\uae30\uc220\uc801 \uc9c0\ud45c: MACD, RSI\uc640 \uac19\uc740 \uc9c0\ud45c\ub97c \ud65c\uc6a9\ud558\uc5ec \uc2dc\uc7a5\uc758 \uc8fc\uae30\uc131\uc744 \uac10\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n4. \uacc4\uc0b0 \uadf8\ub798\ud504 \uc774\ud574\ud558\uae30<\/h2>\n
\uacc4\uc0b0 \uadf8\ub798\ud504\ub294 \ub525\ub7ec\ub2dd\uc5d0\uc11c \ud575\uc2ec\uc801\uc778 \uac1c\ub150\uc73c\ub85c, \ub370\uc774\ud130 \ud750\ub984\uc744 \ub178\ub4dc\uc640 \uc5e3\uc2a4\ub85c \ud45c\ud604\ud55c \uad6c\uc870\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \ub178\ub4dc\ub294 \uc218\ud559\uc801 \uc5f0\uc0b0\uc744 \ub098\ud0c0\ub0b4\uace0, \uc5e3\uc2a4\ub294 \ub370\uc774\ud130\ub97c \ud750\ub974\uac8c \ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubcf5\uc7a1\ud55c \uc5f0\uc0b0\uc744 \ubcf4\ub2e4 \ud6a8\uc728\uc801\uc73c\ub85c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 TensorFlow\uc640 PyTorch<\/h3>\n
\ub450 \uac00\uc9c0 \uc720\uba85\ud55c \uacc4\uc0b0 \uadf8\ub798\ud504 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc778 TensorFlow\uc640 PyTorch\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \ub54c \uc8fc\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. TensorFlow\ub294 \uc815\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud558\uace0, PyTorch\ub294 \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4. \ub3d9\uc801 \uacc4\uc0b0 \uadf8\ub798\ud504\ub294 \ubaa8\ub378\uc744 \ub514\ubc84\uae45\ud558\uace0 \uc218\uc815\ud558\uae30\uac00 \uc6a9\uc774\ud558\uc5ec \ub9ce\uc740 \uc5f0\uad6c\uc790\ub4e4\uc774 \uc120\ud638\ud558\ub294 \ubc29\ud5a5\uc785\ub2c8\ub2e4.<\/p>\n
5. \uc21c\ud658 \uc8fc\uae30\ub85c \uacc4\uc0b0 \uadf8\ub798\ud504 \ud3bc\uce58\uae30<\/h2>\n
\uc21c\ud658 \uc8fc\uae30\ub97c \uacc4\uc0b0 \uadf8\ub798\ud504\uc5d0 \ud1b5\ud569\ud558\ub294 \ubc29\ubc95\uc740 \ub9e4\ub9e4 \uc804\ub7b5\uc758 \uac15\ub825\ud55c \uc608\uce21 \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN; Recurrent Neural Networks)\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\uc640 \uac19\uc740 \uc21c\ucc28\uc801\uc778 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n
5.1 \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)<\/h3>\n
RNN\uc740 \uc774\uc804 \uc0c1\ud0dc\ub97c \uae30\uc5b5\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ub2e4\uc74c \uc0c1\ud0dc\ub97c \uc608\uce21\ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uc2dc\uc7a5 \ub370\uc774\ud130\uc640 \uac19\uc740 \uc2dc\uacc4\uc5f4 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc77c\ubc18\uc801\uc778 RNN\uc740 \uc7a5\uae30\uc801\uc778 \uc758\uc874\uc131\uc744 \ud559\uc2b5\ud558\uae30 \uc5b4\ub824\uc6b4 \ub2e8\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 LSTM(Long Short-Term Memory)<\/h3>\n
RNN\uc758 \ud55c \uc885\ub958\uc778 LSTM\uc740 \uadf8\ub7ec\ud55c \ub2e8\uc810\uc744 \ubcf4\uc644\ud558\uae30 \uc704\ud574 \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc785\ub825 \uac8c\uc774\ud2b8, \ub9dd\uac01 \uac8c\uc774\ud2b8, \ucd9c\ub825 \uac8c\uc774\ud2b8\ub97c \ud1b5\ud574 \uc911\uc694\ud55c \uc815\ubcf4\ub97c \uc7a5\uae30\uc801\uc73c\ub85c \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud65c\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9\uc758 \uc8fc\uae30\uc131\uc744 \ud30c\uc545\ud558\uace0 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.3 GRU(Gated Recurrent Unit)<\/h3>\n
GRU\ub294 LSTM\uc758 \ubcc0\ud615\uc73c\ub85c, \uad6c\uc870\uac00 \uac04\ub2e8\ud558\uba74\uc11c\ub3c4 \ube44\uc2b7\ud55c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. GRU\ub294 \ub450 \uac1c\uc758 \uac8c\uc774\ud2b8\ub9cc\uc73c\ub85c \uc815\ubcf4\ub97c \ucc98\ub9ac\ud558\uc5ec, \uc5f0\uc0b0 \ud6a8\uc728\uc131\uc774 \ud5a5\uc0c1\ub429\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ube60\ub974\uace0 \uac04\ub2e8\ud558\uac8c \uc21c\ud658 \uc8fc\uae30\ub97c \ud65c\uc6a9\ud55c \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
6. \uc2e4\uc2b5: \uc21c\ud658 \uc8fc\uae30\ub97c \uc704\ud55c RNN \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc774\uc81c RNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uace0, \uc21c\ud658 \uc8fc\uae30\ub97c \ud65c\uc6a9\ud55c \uc608\uce21\uc744 \uc9c4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
6.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\uc8fc\uc2dd \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 Python\uc758 yfinance<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \ud2b9\uc815 \uc8fc\uc2dd\uc758 \uacfc\uac70 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\nimport yfinance as yf\n\n# \uc560\ud50c \uc8fc\uc2dd \ub370\uc774\ud130 \uc218\uc9d1\ndata = yf.download('AAPL', start='2020-01-01', end='2023-01-01')\ndata = data['Close'].values\n<\/code><\/pre>\n6.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \uc785\ub825\ud558\uae30 \uc804\uc5d0 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\uace0, \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ub098\ub204\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/p>\n
from sklearn.preprocessing import MinMaxScaler\nimport numpy as np\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled_data = scaler.fit_transform(data.reshape(-1, 1))\n\n# \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac\ntrain_size = int(len(scaled_data) * 0.8)\ntrain_data = scaled_data[0:train_size]\ntest_data = scaled_data[train_size:]\n<\/code><\/pre>\n6.3 RNN \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud574 RNN \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
from keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Dropout\n\n# RNN \ubaa8\ub378 \uc0dd\uc131\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(train_data.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1))\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n<\/code><\/pre>\n6.4 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\ud6c8\ub828 \ub370\uc774\ud130\ub85c \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
model.fit(train_data, epochs=50, batch_size=32)\n<\/code><\/pre>\n6.5 \uc608\uce21 \ubc0f \uacb0\uacfc \uc2dc\uac01\ud654<\/h3>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc774\uc6a9\ud574 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0, \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/p>\n
import matplotlib.pyplot as plt\n\n# \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\npredictions = model.predict(test_data)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.plot(scaler.inverse_transform(test_data), label='Actual Prices')\nplt.plot(scaler.inverse_transform(predictions), label='Predicted Prices')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\ub294 \uc774\ud574\ub97c \uac00\uc9c8 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4. \uc21c\ud658 \uc8fc\uae30\uc758 \uc911\uc694\uc131\uacfc \uc774\ub97c \ud65c\uc6a9\ud55c \uacc4\uc0b0 \uadf8\ub798\ud504\uc758 \ud65c\uc6a9\ubc95\uc744 \ud1b5\ud574 \ud6a8\uacfc\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubbf8\ub798\uc758 \uae08\uc735 \uc2dc\uc7a5\uc740 \ub354\uc6b1 \ubcf5\uc7a1\ud574\uc9c8 \uac83\uc774\uc9c0\ub9cc, \uac15\ub825\ud55c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ubc95\uacfc \uae30\uc220\uc744 \ud1b5\ud574 \uc131\uacf5\uc801\uc778 \ud2b8\ub808\uc774\ub529\uc744 \uc774\ub8f0 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc5ec\ub7ec\ubd84\uc758 \uc131\uacf5\uc801\uc778 \ud2b8\ub808\uc774\ub529\uc744 \uc751\uc6d0\ud569\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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