{"id":3049,"date":"2024-05-01T14:58:22","date_gmt":"2024-05-01T05:58:22","guid":{"rendered":"https:\/\/yorozu.cloudfree.jp\/wordpress\/?p=3049"},"modified":"2024-11-11T08:42:59","modified_gmt":"2024-11-10T23:42:59","slug":"%e6%a0%aa%e4%be%a1%e4%ba%88%e6%b8%ac%ef%bc%88%e3%81%9d%e3%81%ae%ef%bc%92%ef%bc%89","status":"publish","type":"post","link":"https:\/\/yorozu.cloudfree.jp\/wordpress\/?p=3049","title":{"rendered":"\u682a\u4fa1\u4e88\u6e2c\uff08\u305d\u306e\uff12\uff09"},"content":{"rendered":"\n

\u5b9f\u884c\u4f8b\u306f\u300csaka.mokumoku\u300d\u306e\u300cGoogle Colabotry\u300d\u74b0\u5883\u306b\u4fdd\u5b58\u3057\u3066\u3042\u308b<\/p>\n\n\n\n

\u300c\u30a2\u30c3\u30d7\u30eb\u300d\u306e\u682a\u4fa1\u3092\u300c\u56de\u5e30\u554f\u984c\u300d\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\u3059\u308b
\u3000\u3000\uff08\u305d\u306e\uff12\uff09\u306f\u884c\u756a\u53f7\uff12\uff10\u301c\uff14\uff13\u884c\u307e\u3067\u3092\u89e3\u8aac\u3059\u308b<\/p>\n\n\n\n

#\n#\u3000\u3000\u3000\uff11\uff0e\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f\n#\nimport pandas as pd\nimport numpy  as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n#\n%matplotlib inline\n#\n#\u3000\u3000\u3000\uff12\uff0e\u682a\u4fa1\u30c7\u30fc\u30bf\u306e\u53d6\u5f97\n#\n!pip install pandas_datareader\n#\nimport pandas_datareader.data as dr\n#\nstock = dr.DataReader('AAPL',"stooq")\nprint(stock.head())\n#\n#\u3000\u3000\u3000\uff13.\u7fcc\u65e5\u306e\u9ad8\u5024\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\u3092\u8ffd\u52a0\n#\nstock['Next_High'] = stock['High'].shift(1)\nprint(stock.head())\n#\nprint(stock.isnull().sum())\n#\nstock = stock.dropna()\nprint(stock.head())\nprint(stock.shape)\n#\n#\u3000\u3000\u3000\uff14.\u691c\u8a3c\u3001\u76ee\u7684\u5909\u6570\u3068\u8aac\u660e\u5909\u6570\u306b\u5206\u5272\u3059\u308b\n#\ndata_columns = ['Open','High','Low','Close','Volume']\n#\ntest_rows = 500\n#\ntarget = stock['Next_High']\ndata = stock[data_columns]\n#\ntarget_train = target[test_rows:]\ntarget_test = target[:test_rows]\ndata_train = data[test_rows:]\ndata_test = data[:test_rows]\n#\nprint(data.shape)\nprint(target.shape)\nprint(data_train.shape)\nprint(target_train.shape)\nprint(data_test.shape)\nprint(target_test.shape)\n#\n#\u3000\u3000\u3000\uff15.\u7dda\u5f62\u56de\u5e30\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\u3059\u308b\n#\nfrom sklearn.linear_model import LinearRegression\n#\nmodel = LinearRegression()\n#\nmodel.fit(data_train,target_train)\n#\ntarget_predict = model.predict(data_test)\n#\nresult = pd.DataFrame(target_test)\n#\nresult['predict'] = target_predict\n#\nsns.lineplot(data=result)\n#\nsns.lineplot(data=result[:30])\n<\/code><\/pre><\/div>\n\n\n\n
\u4ee5\u4e0b\u306f\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u3068\u89e3\u8aac<\/p>\n\n\n\n
\uff13.\u7fcc\u65e5\u306e\u9ad8\u5024\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\u3092\u8ffd\u52a0<\/strong><\/h4>\n\n\n\n
#\nstock['Next_High'] = stock['High'].shift(1)\nprint(stock.head())<\/code><\/pre><\/div>\n\n\n\n
\u3000\u7fcc\u65e5\u306e\u9ad8\u5024\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\uff08\u5217\uff09\u3092\u8ffd\u52a0\u3059\u308b
\u3000\u3000\u3000\u524d\u65e5\u306b\u7fcc\u65e5\u306e\u9ad8\u5024\u3092\u4e88\u6e2c\u3057\u305f\u3068\u3059\u308b\u30d5\u30a3\u30fc\u30eb\u30c9\u3092\u7528\u610f\u3059\u308b

\u3000\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u4fee\u6b63\u3055\u308c\u308b
\u3000\u3000\u3000\u7fcc\u65e5\u9ad8\u5024\uff08Next_High\uff09\u306b\u7fcc\u65e5\u306e\u9ad8\u5024\uff08High\uff09\u304c\u30bb\u30c3\u30c8\u3055\u308c\u308b<\/p>\n\n\n\n
               Open     High       Low   Close      Volume  Next_High\nDate                                                                 \n2024-05-01  169.580  172.705  169.1100  169.30  50383147.0        NaN\n2024-04-30  173.330  174.990  170.0000  170.33  65934776.0    172.705\n2024-04-29  173.370  176.030  173.1000  173.50  68169419.0    174.990\n2024-04-26  169.880  171.340  169.1800  169.30  44838354.0    176.030\n2024-04-25  169.525  170.610  168.1511  169.89  50558329.0    171.340<\/code><\/pre><\/div>\n\n\n\n
#\nprint(stock.isnull().sum())<\/code><\/pre><\/div>\n\n\n\n
\u3000\u6b20\u640d\u5024\u3092\u78ba\u8a8d\u3059\u308b\u305f\u3081\u306b\u4e2d\u8eab\u3092\u51fa\u529b\u3059\u308b
\u3000\u3000\u3000\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u793a\u3055\u308c\u308b<\/p>\n\n\n\n
Open         0\nHigh         0\nLow          0\nClose        0\nVolume       0\nNext_High    1\ndtype: int64<\/code><\/pre><\/div>\n\n\n\n
\u3000\u201d\u7b26\u53f7\u3042\u308a64\u30d3\u30c3\u30c8\u6574\u6570\u578b\u201d\u306e\u30d5\u30a3\u30fc\u30eb\u30c9\u3067\u201dNext_High\u201d\u306b\uff11\u500b\u3060\u3051\u6b20\u640d\u5024\u304c\u3042\u308b<\/p>\n\n\n\n
#\nstock = stock.dropna()\nprint(stock.head())<\/code><\/pre><\/div>\n\n\n\n
\u3000 \u6b20\u640d\u5024\u306e\u30c7\u30fc\u30bf\u3092\u524a\u9664\u3057\u3001\u6574\u5f62\u3059\u308b
\u3000\u3000\u3000\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u793a\u3055\u308c\u308b\u3001\u65e5\u4ed8\u304c\uff11\u65e5\u305a\u308c\u305f\u5f62\u306b\u306a\u308b<\/p>\n\n\n\n
               Open    High       Low   Close      Volume  Next_High\nDate                                                                \n2024-04-30  173.330  174.99  170.0000  170.33  65934776.0    172.705\n2024-04-29  173.370  176.03  173.1000  173.50  68169419.0    174.990\n2024-04-26  169.880  171.34  169.1800  169.30  44838354.0    176.030\n2024-04-25  169.525  170.61  168.1511  169.89  50558329.0    171.340\n2024-04-24  166.540  169.30  166.2100  169.02  48251835.0    170.610<\/code><\/pre><\/div>\n\n\n\n
#\nprint(stock.shape)<\/code><\/pre><\/div>\n\n\n\n
\u3000\u5168\u4f53\u306e\u69cb\u9020\u3092\u78ba\u8a8d\u3059\u308b
\u3000\u3000\u3000\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u793a\u3055\u308c\u308b\u30011256\u4ef6\u30676\u5217\u306e\u30c7\u30fc\u30bf<\/p>\n\n\n\n
(1256, 6)<\/code><\/pre><\/div>\n\n\n\n
\uff14\uff0e\u691c\u8a3c\u3001\u76ee\u7684\u5909\u6570\u3068\u8aac\u660e\u5909\u6570\u306b\u5206\u5272\u3059\u308b<\/strong><\/h4>\n\n\n\n
#\ndata_columns = ['Open','High','Low','Close','Volume']\n#\ntest_rows = 500\n#\ntarget = stock['Next_High']\ndata = stock[data_columns]\n#\ntarget_train = target[test_rows:]\ntarget_test = target[:test_rows]\ndata_train = data[test_rows:]\ndata_test = data[:test_rows]<\/code><\/pre><\/div>\n\n\n\n
\u3000\u5b66\u7fd2\u30c7\u30fc\u30bf\u3068\u691c\u8a3c\u30c7\u30fc\u30bf\u3068\u306b\u5206\u3051\u308b
\u3000\u3000\u3000\u76ee\u7684\u5909\u6570\uff08target\uff09\u30fb\u30fb\u30fb\u7d50\u679c
\u3000\u3000\u3000\u3000\u3000\u7fcc\u65e5\u9ad8\u5024\u306e\u30c7\u30fc\u30bf1256\u4ef6\u3001\uff11\u5217
\u3000\u3000\u3000\u8aac\u660e\u5909\u6570\uff08data\uff09\u30fb\u30fb\u30fb\u539f\u56e0
\u3000\u3000\u3000\u3000\u3000\u524d\u65e5\u307e\u3067\u306e\u30c7\u30fc\u30bf1256\u4ef6\u3001\uff15\u5217

\u3000\u3000\u3000\u76ee\u7684\u5909\u6570\uff08target\uff09
\u3000\u3000\u3000\u3000\u3000target_train\u30fb\u30fb\u30fb501\u4ef6\u76ee\u301c1256\u4ef6\u76ee\u3001\uff11\u5217
\u3000\u3000\u3000\u3000\u3000target_test\u30fb\u30fb\u30fb\u5148\u982d\u301c500\u4ef6\u76ee\u3001\uff11\u5217
\u3000\u3000\u3000\u8aac\u660e\u5909\u6570\uff08data\uff09
\u3000\u3000\u3000\u3000\u3000data_train\u30fb\u30fb\u30fb501\u4ef6\u76ee\u301c1256\u4ef6\u76ee\u3001\uff15\u5217
\u3000\u3000\u3000\u3000\u3000data_test \u30fb\u30fb\u30fb\u5148\u982d\u301c500\u4ef6\u76ee\u3001\uff15\u5217<\/p>\n\n\n\n
#\nprint(data.shape)\nprint(target.shape)\nprint(data_train.shape)\nprint(target_train.shape)\nprint(data_test.shape)\nprint(target_test.shape)<\/code><\/pre><\/div>\n\n\n\n
\u3000\u5206\u5272\u5f8c\u306e\u30c7\u30fc\u30bf\u69cb\u6210\u3092\u78ba\u8a8d\u3059\u308b<\/p>\n\n\n\n
(1256, 5)\n(1256,)\n(756, 5)\n(756,)\n(500, 5)\n(500,)<\/code><\/pre><\/div>\n\n\n\n
\uff15\uff0e\u7dda\u5f62\u56de\u5e30\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\u3059\u308b<\/strong><\/h4>\n\n\n\n
#\nfrom sklearn.linear_model import LinearRegression\n#\nmodel = LinearRegression()\n#\nmodel.fit(data_train,target_train)<\/code><\/pre><\/div>\n\n\n\n
\u3000\u7dda\u5f62\u56de\u5e30\u3092\u4f7f\u3063\u3066\u6a5f\u68b0\u5b66\u7fd2\u3092\u884c\u3046
\u3000\u3000\u3000\u30e9\u30a4\u30d6\u30e9\u30ea\u300cscikit-learn\u300d\u3092\u547c\u3073\u51fa\u3059
\u3000\u3000\u3000\u6a5f\u68b0\u5b66\u7fd2\u3092\u884c\u3046
\u3000\u3000\u3000\u3000\u3000data_train\uff08\u8aac\u660e\u5909\u6570\u3001\u5b66\u7fd2\u7528\uff09\u3068
\u3000\u3000\u3000\u3000\u3000target_train\uff08\u76ee\u7684\u5909\u6570\u3001\u5b66\u7fd2\u7528\uff09\u3092\u4f7f\u3046<\/p>\n\n\n\n
#\ntarget_predict = model.predict(data_test)<\/code><\/pre><\/div>\n\n\n\n
\u3000\u5b66\u7fd2\u3057\u305f\u3089\u691c\u8a3c\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u682a\u4fa1\u3092\u4e88\u6e2c\u3059\u308b<\/p>\n\n\n\n
#\nresult = pd.DataFrame(target_test)\n#\nresult['predict'] = target_predict<\/code><\/pre><\/div>\n\n\n\n
\u3000\u691c\u8a3c\u30c7\u30fc\u30bf\u306e\u76ee\u7684\u5909\u6570\u3067\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u4f5c\u6210
\u3000\u3000\u3000\u4e88\u6e2c\u7d50\u679c\u3092\u3068\u3057\u3066\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u8ffd\u52a0\u3059\u308b<\/p>\n\n\n\n
#\nsns.lineplot(data=result)\n#\nsns.lineplot(data=result[:30])<\/code><\/pre><\/div>\n\n\n\n
\u3000\u30b0\u30e9\u30d5\u3067\u53ef\u8996\u5316\u3059\u308b
\u3000\u3000\u3000\u901a\u5e38\u306e\u30b0\u30e9\u30d5\u3068\u62e1\u5927\u7248\uff08\uff13\uff10\u65e5\u5206\uff09\u3092\u8868\u793a\u3059\u308b<\/p>\n\n\n\n
\"\"<\/a>
\u5b9f\u884c\u7d50\u679c<\/figcaption><\/figure>\n\n\n\n
\"\"<\/a>
\u62e1\u5927\u7248<\/figcaption><\/figure>\n\n\n\n
\u3000\u3000<\/p>\n\n\n\n
\uff16\uff0e\u7dcf\u62ec<\/strong><\/h4>\n\n\n\n
\u3000\u5168\u4f53\u7684\u306bpredict\uff08\u4e88\u6e2c\u5024\uff09\u306fNext-High\uff08\u7fcc\u65e5\u9ad8\u5024\uff09\u306e\u5f8c\u8ffd\u3044\u3068\u306a\u308b
\u3000\u3000\u3000\u524d\u65e5\u306e\u682a\u4fa1\u3067\u7fcc\u65e5\u306e\u682a\u4fa1\u3092\u4e88\u6e2c\u3057\u3066\u3044\u308b\u306e\u3067\u3001\u3053\u308c\u3067\u6b63\u89e3

\u3000\u8aac\u660e\u5909\u6570\u306e\u9078\u629e\u306e\u7d50\u679c\u306b\u3088\u3063\u3066\u3001\u76ee\u7684\u5909\u6570\u306f\u5de6\u53f3\u3055\u308c\u308b\u3068\u3044\u3046
\u3000\u3000\u3000\u5f53\u305f\u308a\u524d\u306e\u7d50\u679c\u3068\u306a\u308b<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
\u30fb\u300c\u56de\u5e30\u554f\u984c\u300d\u3067\u682a\u4fa1\u3092\u4e88\u6e2c\u3059\u308b\u306b\u3064\u3044\u3066<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[24],"class_list":["post-3049","post","type-post","status-publish","format-standard","hentry","category-99_","tag-24"],"_links":{"self":[{"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/3049","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3049"}],"version-history":[{"count":1,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/3049\/revisions"}],"predecessor-version":[{"id":4696,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/3049\/revisions\/4696"}],"wp:attachment":[{"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3049"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3049"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yorozu.cloudfree.jp\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3049"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}