{"id":29425,"date":"2024-10-27T16:18:23","date_gmt":"2024-10-27T16:18:23","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29425"},"modified":"2024-11-26T06:52:40","modified_gmt":"2024-11-26T06:52:40","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%8c%8c%ec%9d%b4%ec%8d%ac%ec%9d%84-%ec%9d%b4-2","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29425\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud30c\uc774\uc36c\uc744 \uc774\uc6a9\ud55c \ub3d9\uc801 \uacc4\ud68d"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \uae08\uc735 \uc2dc\uc7a5\uc758 \ubcc0\ub3d9\uc131\uacfc \ub370\uc774\ud130\uc758 \uc591\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ud000\ud2b8 \ub9e4\ub9e4\uac00 \ub300\ub450\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ub97c \ub2e4\uc9c0\uace0, \uc774\ub97c \ud30c\uc774\uc36c\uc744 \ud1b5\ud574 \uad6c\ud604\ud574\ubcf4\ub294 \ub3d9\uc801 \uacc4\ud68d\uc5d0 \ub300\ud574\uc11c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac1c\ub150<\/h2>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uac70\ub798\ub97c \uc218\ud589\ud558\ub294 \uc790\ub3d9\ud654\ub41c \ubc29\ubc95\uc73c\ub85c, \uc54c\uace0\ub9ac\uc998\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \ud000\ud2b8 \ub9e4\ub9e4 \uae30\ubc95\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub8f0\uc5d0 \ub530\ub77c \uac70\ub798 \uacb0\uc815\uc744 \ub0b4\ub9ac\ubbc0\ub85c \uac10\uc815\uc801\uc778 \uc694\uc18c\ub97c \ubc30\uc81c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc774 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n

1.1 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc5ed\ud560<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \uae30\uc220\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 \ub370\uc774\ud130\ub97c \ub354 \uae4a\uc774 \uc788\uac8c \ubd84\uc11d\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubcf5\uc7a1\ud55c \uae08\uc735 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0, \uac70\ub798 \uae30\ud68c\ub97c \ud3ec\ucc29\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/p>\n

2. \ud30c\uc774\uc36c\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529<\/h2>\n

\ud30c\uc774\uc36c\uc740 \uadf8 \uac15\ub825\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \uc9c1\uad00\uc801\uc778 \ubb38\ubc95\uc73c\ub85c \uc778\ud574 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \uc139\uc158\uc5d0\uc11c\ub294 \ud30c\uc774\uc36c\uc744 \ud65c\uc6a9\ud558\uc5ec \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud55c \uae30\ubcf8\uc801\uc778 \ud658\uacbd\uc744 \uad6c\ucd95\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

2.1 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h3>\n
pip install numpy pandas matplotlib scikit-learn tensorflow keras<\/code><\/pre>\n
\uc704\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uba74 \ub370\uc774\ud130 \ubd84\uc11d, \uae30\uacc4\ud559\uc2b5 \ubaa8\ub378 \ud559\uc2b5, \ub370\uc774\ud130 \uc2dc\uac01\ud654\ub97c \uc704\ud55c \ud658\uacbd\uc744 \uac16\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.2 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud574\uc11c\ub294 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. Yahoo Finance, Alpha Vantage, Quandl \ub4f1\uc758 API\ub97c \ud1b5\ud574 \uc8fc\uc2dd, \ud658\uc728, \uc554\ud638\ud654\ud3d0 \ub4f1\uc758 \ub370\uc774\ud130\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc608: Yahoo Finance\ub97c \ud1b5\ud55c \ub370\uc774\ud130 \uc218\uc9d1<\/h4>\n
import yfinance as yf\n\n# Apple \uc8fc\uc2dd \ub370\uc774\ud130 \uc218\uc9d1\ndata = yf.download('AAPL', start='2020-01-01', end='2023-01-01')\nprint(data.head())<\/code><\/pre>\n
3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc804\uc5d0 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uacb0\uce21\uce58 \ucc98\ub9ac, \uc815\uaddc\ud654, \ud53c\uccd0 \uc5d4\uc9c0\ub2c8\uc5b4\ub9c1 \ub4f1\uc758 \uc791\uc5c5\uc744 \ud1b5\ud574 \ub370\uc774\ud130\uc758 \uc9c8\uc744 \ub192\uc774\uace0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \uacb0\uce21\uce58 \ucc98\ub9ac<\/h3>\n
data.fillna(method='ffill', inplace=True)<\/code><\/pre>\n
\uc704\uc758 \ucf54\ub4dc\ub294 \uacb0\uce21\uce58\ub97c \uc55e\uc758 \uac12\uc73c\ub85c \ucc44\uc6b0\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
3.2 \ub370\uc774\ud130 \uc815\uaddc\ud654<\/h3>\n
\ubaa8\ub378\uc5d0 \uc785\ub825\ub418\ub294 \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\uba74 \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub192\uc774\uace0 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Min-Max \uc2a4\ucf00\uc77c\ub9c1 \ub610\ub294 Z-score \uc2a4\ucf00\uc77c\ub9c1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler()\nscaled_data = scaler.fit_transform(data[['Close']])<\/code><\/pre>\n
4. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uc120\ud0dd<\/h2>\n
\uc774\uc81c \uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\uc5ec \ud559\uc2b5\uc2dc\ucf1c\uc57c \ud569\ub2c8\ub2e4. \uc8fc\ub85c \uc0ac\uc6a9\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc740 \ud68c\uadc0\ubd84\uc11d, \uacb0\uc815\ud2b8\ub9ac, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8, SVM, LSTM \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \uc608\uce21\uc744 \uc704\ud55c \ud68c\uadc0\ubaa8\ub378<\/h3>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\nX = scaled_data[:-1]\ny = scaled_data[1:]\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n
5. \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc801\uc6a9<\/h2>\n
\ub525\ub7ec\ub2dd\uc744 \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0, LSTM(Long Short Term Memory) \ub124\ud2b8\uc6cc\ud06c\ub97c \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. LSTM\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130 \uc608\uce21\uc5d0 \ud2b9\ud788 \uac15\ub825\ud55c \ubaa8\ub378\ub85c, \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21\uc5d0 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
5.1 LSTM \ubaa8\ub378 \uad6c\uc131<\/h3>\n
import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\n\n# \ub370\uc774\ud130 \uc900\ube44\nX, y = [], []\nfor i in range(60, len(scaled_data)):\n    X.append(scaled_data[i-60:i, 0])\n    y.append(scaled_data[i, 0])\n\nX, y = np.array(X), np.array(y)\n\nX = np.reshape(X, (X.shape[0], X.shape[1], 1))\n\n# \ubaa8\ub378 \uc815\uc758\nmodel = Sequential()\nmodel.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))\nmodel.add(LSTM(units=50))\nmodel.add(Dense(units=1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X, y, epochs=50, batch_size=32)<\/code><\/pre>\n
6. \uc131\uacfc \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4\uc5d0\ub294 \uc131\uacfc\ub97c \ud3c9\uac00\ud558\uc5ec, \uc2e4\uc81c \uac70\ub798 \uc804\ub7b5\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc744\uc9c0\ub97c \ud310\ub2e8\ud574\uc57c \ud569\ub2c8\ub2e4. MSE(Mean Squared Error), MAE(Mean Absolute Error) \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc131\uacfc\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n
6.1 \uc131\uacfc \ube44\uad50<\/h3>\n
from sklearn.metrics import mean_squared_error\n\npredictions = model.predict(X_test)\nmse = mean_squared_error(y_test, predictions)\nprint(f'Mean Squared Error: {mse}') <\/code><\/pre>\n
7. \ub3d9\uc801 \uacc4\ud68d\uc744 \ud1b5\ud55c \ucd5c\uc801\ud654<\/h2>\n
\ub3d9\uc801 \uacc4\ud68d\ubc95(Dynamic Programming, DP)\uc740 \ubcf5\uc7a1\ud55c \ubb38\uc81c\ub97c \ub354 \uac04\ub2e8\ud55c \ud558\uc704 \ubb38\uc81c\ub85c \ub098\ub204\uc5b4 \ud574\uacb0\ud558\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ub3d9\uc801 \uacc4\ud68d\ubc95\uc740 \ub9e4\ub9e4 \uc804\ub7b5\uc758 \ucd5c\uc801\ud654\ub97c \uc704\ud574 \uc774\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.1 \ub3d9\uc801 \uacc4\ud68d\ubc95\uc758 \uae30\ucd08<\/h3>\n
\ub3d9\uc801 \uacc4\ud68d\ubc95\uc744 \uc0ac\uc6a9\ud558\uba74 \uc8fc\uc2dd \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \ud0c0\uc774\ubc0d\uc744 \uace0\ub824\ud558\uc5ec \ucd5c\ub300 \uc774\uc775\uc744 \uc704\ud55c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0c1\ud0dc\uc640 \uacb0\uc815\uc744 \uc815\uc758\ud574\uc57c \ud558\uba70, \uc774\ub97c \uc704\ud574 \uc885\ubaa9\uc758 \uac00\uaca9 \ub370\uc774\ud130\uc640 \ud574\ub2f9 \uc2dc\uc810\uc758 \ubcf4\uc720 \uc8fc\uc2dd \uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.2 \uac00\uce58 \ud568\uc218 \uc815\uc758<\/h3>\n
\uac00\uce58 \ud568\uc218\ub294 \ud2b9\uc815 \uc0c1\ud0dc\uc5d0\uc11c\uc758 \ucd5c\ub300 \ubcf4\uc0c1\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. Q-\ub7ec\ub2dd(Q-Learning)\uacfc \uac19\uc740 \uac15\ud654\ud559\uc2b5 \uae30\ubc95\uc744 \ud1b5\ud574 \uc774 \ud568\uc218\ub97c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.3 \uc608\uc2dc: Q-Learning<\/h3>\n
import numpy as np\n\ndef initialize_q_table(states, actions):\n    return np.zeros((states, actions))\n\ndef update_q_value(q_table, state, action, reward, next_state, alpha, gamma):\n    best_next_action = np.argmax(q_table[next_state])\n    td_target = reward + gamma * q_table[next_state][best_next_action]\n    q_table[state][action] += alpha * (td_target - q_table[state][action])\n<\/code><\/pre>\n
8. \uc2e4\uc81c \uc801\uc6a9 \uc0ac\ub840<\/h2>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc2e4\uc81c\ub85c \uad6c\ud604\ud55c \uc0ac\ub840\ub4e4\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc811\uadfc \ubc29\uc2dd\uacfc \ubaa8\ub378\uc744 \ud1b5\ud574 \uc131\uacfc\ub97c \ub0b4\uace0 \uc788\ub294 \uc0ac\ub840\ub4e4\uc744 \ubd84\uc11d\ud558\uc5ec, \ub3c5\uc790 \uc5ec\ub7ec\ubd84\ub4e4\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uad6c\uccb4\ud654\ud574\ubcf4\uc138\uc694.<\/p>\n
8.1 \uc0ac\ub840 \ubd84\uc11d: 2008\ub144 \uae08\uc735\uc704\uae30<\/h3>\n
2008\ub144 \uae08\uc735\uc704\uae30 \uc0ac\ub840\ub97c \ubd84\uc11d\ud558\uc5ec, \ub2f9\uc2dc\uc758 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \uc608\uce21 \ubaa8\ub378\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uae30\uacc4\ud559\uc2b5 \ubaa8\ub378\uc758 \uc801\uc6a9\uc73c\ub85c \uc5b4\ub5bb\uac8c \uc608\uc0c1\ud558\uc9c0 \ubabb\ud55c \uc0c1\ud669\uc744 \ub300\ube44\ud560 \uc218 \uc788\uc5c8\ub294\uc9c0\ub97c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
8.2 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uae30\uc5c5\uc758 \uc5f0\uad6c\uc640 \uc131\uacfc<\/h3>\n
\uc2e4\uc81c\ub85c \ub9ce\uc740 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uae30\uc5c5\ub4e4\uc774 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uc131\uacfc\ub97c \ub0b4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub4e4\uc758 \uc811\uadfc \ubc29\uc2dd \ubc0f \uc0ac\uc6a9\ub41c \ubaa8\ub378\uc744 \uc0b4\ud3b4\ubcf4\uba70, \ubc30\uc6b8 \uc810\uc744 \uacf5\uc720\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
9. \uacb0\ub860<\/h2>\n
\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud5a5\ud6c4 \ub354 \ub9ce\uc740 \uac00\ub2a5\uc131\uc744 \ubcf4\uc5ec\uc904 \uac83\uc785\ub2c8\ub2e4. \ud30c\uc774\uc36c\uacfc \ud568\uaed8 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\uc591\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub3d9\uc801 \uacc4\ud68d\ubc95\uc744 \ud1b5\ud574 \ub354 \ub098\uc740 \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\uc640 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
10. \ucc38\uace0 \uc790\ub8cc<\/h2>\n