{"id":29067,"date":"2024-10-27T16:16:36","date_gmt":"2024-10-27T16:16:36","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29067"},"modified":"2024-11-26T06:54:47","modified_gmt":"2024-11-26T06:54:47","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-%ec%82%ac%ec%9a%a9%ec%9e%90-%ec%a0%95%ec%9d%98-ope","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29067\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130 \ubd84\uc11d, \uba38\uc2e0\ub7ec\ub2dd(Machine Learning), \ub525\ub7ec\ub2dd(Deep Learning) \uae30\uc220\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \uacfc\uc815\uc740 \ud601\uc2e0\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uace0, \uc790\ub3d9\ud654\ub41c \ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ub370 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc, \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc774\ub780?<\/h2>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc815\ud574\uc9c4 \uaddc\uce59\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc218\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uc774 \uc2dc\uc2a4\ud15c\uc740 \uc2dc\uc7a5 \ub370\uc774\ud130\uc758 \ubd84\uc11d\uc744 \ud1b5\ud574 \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ub9e4\uc218 \ub610\ub294 \ub9e4\ub3c4 \uc8fc\ubb38\uc744 \uc2e4\ud589\ud569\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uc8fc\uc694 \uc7a5\uc810\uc73c\ub85c\ub294 \uc2e0\uc18d\ud55c \uc8fc\ubb38 \uc2e4\ud589, \uac10\uc815\uc758 \ubc30\uc81c, \uadf8\ub9ac\uace0 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \uc2e4\uc2dc\uac04\uc73c\ub85c \ubd84\uc11d\ud560 \uc218 \uc788\ub294 \ub2a5\ub825\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

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

\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4.<\/p>\n

2.1 \uba38\uc2e0\ub7ec\ub2dd<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \ub2e4\uc591\ud55c \uc885\ub958\uc758 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ucc3e\uc544\ub0b4\uace0 \uc774\ub97c \ud65c\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc54c\uace0\ub9ac\uc998\uc758 \uc885\ub958\ub294 \ud06c\uac8c \uac10\ub3c5 \ud559\uc2b5(Supervised Learning), \ube44\uac10\ub3c5 \ud559\uc2b5(Unsupervised Learning), \uac15\ud654 \ud559\uc2b5(Reinforcement Learning)\uc73c\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2.2 \ub525\ub7ec\ub2dd<\/h3>\n

\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(Artificial Neural Network)\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \uc720\ud615\uc73c\ub85c, \ub370\uc774\ud130\uc758 \uacc4\uce35\uc801 \ud45c\ud604\uc744 \ud559\uc2b5\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \uc778\uc2dd\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc73c\uba70, \ucd5c\uadfc\uc5d0\ub294 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub3c4 \uadf8 \uac00\ub2a5\uc131\uc774 \ud0d0\uc0c9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

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

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc774\ud574\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc218\uc9d1, \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ubaa8\ub378 \uc120\ud0dd, \uadf8\ub9ac\uace0 \ubc31\ud14c\uc2a4\ud305(Backtesting) \uacfc\uc815\uc744 \uc774\ud574\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n

3.1 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n

\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uccab \ub2e8\uacc4\ub294 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \ud655\ubcf4\ud558\uc5ec \uacfc\uac70 \ubc0f \uc2e4\uc2dc\uac04 \ub370\uc774\ud130\uc14b\uc744 \uad6c\ucd95\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub300\ud45c\uc801\uc73c\ub85c \uc8fc\uc2dd \uac00\uaca9 \ub370\uc774\ud130, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1\uc744 \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/p>\n

3.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n

\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ub300\ubd80\ubd84 \ubd88\uc644\uc804\ud558\uac70\ub098 \ub178\uc774\uc988\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \uacb0\uce21\uce58\ub97c \ucc98\ub9ac\ud558\uace0, \uc774\uc0c1\uce58\ub97c \uc81c\uac70\ud558\uba70, \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654(normalization)\ud558\ub294 \ub4f1\uc758 \uc791\uc5c5\uc774 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/p>\n

3.3 \ubaa8\ub378 \uc120\ud0dd<\/h3>\n

\uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uc608\uce21\uc5d0 \ud6a8\uacfc\uc801\uc778 \ub2e4\uc591\ud55c \ubaa8\ub378\ub4e4\uc774 \uc788\uc73c\uba70, \uc2dc\uac04\uc5d0 \ub530\ub978 \uc608\uce21\uc744 \uc704\ud574 LSTM(Long Short-Term Memory)\uacfc \uac19\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN) \ubaa8\ub378\uc774 \ub9ce\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n

3.4 \ubc31\ud14c\uc2a4\ud305<\/h3>\n

\ubaa8\ub378\uc774 \uc801\ud569\ud558\ub2e4\uace0 \ud310\ub2e8\ub418\uba74, \uc5ed\uc0ac\uc801\uc778 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud558\uc5ec \ud574\ub2f9 \ubaa8\ub378\uc744 \ud14c\uc2a4\ud2b8\ud558\ub294 \ubc31\ud14c\uc2a4\ud305 \ub2e8\uacc4\ub97c \uac70\uce69\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5 \uc9c0\ud45c\ub97c \ubd84\uc11d\ud558\uace0 \ud544\uc694\uc5d0 \ub530\ub77c \ubaa8\ub378\uc744 \uacbd\uc6b0\uc5d0 \ub530\ub77c \ub2e4\uc2dc \uc870\uc815\ud569\ub2c8\ub2e4.<\/p>\n

4. \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd \uc124\uacc4<\/h2>\n

\ud6a8\uacfc\uc801\uc778 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc744 \uc704\ud55c \ub2e8\uacc4\uc801\uc778 \uc811\uadfc \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n

4.1 OpenAI \ud658\uacbd \uc774\ud574\ud558\uae30<\/h3>\n

OpenAI\ub294 \uc778\uacf5\uc9c0\ub2a5 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \ud6c8\ub828\ud558\ub294 \ub370 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ub3c4\uad6c\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. sklearn, TensorFlow, Keras \ub4f1\uc758 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc190\uc27d\uac8c \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

4.2 \ud658\uacbd \uc124\uc815<\/h3>\n

\uba3c\uc800 \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uace0, \uc0ac\uc6a9\ud560 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc635\ub2c8\ub2e4. Python \ud658\uacbd\uc5d0\uc11c \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

pip install numpy pandas scikit-learn tensorflow<\/code><\/pre>\n
4.3 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac \ud568\uc218 \ub9cc\ub4e4\uae30<\/h3>\n
\uc6f9\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c API\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, pandas\ub97c \ud1b5\ud574 \ub370\uc774\ud130 \ud504\ub808\uc784\uc73c\ub85c \uc27d\uac8c \ub2e4\ub8f0 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ud568\uc218\ub97c \uc815\uc758\ud558\ub294 \uac83\ub3c4 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\ndef preprocess_data(data):\n    # \uacb0\uce21\uce58 \ucc98\ub9ac\n    data = data.fillna(method='ffill')\n    # \ud544\uc694 \uc5c6\ub294 \uc5f4 \uc81c\uac70\n    data = data.drop(columns=['Unnamed: 0'], errors='ignore')\n    return data\n<\/code><\/pre>\n
4.4 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h3>\n
\uadf8 \ub2e4\uc74c\uc73c\ub85c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ud604\ud574\uc57c \ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest) \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX = data.iloc[:, :-1]\ny = data.iloc[:, -1]\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = RandomForestClassifier(n_estimators=100)\nmodel.fit(X_train, y_train)\n<\/code><\/pre>\n
4.5 \ub525\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ud604\ud558\uae30<\/h3>\n
\uc774\uc81c \ud150\uc11c\ud50c\ub85c\uc6b0\ub97c \uc0ac\uc6a9\ud55c \uac04\ub2e8\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc608\uc2dc\ub97c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4:<\/p>\n
import tensorflow as tf\n\nmodel = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)))\nmodel.add(tf.keras.layers.Dense(64, activation='relu'))\nmodel.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.fit(X_train, y_train, epochs=10, batch_size=32)\n<\/code><\/pre>\n
4.6 \ud2b8\ub808\uc774\ub529 \uc2dc\ubbac\ub808\uc774\uc158<\/h3>\n
\ubaa8\ub378\uc774 \uc0dd\uc131\ub418\uba74 \uc2e4\uc81c \uac70\ub798\ub97c \uc2dc\ubbac\ub808\uc774\uc158\ud558\uc5ec \uc804\ub7b5\uc758 \ud6a8\uacfc\uc131\uc744 \ud3c9\uac00\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uc2e4\uc81c \uc2dc\uc7a5 \ub370\uc774\ud130\uc640 \uc0c1\ud638\uc791\uc6a9\ud558\ub294 \ud658\uacbd\uc744 \uad6c\ucd95\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
5. \uc131\ub2a5 \ud3c9\uac00 \ubc0f \uc804\ub7b5 \ucd5c\uc801\ud654<\/h2>\n
\uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \uc9c0\ud45c\ub85c\ub294 \uc0e4\ud504 \ube44\uc728(Sharpe Ratio), \ucd5c\ub300 \ub099\ud3ed(Maximum Drawdown), \uc218\uc775\ub960(Return on Investment, ROI) \ub4f1\uc744 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc131\ub2a5 \ud3c9\uac00 \ud6c4, \ud544\uc694\uc5d0 \ub530\ub77c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd(Hyperparameter Tuning)\uc744 \ud1b5\ud574 \ubaa8\ub378\uc744 \ucd5c\uc801\ud654\ud569\ub2c8\ub2e4.<\/p>\n
5.1 \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00\ud558\uae30<\/h3>\n
from sklearn.metrics import accuracy_score\n\n# \uc608\uce21\npredictions = model.predict(X_test)\npredictions = (predictions > 0.5)\n\n# \uc815\ud655\ub3c4 \ud3c9\uac00\naccuracy = accuracy_score(y_test, predictions)\nprint(f'\ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub294: {accuracy}')\n<\/code><\/pre>\n
5.2 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd<\/h3>\n
GridSearchCV \ub4f1\uc744 \ud65c\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \ucc3e\ub294 \uac83\uc774 \uc720\uc6a9\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc608\uc2dc \ucf54\ub4dc\uc785\ub2c8\ub2e4:<\/p>\n
from sklearn.model_selection import GridSearchCV\n\nparam_grid = {\n    'n_estimators': [50, 100, 200],\n    'max_depth': [10, 20, None],\n}\ngrid_search = GridSearchCV(RandomForestClassifier(), param_grid, cv=3)\ngrid_search.fit(X_train, y_train)\n\nprint(f'\ucd5c\uc801\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130: {grid_search.best_params_}')\n<\/code><\/pre>\n
6. \uc790\ub3d9\ud654\ub41c \uac70\ub798 \uc2dc\uc2a4\ud15c \uad6c\ucd95<\/h2>\n
\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc54c\uace0\ub9ac\uc998\uc73c\ub85c \uc0dd\uc131\ub41c \ub9e4\ub9e4 \uc2e0\ud638\ub97c \ubc14\ud0d5\uc73c\ub85c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc2e4\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uac70\ub798 API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\uc81c \uac70\ub798 \ud50c\ub7ab\ud3fc\uacfc \uc5f0\uacb0\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.<\/p>\n
6.1 API \uc5f0\ub3d9 \uc608\uc2dc<\/h3>\n
\uc608\ub97c \ub4e4\uc5b4, Alpaca API\ub97c \ud65c\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac70\ub798\ub97c \uc790\ub3d9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import alpaca_trade_api as tradeapi\n\nAPI_KEY = 'YOUR_API_KEY'\nSECRET_KEY = 'YOUR_SECRET_KEY'\nBASE_URL = 'https:\/\/paper-api.alpaca.markets'\n\napi = tradeapi.REST(API_KEY, SECRET_KEY, BASE_URL, api_version='v2')\n\n# \ub9e4\uc218\/\ub9e4\ub3c4 \uc8fc\ubb38 \ud568\uc218\ndef place_order(symbol, qty, side):\n    api.submit_order(\n        symbol=symbol,\n        qty=qty,\n        side=side,\n        type='market',\n        time_in_force='gtc'\n    )\n<\/code><\/pre>\n
\uacb0\ub860<\/h2>\n
\uc624\ub298 \uc6b0\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \uad6c\ucd95, \uc131\ub2a5 \ud3c9\uac00, \uadf8\ub9ac\uace0 \uc790\ub3d9\ud654\ub41c \uac70\ub798 \uc2dc\uc2a4\ud15c\uc758 \uad6c\ud604\uae4c\uc9c0 \uc804 \uacfc\uc815\uc744 \uc124\uba85\ud588\uc2b5\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ud2b8\ub808\uc774\ub529\uc740 \ubcc0\ub3d9\uc131\uc774 \ud06c\uace0 \ubd88\ud655\uc2e4\uc131\uc774 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc6b0\ub9ac\uc758 \uc811\uadfc \ubc29\uc2dd\uc744 \ud601\uc2e0\uc801\uc73c\ub85c \ubcc0\ud654\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\uc758 \ubaa9\ud45c\uc640 \ubd84\uc11d\uc758 \uc815\ud655\uc131\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \uc804\ub7b5\uc744 \uc2dc\ub3c4\ud574\ubcf4\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud55c \ub354 \ub9ce\uc740 \uc815\ubcf4\uc640 \uc2ec\ud654\ub41c \ub0b4\uc6a9\uc744 \uc6d0\ud558\uc2e0\ub2e4\uba74, \uad00\ub828 \ucee4\ubba4\ub2c8\ud2f0\ub098 \ud3ec\ub7fc\uc5d0 \ucc38\uc5ec\ud558\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc9c0\uc18d\uc801\uc73c\ub85c \ubc1c\uc804\ud558\ub294 \uae30\uc220\uc744 \ub530\ub77c\uc7a1\uae30 \uc704\ud574\uc11c\ub294 \uc5ec\ub7ec \uc790\ub8cc\ub97c \ucc38\uace0\ud558\uace0 \uc2e4\uc2b5\uc744 \ud1b5\ud574 \uc774\ud574\ub3c4\ub97c \ub192\uc5ec\uac00\ub294 \uac83\uc774 \ud070 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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\ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \ubc29\ubc95\uc5d0 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \ubc29\ubc95”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-29067","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc0ac\uc6a9\uc790 \uc815\uc758 OpenAI \ud2b8\ub808\uc774\ub529 \ud658\uacbd\uc744 \uc124\uacc4\ud558\ub294 \ubc29\ubc95 - 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