{"id":29285,"date":"2024-10-27T16:17:41","date_gmt":"2024-10-27T16:17:41","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29285"},"modified":"2024-11-26T06:53:15","modified_gmt":"2024-11-26T06:53:15","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-%ec%a0%84%eb%9e%b5-%ec%a7%91%eb%9d%bc%ec%9d%b8","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29285\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc804\ub7b5 \uc9d1\ub77c\uc778\uc744 \uc774\uc6a9\ud55c \ubc31\ud14c\uc2a4\ud2b8"},"content":{"rendered":"

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\uc791\uc131\uc790: \uc870\uad11\ud615<\/p>\n

\ub0a0\uc9dc: [\ub0a0\uc9dc]<\/p>\n<\/header>\n

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1. \uc11c\ub860<\/h2>\n

\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uac70\ub798\ub97c \uc790\ub3d9\ud654\ud558\ub294 \ubc29\ubc95\uc73c\ub85c, \ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uad00\uc2ec\uc774 \uae09\uc99d\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uc740 \uadf8\ub7ec\ud55c \uc790\ub3d9\ud654\uc758 \uae30\uc900\uc744 \uc0c8\ub86d\uac8c \uc124\uc815\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc9d1\ub77c\uc778(Zipline)\uc744 \uc0ac\uc6a9\ud558\uc5ec \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ubc31\ud14c\uc2a4\ud2b8\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

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2. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08<\/h2>\n

\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc870\uac74\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uc774 \uc2dc\uc2a4\ud15c\uc740 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uac70\ub798 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\uc5ec, \uc778\uac04 \ud2b8\ub808\uc774\ub354\ubcf4\ub2e4 \ube60\ub974\uace0 \ud6a8\uc728\uc801\uc778 \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc7a5\uc810\uc5d0\ub294 \uac70\ub798 \uc18d\ub3c4, \uac10\uc815\uc758 \ubc30\uc81c, \ub300\ub7c9 \ub370\uc774\ud130 \ucc98\ub9ac \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

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3. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc18c\uac1c<\/h2>\n

\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub354\uc6b1 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uac00\uaca9 \uc608\uce21, \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131 \ub4f1\uc5d0 \ub110\ub9ac \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n<\/section>\n

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4. \uc9d1\ub77c\uc778(Zipline) \uc18c\uac1c<\/h2>\n

\n \uc9d1\ub77c\uc778(Zipline)\uc740 \ud30c\uc774\uc36c \uae30\ubc18\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ubc31\ud14c\uc2a4\ud2b8\uc640 \uc2e4\uc2dc\uac04 \uac70\ub798 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ud604\ud560 \uc218 \uc788\ub294 \ub3c4\uad6c\uc785\ub2c8\ub2e4. \uc9d1\ub77c\uc778\uc740 \uc8fc\ub85c \ub370\uc774\ud130 \uc218\uc9d1, \uc2e0\ud638 \uc0dd\uc131, \uac70\ub798 \uc2e4\ud589 \ub2e8\uacc4\ub97c \ud3ec\ud568\ud55c \uc644\uc804\ud55c \ud2b8\ub808\uc774\ub529 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub610\ud55c, \uae08\uc735 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \ub2e4\uc591\ud55c \ubd84\uc11d \uae30\ub2a5\ub3c4 \uac16\ucd94\uace0 \uc788\uc5b4 \ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc5d0 \uc774\uc0c1\uc801\uc785\ub2c8\ub2e4.\n <\/p>\n

\n \uc124\uce58\ub294 \uc544\ub798\uc758 \uba85\ub839\uc5b4\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install zipline<\/code><\/pre>\n<\/p>\n<\/section>\n
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5. \uc9d1\ub77c\uc778\uc744 \uc774\uc6a9\ud55c \ubc31\ud14c\uc2a4\ud2b8 \uc218\ud589\ud558\uae30<\/h2>\n
5.1. \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
\n                \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uac70\ub798\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. Zipline\uc740 Yahoo Finance, Quandl \ub4f1\uc758 \uc678\ubd80 \ub370\uc774\ud130 \uc18c\uc2a4\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud544\uc694\ud55c \ub370\uc774\ud130\uac00 \uc900\ube44\ub418\uba74, \uc774\ub97c Zipline\uc758 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4.\n            <\/p>\n
5.2. \uc804\ub7b5 \uc815\uc758\ud558\uae30<\/h3>\n
\n                \ub2e4\uc74c \ub2e8\uacc4\ub294 \uac70\ub798 \uc804\ub7b5\uc744 \uc815\uc758\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Moving Average Crossover \uc804\ub7b5\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uc804\ub7b5\uc740 \ub2e8\uae30 \uc774\ub3d9 \ud3c9\uade0\uc120\uc774 \uc7a5\uae30 \uc774\ub3d9 \ud3c9\uade0\uc120\uc744 \uc0c1\ud5a5 \ub3cc\ud30c\ud560 \ub54c \ub9e4\uc218\ud558\uace0, \ud558\ud5a5 \ub3cc\ud30c\ud560 \ub54c \ub9e4\ub3c4\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc774\ub97c \ucf54\ub4dc\ub85c \uad6c\ud604\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\nfrom zipline import algo\n\ndef initialize(context):\n    context.asset = symbol('AAPL')\n    context.short_window = 20\n    context.long_window = 50\n\ndef handle_data(context, data):\n    short_mavg = data.history(context.asset, 'price', context.short_window, '1d').mean()\n    long_mavg = data.history(context.asset, 'price', context.long_window, '1d').mean()\n    \n    if short_mavg > long_mavg:\n        order(context.asset, 10)  # 10\uc8fc \ub9e4\uc218\n    elif short_mavg < long_mavg:\n        order(context.asset, -10)  # 10\uc8fc \ub9e4\ub3c4\n                <\/code><\/pre>\n<\/p>\n
5.3. \ubc31\ud14c\uc2a4\ud2b8 \uc2e4\ud589<\/h3>\n
\n                \uc774\uc81c \uc804\ub7b5\uc744 \uc2e4\ud589\ud558\uc5ec \ubc31\ud14c\uc2a4\ud2b8\ub97c \uc218\ud589\ud569\ub2c8\ub2e4. Zipline\uc5d0\ub294 \ubc31\ud14c\uc2a4\ud2b8\ub97c \uc2e4\ud589\ud558\uae30 \uc704\ud55c \uac04\ub2e8\ud55c \uba54\uc18c\ub4dc\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc758 \ucf54\ub4dc\ub97c \ud1b5\ud574 \ubc31\ud14c\uc2a4\ud2b8\ub97c \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\nfrom zipline import run_algorithm\nfrom datetime import datetime\n\nrun_algorithm(start=datetime(2015, 1, 1), \n               end=datetime(2016, 1, 1), \n               initialize=initialize, \n               capital_base=100000, \n               handle_data=handle_data)\n                <\/code><\/pre>\n<\/p>\n<\/section>\n
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6. \uc804\ub7b5 \ud3c9\uac00 \ubc0f \uc131\uacfc \ubd84\uc11d<\/h2>\n
\n                \ubc31\ud14c\uc2a4\ud2b8\uc758 \uacb0\uacfc\ub97c \ud3c9\uac00\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uac70\ub798 \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \ud310\ub2e8\ud558\uae30 \uc704\ud55c \uc5ec\ub7ec \uac00\uc9c0 \uc9c0\ud45c\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \uc9c0\ud45c\ub85c\ub294 \ucd1d \uc218\uc775\ub960, \uc0e4\ud504 \ube44\uc728, \ucd5c\ub300 \ub099\ud3ed, \uc2b9\ub960 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \uac1c\uc120\ud560 \uc218 \uc788\ub294 \ubc29\ud5a5\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n
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7. \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc804\ub7b5 \uac1c\uc120<\/h2>\n
\n                \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \uac70\ub798 \uc804\ub7b5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc591\ud55c \uae30\uc220 \uc9c0\ud45c\ub97c feature\ub85c \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubd84\uc11d\uc744 \ud1b5\ud574 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac04\ub2e8\ud55c \uc608\uc81c\uc785\ub2c8\ub2e4.\n            <\/p>\n
\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# feature\uc640 label \uc900\ube44\nX = ...  # features \uc0dd\uc131\ny = ...  # \uc885\uac00 \ub370\uc774\ud130\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\npredictions = model.predict(X_test)\n            <\/code><\/pre>\n<\/section>\n
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8. \uacb0\ub860<\/h2>\n
\n                \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uc9d1\ub77c\uc778\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08 \ubc0f \ubc31\ud14c\uc2a4\ud2b8 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\uc758 \uac15\uc88c\uc5d0\uc11c\ub294 \uc2ec\ud654\ub41c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95 \ubc0f \ub2e4\uc591\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uc608\uc815\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc138\uacc4\uc5d0\uc11c \uc131\uacf5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n            <\/p>\n<\/section>\n