\uc624\ub298\ub0a0 \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774 \ub110\ub9ac \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\uc5d0 \ub530\ub77c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uacfc \uac19\uc740 \uae30\ubc95\uc774 \ud22c\uc790 \uc804\ub7b5\uc5d0 \uc810\uc810 \ub354 \ub9ce\uc774 \ucc44\ud0dd\ub418\uace0 \uc788\uc73c\uba70, \ud2b9\ud788 \ud655\ub960\uc801 \uacbd\uc0ac \ud558\uac15\ubc95(SGD)\uc740 \uadf8 \ud6a8\uc728\uc131\uacfc \ube60\ub978 \uc218\ub834 \uc18d\ub3c4\ub85c \uc778\ud574 \ub9ce\uc740 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, scikit-learn<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud574 SGD\ub97c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \uc54c\uace0\ub9ac\uc998 \uc124\uacc4\ub97c \ubaa9\uc801\uc73c\ub85c \ud558\ub294 \uc778\uacf5\uc9c0\ub2a5(AI) \ubd84\uc57c\uc758 \ud55c \uac08\ub798\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ubcf4\ub2e4 \uae4a\uace0 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc774 \ub450 \uac00\uc9c0 \uae30\ubc95\uc744 \ud1b5\ud574 \ub370\uc774\ud130\uc5d0\uc11c \uac00\uce58 \uc788\ub294 \ud328\ud134\uc744 \ucd94\ucd9c\ud558\uace0, \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uc608\uce21 \ubc0f \uc758\uc0ac \uacb0\uc815\uc744 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ub525\ub7ec\ub2dd\uc740 \ubcf5\uc7a1\ud55c \uce35 \uad6c\uc870\ub97c \uac00\uc9c4 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud569\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubc0f \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubc1c\ud718\ud569\ub2c8\ub2e4.<\/p>\n \ud655\ub960\uc801 \uacbd\uc0ac \ud558\uac15\ubc95(Stochastic Gradient Descent, SGD)\uc740 \ucd5c\uc801\ud654 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \uae30\uacc4 \ud559\uc2b5\uc5d0\uc11c\ub294\uc190\uc2e4 \ud568\uc218\ub97c \ucd5c\uc18c\ud654\ud558\uae30 \uc704\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \ubc18\ubaa9\uc801\uc73c\ub85c \uc804\uccb4 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uae30\ubcf4\ub2e4, \ub79c\ub364\ud558\uac8c \uc120\ud0dd\ud55c \uc791\uc740 \ubc30\uce58(batch)\ub97c \uc0ac\uc6a9\ud558\uc5ec \uacbd\ub7c9\ud654\uc640 \uc18d\ub3c4\ub97c \ub192\uc774\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n scikit-learn<\/strong>\uc740 Python\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc704\ud55c \uac00\uc7a5 \uc778\uae30 \uc788\ub294 \ub77c\uc774\ube0c\ub7ec\ub9ac \uc911 \ud558\ub098\ub85c, \uac04\ud3b8\ud55c \uc778\ud130\ud398\uc774\uc2a4\ub97c \uc81c\uacf5\ud558\uba70 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. SGD\ub97c \ud3ec\ud568\ud55c \uc120\ud615 \ubaa8\ub378, \ud68c\uadc0, \ubd84\ub958 \ub4f1 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc774\uc81c scikit-learn\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud655\ub960\uc801 \uacbd\uc0ac \ud558\uac15\ubc95\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc8fc\uac00 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 yfinance<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud569\ub2c8\ub2e4. \uadf8\ub9ac\uace0 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\uc5ec \ubaa8\ub378\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/p>\n SGDClassifier\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \ubd84\uc11d\ud569\ub2c8\ub2e4. \uc608\uce21\ub41c \uacb0\uacfc\ub97c \uc2e4\uc81c \uc8fc\uac00\uc640 \ube44\uad50\ud558\ub294 \uac83\ub3c4 \uc911\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \uacfc\uc815\uc744 \uac70\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Grid Search\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \ucc3e\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec scikit-learn\uc744 \uc0ac\uc6a9\ud55c \ud655\ub960\uc801 \uacbd\uc0ac \ud558\uac15\ubc95(SGD)\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc8fc\uc2dd \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uace0 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd \ubc29\ubc95\uae4c\uc9c0 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n \uc55e\uc73c\ub85c\ub294 \ub354 \ubcf5\uc7a1\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378, LSTM, \uac15\ud654 \ud559\uc2b5 \ub4f1\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354\uc6b1 \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc131\uacf5\uc801\uc778 \ud2b8\ub808\uc774\ub529\uc744 \uc774\ub8e9\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
1.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h3>\n
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1.2 \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h3>\n
2. SGD(\ud655\ub960\uc801 \uacbd\uc0ac \ud558\uac15\ubc95)\ub780?<\/h2>\n
2.1 SGD\uc758 \ub3d9\uc791 \uc6d0\ub9ac<\/h3>\n
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2.2 SGD\uc758 \uc7a5\ub2e8\uc810<\/h3>\n
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3. scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac \uc18c\uac1c<\/h2>\n
3.1 scikit-learn \uc124\uce58\ud558\uae30<\/h3>\n
pip install scikit-learn<\/code><\/pre>\n3.2 scikit-learn\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c<\/h3>\n
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4. SGD\ub97c \uc774\uc6a9\ud55c \uc8fc\uac00 \uc608\uce21 \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h2>\n
4.1 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h3>\n
pip install yfinance<\/code><\/pre>\n\nimport yfinance as yf\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\n# \ub370\uc774\ud130 \uc218\uc9d1\ndf = yf.download(\"AAPL\", start=\"2015-01-01\", end=\"2023-01-01\")\ndf['Return'] = df['Adj Close'].pct_change()\ndf.dropna(inplace=True)\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\nX = df[['Open', 'High', 'Low', 'Volume']]\ny = (df['Return'] > 0).astype(int) # \uc0c1\uc2b9 \uc5ec\ubd80 \uc608\uce21\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n<\/code><\/pre>\n4.2 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.metrics import accuracy_score\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = SGDClassifier(loss='log', max_iter=1000, random_state=42)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \uc815\ud655\ub3c4 \ud3c9\uac00\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\ubaa8\ub378 \uc815\ud655\ub3c4: {accuracy:.2f}\")\n<\/code><\/pre>\n4.3 \uacb0\uacfc \ubd84\uc11d<\/h3>\n
\nimport matplotlib.pyplot as plt\n\n# \uc2e4\uc81c \uc8fc\uac00 \ub370\uc774\ud130\uc640 \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplt.plot(df.index[-len(y_test):], df['Adj Close'][-len(y_test):], label='Actual Price')\nplt.scatter(df.index[-len(y_test):][y_pred == 1], df['Adj Close'][-len(y_test):][y_pred == 1], color='green', label='Predicted Up')\nplt.scatter(df.index[-len(y_test):][y_pred == 0], df['Adj Close'][-len(y_test):][y_pred == 0], color='red', label='Predicted Down')\nplt.legend()\nplt.title('Stock Price Prediction')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.show()\n<\/code><\/pre>\n5. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h2>\n
\nfrom sklearn.model_selection import GridSearchCV\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uadf8\ub9ac\ub4dc \uc124\uc815\nparam_grid = {\n 'loss': ['hinge', 'log'],\n 'alpha': [1e-4, 1e-3, 1e-2],\n 'max_iter': [1000, 1500, 2000]\n}\n\ngrid_search = GridSearchCV(SGDClassifier(), param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\n# \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130\nprint(\"\ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130:\", grid_search.best_params_)\n<\/code><\/pre>\n6. \uacb0\ub860 \ubc0f \ud5a5\ud6c4 \ubc29\ud5a5<\/h2>\n