{"id":29053,"date":"2024-10-27T16:16:33","date_gmt":"2024-10-27T16:16:33","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29053"},"modified":"2024-11-26T06:54:53","modified_gmt":"2024-11-26T06:54:53","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-%eb%b6%84-%eb%8d%b0%ec%9d%b4%ed%84%b0%eb%a5%bc","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29053\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ubd84 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574 \uc790\uccb4 \ubc88\ub4e4 \uc778\uc81c\uc2a4\ud2b8"},"content":{"rendered":"

<\/p>\n

\uc624\ub298\ub0a0 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc810\uc810 \ub354 \ubcf4\ud3b8\ud654\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \uac70\ub798 \uc804\ub7b5 \uac1c\ubc1c\uc5d0 \uc788\uc5b4 \ud070 \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uacfc\ud559\uacfc \uc778\uacf5\uc9c0\ub2a5\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \uc774\uc804\uc5d0\ub294 \ubd88\uac00\ub2a5\ud588\ub358 \ubc29\uc2dd\uc73c\ub85c \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc774\ub97c \ud1b5\ud574 \uac70\ub798 \uacb0\uc815\uc744 \uc790\ub3d9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

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

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780, \uac70\ub798\ub97c \uc218\ud589\ud558\ub294 \uae30\uc900\uc744 \ubbf8\ub9ac \uc124\uc815\ud574 \ub193\uace0 \uc774\ub97c \uc790\ub3d9\uc73c\ub85c \uc2e4\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \ub9d0\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc2e0\uc18d\ud558\uac8c \ubd84\uc11d\ud558\uace0 \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\ub294 \ub2a5\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1 \ub370\uc774\ud130\uc758 \uc911\uc694\uc131<\/h3>\n

\ubaa8\ub4e0 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud569\ub2c8\ub2e4. \uace0\ud488\uc9c8\uc758 \ub370\uc774\ud130\ub294 \ub354 \ub098\uc740 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc8fc\uac00 \ub370\uc774\ud130, \uac70\ub798\ub7c9, \uc7ac\ubb34\uc81c\ud45c, \ub274\uc2a4 \uae30\uc0ac \ub4f1 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc6b0\ub9ac\ub294 \uc8fc\uac00 \ub370\uc774\ud130\uc640 \uac19\uc740 \ubd84 \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n

2. \ubd84 \ub370\uc774\ud130\uc640 \uc790\uccb4 \ubc88\ub4e4 \uc778\uc81c\uc2a4\ud2b8<\/h2>\n

\ubd84 \ub370\uc774\ud130\ub294 \uac70\ub798 \uacb0\uc815\uc5d0 \ub9e4\uc6b0 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \ubd84 \ub2e8\uc704\ub85c \uc218\uc9d1\ub418\ub294 \ub370\uc774\ud130\ub294 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ud3ec\ucc29\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ub610\ud55c, \uc774\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc774 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud560 \uc218 \uc788\ub294 \uae30\ubc18\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n

2.1 \uc790\uccb4 \ubc88\ub4e4 \uc778\uc81c\uc2a4\ud2b8\ub780?<\/h3>\n

\uc790\uccb4 \ubc88\ub4e4 \uc778\uc81c\uc2a4\ud2b8\ub294 \ub370\uc774\ud130\ub97c \uc218\uc9d1, \ucc98\ub9ac \ubc0f \uc800\uc7a5\ud558\ub294 \uacfc\uc815\uc744 \uc790\ub3d9\ud654\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc758 \uc2e0\ub8b0\uc131\uc744 \ub192\uc774\uace0 \ubaa8\ub378\uc758 \ud6c8\ub828\uc5d0 \ud544\uc694\ud55c \ub370\uc774\ud130\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uacf5\uae09\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ub370\uc774\ud130\uc758 \uc815\uc81c \ubc0f \ubcc0\ud658, \ub204\ub77d\uac12 \ucc98\ub9ac, \uc2a4\ucf00\uc77c\ub9c1\uacfc \uac19\uc740 \uc804\ucc98\ub9ac \uc791\uc5c5\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n

3. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n

\ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc774 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uc5ec\uae30\uc11c\ub294 \ud2b9\ud788 \uc8fc\uac00 \uc608\uce21\uc5d0 \ud6a8\uacfc\uc801\uc778 \uc54c\uace0\ub9ac\uc998 \uba87 \uac00\uc9c0\ub97c \uc18c\uac1c\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

3.1 \uc120\ud615 \ud68c\uadc0(Linear Regression)<\/h3>\n

\uc120\ud615 \ud68c\uadc0\ub294 \uc608\uce21 \ubb38\uc81c\uc758 \uac00\uc7a5 \uae30\ubcf8\uc801\uc778 \ud615\ud0dc\ub85c, \ud558\ub098 \uc774\uc0c1\uc758 \ub3c5\ub9bd\ubcc0\uc218\uc640 \uc885\uc18d\ubcc0\uc218 \uac04\uc758 \uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud569\ub2c8\ub2e4.<\/p>\n

import numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\n\n# \ud2b9\uc131\uacfc \ub808\uc774\ube14 \uc120\ud0dd\nX = data[['feature1', 'feature2']]\ny = data['target']\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
3.2 \uacb0\uc815 \ud2b8\ub9ac(Decision Tree)<\/h3>\n
\uacb0\uc815 \ud2b8\ub9ac\ub294 \uc758\uc0ac \uacb0\uc815 \uaddc\uce59\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc608\uce21 \ubaa8\ub378\ub85c, \uc9c1\uad00\uc801\uc73c\ub85c \uacb0\uacfc\ub97c \ud574\uc11d\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.tree import DecisionTreeRegressor\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel = DecisionTreeRegressor()\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\npredictions = model.predict(X_test)\n<\/code><\/pre>\n
3.3 LSTM(Long Short-Term Memory)<\/h3>\n
LSTM\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21\uc5d0 \ud2b9\ud654\ub41c \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN) \uc544\ud0a4\ud14d\ucc98\ub85c, \uacfc\uac70\uc758 \uc815\ubcf4\ub97c \uae30\uc5b5\ud558\uc5ec \ubbf8\ub798\uc758 \uc608\uce21\uc5d0 \ud65c\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
import numpy as np\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\n# (\uc774 \ubd80\ubd84\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c LSTM\uc5d0 \ub9de\uac8c \ubcc0\ud615\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4)\n\n# \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(timesteps, features)))\nmodel.add(LSTM(50))\nmodel.add(Dense(1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud6c8\ub828\nmodel.fit(X_train, y_train, epochs=100, batch_size=32)\n\n# \uc608\uce21\npredictions = model.predict(X_test)\n<\/code><\/pre>\n
4. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \ucd5c\uc801\ud654<\/h2>\n
\ubaa8\ub378\uc744 \ud6c8\ub828\ud55c \ud6c4, \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \ucd5c\uc801\ud654\ud558\ub294 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uc591\ud55c \ud3c9\uac00 \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n
4.1 \ud3c9\uac00 \uc9c0\ud45c<\/h3>\n
\ub300\ud45c\uc801\uc778 \ud3c9\uac00 \uc9c0\ud45c\ub85c\ub294 \ud3c9\uade0 \uc81c\uacf1\uadfc \uc624\ucc28(RMSE), \ud3c9\uade0 \uc808\ub300 \uc624\ucc28(MAE), R2<\/sup> \uac12 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.metrics import mean_squared_error, r2_score\n\n# RMSE \uacc4\uc0b0\nrmse = np.sqrt(mean_squared_error(y_test, predictions))\n\n# R2 \uacc4\uc0b0\nr2 = r2_score(y_test, predictions)\n\nprint('RMSE:', rmse)\nprint('R2:', r2)\n<\/code><\/pre>\n
4.2 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\ub300\ud654\ud558\uae30 \uc704\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uadf8\ub9ac\ub4dc \uc11c\uce58 \ub610\ub294 \ubca0\uc774\uc9c0\uc548 \ucd5c\uc801\ud654\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import GridSearchCV\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uadf8\ub9ac\ub4dc \uc124\uc815\nparam_grid = {\n    'max_depth': [None, 10, 20, 30],\n    'min_samples_split': [2, 5, 10]\n}\n\ngrid_search = GridSearchCV(DecisionTreeRegressor(), param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\n# \ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\nprint('Best parameters:', grid_search.best_params_)\n<\/code><\/pre>\n
5. \uc790\ub3d9 \uac70\ub798 \uc2dc\uc2a4\ud15c \uad6c\ud604<\/h2>\n
\ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\ud55c \uac12\uc744 \uac00\uc9c0\uace0 \uc790\ub3d9 \uac70\ub798 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ube0c\ub85c\ucee4 API\ub97c \ud1b5\ud574 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n
5.1 API \ud1b5\ud569<\/h3>\n
\uc790\ub3d9 \uac70\ub798 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc8fc\uc2dd \uac70\ub798\ub97c \uc704\ud55c API\uc640 \ud1b5\ud569\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\ub7ec \ube0c\ub85c\ucee4\ub4e4\uc774 API\ub97c \uc81c\uacf5\ud558\ubbc0\ub85c \uc774\ub97c \uc774\uc6a9\ud558\uc5ec \uac70\ub798\ub97c \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import requests\n\ndef buy_stock(symbol, amount):\n    # API \ud638\ucd9c \ucf54\ub4dc \uc791\uc131 (\uac00\uc0c1\uc758 \uc608)\n    response = requests.post('https:\/\/api.broker.com\/buy', json={\n        'symbol': symbol,\n        'amount': amount\n    })\n    return response.json()\n<\/code><\/pre>\n
5.2 \uac70\ub798 \uc804\ub7b5 \uc124\uc815<\/h3>\n
\uac70\ub798 \uc804\ub7b5\uc744 \uc815\uc758\ud558\uace0 \uc870\uac74\uc5d0 \ub530\ub77c \ub9e4\ub9e4\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ubaa8\ub378\uc758 \uc608\uce21\uac12\uc774 \ud2b9\uc815 \uae30\uc900\uc744 \ucd08\uacfc\ud560 \uacbd\uc6b0 \uc8fc\uc2dd\uc744 \ub9e4\uc218\ud569\ub2c8\ub2e4.<\/p>\n
if predictions[-1] > threshold:\n    buy_stock('AAPL', 10)\n<\/code><\/pre>\n
6. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130\uc640 \uae30\uc220\uc758 \uc735\ud569\uc744 \ud1b5\ud574 \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \ub3c5\ucc3d\uc801\uc778 \uac70\ub798 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ub370 \ud070 \uc7a0\uc7ac\ub825\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uae30\ubcf8\uc801\uc778 \uc774\ud574\uc640 \uc2e4\uc804 \uc801\uc6a9 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc9c0\uc2dd\uc744 \uc313\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
7. \ucc38\uace0 \uc790\ub8cc<\/h2>\n