\ud604\ub300 \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub354\uc6b1 \uc815\uad50\ud558\uace0 \ud6a8\uc728\uc801\uc778 \uac70\ub798 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 Gradient Boosting Machine(GBM) \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uae08\uc735 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \ub370\uc774\ud130(\uac00\uaca9, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1)\ub97c \ubd84\uc11d\ud558\uc5ec \ucd5c\uc801\uc758 \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \uc774\ub7ec\ud55c \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \ubaa8\ub378\ub9c1 \uae30\uc220\ub85c, \uc9c0\ub3c4\ud559\uc2b5(supervised learning), \ube44\uc9c0\ub3c4\ud559\uc2b5(unsupervised learning), \uc900\uc9c0\ub3c4\ud559\uc2b5(semi-supervised learning) \ub4f1 \ub2e4\uc591\ud55c \ubc29\uc2dd\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc18\uba74, \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc811\uadfc \ubc29\uc2dd\uc73c\ub85c, \uc77c\ubc18\uc801\uc73c\ub85c \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130(\uc608: \uc774\ubbf8\uc9c0, \uc790\uc5f0\uc5b4 \ucc98\ub9ac)\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uae08\uc735 \ub370\uc774\ud130\uc758 \uacbd\uc6b0, \ud6a8\uc728\uc801\uc778 \uc608\uce21\uc744 \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ub3c4 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
Gradient Boosting Machine(GBM)\uc740 \uacb0\uc815 \ud2b8\ub9ac(Decision Tree)\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc559\uc0c1\ube14 \ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. GBM\uc740 \uac01 \ud2b8\ub9ac\uac00 \uc774\uc804 \ud2b8\ub9ac\uc758 \uc624\ucc28\ub97c \ubcf4\uc815\ud558\uba70 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ub2e4\uc74c\uacfc \uac19\uc740 \uc7a5\uc810\uc744 \uac00\uc9d1\ub2c8\ub2e4:<\/p>\n
GBM\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uacfc\uc815\uc744 \ub530\ub985\ub2c8\ub2e4:<\/p>\n
GBM \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud574\ub2f9 \ubaa8\ub378\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud560 \uae08\uc735 \ub370\uc774\ud130\ub97c \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd\uc758 \uacbd\uc6b0, \uacfc\uac70 \uac00\uaca9 \ub370\uc774\ud130 \ubc0f \uad00\ub828 \uc9c0\ud45c\ub97c \uc218\uc9d1\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130\ub97c \uc900\ube44\ud569\ub2c8\ub2e4:<\/p>\n
\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc804\ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n
Python \uc5b8\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec GBM \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc8fc\uc694 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c\ub294 scikit-learn<\/strong>\uacfc XGBoost<\/strong>\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4:<\/p>\n \uc774\uc81c \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\uace0 GBM \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\ub294 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uc5ec \uc131\ub2a5\uc744 \ud655\uc778\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc815\ud655\ub3c4(Accuracy), \uc815\ubc00\ub3c4(Precision), \uc7ac\ud604\uc728(Recall) \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4:<\/p>\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ubaa8\ub378 \ud6c8\ub828 \uc804\uc5d0 \uc124\uc815\ud574\uc57c \ud558\ub294 \ub9e4\uac1c\ubcc0\uc218\ub85c, GBM\uc758 \uacbd\uc6b0 \ub2e4\uc74c\uacfc \uac19\uc740 \ud30c\ub77c\ubbf8\ud130\uac00 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n GridSearchCV\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ud0d0\uc0c9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n \ud6c8\ub828\ub41c GBM \ubaa8\ub378\uc744 \uc2e4\uc81c \uac70\ub798\uc5d0 \uc801\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\uc5d0 \ub530\ub77c \ub9e4\ub9e4 \uacb0\uc815\uc744 \ub0b4\ub824\uc57c \ud569\ub2c8\ub2e4. \uc8fc\ub41c \uc804\ub7b5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac80\uc99d\ud558\uae30 \uc704\ud574 \ubc31\ud14c\uc2a4\ud305\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uacfc\uac70 \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \ubaa8\ub378\uc774 \uc2e4\uc81c\ub85c \uc5b4\ub5a4 \uc131\uacfc\ub97c \ubcf4\uc600\ub294\uc9c0 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n GBM \ubaa8\ub378\uc740 \uba38\uc2e0\ub7ec\ub2dd \uc811\uadfc \ubc29\uc2dd\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 GBM \ubaa8\ub378\uc744 \ud6c8\ub828\ud558\uace0 \uc870\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uc600\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \uae08\uc735 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uc2e4\uc81c \uac70\ub798\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc138\uacc4\ub294 \ub04a\uc784\uc5c6\uc774 \ubcc0\ud654\ud558\uace0 \uc788\uc73c\uba70, \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc640 \uae30\uc220\uc744 \ubc30\uc6b0\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub098\uc544\uac00\uace0 \uc2f6\ub2e4\uba74 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc5f0\uad6c\ud558\uace0, \ubc31\ud14c\uc2a4\ud305 \uacbd\ud5d8\uc744 \uc313\uc73c\uba70 \uc9c0\uc18d\uc801\uc73c\ub85c \ud559\uc2b5\ud574\uc57c \ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\npip install numpy pandas scikit-learn xgboost<\/code><\/pre>\n4.1 GBM \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom xgboost import XGBClassifier\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('financial_data.csv')\n\n# \uc785\ub825 \ubcc0\uc218\uc640 \ud0c0\uac9f \ubcc0\uc218 \uc815\uc758\nX = data.drop(columns=['target'])\ny = data['target']\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# GBM \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud6c8\ub828\nmodel = XGBClassifier()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n4.2 \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
from sklearn.metrics import accuracy_score, classification_report\n\n# \uc608\uce21 \uc218\ud589\ny_pred = model.predict(X_test)\n\n# \ubaa8\ub378 \ud3c9\uac00\naccuracy = accuracy_score(y_test, y_pred)\nprint(f'Accuracy: {accuracy}')\nprint(classification_report(y_test, y_pred))\n<\/code><\/pre>\n5. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h2>\n
\n
5.1 GridSearchCV \ud65c\uc6a9<\/h3>\n
from sklearn.model_selection import GridSearchCV\n\nparam_grid = {\n 'learning_rate': [0.01, 0.1, 0.2],\n 'n_estimators': [100, 200],\n 'max_depth': [3, 5, 7]\n}\n\ngrid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring='accuracy', cv=3)\ngrid_search.fit(X_train, y_train)\n\nprint(\"Best parameters found: \", grid_search.best_params_)\n<\/code><\/pre>\n6. \uc2e4\uc81c \uac70\ub798\uc5d0 \uc751\uc6a9\ud558\uae30<\/h2>\n
\n
6.1 \ubc31\ud14c\uc2a4\ud305<\/h3>\n
def backtest(model, data):\n predictions = model.predict(data)\n returns = np.where(predictions == 1, data['close'].pct_change(), 0)\n cumulative_returns = (1 + returns).cumprod() - 1\n return cumulative_returns\n\ncumulative_returns = backtest(model, X_test)\nprint(cumulative_returns)\n<\/code><\/pre>\n7. \uacb0\ub860<\/h2>\n
\ucc38\uace0 \uc790\ub8cc<\/h2>\n