\uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc218\uc775\uc744 \ucd94\uad6c\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ube14\ub85c\uadf8 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uac1c\ub150\uacfc \ub3c4\uad6c\ub4e4\uc744 \uc18c\uac1c\ud55c \ub4a4, \uc2e4\uc81c\ub85c \uc120\ud615 \ud68c\uadc0\ub97c \ud65c\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc124\uba85\ud569\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uace0 \uc774\ub97c \ud1b5\ud574 \uc608\uce21\uc774\ub098 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd(Deep Learning)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\ub294 \uac83\uc740 \ub370\uc774\ud130\uc758 \uc608\uce21 \uc815\ud655\ub3c4\ub97c \ub192\uc774\uace0, \uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\uc2dc\ud0ac \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
\uc120\ud615 \ud68c\uadc0\ub294 \uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub85c, \uc785\ub825 \ubcc0\uc218\uc640 \ucd9c\ub825 \ubcc0\uc218 \uac04\uc758 \uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc758 \uc608\ub85c, \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uc774\uc804 \uac00\uaca9, \uac70\ub798\ub7c9 \ubc0f \uae30\ud0c0 \uc9c0\ud45c\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798 \uac00\uaca9\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc120\ud615 \ud68c\uadc0\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ud615\ud0dc\ub97c \uac00\uc9d1\ub2c8\ub2e4:<\/p>\n
\n Y = \u03b20 + \u03b21X1 + \u03b22X2 + ... + \u03b2nXn + \u03b5\n<\/pre>\n\uc5ec\uae30\uc11c:<\/p>\n
\uc790\ub3d9 \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774 \uc608\uc81c\uc5d0\uc11c\ub294 Yahoo Finance API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\nimport pandas_datareader.data as web\nfrom datetime import datetime\n\n# \ub370\uc774\ud130 \uc218\uc9d1\nstart = datetime(2020, 1, 1)\nend = datetime(2023, 12, 31)\n\nstock_data = web.DataReader('AAPL', 'yahoo', start, end)\nstock_data.head()<\/code><\/pre>\n\uc774 \ucf54\ub4dc\ub294 Apple Inc. (AAPL)\uc758 \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\uc5d0\ub294 \ub0a0\uc9dc, \uac1c\uc7a5\uac00, \uace0\uac00, \uc800\uac00, \uc885\uac00, \uac70\ub798\ub7c9 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n
4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \uc0ac\uc6a9\ud558\uae30\uc5d0 \uc801\ud569\ud558\ub3c4\ub85d \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \uacb0\uce21\uce58 \ucc98\ub9ac, \ubcc0\ud658, \uc815\uaddc\ud654 \ub4f1\uc758 \ub2e8\uacc4\uac00 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n
4.1 \uacb0\uce21\uce58 \ucc98\ub9ac<\/h3>\n
\uacb0\uce21\uce58\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uc9c1\uc811\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce58\ubbc0\ub85c \uc774\ub97c \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. Pandas\ub97c \uc0ac\uc6a9\ud558\uc5ec \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
# \uacb0\uce21\uce58 \ud655\uc778\nprint(stock_data.isnull().sum())\n\n# \uacb0\uce21\uce58 \uc81c\uac70\nstock_data.dropna(inplace=True)<\/code><\/pre>\n4.2 \ub370\uc774\ud130 \ubcc0\ud658 \ubc0f \uc815\uaddc\ud654<\/h3>\n
\ubaa8\ub378\uc5d0 \uc801\ud569\ud558\ub3c4\ub85d \ub370\uc774\ud130 \ubcc0\ud658 \ubc0f \uc815\uaddc\ud654\uac00 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc885\uac00\ub97c \uc608\uce21\ud558\uace0\uc790 \ud560 \ub54c, \uae30\uc874 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud558\uc5ec \ud2b9\uc131\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
# \ud2b9\uc131 \ubcc0\uc218 \uc0dd\uc131\nstock_data['Return'] = stock_data['Adj Close'].pct_change()\nstock_data['SMA_5'] = stock_data['Adj Close'].rolling(window=5).mean()\nstock_data['SMA_20'] = stock_data['Adj Close'].rolling(window=20).mean()\nstock_data.dropna(inplace=True)<\/code><\/pre>\n5. \ub370\uc774\ud130 \ubd84\ud560<\/h2>\n
\ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud55c \ud6c4, \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud574 \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 70%\ub294 \ud6c8\ub828\uc5d0, 30%\ub294 \ud14c\uc2a4\ud2b8\uc5d0 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = stock_data[['Return', 'SMA_5', 'SMA_20']]\ny = stock_data['Adj Close']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)<\/code><\/pre>\n6. \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\uc774\uc81c \ub370\uc774\ud130\ub97c \uc900\ube44\ud588\uc73c\ub2c8 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Scikit-Learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uba74 \uc27d\uace0 \ube60\ub974\uac8c \ubaa8\ub378\uc744 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.linear_model import LinearRegression\n\n# \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud559\uc2b5\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n7. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\ud559\uc2b5\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc608\uce21\uac12\uc744 \uc0dd\uc131\ud558\uace0, \uc774\ub97c \uc2e4\uc81c\uac12\uacfc \ube44\uad50\ud569\ub2c8\ub2e4. \ub2e4\uc591\ud55c \ud3c9\uac00 \uc9c0\ud45c\uac00 \uc788\uc9c0\ub9cc, \uc5ec\uae30\uc11c\ub294 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(Mean Squared Error, MSE)\uc640 R\u00b2 \uc810\uc218\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import mean_squared_error, r2_score\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \ud3c9\uac00 \uc9c0\ud45c \uacc4\uc0b0\nmse = mean_squared_error(y_test, y_pred)\nr_squared = r2_score(y_test, y_pred)\n\nprint(f'MSE: {mse}')\nprint(f'R\u00b2: {r_squared}') # 0\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \ub098\uc05c \ubaa8\ub378, 1\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \uc88b\uc740 \ubaa8\ub378<\/code><\/pre>\n8. \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654<\/h2>\n
\ubaa8\ub378\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uba74 \ub354\uc6b1 \uc9c1\uad00\uc801\uc73c\ub85c \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Matplotlib\uacfc Seaborn\uc744 \ud65c\uc6a9\ud558\uc5ec \uc608\uce21 \uacb0\uacfc\ub97c \uadf8\ub798\ud504\ub85c \ub098\ud0c0\ub0b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\nimport seaborn as sns\n\nsns.set(style='whitegrid')\n\nplt.figure(figsize=(14, 7))\nplt.plot(y_test.index, y_test, label='Actual', color='blue')\nplt.plot(y_test.index, y_pred, label='Predicted', color='orange')\nplt.title('Actual vs Predicted Prices')\nplt.xlabel('Date')\nplt.ylabel('Price')\nplt.legend()\nplt.show()<\/code><\/pre>\n9. \ucd5c\uc801\ud654 \ubc0f \ud29c\ub2dd<\/h2>\n
\uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc774 \uc644\ub8cc\ub41c \ud6c4, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc774\ub098 \ud53c\uccd0 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1\uc744 \ud1b5\ud574 \ubaa8\ub378 \uc131\ub2a5\uc744 \ub354\uc6b1 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Grid Search, Random Search \ub4f1\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \ucc3e\ub294 \ubc29\ubc95\uc744 \uace0\ub824\ud574 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
10. \ud30c\uc774\ud504\ub77c\uc778 \uad6c\ucd95<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc2e4\uc81c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc5d0 \ud1b5\ud569\ud558\uae30 \uc704\ud574 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uad6c\ucd95\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \ud559\uc2b5 \ubc0f \uc608\uce21, \ub9ac\ubc38\ub7f0\uc2f1 \ub4f1 \uc5ec\ub7ec \ub2e8\uacc4\ub97c \ud1b5\ud569\ud558\uc5ec \uc790\ub3d9\ud654\ub41c \uc2dc\uc2a4\ud15c\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
11. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08\ubd80\ud130 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uae4c\uc9c0 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub2e8\uc21c\ud55c \ub370\uc774\ud130 \ubd84\uc11d\uc744 \ub118\uc5b4, \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c \ubc0f \uac1c\uc120\uc774 \ud544\uc694\ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \uc120\ud615 \ud68c\uadc0\ub97c \uc2dc\uc791\uc73c\ub85c \uc5ec\ub7ec \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \uc815\uad50\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
12. \ucc38\uace0 \uc790\ub8cc<\/h2>\n\n- \u201cHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow\u201d by Aur\u00e9lien G\u00e9ron<\/li>\n
- \u201cPython for Finance\u201d by Yves Hilpisch<\/li>\n
- Scikit-Learn Documentation: https:\/\/scikit-learn.org\/stable\/<\/a><\/li>\n
- Matplotlib Documentation: https:\/\/matplotlib.org\/stable\/index.html<\/a><\/li>\n<\/ul>\n
\uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \ub370\uc774\ud130\uc640 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc73c\ub85c \ud568\uaed8 \ubc1c\uc804\ud574 \ub098\uac00\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"