1. \uc11c\ub860<\/h2>\n
\n \ube44\ud2b8\ucf54\uc778\uc740 \uc9c0\ub09c \uba87 \ub144\uac04 \uadf9\uc2ec\ud55c \uac00\uaca9 \ubcc0\ub3d9\uc131\uc744 \ubcf4\uc600\uc73c\uba70, \uc774\ub294 \ud22c\uc790\uc790\uc640 \ud2b8\ub808\uc774\ub354\uc5d0\uac8c \ud070 \uae30\ud68c\ub97c \uc81c\uacf5\ud588\uc2b5\ub2c8\ub2e4. \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uc54c\uace0\ub9ac\uc998\uacfc \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub798\ubc0d\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2dc\uc7a5\uc744 \ubd84\uc11d\ud558\uace0 \ube60\ub974\uac8c \uac70\ub798\ub97c \uc2e4\ud589\ud558\uc5ec \uc218\uc775\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\ub3c4\ub85d \ub3d5\uc2b5\ub2c8\ub2e4. \ucd5c\uadfc \ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \ubcf4\ub2e4 \uc815\uad50\ud55c \uc608\uce21 \ubaa8\ub378\uacfc \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uac8c \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ubcf8 \ud3ec\uc2a4\ud2b8\uc5d0\uc11c\ub294 \ube44\ud2b8\ucf54\uc778\uacfc \uc8fc\uc694 \uacbd\uc81c \uc9c0\ud45c \uac04\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \ubd84\uc11d\ud558\uace0, \uc774\ub7ec\ud55c \ubd84\uc11d\uc744 \ud1b5\ud574 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n
2. \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc758 \uac1c\uc694<\/h2>\n
\n \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uae30\ubcf8\uc801\uc73c\ub85c \ub2e4\uc74c\uc758 \uacfc\uc815\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.\n <\/p>\n
- \n
- \ub370\uc774\ud130 \uc218\uc9d1:<\/strong> \uacfc\uac70\uc758 \uac00\uaca9 \ub370\uc774\ud130\uc640 \uad00\ub828\ub41c \uacbd\uc81c \uc9c0\ud45c\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/li>\n
- \ub370\uc774\ud130 \uc804\ucc98\ub9ac:<\/strong> \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uae30 \uc88b\uc740 \ud615\ud0dc\ub85c \uac00\uacf5\ud569\ub2c8\ub2e4.<\/li>\n
- \ubaa8\ub378 \ud559\uc2b5:<\/strong> \uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4.<\/li>\n
- \ubaa8\ub378 \ud3c9\uac00:<\/strong> \ud559\uc2b5\ud55c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0 \ud29c\ub2dd\ud569\ub2c8\ub2e4.<\/li>\n
- \ub9e4\ub9e4 \uc2e4\ud589:<\/strong> \ubaa8\ub378\uc73c\ub85c\ubd80\ud130 \uc0dd\uc131\ub41c \uc2e0\ud638\uc5d0 \ub530\ub77c \ub9e4\ub9e4\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3. \ub370\uc774\ud130 \uc218\uc9d1<\/h2>\n
\n \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub294 \uc5ec\ub7ec \uc628\ub77c\uc778 \uc11c\ube44\uc2a4 API\ub97c \ud1b5\ud574 \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc73c\ub85c ‘CoinGecko’, ‘CoinMarketCap’ \ub4f1\uc774 \uc788\uc73c\uba70, S&P 500 \ub4f1\uc758 \uacbd\uc81c \uc9c0\ud45c\ub294 ‘Yahoo Finance’\uc640 \uac19\uc740 \uc11c\ube44\uc2a4\uc5d0\uc11c \uc81c\uacf5\ub429\ub2c8\ub2e4.\n <\/p>\n
\uc608\uc81c: \ube44\ud2b8\ucf54\uc778\uacfc S&P 500 \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
\nimport pandas as pd\nimport yfinance as yf\n\n# \ube44\ud2b8\ucf54\uc778 \ub370\uc774\ud130 \uc218\uc9d1\nbtc_data = yf.download('BTC-USD', start='2020-01-01', end='2023-01-01')\n\n# S&P 500 \ub370\uc774\ud130 \uc218\uc9d1\nsp500_data = yf.download('^GSPC', start='2020-01-01', end='2023-01-01')\n\n# \ub370\uc774\ud130 \ud655\uc778\nprint(btc_data.head())\nprint(sp500_data.head())\n <\/pre>\n4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\n \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub294 \uc815\uc81c\uacfc\uc815\uc744 \ud544\uc694\ub85c \ud569\ub2c8\ub2e4. \uacb0\uce21\uac12\uc744 \ucc98\ub9ac\ud558\uace0, \ud544\uc694\ud55c \ud53c\ucc98\ub97c \uc120\ud0dd\ud558\uc5ec \ubaa8\ub378\uc5d0 \uc785\ub825\ud560 \uc218 \uc788\ub3c4\ub85d \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4.\n <\/p>\n
\uc608\uc81c: \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n# \uacb0\uce21\uac12 \ucc98\ub9ac\nbtc_data.fillna(method='ffill', inplace=True)\nsp500_data.fillna(method='ffill', inplace=True)\n\n# \ube44\ud2b8\ucf54\uc778\uacfc S&P 500 \uc885\uac00\ub9cc \uc120\ud0dd\nbtc_close = btc_data['Close']\nsp500_close = sp500_data['Close']\n\n# \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d\uc744 \uc704\ud55c \ub370\uc774\ud130\ud504\ub808\uc784 \uc0dd\uc131\ndata = pd.DataFrame({'BTC': btc_close, 'S&P500': sp500_close})\ndata.dropna(inplace=True)\n\n# \uacb0\uacfc \ud655\uc778\nprint(data.head())\n <\/pre>\n5. \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d<\/h2>\n
\n \ube44\ud2b8\ucf54\uc778\uacfc S&P 500\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \ubd84\uc11d\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc774 \uc788\uc744 \uc218 \uc788\uc9c0\ub9cc, \uc5ec\uae30\uc11c\ub294 \ud53c\uc5b4\uc2a8 \uc0c1\uad00 \uacc4\uc218\ub97c \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\uc608\uc81c: \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d<\/h3>\n
\n# \uc0c1\uad00\uad00\uacc4 \ubd84\uc11d\ncorrelation = data.corr()\nprint(correlation)\n <\/pre>\n
6. \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h2>\n
\n \uc774\uc81c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uc5ec \ube44\ud2b8\ucf54\uc778\uc758 \uac00\uaca9\uc744 \uc608\uce21\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c \ub0a0\uc758 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\uc608\uc81c: \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\n\n# Feature\uc640 Target \uc124\uc815\nX = data[['S&P500']]\ny = data['BTC']\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\npredictions = model.predict(X_test)\n\n# \uacb0\uacfc \ucd9c\ub825\nprint(predictions)\n <\/pre>\n
7. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uac1c\uc120<\/h2>\n
\n \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uacb0\uc815\uacc4\uc218 (R\u00b2)\uc640 \uac19\uc740 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\uc608\uc81c: \ubaa8\ub378 \ud3c9\uac00<\/h3>\n
\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \ubaa8\ub378 \ud3c9\uac00\nmse = mean_squared_error(y_test, predictions)\nr2 = r2_score(y_test, predictions)\n\nprint(f'MSE: {mse}, R\u00b2: {r2}')\n <\/pre>\n8. \uacb0\ub860<\/h2>\n
\n \ubcf8 \ud3ec\uc2a4\ud305\uc744 \ud1b5\ud574 \ube44\ud2b8\ucf54\uc778\uacfc S&P 500\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \uba38\uc2e0\ub7ec\ub2dd\uc73c\ub85c \ubd84\uc11d\ud558\uace0, \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubd84\uc11d\uacfc \uc608\uce21 \ubaa8\ub378\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uace0\ubcc0\ub3d9\uc131 \uc790\uc0b0\uc5d0 \ub9e4\uc6b0 \uc720\uc6a9\ud558\uac8c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \uc778\uacf5\uc9c0\ub2a5 \uae30\uc220\uc758 \ubc1c\uc804\uacfc \uad00\ub828\ub41c \ub370\uc774\ud130 \ubd84\uc11d \uae30\ubc95\uc758 \ud601\uc2e0\uc73c\ub85c \uc778\ud574 \ub354\uc6b1 \uc815\uad50\ud55c \ud22c\uc790 \uc804\ub7b5\uc774 \uac00\ub2a5\ud560 \uac83\uc73c\ub85c \uae30\ub300\ub429\ub2c8\ub2e4.\n <\/p>\n<\/article>\n
- \ub370\uc774\ud130 \uc804\ucc98\ub9ac:<\/strong> \uc218\uc9d1\ub41c \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uae30 \uc88b\uc740 \ud615\ud0dc\ub85c \uac00\uacf5\ud569\ub2c8\ub2e4.<\/li>\n