{"id":31403,"date":"2024-10-28T12:01:46","date_gmt":"2024-10-28T12:01:46","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31403"},"modified":"2024-11-26T06:45:16","modified_gmt":"2024-11-26T06:45:16","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d%ea%b3%bc-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d%ec%9d%84-%ed%99%9c%ec%9a%a9%ed%95%9c-%ec%9e%90%eb%8f%99%eb%a7%a4%eb%a7%a4-%ec%8b%a0%ea%b2%bd%eb%a7%9d%ec%9d%84-%ec%9d%b4","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31403\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud55c \uac00\uaca9 \ubcc0\ub3d9\uc131 \uc608\uce21 \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\uc744 \uc774\uc6a9\ud574 \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131 \uc608\uce21\ud558\uae30."},"content":{"rendered":"

<\/p>\n

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

\n \ube44\ud2b8\ucf54\uc778\uc740 \uadf8 \uc790\uccb4\ub85c \ub9e4\ub825\uc801\uc778 \ud22c\uc790 \uc790\uc0b0\uc774\uc9c0\ub9cc, \uac00\uaca9 \ubcc0\ub3d9\uc131\uc774 \ub9e4\uc6b0 \ud07d\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\uace0 \ud65c\uc6a9\ud558\uae30 \uc704\ud574 \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc740 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\uc744 \ud65c\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\ub294 \uae30\ucd08\ub97c \ub2e4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

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

\n \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \uc608\uce21\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud328\ud134\uc744 \ubc30\uc6b0\uace0 \uc774\ub97c \ud1a0\ub300\ub85c \uc608\uce21\uc774\ub098 \uacb0\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uce35\uc758 \uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

3. \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\ub780?<\/h2>\n

\n \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\uc740 \uc5ec\ub7ec \uac1c\uc758 \uce35\uc73c\ub85c \uad6c\uc131\ub41c \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc73c\ub85c, \uc785\ub825\uce35, \uc228\uaca8\uc9c4 \uce35, \ucd9c\ub825\uce35\uc73c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \uce35\uc758 \ub178\ub4dc\ub294 \ub2e4\uc74c \uce35\uc758 \ub178\ub4dc\uc640 \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ud65c\uc131\ud654 \ud568\uc218\ub97c \ud1b5\ud574 \ucd9c\ub825\uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4. MLP\ub294 \ubcf5\uc7a1\ud55c \ube44\uc120\ud615 \ud568\uc218\ub97c \ud559\uc2b5\ud558\ub294 \ub370 \ud2b9\ud788 \uc720\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n

4. \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c \uc608\uce21<\/h2>\n

\n \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uc608\uce21\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \uc774\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc608\ub97c \ub4e4\uc5b4 \uac70\ub798\uc18c API\ub97c \uc774\uc6a9\ud574 \ub370\uc774\ud130\ub97c \ubc1b\uc544\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

4.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
import pandas as pd\n\n# \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c CSV \ud30c\uc77c\uc5d0\uc11c \ub85c\ub4dc\ud569\ub2c8\ub2e4.\ndf = pd.read_csv('bitcoin_price.csv')\nprint(df.head())<\/code><\/pre>\n
4.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n    \uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ub300\ubd80\ubd84 \uacb0\uce21\uce58\ub098 \uc7a1\uc74c\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\ubbc0\ub85c, \uc801\uc808\ud55c \uc804\ucc98\ub9ac\ub97c \ud1b5\ud574 \ubaa8\ub378\uc5d0 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud574\uc57c \ud569\ub2c8\ub2e4. \ubcf4\ud1b5 \uac00\uaca9\uc758 \ucc28\uc774, \ub85c\uadf8 \ubcc0\ud658 \ub4f1\uc758 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc815\uc81c\ud569\ub2c8\ub2e4.\n<\/p>\n
# \uacb0\uce21\uce58 \uc81c\uac70\ndf.dropna(inplace=True)\n\n# \uac00\uaca9 \ubcc0\ub3d9\uc131 \uacc4\uc0b0 (\ub85c\uadf8 \uc218\uc775\ub960)\ndf['returns'] = df['Close'].pct_change()\ndf.dropna(inplace=True)<\/code><\/pre>\n
4.3 \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac<\/h3>\n
\n    \ubaa8\ub378 \ud6c8\ub828\uc744 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130\ub97c \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ubd84\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 70-80%\uc758 \ub370\uc774\ud130\ub97c \ud6c8\ub828\uc5d0 \uc0ac\uc6a9\ud558\uace0, \ub098\uba38\uc9c0 20-30%\ub97c \ud14c\uc2a4\ud2b8\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = df[['Open', 'High', 'Low', 'Volume']]\ny = df['returns']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n
5. MLP \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\n    \uc774\uc81c \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP) \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \ucc28\ub840\uc785\ub2c8\ub2e4. Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc27d\uac8c \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
from keras.models import Sequential\nfrom keras.layers import Dense\n\n# MLP \ubaa8\ub378 \uc0dd\uc131\nmodel = Sequential()\nmodel.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dense(1, activation='linear'))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(loss='mean_squared_error', optimizer='adam')<\/code><\/pre>\n
6. \ubaa8\ub378 \ud6c8\ub828<\/h2>\n
\n    \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0a4\uae30 \uc704\ud574 fit() \uba54\uc11c\ub4dc\ub97c \ud638\ucd9c\ud569\ub2c8\ub2e4.\n<\/p>\n
model.fit(X_train, y_train, epochs=100, batch_size=10)<\/code><\/pre>\n
7. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\n    \ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud569\ub2c8\ub2e4. \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uc774\ub97c \uc2e4\uc81c \uac12\uacfc \ube44\uad50\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
y_pred = model.predict(X_test)\n\nfrom sklearn.metrics import mean_squared_error\nmse = mean_squared_error(y_test, y_pred)\nprint(f'Mean Squared Error: {mse}') <\/code><\/pre>\n
8. \uc608\uce21 \uacb0\uacfc \uc2dc\uac01\ud654<\/h2>\n
\n    \uc608\uce21 \uc131\uacfc\ub97c \ud55c\ub208\uc5d0 \ud655\uc778\ud558\uae30 \uc704\ud574 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Matplotlib \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uadf8\ub798\ud504\ub97c \uadf8\ub9bd\ub2c8\ub2e4.\n<\/p>\n
import matplotlib.pyplot as plt\n\nplt.figure(figsize=(12, 6))\nplt.plot(y_test.values, label='Actual')\nplt.plot(y_pred, label='Predicted', alpha=0.7)\nplt.title('Bitcoin Return Prediction')\nplt.xlabel('Time Step')\nplt.ylabel('Returns')\nplt.legend()\nplt.show()<\/code><\/pre>\n
9. \uacb0\ub860<\/h2>\n
\n    \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP)\uc744 \uc0ac\uc6a9\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6e0\uc2b5\ub2c8\ub2e4. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \uc608\uce21 \uacb0\uacfc\ub97c \uc2e4\uc81c \ub9e4\ub9e4 \uc804\ub7b5\uc5d0 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud560\uc9c0\ub97c \uace0\ubbfc\ud574\uc57c \ud569\ub2c8\ub2e4. \ucd94\uac00\uc801\uc73c\ub85c, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \ub2e4\uc591\ud55c \uc2e0\uacbd\ub9dd \uad6c\uc870, \uadf8\ub9ac\uace0 \ub2e4\ub978 \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \ud65c\uc6a9\ud55c \uc2ec\ud654 \uc5f0\uad6c\ub97c \ud1b5\ud574 \ub354\uc6b1 \ud5a5\uc0c1\ub41c \uc131\uacfc\ub97c \uae30\ub300\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
10. \ucc38\uace0 \uc790\ub8cc<\/h2>\n