\uc21c\ud658 \uc2e0\uacbd\ub9dd(Recurrent Neural Network, RNN)<\/strong> \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n # \uc608\uce21 \ubaa8\ub378 \uad6c\ucd95\uc744 \uc704\ud55c Python \ucf54\ub4dc \uc608\uc2dc\n from sklearn.ensemble import RandomForestRegressor\n model = RandomForestRegressor()\n model.fit(X_train, y_train)\n predictions = model.predict(X_test)\n <\/pre>\n4.2 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \ucd5c\uc801\ud654<\/h3>\n
\uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \uc704\ud5d8\uacfc \uc218\uc775\uc744 \uace0\ub824\ud558\uc5ec \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \ucd5c\uc801\ud654\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac15\ud654 \ud559\uc2b5(Reinforcement Learning)<\/strong> \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uba74 \ub3d9\uc801\uc73c\ub85c \ubcc0\ud654\ud558\ub294 \uc2dc\uc7a5 \ud658\uacbd\uc5d0 \ub9de\ucdb0 \ucd5c\uc801\uc758 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n # \uac15\ud654 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998 \uc608\uc2dc\n import gym\n env = gym.make('StockTrading-v0')\n model = SomeReinforcementLearningModel()\n model.fit(env)\n <\/pre>\n5. \ub300\uccb4 \ub370\uc774\ud130\ub97c \ud1b5\ud55c \uacbd\uc7c1\ub825 \uac15\ud654<\/h2>\n
\ub300\uccb4 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uba74 \uc608\uce21 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub04c\uc5b4\uc62c\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \ubaa8\ub378\uc774 \ub300\uccb4 \ub370\uc774\ud130\ub97c \uc5b4\ub5bb\uac8c \ucc98\ub9ac\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc774\ud574\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4\u3002<\/p>\n
5.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ub300\uccb4 \ub370\uc774\ud130\ub294 \uc885\uc885 \ube44\uc815\ud615 \ub370\uc774\ud130\uc774\ubbc0\ub85c \uc801\uc808\ud55c \uc804\ucc98\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\uac70\ub098, \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\n # \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc608\uc2dc\n import pandas as pd\n from sklearn.feature_extraction.text import TfidfVectorizer\n\n df = pd.read_csv('social_media_data.csv')\n vectorizer = TfidfVectorizer()\n X = vectorizer.fit_transform(df['text_column'])\n <\/pre>\n5.2 \uc608\uce21 \uc131\ub2a5 \ud5a5\uc0c1<\/h3>\n
\ub300\uccb4 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc18c\uc15c \ubbf8\ub514\uc5b4 \uac10\uc815 \ubd84\uc11d\uc744 \ud1b5\ud574 \ud2b9\uc815 \uc8fc\uc2dd\uc5d0 \ub300\ud55c \uc2dc\uc7a5\uc758 \uac10\uc815\uc744 \ubc18\uc601\ud558\uac70\ub098, \uc704\uc131 \uc774\ubbf8\uc9c0 \ubd84\uc11d\uc744 \ud1b5\ud574 \uae30\uc5c5\uc758 \uc7ac\uace0 \uc218\uc900\uc744 \uc608\uce21\ud558\ub294 \uac83\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
6. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ucd95<\/h2>\n
\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud55c \uc911\uc694\ud55c \ub2e8\uacc4\ub294 \uc804\ub7b5 \uac1c\ubc1c, \ub370\uc774\ud130 \uc218\uc9d1, \uadf8\ub9ac\uace0 \uc2dc\uc2a4\ud15c \uad6c\ud604\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ud544\uc694\ud55c \uac01 \ub2e8\uacc4 \ubcc4\ub85c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \uc804\ub7b5 \uac1c\ubc1c<\/h3>\n
\ubaa9\ud45c \uc2dc\uc7a5\uacfc \uac70\ub798 \uc2a4\ud0c0\uc77c\uc5d0 \ub9de\ub294 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2a4\uc719 \ud2b8\ub808\uc774\ub529, \ub370\uc774 \ud2b8\ub808\uc774\ub529, \ub610\ub294 \ub871-\uc20f \uc804\ub7b5 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uc804\ub7b5\ubcc4\ub85c \ud544\uc694\ud55c \ub370\uc774\ud130\uc640 \uc54c\uace0\ub9ac\uc998\uc744 \uc815\uc758\ud558\uace0 \uc2e4\ud5d8\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
6.2 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc218\uc9d1\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. API, \uc6f9 \uc2a4\ud06c\ub798\ud551, \ub610\ub294 \uacf5\uac1c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc73c\uba70, \uc774 \uacfc\uc815\uc5d0\uc11c \ub300\uccb4 \ub370\uc774\ud130\ub3c4 \ud3ec\ud568\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6.3 \uc2dc\uc2a4\ud15c \uad6c\ud604<\/h3>\n
\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ub370\uc774\ud130 \uc218\uc9d1, \ubaa8\ub378 \ud559\uc2b5, \uadf8\ub9ac\uace0 \uc2e4\uc81c \ub9e4\ub9e4 \uc2e4\ud589 \uacfc\uc815\uc744 \uc544\uc6b0\ub974\ub294 \ud1b5\ud569 \uc2dc\uc2a4\ud15c\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ud30c\uc774\uc36c(Python)<\/strong>\uacfc \uac19\uc740 \ud504\ub85c\uadf8\ub798\ubc0d \uc5b8\uc5b4\ub97c \ud65c\uc6a9\ud558\uc5ec \ubd07\uc744 \uac1c\ubc1c\ud558\uace0 \ud14c\uc2a4\ud2b8\ud569\ub2c8\ub2e4.<\/p>\n\n # \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc744 \uc704\ud55c \uad6c\uc870 \uc608\uc2dc\n import time\n\n while True:\n market_data = collect_market_data()\n signals = model.predict(market_data)\n execute_trade(signals)\n time.sleep(60) # 1\ubd84 \uac04\uaca9\uc73c\ub85c \uc2e4\ud589\n <\/pre>\n7. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b8\ub808\uc774\ub354\ub4e4\uc5d0\uac8c \uc0c8\ub85c\uc6b4 \uae30\ud68c\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub300\uccb4 \ub370\uc774\ud130\uc758 \ub4f1\uc7a5\uc740 \uc774 \ubcc0\ud654\uc5d0 \ub354 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \ub354\ud574\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130, \ucca0\uc800\ud55c \uc804\ub7b5 \uc218\ub9bd, \uadf8\ub9ac\uace0 \uc9c0\uc18d\uc801\uc778 \ubaa8\ub2c8\ud130\ub9c1\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \ubbf8\ub798\ub294 \ub354\uc6b1 \ubc1d\uc73c\uba70, \uc774\ub97c \ud65c\uc6a9\ud558\ub294 \ud2b8\ub808\uc774\ub354\ub4e4\uc740 \ub354\uc6b1 \uacbd\uc7c1\ub825\uc774 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
8. \ucc38\uace0\uc790\ub8cc<\/h2>\n
\uc774 \uae00\uc5d0\uc11c \ub2e4\ub8ec \ub0b4\uc6a9\uc5d0 \ub300\ud55c \ub354 \ub9ce\uc740 \uc815\ubcf4\ub294 \ub2e4\uc74c\uc758 \uc790\ub8cc\ub97c \ucc38\uc870\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4:<\/p>\n
\n- Machine Learning for Asset Managers, Marcos L\u00f3pez de Prado<\/li>\n
- Advances in Financial Machine Learning, Marcos L\u00f3pez de Prado<\/li>\n
- Deep Learning for Finance, Zura Kakushadze and Htensor Team<\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ucd5c\uadfc \uae08\uc735\uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774 \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uae30\uc220\ub4e4\uc774 \uc5b4\ub5bb\uac8c \ubc1c\uc804\ud574 \uc654\ub294\uc9c0, \uadf8\ub9ac\uace0 \ub300\uccb4 \ub370\uc774\ud130\uac00 \uc5b4\ub5bb\uac8c \uc774 \ud601\uc2e0\uc801 \ubcc0\ud654\uc5d0 \uae30\uc5ec\ud558\uace0 \uc788\ub294\uc9c0\ub97c \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ud559\uc2b5\ud558\uc5ec \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc18\uba74, \ub525\ub7ec\ub2dd\uc740 \uc2e0\uacbd\ub9dd(Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ub300\uccb4 \ub370\uc774\ud130 \ud601\uba85”<\/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-28904","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, \ub300\uccb4 \ub370\uc774\ud130 \ud601\uba85 - 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