\uc624\ub298\ub0a0 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc810\uc810 \ub354 \ub9ce\uc740 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc758\uc0ac \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc73c\uba70, \uc774\ub294 \ud2b9\ud788 \uae08\uc735 \ub370\uc774\ud130\uc640 \uac19\uc774 \ubc29\ub300\ud55c \uc591\uc758 \ub370\uc774\ud130\uac00 \uc874\uc7ac\ud558\ub294 \ubd84\uc57c\uc5d0\uc11c\ub294 \ub354\uc6b1 \uc911\uc694\ud558\uac8c \uc5ec\uaca8\uc9d1\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd(ML)\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21\uc744 \uc218\ud589\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ubc18\uba74 \ub525\ub7ec\ub2dd(DL)\uc740 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uc5ec, \ub354\uc6b1 \ubcf5\uc7a1\ud558\uace0 \ube44\uc120\ud615\uc801\uc778 \ud328\ud134 \ubd84\uc11d\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\ub294 \uc774\ub4e4 \uae30\uc220\uc744 \ud1b5\ud574 \uc2dc\uc7a5\uc758 \ubcc0\ub3d9\uc131\uc744 \uc608\uce21\ud558\uace0, \ucd5c\uc801\uc758 \ub9e4\ub9e4 \uc2dc\uc810\uc744 \ucc3e\uc544\ub0b4\ub294 \ub370 \ud65c\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc5d0\ub294 \uc5ec\ub7ec \uc885\ub958\uac00 \uc788\uc73c\uba70, \uc77c\ubc18\uc801\uc73c\ub85c \uc544\ub798\uc640 \uac19\uc740 \uc885\ub958\ub85c \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \ub2e4\uce35 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\uc758 \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \uc8fc\uc694 \ucef4\ud3ec\ub10c\ud2b8\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n
\ud310\ub2e4\uc2a4\ub294 \ud30c\uc774\uc36c\uc758 \ub370\uc774\ud130 \ubd84\uc11d \ub77c\uc774\ube0c\ub7ec\ub9ac\ub85c, \ub370\uc774\ud130 \uc870\uc791 \ubc0f \ubd84\uc11d\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uae30\ub2a5\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \ud310\ub2e4\uc2a4\ub97c \ud65c\uc6a9\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uba3c\uc800, \uae08\uc735 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uacf5\uacf5 \ub370\uc774\ud130 API\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ubc1b\uc744 \uc218 \uc788\uace0 \ub85c\uceec \ud30c\uc77c\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc77d\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc2dc\ub294 CSV \ud30c\uc77c\uc5d0\uc11c \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4:<\/p>\n
import pandas as pd\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv')\nprint(data.head())<\/code><\/pre>\n2.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ub85c\ub529\ud55c \ub370\uc774\ud130\ub294 \uc885\uc885 \uacb0\uce21\uce58\ub098 \uc774\uc0c1\uce58\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\ub294 \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
# \uacb0\uce21\uce58 \uc81c\uac70\ndata = data.dropna()\n\n# \ub610\ub294 \uacb0\uce21\uce58\ub97c \ud3c9\uade0\uac12\uc73c\ub85c \ub300\uccb4\ud558\uae30\ndata.fillna(data.mean(), inplace=True)<\/code><\/pre>\n2.3 \uc6d0\uac70\ub9ac \ub370\uc774\ud130 \uc811\uadfc<\/h3>\n
\uc6d0\uac70\ub9ac \ub370\uc774\ud130 \uc811\uadfc\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Yahoo Finance API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc9c1\uc811 \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
import yfinance as yf\n\n# \uc560\ud50c\uc758 \uc8fc\uc2dd \ub370\uc774\ud130 \uac00\uc838\uc624\uae30\nticker = 'AAPL'\ndata = yf.download(ticker, start='2020-01-01', end='2023-01-01')\nprint(data.head())<\/code><\/pre>\n3. \ud544\uc218 \ub77c\uc774\ube0c\ub7ec\ub9ac \ubc0f \uc124\uce58 \ubc29\ubc95<\/h2>\n
\ud504\ub85c\uc81d\ud2b8\uc5d0 \uc0ac\uc6a9\ud560 \ud544\uc218 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \uc544\ub798 \ucee4\ub9e8\ub4dc\ub97c \ud1b5\ud574 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
pip install pandas numpy scikit-learn tensorflow yfinance<\/code><\/pre>\n4. \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\uc774\uc81c \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ub2e8\uacc4\ub85c \ub118\uc5b4\uac11\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uc608\uc2dc\ub97c \ud1b5\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ub370\uc774\ud130 \ubd84\ud560<\/h3>\n
\uac00\uc7a5 \uba3c\uc800, \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\n\nX = data.drop('Close', axis=1) # \ud2b9\uc131\ny = data['Close'] # \ubaa9\ud45c \ubcc0\uc218\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n4.2 \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
\uc774\uc81c \ubaa8\ub378\uc744 \ud6c8\ub828\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc120\ud615 \ud68c\uadc0\ub97c \uc774\uc6a9\ud55c \uac04\ub2e8\ud55c \uc608\uc2dc\uc785\ub2c8\ub2e4:<\/p>\n
from sklearn.linear_model import LinearRegression\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)<\/code><\/pre>\n4.3 \uc608\uce21 \ubc0f \ud3c9\uac00<\/h3>\n
\ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud14c\uc2a4\ud2b8 \uc138\ud2b8\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc608\uce21 \uacb0\uacfc\ub97c \ud3c9\uac00\ud558\uae30 \uc704\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4:<\/p>\n
predictions = model.predict(X_test)\nfrom sklearn.metrics import mean_squared_error\n\nmse = mean_squared_error(y_test, predictions)\nprint('Mean Squared Error:', mse)<\/code><\/pre>\n5. \ub525\ub7ec\ub2dd \ubaa8\ub378<\/h2>\n
\ub2e4\uc74c\uc73c\ub85c TensorFlow\ub97c \ud65c\uc6a9\ud55c \uac04\ub2e8\ud55c \ub2e4\uce35 \ud37c\uc149\ud2b8\ub860(MLP) \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import tensorflow as tf\n\n# \ubaa8\ub378 \uad6c\ucd95\nmodel = tf.keras.Sequential([\n tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n tf.keras.layers.Dense(32, activation='relu'),\n tf.keras.layers.Dense(1) # \ucd9c\ub825 \ub808\uc774\uc5b4\n])\n\n# \ubaa8\ub378 \ucef4\ud30c\uc77c\nmodel.compile(optimizer='adam', loss='mean_squared_error')\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X_train, y_train, epochs=50, validation_split=0.2)<\/code><\/pre>\n5.1 \uc608\uce21 \ubc0f \ud3c9\uac00<\/h3>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\ub3c4 \ube44\uc2b7\ud569\ub2c8\ub2e4:<\/p>\n
predictions = model.predict(X_test)\n\n# MSE \ud3c9\uac00\nmse = mean_squared_error(y_test, predictions)\nprint('Mean Squared Error:', mse)<\/code><\/pre>\n6. \ud2b8\ub808\uc774\ub529 \uc804\ub7b5 \uad6c\ud604<\/h2>\n
\uc774\uc81c \ud6c8\ub828\ub41c \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uc2e4\uc81c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uac04\ub2e8\ud55c \uc870\uac74\ubd80 \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:<\/p>\n
def trading_strategy(predictions):\n # \ub9e4\ub9e4 \uc2e0\ud638 \uc0dd\uc131\n signals = []\n for pred in predictions:\n if pred > current_price: # \ud604\uc7ac \uac00\uaca9\ubcf4\ub2e4 \ub192\uc73c\uba74 \ub9e4\uc218\n signals.append('Buy')\n else:\n signals.append('Sell')\n return signals\n<\/code><\/pre>\n\uc704\uc758 \ub85c\uc9c1\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\ud55c \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uae30\ubc18\uc73c\ub85c \uc2e4\uc2dc\uac04 \ud2b8\ub808\uc774\ub529\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7. \ub9c8\ubb34\ub9ac \ubc0f \ud5a5\ud6c4 \ubc29\ud5a5<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\uc640 \uc6d0\uac70\ub9ac \ub370\uc774\ud130 \uc811\uadfc \ubc29\ubc95\uc744 \uc18c\uac1c\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc55e\uc73c\ub85c\ub294 \ub354 \ubcf5\uc7a1\ud55c \uc54c\uace0\ub9ac\uc998 \ubc0f \uc2e4\uc2dc\uac04 \ub370\uc774\ud130 \ud53c\ub4dc\ub97c \ud65c\uc6a9\ud55c \uace0\uae09 \uc804\ub7b5\uc73c\ub85c \ub098\uc544\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc778 \ud559\uc2b5\uacfc \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ub098\ub9cc\uc758 \ud2b8\ub808\uc774\ub529 \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud574\ubcf4\uc138\uc694.<\/p>\n
\ucc38\uace0\ubb38\ud5cc<\/h2>\n\n- \uc7a5\ubcd1\ud0c1, “Python \ub370\uc774\ud130 \ubd84\uc11d”, \ud55c\ube5b\ubbf8\ub514\uc5b4, 2019.<\/li>\n
- \uae40\uc870\uc644, “Deep Learning for Finance”, Springer, 2020.<\/li>\n
- YFinance \uacf5\uc2dd \ubb38\uc11c: YFinance<\/a><\/li>\n<\/ul>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"