\n\uc11c\ub860<\/h2>\n
\ucd5c\uadfc \uba87 \ub144 \uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd(ML)\uacfc \ub525\ub7ec\ub2dd(DL)\uc774 \ud601\uc2e0\uc801\uc73c\ub85c \ubcc0\ud654\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae30\uc220\ub4e4\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc124\uacc4, \ucd5c\uc801\ud654, \uadf8\ub9ac\uace0 \uc2e4\ud589\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529<\/strong>\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc9d1\ub77c\uc778(Jupyter Notebook\uc744 \ud1b5\ud55c ML \uc2e4\uc2b5)<\/strong>\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/section>\n\n1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574<\/h2>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \uc790\uc0b0\uc758 \ub9e4\ub9e4 \uacb0\uc815\uc744 \uc790\ub3d9\ud654\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \ub9e4\ub9e4 \ubc29\uc2dd\uacfc\ub294 \ub2ec\ub9ac, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc778\uc9c0 \ud3b8\ud5a5\uc744 \ucd5c\uc18c\ud654\ud558\uace0 \ube60\ub974\uace0 \ud6a8\uc728\uc801\uc778 \uac70\ub798\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc7a5\uc810\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\ub4e4\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uc18d\ub3c4:<\/strong> \uc54c\uace0\ub9ac\uc998\uc740 \uc0ac\ub78c\ubcf4\ub2e4 \ud6e8\uc52c \ube60\ub978 \uc18d\ub3c4\ub85c \uc791\uc5c5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc815\ud655\uc131:<\/strong> \uc218\ud559\uc801 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac10\uc815\uc801 \ud310\ub2e8\uc744 \ubc30\uc81c\ud558\uace0, \uaddc\uce59\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uacb0\uc815\uc774 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/li>\n
- \uac1c\uc778\ud654:<\/strong> \ub2e4\uc591\ud55c \uc804\ub7b5\uc744 \ud14c\uc2a4\ud2b8\ud558\uace0 \uc790\uc2e0\ub9cc\uc758 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\ub294 \uc720\uc5f0\uc131\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<\/section>\n\n
2. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \uae30\uc220\uc801\uc778 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ubc18\uba74, \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c\uc11c \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \uace0\ucc28\uc6d0 \ub370\uc774\ud130 \ucc98\ub9ac \ubc0f \ubcf5\uc7a1\ud55c \ud328\ud134 \uc778\uc2dd\uc5d0 \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
2.1 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc885\ub958<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ud06c\uac8c \uc138 \uac00\uc9c0 \uc720\ud615\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4:<\/p>\n
\n- \uc9c0\ub3c4\ud559\uc2b5(Supervised Learning):<\/strong> \ub808\uc774\ube14\uc774 \uc788\ub294 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uc5ec \uacb0\uacfc\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n
- \ube44\uc9c0\ub3c4\ud559\uc2b5(Unsupervised Learning):<\/strong> \ub808\uc774\ube14\uc774 \uc5c6\ub294 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \uc774\ud574\ud558\uace0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n
- \uac15\ud654\ud559\uc2b5(Reinforcement Learning):<\/strong> \uc5d0\uc774\uc804\ud2b8\uac00 \ud658\uacbd\uacfc \uc0c1\ud638\uc791\uc6a9\ud558\uba70 \ubcf4\uc0c1\uc744 \uadf9\ub300\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
2.2 \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uc6d0\ub9ac<\/h3>\n
\ub525\ub7ec\ub2dd\uc740 \uc8fc\ub85c \uc2e0\uacbd\ub9dd<\/strong>\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc2e0\uacbd\ub9dd\uc740 \uc5ec\ub7ec \uce35\uc73c\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc73c\uba70, \uc785\ub825\uce35, \uc740\ub2c9\uce35, \ucd9c\ub825\uce35\uc73c\ub85c \ub098\ub269\ub2c8\ub2e4. \uac01 \uce35\uc758 \ub178\ub4dc\ub294 \uc774\uc804 \uce35\uc758 \ub178\ub4dc\uc640 \uc5f0\uacb0\ub418\uc5b4 \uac00\uc911\uce58\ub97c \ud1b5\ud574 \uc2e0\ud638\ub97c \uc804\ub2ec\ud569\ub2c8\ub2e4.<\/p>\n<\/section>\n\n3. \uc9d1\ub77c\uc778\uc744 \ud65c\uc6a9\ud55c \uc2e4\uc2b5 \ud658\uacbd \uad6c\ucd95<\/h2>\n
\uc9d1\ub77c\uc778\uc740 \ud30c\uc774\uc36c \uae30\ubc18\uc758 \ub370\uc774\ud130 \uc0ac\uc774\uc5b8\uc2a4 \ud658\uacbd\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ud234\ud0b7\uc785\ub2c8\ub2e4. Jupyter Notebook\uc744 \ud65c\uc6a9\ud558\uc5ec \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uad6c\ucd95\ud558\uace0 \ud14c\uc2a4\ud2b8\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ub2e8\uacc4\ub97c \ud1b5\ud574 \uc9d1\ub77c\uc778\uc744 \uc124\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4:<\/p>\n
3.1 \uc9d1\ub77c\uc778 \uc124\uce58<\/h3>\n
\uc9d1\ub77c\uc778\uc740 pip<\/code>\ub97c \uc774\uc6a9\ud558\uc5ec \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \ud1b5\ud574 \uc124\uce58\ud569\ub2c8\ub2e4:<\/p>\npip install zipline<\/code><\/pre>\n3.2 \uc9d1\ub77c\uc778 \ub370\uc774\ud130 \uac00\uc838\uc624\uae30<\/h3>\n
\uc9d1\ub77c\uc778\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc744 \ub85c\ub4dc\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130, \uac70\ub798\ub7c9 \ub370\uc774\ud130 \ub4f1\uc744 \ud3ec\ud568\ud55c CSV \ud30c\uc77c\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
import pandas as pd\ndata = pd.read_csv('stock_data.csv')<\/code><\/pre>\n<\/section>\n\n4. \ud000\ud2b8 \ub9e4\ub9e4 \uc804\ub7b5 \uac1c\ubc1c<\/h2>\n
\uc774\uc81c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ud575\uc2ec\uc774 \ub418\ub294 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ud53c\uccd0 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1<\/h3>\n
\ud53c\uccd0 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud55c \uc911\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \uc8fc\uac00 \ub370\uc774\ud130\uc5d0\uc11c \uc720\uc6a9\ud55c \ud53c\uccd0\ub97c \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ub3d9 \ud3c9\uade0, \ubcc0\ub3d9\uc131, \uac70\ub798\ub7c9 \ubcc0\ud654\ub97c \ud53c\uccd0\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
data['moving_average'] = data['close'].rolling(window=20).mean()<\/code><\/pre>\n4.2 \ubaa8\ub378 \uc120\ud0dd \ubc0f \ud559\uc2b5<\/h3>\n
\uc801\uc808\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c Decision Tree, Random Forest, XGBoost\uc640 \uac19\uc740 \ubaa8\ub378\ub4e4\uc774 \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc544\ub798\ub294 Random Forest \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
from sklearn.ensemble import RandomForestClassifier\nclf = RandomForestClassifier()\nclf.fit(X_train, y_train)<\/code><\/pre>\n4.3 \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ubaa8\ub378 \ud559\uc2b5 \ud6c4, \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc8fc\uac00\uac00 \uc774\ub3d9 \ud3c9\uade0\uc744 \ucd08\uacfc\ud560 \ub54c \ub9e4\uc218 \uc2e0\ud638\ub97c, \ud558\ud68c\ud560 \ub54c \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
data['buy_signal'] = (data['close'] > data['moving_average']).astype(int)\ndata['sell_signal'] = (data['close'] < data['moving_average']).astype(int)<\/code><\/pre>\n<\/section>\n\n5. \uc804\ub7b5 \ud3c9\uac00 \ubc0f \ucd5c\uc801\ud654<\/h2>\n
\uac1c\ubc1c\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \ud3c9\uac00\ud558\uace0 \ucd5c\uc801\ud654\ud558\ub294 \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uc131\ub2a5 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uc744 \uac80\uc99d\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \uc131\uacfc \uc9c0\ud45c<\/h3>\n
\uc131\uacfc \uc9c0\ud45c\uc5d0\ub294 \uc218\uc775\ub960, \ubcc0\ub3d9\uc131, \ucd5c\ub300 \ub099\ud3ed \ub4f1\uc774 \ud3ec\ud568\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uc131\uacfc\ub97c \uacc4\uc0b0\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
returns = data['close'].pct_change()\ncumulative_returns = (1 + returns).cumprod()<\/code><\/pre>\n5.2 \uadf8\ub9ac\ub4dc \uc11c\uce58<\/h3>\n
\uadf8\ub9ac\ub4dc \uc11c\uce58\ub97c \ud1b5\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucd5c\uc801\ud654\ud558\uc5ec \ubaa8\ub378 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
from sklearn.model_selection import GridSearchCV\nparam_grid = {'n_estimators': [10, 50, 100], 'max_depth': [None, 10, 20]}\ngrid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)<\/code><\/pre>\n<\/section>\n\n\uacb0\ub860<\/h2>\n
\uc774 \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 \ub2e4\uc591\ud55c \uac1c\ub150\uacfc \uc9d1\ub77c\uc778\uc744 \ud1b5\ud55c \uc2e4\uc2b5\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc131\uacf5\uc801\uc778 \uac70\ub798 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc790\uc2e0\uc758 \uc804\ub7b5\uc744 \uac1c\uc120\ud558\uace0 \ucd5c\uc801\ud654\ud558\uae38 \ubc14\ub78d\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uacfc \ub525\ub7ec\ub2dd \uad6c\uc870\ub97c \uc5f0\uad6c\ud558\uace0 \uc801\uc6a9\ud558\uc5ec \ub354\uc6b1 \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/section>\n<\/article>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc11c\ub860 \ucd5c\uadfc \uba87 \ub144 \uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd(ML)\uacfc \ub525\ub7ec\ub2dd(DL)\uc774 \ud601\uc2e0\uc801\uc73c\ub85c \ubcc0\ud654\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae30\uc220\ub4e4\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc124\uacc4, \ucd5c\uc801\ud654, \uadf8\ub9ac\uace0 \uc2e4\ud589\uc744 \ud3ec\ud568\ud558\uc5ec \ub2e4\uc591\ud55c \ubc29\uc2dd\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \uc9d1\ub77c\uc778(Jupyter Notebook\uc744 \ud1b5\ud55c ML \uc2e4\uc2b5)\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4. 1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc774\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc9d1\ub77c\uc778”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-29329","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc9d1\ub77c\uc778 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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