\uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ub9e4\ub9e4 \uc804\ub7b5 \uac1c\ubc1c\uc740 \ud22c\uc790\uc790\ub4e4\uc5d0\uac8c \ub9e4\uc6b0 \uc911\uc694\ud55c \uc601\uc5ed\uc785\ub2c8\ub2e4. \ud2b9\ud788 \uba38\uc2e0\ub7ec\ub2dd(Machine Learning) \ubc0f \ub525\ub7ec\ub2dd(Deep Learning) \uc54c\uace0\ub9ac\uc998\uc774 \ubc1c\uc804\ud558\uba74\uc11c, \ub370\uc774\ud130 \uae30\ubc18\uc758 \ud2b8\ub808\uc774\ub529 \uc811\uadfc \ubc29\uc2dd\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression) \ubd84\uc11d\uc744 \ud1b5\ud574 \uac00\uaca9 \uc6c0\uc9c1\uc784 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\ub294 \ucd08\ubcf4\uc790\ubd80\ud130 \uc804\ubb38\uac00\uae4c\uc9c0 \ubaa8\ub450 \uc774\ud574\ud560 \uc218 \uc788\ub3c4\ub85d \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \ub3c5\ub9bd \ubcc0\uc218\uc640 \uc885\uc18d \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud558\ub294 \ud1b5\uacc4\uc801 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc885\uc18d \ubcc0\uc218\uac00 \uc774\uc9c4\ud615(binary)\uc77c \ub54c \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uac00\uaca9\uc774 \uc624\ub97c\uc9c0 \ub0b4\ub9b4\uc9c0\ub97c \uc608\uce21\ud558\ub294 \ubb38\uc81c\uc5d0\uc11c\ub294 ‘\uac00\uaca9 \uc0c1\uc2b9(1)’\uacfc ‘\uac00\uaca9 \ud558\ub77d(0)’\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc120\ud615 \ud68c\uadc0\uc758 \ud655\uc7a5\uc73c\ub85c, \uc77c\ubc18\uc801\uc778 \uc120\ud615 \ubc29\uc815\uc2dd\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218(logistic function)\ub97c \uc801\uc6a9\ud558\uc5ec \ucd9c\ub825\uc744 \ud655\ub960\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\ud0dc\ub97c \uac00\uc9d1\ub2c8\ub2e4:<\/p>\n
h(x) = 1 \/ (1 + e^(-z)), z = \u03b20 + \u03b21*x1 + \u03b22*x2 + ... + \u03b2n*xn<\/code><\/pre>\n\uc5ec\uae30\uc11c \u03b2<\/code>\ub294 \ubaa8\ub378\uc758 \ud30c\ub77c\ubbf8\ud130, x<\/code>\ub294 \ub3c5\ub9bd \ubcc0\uc218, e<\/code>\ub294 \uc790\uc5f0\uc0c1\uc218\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218\ub294 0\uacfc 1 \uc0ac\uc774\uc758 \uac12\uc744 \ucd9c\ub825\ud558\uc5ec \ud074\ub798\uc2a4 \ud655\ub960\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n1.2 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc758 \ud2b9\uc9d5<\/h2>\n\n- \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc801\ud569\ud558\ub2e4.<\/li>\n
- \ucd9c\ub825\uac12\uc740 \ud655\ub960\ub85c \ud574\uc11d\ud560 \uc218 \uc788\ub2e4.<\/li>\n
- \uc120\ud615 \ud68c\uadc0\uc640 \ube44\uad50\ud574 \uacfc\uc801\ud569(overfitting)\uc5d0 \uac15\ud558\ub2e4.<\/li>\n
- \ud574\uc11d\uc774 \uc6a9\uc774\ud558\uace0 \uc9c1\uad00\uc801\uc774\ub2e4.<\/li>\n<\/ul>\n
2. \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uac00\uaca9 \uc608\uce21<\/h2>\n
\uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc608\uce21 \ubaa8\ub378\uc740 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \ub370\uc774\ud130\uac00 \uc120\ud615\uc801\uc73c\ub85c \uad6c\ubd84\ub420 \ub54c \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n
2.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub370\uc774\ud130 \uc218\uc9d1\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1 \ub2e4\uc591\ud55c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ubaa8\ub378\uc5d0 \uc801\ud569\ud558\ub3c4\ub85d \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac \uacfc\uc815\uc5d0\uc11c\ub294 \uacb0\uce21\uac12 \ucc98\ub9ac, \ubc94\uc8fc\ud615 \ubcc0\uc218 \uc778\ucf54\ub529, \ud53c\ucc98 \uc2a4\ucf00\uc77c\ub9c1 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Pandas \ud328\ud0a4\uc9c0\ub97c \uc774\uc6a9\ud558\uc5ec \uacb0\uce21\uac12\uc744 \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
import pandas as pd\n\ndata = pd.read_csv('stock_data.csv')\ndata.fillna(method='ffill', inplace=True)<\/code><\/pre>\n2.3 \ud53c\ucc98 \uc120\ud0dd \ubc0f \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1<\/h3>\n
\uc608\uce21\ud560 \uc885\uc18d \ubcc0\uc218\uc640 \uadf8\uc640 \uad00\ub828\ub41c \ub3c5\ub9bd \ubcc0\uc218\ub97c \uc120\uc815\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uae30\uc220\uc801 \uc9c0\ud45c\uc640 \uac19\uc740 \ucd94\uac00\uc801\uc778 \ud53c\ucc98\ub97c \uc0dd\uc131\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ub3d9 \ud3c9\uade0(Moving Averages), \uc0c1\ub300 \uac15\ub3c4 \uc9c0\uc218(Relative Strength Index) \ub4f1\uc744 \ud53c\ucc98\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.4 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 \ub370\uc774\ud130\ub97c \ud559\uc2b5 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 70%\uc758 \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc5d0 \uc0ac\uc6a9\ud558\uace0, 30%\ub294 \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00\ub97c \uc704\ud574 reserved \ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = data[['feature1', 'feature2', ...]]\ny = data['target']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/code><\/pre>\n\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4:<\/p>\n
from sklearn.linear_model import LogisticRegression\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n3. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc815\ud655\ub3c4(Accuracy), \uc815\ubc00\ub3c4(Precision), \uc7ac\ud604\uc728(Recall), F1 Score \ub4f1\uc774 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import classification_report, confusion_matrix\n\ny_pred = model.predict(X_test)\nprint(classification_report(y_test, y_pred))<\/code><\/pre>\n3.1 \ud63c\ub3d9 \ud589\ub82c(Confusion Matrix)<\/h3>\n
\ud63c\ub3d9 \ud589\ub82c\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \uc9c1\uad00\uc801\uc73c\ub85c \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc798\ubabb\ub41c \uc608\uce21\uc744 \ud55c \uacbd\uc6b0\uc640 \uc62c\ubc14\ub978 \uc608\uce21\uc744 \ud55c \uacbd\uc6b0\ub97c \uad6c\ubd84\ud558\uc5ec \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4:<\/p>\n
import matplotlib.pyplot as plt\nimport seaborn as sns\n\nconf_matrix = confusion_matrix(y_test, y_pred)\nsns.heatmap(conf_matrix, annot=True, fmt='d')\nplt.xlabel('Predicted')\nplt.ylabel('Actual')\nplt.title('Confusion Matrix')\nplt.show()<\/code><\/pre>\n4. \uacfc\uc801\ud569 \ubc29\uc9c0<\/h2>\n
\ubaa8\ub378\uc774 \ud6c8\ub828 \ub370\uc774\ud130\uc5d0 \uacfc\uc801\ud569\ub420 \uacbd\uc6b0, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0\uc11c \uc131\ub2a5\uc774 \uc800\ud558\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. K-\uacb9 \uad50\ucc28\uac80\uc99d(K-Fold Cross Validation)\uc744 \ud1b5\ud574 \uc774\ub97c \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import cross_val_score\n\nscores = cross_val_score(model, X, y, cv=5)\nprint('Cross-Validation Scores:', scores)<\/code><\/pre>\n5. \uc804\ub7b5 \uad6c\ucd95<\/h2>\n
\uc774\uc81c \uc608\uce21 \ubaa8\ub378\uc774 \uc900\ube44\ub418\uc5c8\uc73c\ubbc0\ub85c, \uc774\ub97c \uc2e4\uc804 \ub9e4\ub9e4 \uc804\ub7b5\uc73c\ub85c \uc804\ud658\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd\uc758 \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ub85c\uc9c1\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \ud655\ub960 \ucd9c\ub825\uc744 \uae30\ubc18\uc73c\ub85c \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ubaa8\ub378\uc774 0.5 \uc774\uc0c1\uc758 \ud655\ub960\ub85c \uac00\uaca9 \uc0c1\uc2b9\uc744 \uc608\uce21\ud560 \uacbd\uc6b0 \ub9e4\uc218 \uc2e0\ud638\ub97c, \ubc18\ub300\uc758 \uacbd\uc6b0 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \ubc1c\uc0dd\uc2dc\ud0b5\ub2c8\ub2e4:<\/p>\n
probabilities = model.predict_proba(X_test)[:, 1]\nsignals = (probabilities >= 0.5).astype(int)<\/code><\/pre>\n6. \uc2e4\uc804 \uc801\uc6a9 \ubc0f \uc131\uacfc \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc744 \uc2e4\uc804 \ub9e4\ub9e4\uc5d0 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \uc9c0\uc18d\uc801\uc73c\ub85c \uc804\ub7b5\uc744 \ud3c9\uac00\ud558\uace0 \uc870\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uc131\uacfc\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\uace0, \uac01 \uac70\ub798\uc5d0 \ub300\ud55c \uc190\uc775\uc744 \uae30\ub85d\ud569\ub2c8\ub2e4.<\/p>\n
\uc131\uacfc \ucd94\uc801\uc744 \uc704\ud55c \uc131\uacfc \uc9c0\ud45c\ub85c\ub294 \ub204\uc801 \uc218\uc775\ub960(Cumulative Return), \ucd5c\ub300 \ub099\ud3ed(Max Drawdown), \uc0e4\ud504 \ube44\uc728(Sharpe Ratio) \ub4f1\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import numpy as np\n\ndef calculate_cumulative_return(prices):\n return (prices[-1] - prices[0]) \/ prices[0]\n\ncumulative_return = calculate_cumulative_return(prices)\nprint('Cumulative Return:', cumulative_return)<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubd84\uc11d\uc744 \ud65c\uc6a9\ud55c \uac00\uaca9 \uc6c0\uc9c1\uc784 \uc608\uce21 \ubc0f \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ub97c \ub2e4\ub918\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud1b5\ud574 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ud22c\uc790 \uc804\ub7b5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \ubaa8\ub378 \uac1c\uc120\uc744 \ud1b5\ud574 \ub354\uc6b1 \ub098\uc740 \uc131\uacfc\ub97c \uae30\ub300\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
8. \ucc38\uace0 \ubb38\ud5cc<\/h2>\n\n- \uc774\uc6a9, “\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc774\ud574”, \ub370\uc774\ud130\uc0ac\uc774\uc5b8\uc2a4 \ucd9c\ud310\uc0ac.<\/li>\n
- \uc2a4\ud2f0\ube10\uacfc \uc5d0\ub450\uc544\ub974\ub3c4, “\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc5d0 \ub300\ud55c \uc2ec\uce35 \ubd84\uc11d”, \ud1b5\uacc4\ud559\ud68c \uc800\ub110, 2021.<\/li>\n
- \ud30c\uc774\uc36c \uba38\uc2e0\ub7ec\ub2dd, “\uc0ac\ub840 \uc5f0\uad6c”, O’Reilly Media, 2018.<\/li>\n<\/ul>\n
9. \ucd94\uac00 \uc790\ub8cc<\/h2>\n
\uc774 \uac15\uc88c\uc5d0 \ub300\ud55c \ud53c\ub4dc\ubc31\uc774\ub098 \uc9c8\ubb38\uc774 \uc788\uc73c\uc2dc\uba74 \ub313\uae00\ub85c \ub0a8\uaca8\uc8fc\uc2ed\uc2dc\uc624. \ucd94\uac00\uc801\uc778 \uc790\ub8cc \uc694\uccad\uc774\ub098 \ud2b9\uc815 \uc8fc\uc81c\uc5d0 \ub300\ud55c \uc124\uba85\uc744 \uc6d0\ud558\uc2dc\uba74 \uae30\uaebc\uc774 \ub3c4\uc640\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc990\uac70\uc6b4 \ud2b8\ub808\uc774\ub529 \ub418\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"