc774\ud0b7\ub7f0(Scikit-learn) \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec \uc2e4\uc6a9\uc801\uc778 \uc608\uc81c\ub97c \ud1b5\ud574 \uc774\ub860\uc744 \uad6c\uccb4\ud654\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \ucef4\ud4e8\ud130\uac00 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub3c4\ub85d \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 \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc774\ub7ec\ud55c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uae08\uc735 \uc790\uc0b0\uc744 \ub9e4\ub9e4\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/p>\n
\uc120\ud615 \ud68c\uadc0\ub294 \ub3c5\ub9bd \ubcc0\uc218\uc640 \uc885\uc18d \ubcc0\uc218\uc640\uc758 \uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. OLS\ub294 \uc794\ucc28 \uc81c\uacf1\ud569(Squared Errors)\uc744 \ucd5c\uc18c\ud654\ud568\uc73c\ub85c\uc368 \ud68c\uadc0\uc120\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/p>\n
OLS \ud68c\uadc0 \ubaa8\ub378\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ub429\ub2c8\ub2e4:<\/p>\n
Y = \u03b20 + \u03b21 * X1 + \u03b22 * X2 + ... + \u03b2n * Xn + \u03b5<\/code><\/pre>\n\uc5ec\uae30\uc11c, Y\ub294 \uc885\uc18d \ubcc0\uc218, X\ub294 \ub3c5\ub9bd \ubcc0\uc218, \u03b2\ub294 \ud68c\uadc0 \uacc4\uc218, \u03b5\ub294 \uc624\ucc28\ud56d\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/p>\n
2.2 OLS \ud68c\uadc0\uc758 \uac00\uc815<\/h3>\n\n- \uc120\ud615\uc131: \uc885\uc18d \ubcc0\uc218\uc640 \ub3c5\ub9bd \ubcc0\uc218 \uac04\uc758 \uc120\ud615 \uad00\uacc4.<\/li>\n
- \ub3c5\ub9bd\uc131: \ubaa8\ub4e0 \uc624\ucc28\ub294 \uc11c\ub85c \ub3c5\ub9bd\uc801\uc774\uc5b4\uc57c \ud568.<\/li>\n
- \uc815\uaddc\uc131: \uc624\ucc28\ub294 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub77c\uc57c \ud568.<\/li>\n
- \uc774\ubd84\uc0b0\uc131: \uc624\ucc28\uc758 \ubd84\uc0b0\uc774 \ubaa8\ub4e0 \ub3c5\ub9bd \ubcc0\uc218\uc5d0 \ub300\ud574 \uc77c\uc815\ud574\uc57c \ud568.<\/li>\n<\/ul>\n
3. \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc774\uc6a9\ud55c OLS \ud68c\uadc0 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc0ac\uc774\ud0b7\ub7f0\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc704\ud55c Python \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uacfc \ub3c4\uad6c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uc139\uc158\uc5d0\uc11c\ub294 pandas, NumPy\uc640 \ud568\uaed8 \uc0ac\uc774\ud0b7\ub7f0\uc744 \ud65c\uc6a9\ud558\uc5ec OLS \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\uae08\uc735 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 Pandas\ub97c \uc774\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\ndata = pd.read_csv('stock_data.csv')<\/code><\/pre>\n\uc704 \ucf54\ub4dc\ub294 ‘stock_data.csv’ \ud30c\uc77c\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc5d0\ub294 \ub0a0\uc9dc, \uac1c\uc7a5\uac00, \uace0\uac00, \uc800\uac00, \uc885\uac00, \uac70\ub798\ub7c9 \ub4f1\uc758 \uc815\ubcf4\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ubaa8\ub378\ub9c1\uc744 \uc704\ud574 \ud544\uc694\ud55c \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\uace0 \ubcc0\uc218\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/p>\n
data.fillna(method='ffill', inplace=True)\ndata['Returns'] = data['Close'].pct_change()<\/code><\/pre>\n\uc5ec\uae30\uc11c\ub294 \uacb0\uce21\uce58\ub97c \uc774\uc804 \uac12\uc73c\ub85c \ucc44\uc6b0\uace0, \uc885\uac00\uc758 \uc218\uc775\ub960\uc744 \uc0c8\ub85c\uc6b4 \uc5f4\ub85c \ucd94\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.3 \ud6c8\ub828\uacfc \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac<\/h3>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574 \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ub098\ub204\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = data[['Open', 'High', 'Low', 'Volume']]\ny = data['Close']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n3.4 OLS \ud68c\uadc0 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\uc0ac\uc774\ud0b7\ub7f0\uc758 LinearRegression \ud074\ub798\uc2a4\ub97c \uc774\uc6a9\ud558\uc5ec OLS \ud68c\uadc0 \ubaa8\ub378\uc744 \ud6c8\ub828\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.linear_model import LinearRegression\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n3.5 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc608\uce21\uac12\uacfc \uc2e4\uc81c\uac12\uc744 \ube44\uad50\ud558\uace0, R\u00b2 \uc810\uc218\ub97c \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import mean_squared_error, r2_score\n\ny_pred = model.predict(X_test)\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)<\/code><\/pre>\n\uc774\uc81c mse<\/code>\uc640 r2<\/code>\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n4. OLS \ud68c\uadc0\uc758 \ud55c\uacc4<\/h2>\n
OLS \ud68c\uadc0\ub294 \uac04\ub2e8\ud558\uac8c \uc774\ud574\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \ub2e4\uc74c\uacfc \uac19\uc740 \uba87 \uac00\uc9c0 \ud55c\uacc4\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4:<\/p>\n
\n- \ube44\uc120\ud615 \uad00\uacc4\ub97c \uc798 \uc124\uba85\ud558\uc9c0 \ubabb\ud568.<\/li>\n
- \uc0c1\uad00\uad00\uacc4\uc640 \uc778\uacfc\uad00\uacc4\ub97c \ud63c\ub3d9\ud560 \uc218 \uc788\uc74c.<\/li>\n
- \uc774\uc0c1\uce58\uc5d0 \ubbfc\uac10\ud568.<\/li>\n<\/ul>\n
5. \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ubc1c\uc804 \ubc29\ud5a5<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ubc1c\uc804 \ubc29\ud5a5\uc740 \ub9e4\uc6b0 \ub2e4\uc591\ud569\ub2c8\ub2e4. OLS \ud68c\uadc0 \ubaa8\ub378 \uc678\uc5d0\ub3c4 \ubcf5\uc7a1\ud55c \uc2e0\uacbd\ub9dd, \uc758\uc0ac\uacb0\uc815 \ub098\ubb34, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ub4f1 \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \uc608\uce21 \uc131\ub2a5\uc744 \ud655\ubcf4\ud558\ub294 \uac83\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \ubaa8\ub378 \ub2e4\uac01\ud654<\/h3>\n
\ub2e8\uc77c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ub300\uc2e0 \uc5ec\ub7ec \ubaa8\ub378\uc744 \uc870\ud569\ud558\uc5ec \uc0ac\uc6a9\ud558\ub294 \uc559\uc0c1\ube14 \uae30\ubc95\uc758 \ud65c\uc6a9\uc774 \uc99d\uac00\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc608\uce21\uc758 \uc815\ud655\uc131\uc744 \ub192\uc774\uae30 \uc704\ud55c \ubc29\ubc95 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.<\/p>\n
5.2 \uac15\ud654 \ud559\uc2b5\uc758 \uc801\uc6a9<\/h3>\n
\uac15\ud654 \ud559\uc2b5 \uae30\uc220\uc744 \ud1b5\ud574 \uc2dc\uc7a5\uc758 \ubcc0\ud654\uc5d0 \ub530\ub77c \ubaa8\ub378\uc774 \uc2a4\uc2a4\ub85c \ud559\uc2b5\ud558\uace0 \uc801\uc751\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \ubcc0\ub3d9\uc131\uc774 \ud070 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc720\uc6a9\ud560 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c\uae4c\uc9c0 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, OLS \ud68c\uadc0\uc758 \uae30\ucd08\uc640 \uc0ac\uc774\ud0b7\ub7f0\uc744 \uc774\uc6a9\ud55c \uc2e4\uc2b5 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub3c4 \uc774 \uae30\uc220\ub4e4\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \uc8fc\ub825\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\uc778\uacf5\uc9c0\ub2a5\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc740 \uae08\uc735 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ud070 \uc7a0\uc7ac\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. OLS \ud68c\uadc0 \ubaa8\ub378\uc744 \uc2dc\uc791\uc73c\ub85c, \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354\uc6b1 \uc815\uad50\ud55c \uac70\ub798 \uc804\ub7b5 \uc218\ub9bd\uc774 \uac00\ub2a5\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\ucd5c\uc885\uc801\uc73c\ub85c, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc131\uacf5\uc801\uc778 \uac70\ub798\ub294 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uc608\uce21\uc5d0 \ub2ec\ub824 \uc788\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud1b5\ud574 \ubcf4\ub2e4 \uccb4\uacc4\uc801\uc774\uace0 \uacfc\ud559\uc801\uc778 \uc811\uadfc \ubc29\uc2dd\uc744 \ucc44\ud0dd\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n