{"id":31391,"date":"2024-10-28T12:01:42","date_gmt":"2024-10-28T12:01:42","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=31391"},"modified":"2024-11-26T06:45:18","modified_gmt":"2024-11-26T06:45:18","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-%eb%aa%a8%eb%8d%b8-%eb%b0%b0%ed%8f%ac-%eb%b0%8f","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/31391\/","title":{"rendered":"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, \ubaa8\ub378 \ubc30\ud3ec \ubc0f \ubaa8\ub2c8\ud130\ub9c1 Flask\ub97c \uc774\uc6a9\ud55c \uc6f9 \uc11c\ubc84 \uad6c\ucd95\uc744 \ud1b5\ud574 \ub9e4\ub9e4 \ubaa8\ub378 \ubc30\ud3ec \ubc0f \ubaa8\ub2c8\ud130\ub9c1."},"content":{"rendered":"

<\/p>\n

\ucd5c\uadfc \uba87 \ub144 \ub3d9\uc548 \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc131\uc774 \uae09\uc99d\ud558\uba74\uc11c \ub9ce\uc740 \ud22c\uc790\uc790\ub4e4\uc774 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \uc8fc\ubaa9\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud1b5\ud574 \uc774\ub7ec\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\ub294 \uac00\ub2a5\uc131\uc774 \uc5f4\ub838\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uace0, Flask\ub97c \uc774\uc6a9\ud574 \ubaa8\ub378\uc744 \ubc30\ud3ec \ubc0f \ubaa8\ub2c8\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uae08\uc735 \ub370\uc774\ud130 \ubd84\uc11d \uac1c\uc694<\/h2>\n

\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ud604\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \ubd84\uc11d\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub294 \uc5ec\ub7ec API\ub97c \ud1b5\ud574 \ubc1b\uc744 \uc218 \uc788\uc73c\uba70, \uc5ec\uae30\uc11c\ub294 Binance\uc758 API\ub97c \uc608\ub85c \ub4e4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1.1 Binance API \uc0ac\uc6a9<\/h3>\n
\nimport requests\n\ndef fetch_bitcoin_data():\n    url = \"https:\/\/api.binance.com\/api\/v3\/klines\"\n    params = {\n        'symbol': 'BTCUSDT',\n        'interval': '1h',\n        'limit': 1000\n    }\n    response = requests.get(url, params=params)\n    data = response.json()\n    return data\n\nbitcoin_data = fetch_bitcoin_data()\nprint(bitcoin_data)\n<\/code><\/pre>\n
\uc704\uc758 \ucf54\ub4dc\ub294 Binance API\ub97c \ud638\ucd9c\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \uc608\uc81c\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 1\uc2dc\uac04 \uac04\uaca9\uc758 \ub9c8\uc9c0\ub9c9 1000\uac1c\uc758 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc635\ub2c8\ub2e4.<\/p>\n
2. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\ub370\uc774\ud130\ub97c \uc218\uc9d1\ud55c \ud6c4, \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9 \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c\ub294 LSTM(Long Short-Term Memory)\uacfc \uac19\uc740 \uc2dc\uacc4\uc5f4 \ubaa8\ub378\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\ube44\ud2b8\ucf54\uc778 \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \uc801\ud569\ud55c \ud615\uc2dd\uc73c\ub85c \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub370\uc774\ud130\uc758 \ub0a0\uc9dc \ubc0f \uac00\uaca9\uc744 \ubd84\ub9ac\ud558\uace0, \ud544\uc694\uc5d0 \ub530\ub77c \uc815\uaddc\ud654\ud569\ub2c8\ub2e4.<\/p>\n
\nimport numpy as np\nimport pandas as pd\n\ndef preprocess_data(data):\n    df = pd.DataFrame(data, columns=['Open Time', 'Open', 'High', 'Low', 'Close', 'Volume', 'Close Time', 'Quote Asset Volume', 'Number of Trades', 'Taker Buy Base Asset Volume', 'Taker Buy Quote Asset Volume', 'Ignore'])\n    df['Close'] = df['Close'].astype(float)\n    df['Date'] = pd.to_datetime(df['Open Time'], unit='ms')\n    df.set_index('Date', inplace=True)\n    return df['Close'].values\n\nclose_prices = preprocess_data(bitcoin_data)\n<\/code><\/pre>\n
2.2 \ubaa8\ub378 \uc0dd\uc131<\/h3>\n
LSTM \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uc5ec \ube44\ud2b8\ucf54\uc778 \uac00\uaca9\uc744 \uc608\uce21\ud569\ub2c8\ub2e4. Keras\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \uad6c\ucd95\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Dropout\n\ndef create_model(input_shape):\n    model = Sequential()\n    model.add(LSTM(50, return_sequences=True, input_shape=input_shape))\n    model.add(Dropout(0.2))\n    model.add(LSTM(50, return_sequences=False))\n    model.add(Dropout(0.2))\n    model.add(Dense(25))\n    model.add(Dense(1))\n    model.compile(optimizer='adam', loss='mean_squared_error')\n    return model\n\nX_train, y_train = ...  # \uc5ec\uae30\uc11c \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ub9ac\ud569\ub2c8\ub2e4.\nmodel = create_model((X_train.shape[1], X_train.shape[2]))\nmodel.fit(X_train, y_train, batch_size=1, epochs=10)\n<\/code><\/pre>\n
3. \ubaa8\ub378 \ubc30\ud3ec<\/h2>\n
\uae30\uacc4 \ud559\uc2b5 \ubaa8\ub378\uc744 \uc0dd\uc131\ud55c \ud6c4, \uc774 \ubaa8\ub378\uc744 Flask \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \ud1b5\ud574 \ubc30\ud3ec\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc678\ubd80\uc5d0\uc11c \ubaa8\ub378\uc5d0 \uc811\uadfc\ud558\uc5ec \uc608\uce21 \uacb0\uacfc\ub97c \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 Flask \uc124\uc815<\/h3>\n
\uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 Flask \uc11c\ubc84\ub97c \uc124\uc815\ud558\uace0, REST API \uc5d4\ub4dc\ud3ec\uc778\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\uac00 POST \uc694\uccad\uc744 \ubcf4\ub0c4\uc73c\ub85c\uc368 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21\uc744 \uc694\uccad\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\nfrom flask import Flask, request, jsonify\nimport numpy as np\n\napp = Flask(__name__)\n\n@app.route('\/predict', methods=['POST'])\ndef predict():\n    data = request.get_json(force=True)\n    # \ub370\uc774\ud130 \ucc98\ub9ac \ubc0f \uc608\uce21\n    prediction = model.predict(data['input'])  # \uc785\ub825 \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \ubcf4\ub0c4\n    return jsonify({'prediction': prediction.tolist()})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n<\/code><\/pre>\n
3.2 Flask \uc11c\ubc84 \uc2e4\ud589<\/h3>\n
\uc704 \ucf54\ub4dc\ub85c Flask \uc11c\ubc84\ub97c \uc2e4\ud589\ud558\uba74 \uc608\uce21 \uc694\uccad\uc744 \ubcf4\ub0bc \uc218 \uc788\ub294 \uc5d4\ub4dc\ud3ec\uc778\ud2b8\uac00 \uc0dd\uc131\ub429\ub2c8\ub2e4. POST \uc694\uccad\uc744 \ud1b5\ud574 \ubaa8\ub378\uc5d0 \ub370\uc774\ud130\ub97c \uc804\uc1a1\ud558\uace0, \uc608\uce21\uac12\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4. \ubaa8\ub2c8\ud130\ub9c1 \ubc0f \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc744 \ubc30\ud3ec\ud55c \ud6c4, \uadf8 \uc131\ub2a5\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud558\uace0 \ud3c9\uac00\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc608\uce21\uc774 \uc5bc\ub9c8\ub098 \uc815\ud655\ud55c\uc9c0 \ud655\uc778\ud558\uace0, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\uae30 \uc704\ud574 \ud544\uc694\ud55c \uc870\uce58\ub97c \ucde8\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
4.1 \uc131\ub2a5 \ubaa8\ub2c8\ud130\ub9c1 \ub3c4\uad6c<\/h3>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uc9c0\uc18d\uc801\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud558\uae30 \uc704\ud574 Grafana\uc640 Prometheus\uc640 \uac19\uc740 \ub3c4\uad6c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub3c4\uad6c\ub97c \ud1b5\ud574 API\uc758 \uc694\uccad \uc218, \uc2e4\ud328\uc728 \ubc0f \uae30\ud0c0 \uc9c0\ud45c\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ubaa8\ub2c8\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.2 \ubaa8\ub378 \uc5c5\ub370\uc774\ud2b8<\/h3>\n
\ube44\ud2b8\ucf54\uc778 \uc2dc\uc7a5\uc740 \ub9e4\uc6b0 \ubcc0\ub3d9\uc131\uc774 \ud06c\uae30 \ub54c\ubb38\uc5d0, \ubaa8\ub378\uc744 \uc8fc\uae30\uc801\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8\ud558\uc5ec \ucd5c\uc2e0 \ub370\uc774\ud130\ub97c \ubc18\uc601\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc815\ud655\uc131\uc744 \uadf9\ub300\ud654\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n
\n# \uc608: \ub9e4\uc77c \ubaa8\ub378\uc744 retrain \ud558\ub294 \uc2a4\ucf00\uc904\ub7ec\ub97c \uc124\uc815\nimport schedule\nimport time\n\ndef retrain_model():\n    # \ubaa8\ub378\uc744 \uc7ac\ud6c8\ub828\ud558\ub294 \ucf54\ub4dc\n    pass\n\nschedule.every().day.at(\"00:00\").do(retrain_model)\n\nwhile True:\n    schedule.run_pending()\n    time.sleep(1)\n<\/code><\/pre>\n
5. \uacb0\ub860<\/h2>\n
\uc774\ubc88 \ube14\ub85c\uadf8 \uae00\uc5d0\uc11c\ub294 \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uace0 \uc774\ub97c Flask\ub97c \uc774\uc6a9\ud558\uc5ec \ubc30\ud3ec\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc218\uc9d1\uc5d0\uc11c \ubaa8\ub378 \ud6c8\ub828, \ubc30\ud3ec, \ubaa8\ub2c8\ud130\ub9c1\uae4c\uc9c0\uc758 \uc804\uccb4 \uacfc\uc815\uc744 \uc124\uba85\ud558\uc600\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uc5ec\ub7ec\ubd84\ub3c4 \uc790\uc2e0\ub9cc\uc758 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uace0 \uc6b4\uc601\ud574 \ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
6. \ucc38\uace0 \uc790\ub8cc<\/h3>\n