{"id":20922,"date":"2018-04-17T13:40:29","date_gmt":"2018-04-17T04:40:29","guid":{"rendered":"https:\/\/www.creationline.com\/?p=20922"},"modified":"2018-04-17T13:40:29","modified_gmt":"2018-04-17T04:40:29","slug":"%e3%83%87%e3%82%a3%e3%83%bc%e3%83%97%e3%83%a9%e3%83%bc%e3%83%8b%e3%83%b3%e3%82%b0%e3%81%a7bitcoin%e3%81%ae%e4%be%a1%e6%a0%bc%e3%82%92%e4%ba%88%e6%b8%ac%e3%81%97%e3%81%a6%e8%a6%8b%e3%82%8b-deep-learni","status":"publish","type":"post","link":"https:\/\/www.creationline.com\/tech-blog\/data-management\/dataanalytics\/20922","title":{"rendered":"\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3067Bitcoin\u306e\u4fa1\u683c\u3092\u4e88\u6e2c\u3057\u3066\u898b\u308b #Deep Learning #Keras"},"content":{"rendered":"

\u3053\u3093\u306b\u3061\u306f\u3001\u30af\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u30e9\u30a4\u30f3\u306e\u6731\u3067\u3059\u3002<\/p>\n

\u6700\u8fd1\u3001ICO\uff08Initial Coin offering\uff09\u304c\u975e\u5e38\u306b\u30db\u30c3\u30c8\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u6587\u5b57\u901a\u308a\u3001ICO\u306f\u4f01\u696d\u304c\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u521d\u671f\u6bb5\u968e\u3067\u30b3\u30a4\u30f3\uff08\u4eee\u60f3\u901a\u8ca8\uff09\u3092\u4f7f\u3063\u3066\u8cc7\u91d1\u3092\u8abf\u9054\u3059\u308b\u4ed5\u7d44\u307f\u3067\u3059\u3002ICO\u306b\u3088\u3063\u3066\u30b9\u30bf\u30fc\u30c8\u30a2\u30c3\u30d7\u3067\u3082\u7c21\u5358\u306b\u4e8b\u696d\u3092\u7acb\u3066\u308b\u305f\u3081\u306e\u8cc7\u91d1\u3092\u8abf\u9054\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u4eca\u56de\u306f\u4eee\u60f3\u901a\u8ca8\u306e\u4e2d\u3067\u3082\u7279\u306b\u6709\u540d\u306aBitcoin\u306e\u4fa1\u683c\u3092\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3067\u4e88\u6e2c\u3057\u3066\u307f\u307e\u3057\u305f\u3002\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u51e6\u7406\u306b\u5f97\u610f\u3068\u3059\u308bRNN\uff08Recurrent Neural Network\uff09\u306e\u4e00\u7a2e\u3067\u3042\u308bLSTM\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u4eca\u56de\u6271\u3046\u30c7\u30fc\u30bf\u304cBitcoin\u306e\u4fa1\u683c\u306e\u307f\u3001\u4ed6\u306e\u30c7\u30fc\u30bf\u306f\u4e00\u5207\u4f7f\u3044\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u4f46\u3057\u3001\u7d50\u8ad6\u304b\u3089\u8a00\u3046\u3068\u3001\u3053\u306e\u307e\u307e\u3060\u3068\u4e0a\u624b\u304f\u4e88\u6e2c\u3067\u304d\u305a\u3001\u4ed6\u306e\u30c7\u30fc\u30bf\uff08\u7279\u5fb4\u91cf\uff09\u304c\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u3002\u4eca\u56de\u306f\u52c9\u5f37\u304c\u76ee\u7684\u306a\u306e\u3067\u3001\u7279\u5fb4\u91cf\u3092\u63a2\u3059\u306e\u306f\u307e\u305f\u5225\u306e\u6a5f\u4f1a\u306b\u3057\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n

\u4eca\u56de\u306e\u5b9f\u88c5\u306fKeras\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u3063\u3066\u307f\u307e\u3057\u305f\u304c\u3001\u6240\u611f\u3068\u3057\u3066\u306f\u3068\u306b\u304b\u304f\u30b3\u30fc\u30c6\u30a3\u30f3\u30b0\u306e\u91cf\u304c\u5c11\u306a\u3044\u3067\u3059\u3002\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u7406\u8ad6\u306f\u8907\u96d1\u3067\u3059\u304c\u3001\u3053\u3093\u306a\u77ed\u3044\u671f\u9593\u3067\u5b9f\u88c5\u3067\u304d\u308b\u306e\u304c\u9a5a\u304d\u3067\u3059\u3002<\/p>\n

\u3067\u306f\u3001\u65e9\u901f\u672c\u984c\u306b\u5165\u308a\u305f\u3044\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n

\u30c7\u30fc\u30bf\u3092\u6e96\u5099<\/h2>\n

\u30c7\u30fc\u30bf\u306e\u53d6\u5f97<\/h3>\n

\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u306f\u5927\u624b\u4eee\u60f3\u901a\u8ca8\u53d6\u5f15\u6240\u306ePoloniex\u304b\u3089\u53d6\u3063\u3066\u304d\u307e\u3059\u3002Python\u7528\u306eAPI\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u308b\u306e\u3067\u3001API\u7d4c\u7531\u3067\u53d6\u5f97\u3057\u307e\u3059\u3002<\/p>\n

\nimport poloniex\nimport time\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef return_chart_data(pair, period, day):\n    polo = poloniex.Poloniex()\n    chart_data = polo.returnChartData(pair,\n                                      period=period,\n                                      start=time.mktime(time.strptime(start_time, \"%Y-%m-%d %H:%M\")),\n                                      end=time.mktime(time.strptime(end_time, \"%Y-%m-%d %H:%M\")))\n    df = pd.DataFrame(chart_data)\n    df[\"datetime\"] = pd.to_datetime(df[\"date\"].astype(int) , unit=\"s\")\n    df = df.set_index(\"datetime\")\n    fig, ax1 = plt.subplots(figsize=(20, 10))\n    ax1.plot(pd.to_datetime(df[\"date\"].astype(int) , unit=\"s\"),\n             df[\"close\"].astype(np.float32), label = \"Coin Price\", color=\"deeppink\")\n    ax1.legend(loc=2, fontsize=14)\n    ax1.tick_params(labelsize=14)\n    plt.show()\n    return df\n\n# USDT_BTC\u30b3\u30a4\u30f3\u30da\u30a2\u306e\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3001\u30c7\u30fc\u30bf\u9593\u9694\u306f300s\u3001\u671f\u9593\u306f2018-03-30 00:00\u304b\u30892018-04-01 00:00\u307e\u3067\ndata_df = return_chart_data(\"USDT_BTC\", 300, \"2018-03-30 00:00\", \"2018-04-01 00:00\")\n<\/pre>\n
\u203b\u4f59\u8ac7\u3067\u3059\u304c\u3001\u3053\u3053\u306eUSDT\u306fUSD\u3067\u306f\u306a\u304fTether\u3068\u3044\u3046\u4eee\u60f3\u901a\u8ca8\u3067\u3059\u3002\u3057\u304b\u3082Tether\u304c\u4eca\u6d41\u884c\u306a\u5206\u6563\u578b\u3067\u306f\u306a\u304f\u96c6\u4e2d\u578b\u3067\u3059\u30021Tether=1USD\u306a\u306e\u3067\u3001USD\u3068\u307f\u306a\u3057\u3066\u3082\u5927\u4e08\u592b\u3067\u3059\u3002<\/p>\n
Poloniex\u304b\u3089\u53d6\u5f97\u3057\u305f\u30c7\u30fc\u30bf\u306edate\u3068close\u5217\u304c\u4eca\u56de\u306e\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306b\u306a\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306fdate\u3092\u6a2a\u8ef8\u306b\u3001close\u3092\u7e26\u8ef8\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u305f\u30b0\u30e9\u30d5\u3067\u3059\u3002
\n\"\"<\/p>\n
\u30c7\u30fc\u30bf\u306e\u968e\u5dee\u3092\u53d6\u308b\uff08differencing\uff09<\/h3>\n
Bitcoin\u4fa1\u683c\u306e\u3088\u3046\u306a\u30d5\u30a1\u30a4\u30ca\u30f3\u30b7\u30e3\u30eb\u30c7\u30fc\u30bf\u306f\u57fa\u672cRandom walk\u306e\u6027\u8cea\u3092\u6301\u3063\u3066\u3044\u307e\u3059\u3002Random walk\u3068\u306f\u4ee5\u4e0b\u306e\u3053\u3068\u3092\u6307\u3057\u307e\u3059\u3002
\n- \u9577\u671f\u306e\u4e0b\u843d\u304b\u4e0a\u6607\u306e\u30c8\u30ec\u30f3\u30c9\u304c\u3042\u308b
\n- \u6025\u306b\u4e88\u6e2c\u4e0d\u80fd\u306a\u65b9\u5411\u306b\u5909\u5316\u3059\u308b\u3053\u3068\u304c\u3042\u308b<\/p>\n
\u3053\u3046\u3044\u3046\u6027\u8cea\u3092\u6301\u3064\u30c7\u30fc\u30bf\u3092\u975e\u5b9a\u5e38\u30c7\u30fc\u30bf\u3068\u547c\u3073\u307e\u3059\u3002\u5206\u6790\u3092\u884c\u3046\u305f\u3081\u306b\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u30c8\u30ec\u30f3\u30c9\u3092\u62bd\u51fa\u3057\u3001\u5b9a\u5e38\u7684\u306a\u30c7\u30fc\u30bf\u306b\u5909\u5316\u3055\u305b\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002
\n\u3053\u3053\u3067\u306f\u3001\u30c7\u30fc\u30bf\u306e\u30c8\u30ec\u30f3\u30c9\u3092\u53d6\u308a\u9664\u304d\u307e\u3059\u3002\u968e\u5dee\uff08differencing\uff09\u3068\u3044\u3046\u624b\u6cd5\u3092\u4f7f\u3044\u307e\u3059\u304c\u3001\u4ee5\u4e0b\u306e\u6570\u5f0f\u3067\u884c\u3044\u307e\u3059\u304c\u3001close_diffed(t)\u304c\u6642\u523bclose(t)\u3068\u6642\u523bclose(t-1)\u306e\u968e\u5dee\u306b\u306a\u308a\u307e\u3059\u3002\u307e\u305f\u3001Bitcoin\u4fa1\u683c\u306b\u306f\u7279\u306b\u5468\u671f\u6027\u304c\u898b\u5f53\u305f\u3089\u306a\u3044\u306e\u3067\u3001\u4eca\u56de\u306f\u96a3\u63a5\u306e\u6642\u523b\u306e\u968e\u5dee\u3092\u53d6\u308a\u307e\u3059\u306e\u3067\u3001\u968e\u6570\u304c1\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
\nclose_diffed(t)=close(t)\u2212close(t\u22121).\n<\/pre>\n
\u4f46\u3057\u3001\u5f8c\u7a0b\u306e\u4e88\u6e2c\u7d50\u679c\u3067\u304a\u898b\u305b\u3057\u307e\u3059\u304c\u3001\u3053\u3046\u3044\u3046\u3084\u308a\u65b9\u3067\u306f\u3001close(t)\u306e\u4e88\u6e2c\u7d50\u679c\u304cclose(t-1)\u306e\u89b3\u6e2c\u5024\u3068\u4f3c\u305f\u3088\u3046\u306a\u30c8\u30ec\u30f3\u30c9\u306b\u306a\u308a\u307e\u3059\u3002\u4e88\u6e2c\u304c\u89b3\u6e2c\u3088\u308a\u4e00\u6b69\u9045\u308c\u3066\u3044\u308b\u611f\u3058\u306b\u306a\u308a\u307e\u3059\u3002
\n\u3082\u3057\u30c7\u30fc\u30bf\u306b\u5468\u671f\u6027(\u5b63\u7bc0\u6027\u7b49)\u304c\u5b58\u5728\u3059\u308b\u306e\u3067\u3042\u308c\u3070\u3001\u304b\u306a\u308a\u6709\u529b\u306a\u65b9\u6cd5\u3067\u3059\u3002<\/p>\n
\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\ndef difference_data(data_df):\n    diff_interval = 1\n    data = data_df[\"close\"].values\n    diff = np.empty((0,1), np.float32)\n    for i in range(diff_interval, len(data)):\n        value = data[i] - data[i - diff_interval]\n        diff = np.append(diff, value)\n\n    data_df = data_df.iloc[self.diff_interval:, :]\n    return diff, data_df\n\ndata_diff, data_df = difference_data(data_df)\n<\/pre>\n
\u968e\u5dee\u3092\u3068\u3063\u305f\u30c7\u30fc\u30bf\u304c\u4ee5\u4e0b\u306e\u30b0\u30e9\u30d5\u306b\u306a\u308a\u307e\u3059\u3002
\n\"\"<\/p>\n
\u7279\u5fb4\u91cf\u306e\u6e96\u5099<\/h3>\n
\u4eca\u56de\u6271\u3046\u30c7\u30fc\u30bf\u304cBitcoin\u4fa1\u683c\u306e\u307f\u306a\u306e\u3067\u3001\u5b66\u7fd2\u3068\u4e88\u6e2c\u3092\u884c\u3046\u305f\u3081\u306b\u3001\u7279\u5fb4\u91cf\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002
\n\u4eca\u56de\u306ft\u6642\u523b\u306e\u30c7\u30fc\u30bfclose(t)\u306e\u7279\u5fb4\u91cf\u3092close(t-30)...close(t-1)\u306e\u7cfb\u5217\u3068\u3057\u307e\u3059\u3002\u3053\u308c\u3067\u4e00\u3064\u306e\u89b3\u6e2c\u5024\u306b\u305f\u3044\u3057\u306630\u500b\u306e\u7279\u5fb4\u91cf\u304c\u5f97\u307e\u3057\u305f\u3002
\n\u3053\u306e\u5f8c\u3001close(t-30)...close(t-1)\u306e\u7cfb\u5217\u3067close(t)\u3092\u4e88\u6e2c\u3059\u308b\u308f\u3051\u3067\u3059\u3002<\/p>\n
\ndef generate_series_data_for_supervised_learning(data_array, datetime_array):\n    t = np.empty((0,1), int)\n    x = np.empty((0,30), np.float32)\n    y = np.empty((0,1), np.float32)\n\n    m = len(data_array)\n    for n in range(30, m):\n        new_t = np.array([[datetime_array[n]]])\n        new_x = np.array([data_array[n-30: n]])\n        new_y = np.array([[data_array[n]]])\n        t = np.append(t, new_t, axis=0)\n        x = np.append(x, new_x, axis=0)\n        y = np.append(y, new_y, axis=0)\n\n    self.dataframe = self.dataframe.iloc[30:, :]\n    return t, np.concatenate([x, y], axis=1)\n\ntime_array, data_array = generate_series_data_for_supervised_learning(data_diff, data_df.index)\n<\/pre>\n
\u30c7\u30fc\u30bf\u306e\u5206\u5272<\/h3>\n
\u4eca\u56de\u306f\u6559\u5e2b\u30c7\u30fc\u30bf\uff08\u4ea4\u5dee\u691c\u8a3c\u30c7\u30fc\u30bf\u3092\u542b\u3080\uff0980%\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf20\uff05\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u306e\u305f\u3081\u30b7\u30e3\u30d5\u30eb\u306f\u884c\u3044\u307e\u305b\u3093\u3002<\/p>\n
\ndef split_data(data_array, datetime_array):
\n    m = len(data_array)<\/p>\n
    train_batches = int(m * 0.8)<\/p>\n
    # 80% training data(cv included), 20% test data
\n    m_train = train_batches<\/p>\n
    time_train, time_test = datetime_array<\/p>\n","protected":false},"excerpt":{"rendered":"
\u3053\u3093\u306b\u3061\u306f\u3001\u30af\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u30e9\u30a4\u30f3\u306e\u6731\u3067\u3059\u3002 \u6700\u8fd1\u3001ICO\uff08Initial Coin offering\uff09\u304c\u975e\u5e38\u306b\u30db\u30c3\u30c8\u306a\u30ad\u30fc\u30ef\u30fc\u30c9\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u6587\u5b57\u901a\u308a\u3001ICO\u306f\u4f01\u696d\u304c\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u521d\u671f\u6bb5\u968e\u3067\u30b3\u30a4\u30f3\uff08\u4eee\u60f3\u901a\u8ca8\uff09\u3092\u4f7f\u3063\u3066\u8cc7 […]<\/p>\n","protected":false},"author":1,"featured_media":20924,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[83,124],"tags":[],"class_list":["post-20922","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dataanalytics","category-j-zhu"],"acf":[],"yoast_head":"\n\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3067Bitcoin\u306e\u4fa1\u683c\u3092\u4e88\u6e2c\u3057\u3066\u898b\u308b #Deep Learning #Keras - 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