\n \uc624\ub298\ub0a0 \uae08\uc735 \uc2dc\uc7a5\uc740 \uc810\uc810 \ub354 \ubcf5\uc7a1\ud574\uc9c0\uace0 \uc788\uc73c\uba70, \ub530\ub77c\uc11c \ud22c\uc790 \uc804\ub7b5\ub3c4 \uc9c4\ud654\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uc778\uacf5\uc9c0\ub2a5(AI)\uc640 \uba38\uc2e0\ub7ec\ub2dd(ML)\uc758 \ubc1c\uc804\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubc0f \ub871\/\uc20f \uc804\ub7b5\uc744 \uad6c\ud604\ud558\ub294 \ub370 \uac15\ub825\ud55c \ub3c4\uad6c\uac00 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uc77c\ubcf8 \uc8fc\uc2dd \uc2dc\uc7a5\uc744 \ub300\uc0c1\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ub871\/\uc20f \uc2dc\uadf8\ub110 \uc0dd\uc131 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\n \ub871\/\uc20f \uc804\ub7b5\uc740 \ud22c\uc790\uc790\uac00 \ud2b9\uc815 \uc790\uc0b0\uc744 \ub9e4\uc218(\ub871)\ud558\uace0 \ub3d9\uc2dc\uc5d0 \ub2e4\ub978 \uc790\uc0b0\uc744 \ub9e4\ub3c4(\uc20f)\ud558\uc5ec \uc2dc\uc7a5\uc758 \ubcc0\ub3d9\uc131\uc744 \ud65c\uc6a9\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc804\ub7b5\uc740 \uc790\uc0b0 \uac00\uaca9\uc758 \uc0c1\ub300\uc801\uc778 \ubcc0\ud654\ub97c \ud1b5\ud574 \uc218\uc775\uc744 \ucc3d\ucd9c\ud558\ub294 \ub370 \ucd08\uc810\uc744 \ub9de\ucd94\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubcf8 \uc8fc\uc2dd \uc2dc\uc7a5\uc740 \uc218\ub9ce\uc740 \ud22c\uc790\uc790\uc640 \ud2b8\ub808\uc774\ub354\uac00 \ud65c\ub3d9\ud558\ub294 \uacf3\uc73c\ub85c, \uc774\ub7ec\ud55c \uc804\ub7b5\uc744 \ud14c\uc2a4\ud2b8\ud558\uace0 \uad6c\ud604\ud558\uae30\uc5d0 \ub9e4\uc6b0 \ub9e4\ub825\uc801\uc785\ub2c8\ub2e4.\n<\/p>\n
\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubc0f \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \ubc18\uba74 \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\uc640 \ub192\uc740 \uacc4\uc0b0 \uc131\ub2a5\uc744 \ud544\uc694\ub85c \ud558\uc9c0\ub9cc, \uadf8\ub9cc\ud07c \ub354 \uc815\uad50\ud55c \uc608\uce21\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n<\/p>\n
\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub824\uba74 \uba3c\uc800 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubcf8 \uc8fc\uc2dd \uc2dc\uc7a5\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \uc18c\uac1c\ud569\ub2c8\ub2e4.\n<\/p>\n
\n \uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \uacb0\uce21\uce58 \ucc98\ub9ac, \ub370\uc774\ud130 \uc815\uaddc\ud654, \ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ub4f1\uc758 \uc791\uc5c5\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \uc801\ud569\ud55c \ud615\ud0dc\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4.\n<\/p>\n
import pandas as pd\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('japan_stock_data.csv')\n\n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata = data.fillna(method='ffill')\n\n# \uc815\uaddc\ud654\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\ndata_scaled = scaler.fit_transform(data[['Close']])\n<\/code><\/pre>\n<\/div>\n3. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\n \uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, \uadf8\ub9ac\uace0 \uc9c0\uc6d0\ubca1\ud130 \uae30\uacc4(SVM)\uc640 \uac19\uc740 \ubc29\ubc95\uc744 \ud65c\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
3.1 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0<\/h3>\n
\n \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc801\ud569\ud55c \uac04\ub2e8\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc8fc\uc2dd\uc758 \uac00\uaca9 \uc0c1\uc2b9 \ub610\ub294 \ud558\ub77d \uc5ec\ubd80\ub97c \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\n\n# \ud53c\ucc98 \uc0dd\uc131\ndata['Returns'] = data['Close'].pct_change()\ndata['Signal'] = (data['Returns'] > 0).astype(int)\n\n# \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ub098\ub204\uae30\nX = data[['Close']]\ny = data['Signal']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n<\/div>\n3.2 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8<\/h3>\n
\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub294 \ub2e4\uc218\uc758 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc559\uc0c1\ube14\ud558\uc5ec \uc608\uce21 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ud2b9\ud788 \ube44\uc120\ud615 \uad00\uacc4\ub97c \uc798 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\nfrom sklearn.ensemble import RandomForestClassifier\n\n# \ubaa8\ub378 \ud6c8\ub828\nrf_model = RandomForestClassifier(n_estimators=100, random_state=42)\nrf_model.fit(X_train, y_train)\n<\/code><\/pre>\n<\/div>\n3.3 \uc9c0\uc6d0\ubca1\ud130 \uae30\uacc4(SVM)<\/h3>\n
\n \uc9c0\uc6d0\ubca1\ud130 \uae30\uacc4\ub294 \ud2b9\ud788 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc774\ub294 \ubd84\ub958 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub3c4 \uc801\uc808\ud788 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\nfrom sklearn.svm import SVC\n\n# \ubaa8\ub378 \ud6c8\ub828\nsvm_model = SVC(kernel='linear')\nsvm_model.fit(X_train, y_train)\n<\/code><\/pre>\n<\/div>\n4. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604<\/h2>\n
\n \ub525\ub7ec\ub2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\uc11c\ub294 TensorFlow\uc640 Keras\ub97c \ud65c\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc0dd\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n
4.1 Keras\ub97c \uc774\uc6a9\ud55c \uc2e0\uacbd\ub9dd \uad6c\ud604<\/h3>\n
\n Keras\ub294 \ud504\ub85c\ud1a0\ud0c0\uc785\uc744 \uc2e0\uc18d\ud558\uac8c \uc81c\uc791\ud560 \uc218 \uc788\ub294 \uace0\uc218\uc900\uc758 \ub525\ub7ec\ub2dd API\uc785\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uad6c\ud604 \ucf54\ub4dc\uc785\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\n# \ubaa8\ub378 \uad6c\uc131\nmodel = keras.Sequential([\n keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n keras.layers.Dense(64, activation='relu'),\n keras.layers.Dense(1, activation='sigmoid')\n])\n\n# \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n\n# \ud6c8\ub828\nmodel.fit(X_train, y_train, epochs=10, batch_size=32)\n<\/code><\/pre>\n<\/div>\n5. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\n \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uc5ec \uc131\ub2a5\uc744 \ud655\uc778\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \ud63c\ub3d9 \ud589\ub82c, \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728 \ub4f1\uc744 \ud65c\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc815\ub7c9\uc801\uc73c\ub85c \uce21\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\ny_pred_classes = (y_pred > 0.5).astype(int)\n\n# \uc131\ub2a5 \ud3c9\uac00\nprint(classification_report(y_test, y_pred_classes))\nprint(confusion_matrix(y_test, y_pred_classes))\n<\/code><\/pre>\n<\/div>\n6. \ub871\/\uc20f \uc2dc\uadf8\ub110 \uc0dd\uc131<\/h2>\n
\n \ucd5c\uc885\uc801\uc73c\ub85c \uc608\uce21\ub41c \uacb0\uacfc\ub97c \ud65c\uc6a9\ud558\uc5ec \ub871\/\uc20f \uc2dc\uadf8\ub110\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc608\uc0c1 \uc0c1\uc2b9\uc774 \uc788\ub2e4\uba74 \ub871 \ud3ec\uc9c0\uc158, \ud558\ub77d\ud560 \uac83\uc73c\ub85c \uc608\uc0c1\ub41c\ub2e4\uba74 \uc20f \ud3ec\uc9c0\uc158\uc744 \ucde8\ud569\ub2c8\ub2e4.\n<\/p>\n
\uc554\ud638\ud654\ub41c \uc608\uc81c \ucf54\ub4dc<\/h3>\n\ndata['Predicted_Signal'] = model.predict(data[['Close']])\ndata['Long_Signal'] = (data['Predicted_Signal'] > 0.5).astype(int)\ndata['Short_Signal'] = (data['Predicted_Signal'] <= 0.5).astype(int)\n<\/code><\/pre>\n<\/div>\n7. \uacb0\ub860 \ubc0f \ud5a5\ud6c4 \uc791\uc5c5<\/h2>\n
\n \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ub871\/\uc20f \uc2dc\uadf8\ub110 \uc0dd\uc131\uc740 \uc77c\ubcf8 \uc8fc\uc2dd \uc2dc\uc7a5\uc5d0\uc11c\ub3c4 \uc720\uc758\ubbf8\ud55c \uacb0\uacfc\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud3c9\uac00, \uadf8\ub9ac\uace0 \uc2dc\uadf8\ub110 \uc0dd\uc131\uae4c\uc9c0\uc758 \uc804 \uacfc\uc815\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\n \ud5a5\ud6c4\uc5d0\ub294 \ub354 \ub9ce\uc740 \ud53c\ucc98\ub97c \ucd94\uac00\ud558\uac70\ub098 \ub2e4\ub978 \uc54c\uace0\ub9ac\uc998\uc744 \uc2dc\ub3c4\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c, \uac15\ud654\ud559\uc2b5 \ub4f1\uc758 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uc5ec \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ud6a8\uc728\uc131\uc744 \ud55c\uce35 \ub354 \ub192\uc77c \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"