\ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0\uc758 \uac70\ub798\ub294 \ucd5c\uadfc \uba87 \ub144 \uac04 \ub300\ud3ed \uc131\uc7a5\ud558\uace0 \uc788\uc73c\uba70, \uc774\uc640 \ud568\uaed8 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc5d0 \ub300\ud55c \uad00\uc2ec\ub3c4 \ub192\uc544\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uc0ac\uc804\uc5d0 \uc124\uc815\ud55c \uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \uac70\ub798\ub97c \uc218\ud589\ud558\ubbc0\ub85c, \ud22c\uc790\uc790\uc758 \uac10\uc815\uc801 \uc694\uc18c\ub97c \ubc30\uc81c\ud560 \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd(ML)\uacfc \ub525\ub7ec\ub2dd(DL)\uc740 \uc774\ub7ec\ud55c \uc2dc\uc2a4\ud15c\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud558\uace0 \uc608\uce21 \ub2a5\ub825\uc744 \ub192\uc774\uae30 \uc704\ud55c \uc911\uc694\ud55c \uae30\uc220\ub85c \uc790\ub9ac \uc7a1\uc558\uc2b5\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0 \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \uba85\uc2dc\uc801 \ud504\ub85c\uadf8\ub798\ubc0d \uc5c6\uc774 \ub370\uc774\ud130\uc5d0\uc11c \ud559\uc2b5\ud558\uc5ec \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc740 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud328\ud134\uc744 \uc778\uc2dd\ud558\uace0, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \ubbf8\ub798\uc758 \uacb0\uacfc\ub97c \uc608\uce21\ud569\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc774 \uc874\uc7ac\ud558\uba70, \uadf8 \uc911 \uc77c\ubd80\ub294:<\/p>\n
\ub525\ub7ec\ub2dd\uc740 \ub2e4\uce35 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ud1b5\ud574 \ud615\uc131\ub41c \ubaa8\ub378\ub85c, \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc740 \uc774\ubbf8\uc9c0 \uc778\uc2dd, \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc758 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc218\ud589\ud569\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \uc774\uc6a9\ud574 \uc608\uce21 \ubaa8\ub378\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574\uc11c\ub294 \uba3c\uc800 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130 \ubc0f \uac70\ub798\ub7c9\uc744 \ud3ec\ud568\ud55c \uc5ec\ub7ec \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ub370\uc774\ud130 \uc18c\uc2a4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\ub294 \ubaa8\ub378\uc774 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ud615\ud0dc\ub85c \uac00\uacf5\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uace0 \ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \uc608\ub97c \ub4e4\uc5b4:<\/p>\n
\ud559\uc2b5\ud55c \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \uc2e4\uc81c \uc790\ub3d9\ub9e4\ub9e4 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c\uacfc \uac19\uc740 \uac70\ub798 \uc804\ub7b5\uc744 \uad6c\uc0c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\ubaa8\ub378\uacfc \uc54c\uace0\ub9ac\uc998 \uad6c\ud604 \ud6c4\uc5d0\ub294 \uc2e4\uc81c \uac70\ub798\uc18c\uc640 \uc5f0\ub3d9\ud558\uc5ec \uc2e4\uc2dc\uac04 \uac70\ub798\ub97c \uc218\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud574\uc57c \ud569\ub2c8\ub2e4. \ubcf4\ud1b5 \ub2e4\uc74c\uc758 \uacfc\uc815\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4:<\/p>\n
\uc5ec\uae30\uc11c\ub294 Python\uc744 \uc0ac\uc6a9\ud574 \uac04\ub2e8\ud55c \ube44\ud2b8\ucf54\uc778 \uc608\uce21 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uc608\uc81c \ucf54\ub4dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ucf54\ub4dc\ub294 Keras \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc774\uc6a9\ud558\uc5ec LSTM \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0, Binance API\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uac00\uc838\uc624\ub294 \ubc29\uc2dd\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n
!pip install numpy pandas matplotlib tensorflow --upgrade\n!pip install python-binance<\/code><\/pre>\n4.2. \ub370\uc774\ud130 \uc218\uc9d1 \ucf54\ub529<\/h3>\nfrom binance.client import Client\nimport pandas as pd\n\n# Binance API \ud0a4\uc640 \ube44\ubc00 \ud0a4 \uc785\ub825\napi_key = 'YOUR_API_KEY'\napi_secret = 'YOUR_API_SECRET'\nclient = Client(api_key, api_secret)\n\n# \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130 \uac00\uc838\uc624\uae30\ndef get_historical_data(symbol, interval, start_time):\n klines = client.get_historical_klines(symbol, interval, start_time)\n data = pd.DataFrame(klines, columns=['Open Time', 'Open', 'High', 'Low', 'Close', \n 'Volume', 'Close Time', 'Quote Asset Volume', \n 'Number of Trades', 'Taker Buy Base Asset Volume', \n 'Taker Buy Quote Asset Volume', 'Ignore'])\n data['Close'] = data['Close'].astype(float)\n return data[['Close']]\n\n# \ub370\uc774\ud130 \uc218\uc9d1\ndata = get_historical_data('BTCUSDT', Client.KLINE_INTERVAL_1HOUR, \"1 month ago UTC\")\nprint(data.head())<\/code><\/pre>\n4.3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\nimport numpy as np\n\n# \ub370\uc774\ud130 \uc815\uaddc\ud654\nfrom sklearn.preprocessing import MinMaxScaler\n\nscaler = MinMaxScaler(feature_range=(0, 1))\nscaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))\n\n# \ub370\uc774\ud130\uc14b \uc0dd\uc131\ndef create_dataset(data, time_step=1):\n X, y = [], []\n for i in range(len(data) - time_step - 1):\n X.append(data[i:(i + time_step), 0])\n y.append(data[i + time_step, 0])\n return np.array(X), np.array(y)\n\ntime_step = 60\nX, y = create_dataset(scaled_data, time_step)\nX = X.reshape(X.shape[0], X.shape[1], 1)\nprint(X.shape, y.shape)<\/code><\/pre>\n4.4. \ubaa8\ub378 \uad6c\uc131 \ubc0f \ud559\uc2b5<\/h3>\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout\n\n# LSTM \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)))\nmodel.add(Dropout(0.2))\nmodel.add(LSTM(50, return_sequences=False))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(25))\nmodel.add(Dense(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, batch_size=1, epochs=1)<\/code><\/pre>\n4.5. \uc608\uce21 \ubc0f \uc2dc\uac01\ud654<\/h3>\n# \uc608\uce21\ntrain_predict = model.predict(X)\ntrain_predict = scaler.inverse_transform(train_predict)\n\n# \uc2dc\uac01\ud654\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(14, 5))\nplt.plot(data['Close'].values, label='\uc2e4\uc81c \ube44\ud2b8\ucf54\uc778 \uac00\uaca9', color='blue')\nplt.plot(range(time_step, time_step + len(train_predict)), train_predict, label='\uc608\uce21 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9', color='red')\nplt.title('\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21')\nplt.xlabel('\uc2dc\uac04')\nplt.ylabel('\uac00\uaca9')\nplt.legend()\nplt.show()<\/code><\/pre>\n5. \uacb0\ub860<\/h2>\n
\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uae09\ubcc0\ud558\ub294 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c \uac70\ub798\uc758 \ud6a8\uc728\uc131\uc744 \ub192\uc774\ub294 \ub370 \uae30\uc5ec\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \uc2dc\uc791\ud558\uc5ec, \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uad6c\ucd95 \uacfc\uc815 \ubc0f \uac04\ub2e8\ud55c \uc608\uc81c \ucf54\ub4dc\ub97c \ud1b5\ud574 \uc2e4\uc9c8\uc801\uc778 \uc774\ud574\ub97c \ub3c4\ubaa8\ud558\uc600\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4\uc5d0\ub294 \ub2e4\uc591\ud55c \uc804\ub7b5\uacfc \uace0\uae09 \ubaa8\ub378\uc744 \ud0d0\uad6c\ud558\uc5ec \ub354\uc6b1 \uc815\uad50\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \ubc1c\uc804\uc2dc\ud0ac \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
\uc774 \uae00\uc774 \uc5ec\ub7ec\ubd84\uc758 \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"