{"id":29449,"date":"2024-10-27T16:18:30","date_gmt":"2024-10-27T16:18:30","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29449"},"modified":"2024-11-26T06:52:35","modified_gmt":"2024-11-26T06:52:35","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ed%8e%98%ec%96%b4-%ed%8a%b8%eb%a0%88%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29449\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud398\uc5b4 \ud2b8\ub808\uc774\ub529 \uc2e4\uc81c \uad6c\ud604"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ub370\uc774\ud130 \uae30\ubc18\uc758 \uac70\ub798 \ubc29\uc2dd\uc774 \uc810\uc810 \uc8fc\ubaa9\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ud000\ud2b8(Quantitative) \ud2b8\ub808\uc774\ub529\uc774 \uadf8 \uc911\uc2ec\uc5d0 \uc11c \uc788\uc73c\uba70, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc774 \uc0ac\uc6a9\uc790\uc5d0\uac8c \ub192\uc740 \uc218\uc775\ub960\uc744 \uc548\uaca8\uc904 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uacfc \ud2b9\ud788 \ud398\uc5b4 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ucd08<\/h2>\n

1.1 \uba38\uc2e0\ub7ec\ub2dd(Machine Learning)\ub780?<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\ub85c, \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ubc30\uc6b0\uace0 \uc608\uce21 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uc218 \uc788\ub3c4\ub85d \uc2dc\uc2a4\ud15c\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uac04\ub2e8\ud788 \ub9d0\ud574, \ub370\uc774\ud130\uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \uaddc\uce59\uc744 \ucc3e\uc544\ub0b4\uc5b4 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud558\ub294 \uacfc\uc815\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/p>\n

1.2 \ub525\ub7ec\ub2dd(Deep Learning)\ub780?<\/h3>\n

\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Networks)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub2e4\uce35 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774 \uae30\uc220\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \uc74c\uc131 \uc778\uc2dd \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4.<\/p>\n

2. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uac1c\uc694<\/h2>\n

2.1 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc815\uc758<\/h3>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc0ac\uc804\uc5d0 \uc815\uc758\ub41c \uac70\ub798 \uaddc\uce59\uc744 \ubc14\ud0d5\uc73c\ub85c \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc774 \uc790\ub3d9\uc73c\ub85c \uc2dc\uc7a5\uc5d0 \uc8fc\ubb38\uc744 \ub123\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc740 \uac10\uc815\uc801 \uc694\uc778\uc744 \ubc30\uc81c\ud558\uace0, \uc2e0\uc18d\ud55c \uc758\uc0ac \uacb0\uc815\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n

2.2 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uc758 \uc5ed\ud560<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ubbf8\ub798\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\ub294 \ub370 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc131\uacfc\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3. \ud398\uc5b4 \ud2b8\ub808\uc774\ub529(Pair Trading) \uc774\ud574\ud558\uae30<\/h2>\n

3.1 \ud398\uc5b4 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uac1c\ub150<\/h3>\n

\ud398\uc5b4 \ud2b8\ub808\uc774\ub529\uc740 \ub450 \uac1c\uc758 \uc0c1\uad00\uad00\uacc4\uac00 \uc788\ub294 \uc790\uc0b0 \uac04\uc758 \uac00\uaca9 \ucc28\uc774\ub97c \uc774\uc6a9\ud574 \uac70\ub798\ud558\ub294 \uc804\ub7b5\uc785\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c\ub294 \ud55c \uc790\uc0b0\uc744 \ub9e4\uc218\ud558\uace0 \ub2e4\ub978 \uc790\uc0b0\uc744 \ub9e4\ub3c4\ud558\uc5ec \uc218\uc775\uc744 \ucd94\uad6c\ud569\ub2c8\ub2e4. \uc774 \uc804\ub7b5\uc740 \uc2dc\uc7a5\uc758 \ube44\ud6a8\uc728\uc131\uc744 \ud65c\uc6a9\ud558\uc5ec \uc704\ud5d8\uc744 \uc904\uc774\uba70 \uc218\uc775\uc744 \ucd94\uad6c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

3.2 \ud398\uc5b4 \ud2b8\ub808\uc774\ub529\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810<\/h3>\n

\uc774 \uc804\ub7b5\uc758 \uac00\uc7a5 \ud070 \uc7a5\uc810\uc740 \uc2dc\uc7a5 \uc911\ub9bd\uc801\uc778 \uc131\uaca9\uc785\ub2c8\ub2e4. \uc989, \uc2dc\uc7a5\uc758 \ubc29\ud5a5\uc131\uacfc \uad00\uacc4\uc5c6\uc774 \uc218\uc775\uc744 \ucd94\uad6c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, \uc0c1\uad00\uad00\uacc4\uac00 \uc9c0\uc18d\ub418\uc9c0 \uc54a\uac70\ub098, \uc608\uc0c1\uacfc \ub2e4\ub978 \ubc29\ubc95\uc73c\ub85c \uac00\uaca9\uc774 \uc6c0\uc9c1\uc77c \uacbd\uc6b0 \uc190\uc2e4\uc744 \uc785\uc744 \uc218 \uc788\ub294 \uc704\ud5d8\ub3c4 \uc874\uc7ac\ud569\ub2c8\ub2e4.<\/p>\n

4. \ud398\uc5b4 \ud2b8\ub808\uc774\ub529 \uad6c\ud604 \uacfc\uc815<\/h2>\n

4.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n

\ud398\uc5b4 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc8fc\uac00 \ub370\uc774\ud130, \uac70\ub798\ub7c9 \ub370\uc774\ud130 \ub4f1 \ub2e4\uc591\ud55c \uc694\uc18c\ub97c \ud3ec\ud568\ud558\ub294 \ub370\uc774\ud130\ud504\ub808\uc784\uc744 \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub450 \uc790\uc0b0 \uac04\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \ubd84\uc11d\ud558\uace0 \ud544\uc694 \uc2dc \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.<\/p>\n

import pandas as pd\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\ndata.head()<\/code><\/pre>\n
4.2 \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d<\/h3>\n
\uc0c1\uad00\uad00\uacc4\ub97c \ubd84\uc11d\ud558\uae30 \uc704\ud574 \ud53c\uc5b4\uc2a8 \uc0c1\uad00\uacc4\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub450 \uc790\uc0b0\uc758 \uac00\uaca9 \ubcc0\ub3d9 \ud328\ud134\uc744 \ubd84\uc11d\ud558\uc5ec \uc0c1\uad00\uad00\uacc4\uac00 \ub192\uc740 \uc790\uc0b0 \uc30d\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/p>\n
# \uc0c1\uad00\uad00\uacc4 \uacc4\uc0b0\ncorrelation = data[['asset1', 'asset2']].corr()\nprint(correlation)<\/code><\/pre>\n
4.3 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\uc120\ud0dd\ub41c \uc790\uc0b0 \uc30d\uc744 \uae30\ubc18\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ucf1c \uc608\uc0c1 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud574\ubcfc \uc218 \uc788\uc73c\uba70, \ud544\uc694\ud55c \uacbd\uc6b0 hyperparameter tuning\uc744 \uc9c4\ud589\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\n\n# \ub370\uc774\ud130 \ub098\ub204\uae30\nX = data[['feature1', 'feature2']]\ny = data['target']\n\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 = RandomForestRegressor()\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n
4.4 \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n
\ud3c9\uade0\ud68c\uadc0 \uae30\ubc18\uc758 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\uae30 \uc704\ud574 Z-\uc2a4\ucf54\uc5b4\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Z-\uc2a4\ucf54\uc5b4\uac00 \ud2b9\uc815 \uc784\uacc4\uce58\ub97c \ucd08\uacfc\ud560 \uacbd\uc6b0 \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \ubc1c\uc0dd\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
def generate_signals(data):\n    data['spread'] = data['asset1'] - data['asset2']\n    data['z_score'] = (data['spread'] - data['spread'].mean()) \/ data['spread'].std()\n    \n    data['long_signal'] = (data['z_score'] < -1).astype(int)\n    data['short_signal'] = (data['z_score'] > 1).astype(int)\n    \n    return data\n\nsignals = generate_signals(data)\n<\/code><\/pre>\n
4.5 \uac70\ub798 \uc2e4\ud589<\/h3>\n
\uac70\ub798 \uc2e0\ud638\ub97c \uae30\ubc18\uc73c\ub85c \uc2e4\uc81c \uac70\ub798\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4. \uc2e0\ud638\uac00 \ubc1c\uc0dd\ud558\uba74 \ud574\ub2f9 \uc790\uc0b0\uc744 \ub9e4\uc218\ud558\uac70\ub098 \ub9e4\ub3c4\ud558\uace0, \uc774\ud6c4 \uc218\uc775\uacfc \uc190\uc2e4\uc744 \uae30\ub85d\ud558\uc5ec \uc131\uacfc\ub97c \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/p>\n
# \uac70\ub798 \uc2e4\ud589 \ub85c\uc9c1\nfor index, row in signals.iterrows():\n    if row['long_signal']:\n        execute_trade('buy', row['asset1'])\n        execute_trade('sell', row['asset2'])\n    elif row['short_signal']:\n        execute_trade('sell', row['asset1'])\n        execute_trade('buy', row['asset2'])\n<\/code><\/pre>\n
5. \uc131\uacfc \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h2>\n
5.1 \uc131\uacfc \ud3c9\uac00 \uae30\uc900<\/h3>\n
\uc131\uacfc\ub97c \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc54c\ud30c, \uc0e4\ud504 \ube44\uc728, \ucd5c\ub300 \uc190\uc2e4 \uac19\uc740 \uc9c0\ud45c\ub97c \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc9c0\ud45c\ub4e4\uc740 \uc804\ub7b5\uc758 \uc720\ud6a8\uc131\uacfc \ub9ac\uc2a4\ud06c\ub97c \ud3c9\uac00\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.<\/p>\n
def evaluate_performance(trades):\n    # \uc131\uacfc \ud3c9\uac00 \ub85c\uc9c1 \uad6c\ud604\n    # \uc608: \uc54c\ud30c, \uc0e4\ud504 \ube44\uc728, \ucd5c\ub300 \uc190\uc2e4 \ub4f1 \uacc4\uc0b0\n    pass\n<\/code><\/pre>\n
5.2 \ubaa8\ub378 \uac1c\uc120 \uc804\ub7b5<\/h3>\n
\uc131\uacfc \ud3c9\uac00 \ud6c4 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc774\uae30 \uc704\ud55c \ubc29\ubc95\ub860\uc744 \ubaa8\uc0c9\ud569\ub2c8\ub2e4. \ucd94\uac00 \ud53c\ucc98 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1, \ubaa8\ub378 \ubcf5\uc7a1\ub3c4 \uc99d\uac00, \ub354 \ub098\uc740 \ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \ub4f1\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \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\uc744 \uc774\ud574\ud558\uace0, \ud398\uc5b4 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc2e4\uc81c\ub85c \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uae30\ubc18\uc758 \uc790\ub3d9\ub9e4\ub9e4\ub294 \ud22c\uc790\uc5d0 \uc788\uc5b4 \uae30\ud68c\uc640 \ub9ac\uc2a4\ud06c\ub97c \ub3d9\uc2dc\uc5d0 \uc81c\uc2dc\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c \uad00\ub828 \uc9c0\uc2dd\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \ud559\uc2b5\ud558\uace0 \uc2e4\ud5d8\ud558\ub294 \uc790\uc138\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
7. \ucc38\uace0 \uc790\ub8cc<\/h2>\n
\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uad00\ub828 \ucc38\uace0 \uc790\ub8cc\ub97c \uba87 \uac00\uc9c0 \uc18c\uac1c\ud569\ub2c8\ub2e4:<\/p>\n