\uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uacfc\uc801\ud569(overfitting) \ubc29\uc9c0 \uac00\ub2a5<\/li>\n<\/ul>\n2. \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130 \uc218\uc9d1<\/h2>\n
\ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub294 \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 pandas<\/strong>\ub97c \ud65c\uc6a9\ud558\uc5ec CSV \ud30c\uc77c\ub85c\ubd80\ud130 \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/p>\nimport pandas as pd\n\n# \ube44\ud2b8\ucf54\uc778 \uac00\uaca9 \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('bitcoin_price.csv')\ndata.head()<\/code><\/pre>\n\uc5ec\uae30\uc11c ‘bitcoin_price.csv’\ub294 \ube44\ud2b8\ucf54\uc778\uc758 \ub0a0\uc9dc\uc640 \uac00\uaca9 \uc815\ubcf4\ub97c \ud3ec\ud568\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc694 \uc5f4\uc740 \ub0a0\uc9dc(date), \uc885\uac00(close)\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub370\uc774\ud130\uc758 \uc804\ucc98\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \uc911\uc694\ud55c \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ub9e4\uc218\/\ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
3.1. \ud2b9\uc9d5 \uc0dd\uc131<\/h3>\n
\uac00\uaca9 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ucd94\uac00\uc801\uc778 \ud2b9\uc9d5\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\ub3d9 \ud3c9\uade0(Moving Average), \uc0c1\ub300 \uac15\ub3c4 \uc9c0\uc218(Relative Strength Index, RSI) \ub4f1\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import numpy as np\n\n# \uc774\ub3d9 \ud3c9\uade0\ndata['SMA_30'] = data['close'].rolling(window=30).mean()\ndata['SMA_100'] = data['close'].rolling(window=100).mean()\n\n# \uc0c1\ub300 \uac15\ub3c4 \uc9c0\uc218(RSI) \uacc4\uc0b0\ndef calculate_rsi(data, window=14):\n delta = data['close'].diff()\n gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()\n loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()\n rs = gain \/ loss\n return 100 - (100 \/ (1 + rs))\n\ndata['RSI'] = calculate_rsi(data)<\/code><\/pre>\n3.2. \ud0c0\uac9f \ub808\uc774\ube14 \uc0dd\uc131<\/h3>\n
\ube44\ud2b8\ucf54\uc778\uc758 \ub9e4\uc218\uc640 \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\uae30 \uc704\ud574 \ud0c0\uac9f \ub808\uc774\ube14\uc744 \ub9cc\ub4e4 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c \ub0a0\uc758 \uc885\uac00\uac00 \uc624\ub298\uc758 \uc885\uac00\ubcf4\ub2e4 \ub192\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub9e4\uc218(1), \ub0ae\uc740 \uacbd\uc6b0\uc5d0\ub294 \ub9e4\ub3c4(0)\ub85c \ub808\uc774\ube14\ub9c1\ud569\ub2c8\ub2e4.<\/p>\n
data['Target'] = np.where(data['close'].shift(-1) > data['close'], 1, 0)<\/code><\/pre>\n4. \ub370\uc774\ud130 \ubd84\ud560 \ubc0f \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
\ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub208 \ud6c4, SVM \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 scikit-learn\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# \ud2b9\uc131\uacfc \ud0c0\uac9f \uc124\uc815\nfeatures = data[['SMA_30', 'SMA_100', 'RSI']].dropna()\ntarget = data['Target'][features.index]\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n\n# SVM \ubaa8\ub378 \ud559\uc2b5\nmodel = SVC(kernel='rbf')\nmodel.fit(X_train, y_train)<\/code><\/pre>\n5. \ubaa8\ub378 \ud3c9\uac00<\/h2>\n
\ud559\uc2b5\ud55c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uc131\ub2a5\uc744 \ud655\uc778\ud569\ub2c8\ub2e4.<\/p>\n
# \uc608\uce21 \uc218\ud589\ny_pred = model.predict(X_test)\n\n# \uc131\ub2a5 \ud3c9\uac00\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))<\/code><\/pre>\n6. \uc790\ub3d9\ub9e4\ub9e4 \uc804\ub7b5 \uad6c\ud604<\/h2>\n
\uc608\uce21 \uacb0\uacfc\ub97c \uae30\ubc18\uc73c\ub85c \uc2e4\uc81c \ub9e4\ub9e4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ud604\ud569\ub2c8\ub2e4. \ube44\ud2b8\ucf54\uc778 \uac70\ub798\uc18c\uc758 API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc8fc\ubb38\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 Binance API\ub97c \uc0ac\uc6a9\ud55c \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n
from binance.client import Client\n\n# Binance API \ud074\ub77c\uc774\uc5b8\ud2b8 \uc124\uc815\napi_key = 'YOUR_API_KEY'\napi_secret = 'YOUR_API_SECRET'\nclient = Client(api_key, api_secret)\n\ndef place_order(signal):\n if signal == 1: # \ub9e4\uc218 \uc2e0\ud638\n client.order_market_buy(symbol='BTCUSDT', quantity=0.001) # \uc218\ub7c9\uc740 \uc870\uc815 \ud544\uc694\n elif signal == 0: # \ub9e4\ub3c4 \uc2e0\ud638\n client.order_market_sell(symbol='BTCUSDT', quantity=0.001) # \uc218\ub7c9\uc740 \uc870\uc815 \ud544\uc694\n\n# \uc608\uce21\ub41c \uc2e0\ud638\ub97c \uae30\ubc18\uc73c\ub85c \uc8fc\ubb38 \uc2e4\ud589\nlatest_data = features.iloc[-1]\npredicted_signal = model.predict(latest_data.values.reshape(1, -1))[0]\nplace_order(predicted_signal)<\/code><\/pre>\n\uacb0\ub860<\/h2>\n
\uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \ube44\ud2b8\ucf54\uc778 \uac70\ub798\uc5d0\uc11c \uc218\uc775\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\ub294 \uc88b\uc740 \ubc29\ubc95\uc785\ub2c8\ub2e4. SVM\uc744 \ud65c\uc6a9\ud55c \ub9e4\ub9e4 \uc608\uce21 \uc2dc\uc2a4\ud15c\uc740 \ub370\uc774\ud130 \uc218\uc9d1, \uc804\ucc98\ub9ac, \ubaa8\ub378 \ud6c8\ub828 \ubc0f \ud3c9\uac00\ub97c \ud3ec\ud568\ud55c \uc5ec\ub7ec \ub2e8\uacc4\ub97c \uac70\uccd0 \uad6c\ucd95\ub429\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ud56d\uc0c1 \uc2dc\uc7a5\uc758 \ubcc0\ub3d9\uc131\uacfc \ub9ac\uc2a4\ud06c\ub97c \uace0\ub824\ud574\uc57c \ud558\uba70, \uc774 \uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud558\uae30 \uc804\uc5d0 \ucda9\ubd84\ud55c \ud14c\uc2a4\ud2b8\uc640 \uac80\uc99d\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\uc774\uc640 \uac19\uc740 \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uad6c\ud604\uc5d0 \uc788\uc5b4 \ub370\uc774\ud130\ub97c \ucca0\uc800\ud788 \ubd84\uc11d\ud558\uace0, \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc744 \uc2dc\ub3c4\ud574 \ubcf4\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. SVM \uc678\uc5d0\ub3c4 \uc5ec\ub7ec \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc774 \uc788\uc73c\ub2c8 \uc0c1\ud669\uc5d0 \ub9de\ub294 \uac00\uc7a5 \uc801\ud569\ud55c \ubc29\ubc95\uc744 \ucc3e\uc544\ubcf4\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc778\uacf5\uc9c0\ub2a5(Artificial Intelligence, AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd(Machine Learning, ML)\uc758 \ud65c\uc6a9\uc774 \uac10\uc9c0\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc790\uc0b0 \uac00\uaca9 \uc608\uce21\uacfc \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uac1c\ubc1c\uc5d0 \ub300\ud55c \uad00\uc2ec\uc774 \ub192\uc544\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 Support Vector Machine(SVM)\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc608\uce21 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc744 \uc704\ud55c \ub2e8\uacc4\ubcc4 \uac00\uc774\ub4dc\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. 1. \uc9c0\uc6d0 \ubca1\ud130 \uba38\uc2e0(SVM) \uc774\ud574\ud558\uae30 SVM\uc740 \ubd84\ub958 \ubc0f \ud68c\uadc0 \ubd84\uc11d\uc5d0 \uc0ac\uc6a9\ub418\ub294 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Support Vector Machine (SVM) \uae30\ubc18 \ub9e4\ub9e4 \uc608\uce21 SVM\uc744 \uc774\uc6a9\ud574 \ub9e4\uc218 \ub9e4\ub3c4 \uc2e0\ud638 \uc0dd\uc131.”<\/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-31365","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Support Vector Machine (SVM) \uae30\ubc18 \ub9e4\ub9e4 \uc608\uce21 SVM\uc744 \uc774\uc6a9\ud574 \ub9e4\uc218 \ub9e4\ub3c4 \uc2e0\ud638 \uc0dd\uc131. - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/31365\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Support Vector Machine (SVM) \uae30\ubc18 \ub9e4\ub9e4 \uc608\uce21 SVM\uc744 \uc774\uc6a9\ud574 \ub9e4\uc218 \ub9e4\ub3c4 \uc2e0\ud638 \uc0dd\uc131. - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc778\uacf5\uc9c0\ub2a5(Artificial Intelligence, AI)\uacfc \uba38\uc2e0\ub7ec\ub2dd(Machine Learning, ML)\uc758 \ud65c\uc6a9\uc774 \uac10\uc9c0\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\uc911\uc5d0\uc11c\ub3c4 \ube44\ud2b8\ucf54\uc778\uacfc \uac19\uc740 \uc554\ud638\ud654\ud3d0 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc790\uc0b0 \uac00\uaca9 \uc608\uce21\uacfc \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c \uac1c\ubc1c\uc5d0 \ub300\ud55c \uad00\uc2ec\uc774 \ub192\uc544\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 Support Vector Machine(SVM)\uc744 \uc774\uc6a9\ud55c \ube44\ud2b8\ucf54\uc778 \uc790\ub3d9\ub9e4\ub9e4 \uc608\uce21 \uc2dc\uc2a4\ud15c \uad6c\ucd95\uc744 \uc704\ud55c \ub2e8\uacc4\ubcc4 \uac00\uc774\ub4dc\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. 1. \uc9c0\uc6d0 \ubca1\ud130 \uba38\uc2e0(SVM) \uc774\ud574\ud558\uae30 SVM\uc740 \ubd84\ub958 \ubc0f \ud68c\uadc0 \ubd84\uc11d\uc5d0 \uc0ac\uc6a9\ub418\ub294 … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd\uacfc \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4, Support Vector Machine (SVM) \uae30\ubc18 \ub9e4\ub9e4 \uc608\uce21 SVM\uc744 \uc774\uc6a9\ud574 \ub9e4\uc218 \ub9e4\ub3c4 \uc2e0\ud638 \uc0dd\uc131."\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/31365\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T12:01:32+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:45:23+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" 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