MA_RCI_Interactive_Chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd41e400e13846dab6d690d25345883e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "interactive(children=(Dropdown(description='Bar', options=('Candle', 'Heikin'), value='Candle'), Dropdown(description='Period', index=5, options=('1m', '3m', '5m', '15m', '30m', '1h', '2h', '3h', '4h', '6h', '12h', '1d'), value='1h'), IntSlider(value=100, description='Bars', max=300, min=60, step=20), IntSlider(value=9, description='SMA1', min=1), IntSlider(value=36, description='SMA2', min=1), IntSlider(value=52, description='SMA3', min=1), Output()), _dom_classes=('widget-interact',))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import time, calendar, pytz, requests, math\n",
    "from datetime import datetime, timedelta\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mpl_finance as mpf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "from IPython.display import display, clear_output\n",
    "from ipywidgets import interact, IntSlider, Dropdown\n",
    "%matplotlib inline\n",
    "\n",
    "plt.ion()\n",
    "\n",
    "ALLOWED_PERIOD = {\n",
    "    \"1m\": [\"1m\", 1,    1],  \"3m\": [\"1m\",  3,   3],\n",
    "    \"5m\": [\"5m\", 1,    5], \"15m\": [\"5m\",  3,  15], \"30m\": [\"5m\", 6, 30],\n",
    "    \"1h\": [\"1h\", 1,   60],  \"2h\": [\"1h\",  2, 120],\n",
    "    \"3h\": [\"1h\", 3,  180],  \"4h\": [\"1h\",  4, 240],\n",
    "    \"6h\": [\"1h\", 6,  360], \"12h\": [\"1h\", 12, 720],\n",
    "    \"1d\": [\"1d\", 1, 1440],\n",
    "    # not support yet '3d', '1w', '2w', '1m'\n",
    "}\n",
    "\n",
    "bar        = \"Candle\" # チャートタイプ(ロウソク足/平均足)\n",
    "period     = \"1h\"     # 時間足\n",
    "chart_bars = 100      # チャートバー表示件数\n",
    "sma1_term  = 9        # SMA1期間\n",
    "sma2_term  = 36       # SMA2期間\n",
    "sma3_term  = 52       # SMA3期間\n",
    "df_base    = pd.DataFrame(index=[], columns=[]) # OHLCV + RCI\n",
    "df_chart   = pd.DataFrame(index=[], columns=[]) # OHLCV + RCI + Regression\n",
    "fig        = None\n",
    "ax1        = None\n",
    "ax2        = None\n",
    "ax3        = None\n",
    "bg1        = None\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# メイン処理\n",
    "#---------------------------------------------------------------------\n",
    "def main():\n",
    "    global period\n",
    "    global df_base\n",
    "\n",
    "    # 初回チャート作成\n",
    "    createChart()\n",
    "    # OHLCVデータ+平均足取得\n",
    "    df_base = fetch_ohlcv_df(period=period, count=360, reverse=False, partial=True, \\\n",
    "                             tstype=\"UTS\", heikin=True)\n",
    "    # index設定\n",
    "    df_base = df_base.set_index(\"timestamp\")\n",
    "    # RCI取得\n",
    "    attachRCI()\n",
    "    # SMA取得\n",
    "    attachSMA()\n",
    "    # チャート更新\n",
    "    updateChart()\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# チャート作成\n",
    "#---------------------------------------------------------------------\n",
    "def createChart():\n",
    "    global fig\n",
    "    global ax1\n",
    "    global ax2\n",
    "    global ax3\n",
    "    global bg1\n",
    "\n",
    "    # 描画領域を作成\n",
    "    # Figure(図全体)\n",
    "    fig = plt.figure(figsize   =(10, 8),   # 領域サイズ(インチ指定)\n",
    "                     dpi       =100,      # 解像度\n",
    "                     facecolor =\"#1E1E1E\",# 背景色\n",
    "                     edgecolor =\"k\")      # 外枠色\n",
    "    fig.autofmt_xdate()                   # x軸のオートフォーマット\n",
    "    # チャート配置割り\n",
    "    gs = gridspec.GridSpec(4, 1)\n",
    "    # チャート間隔\n",
    "    plt.subplots_adjust(wspace=0.0, hspace=0.0)\n",
    "    # アスペクト比\n",
    "    plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n",
    "\n",
    "    # ax1:メインチャート(ロウソクバー)\n",
    "    ax1 = plt.subplot(gs[0:3, 0])\n",
    "    # ax2:メインチャー(出来高)\n",
    "    ax2 = ax1.twinx()\n",
    "    # ax3:サブチャート\n",
    "    ax3 = plt.subplot(gs[3, 0], sharex=ax1)\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# RCI取得\n",
    "#---------------------------------------------------------------------\n",
    "def attachRCI():\n",
    "    global bar\n",
    "    global df_base\n",
    "\n",
    "    lst_ts    = df_base.index.tolist()\n",
    "    lst_close = df_base[\"close\"].tolist() if bar == \"Candle\" else df_base[\"heikin_close\"].tolist()\n",
    "    lst_ts    = lst_ts[::-1]\n",
    "    lst_close = lst_close[::-1]\n",
    "    df_rci    = get_rci_df(lst_ts, lst_close, 300)\n",
    "    # index設定\n",
    "    df_rci  = df_rci.set_index(\"timestamp\")\n",
    "    # OHLCVとRCIをマージ(外部結合)\n",
    "    df_base = df_base.join(df_rci, how=\"outer\")\n",
    "    # 欠損値補間(全体0補間)\n",
    "    df_base.fillna(0, inplace=True)\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# SMA取得\n",
    "#---------------------------------------------------------------------\n",
    "def attachSMA():\n",
    "    global bar\n",
    "    global sma1_term\n",
    "    global sma2_term\n",
    "    global sma3_term\n",
    "    global df_base\n",
    "    global df_chart\n",
    "\n",
    "    sr_close = df_base[\"close\"] if bar == \"Candle\" else df_base[\"heikin_close\"]\n",
    "    df_chart = df_base.copy()\n",
    "    df_chart[\"SMA1\"] = sr_close.rolling(window=sma1_term, min_periods=1).mean()\n",
    "    df_chart[\"SMA2\"] = sr_close.rolling(window=sma2_term, min_periods=1).mean()\n",
    "    df_chart[\"SMA3\"] = sr_close.rolling(window=sma3_term, min_periods=1).mean()\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# チャート更新\n",
    "#---------------------------------------------------------------------\n",
    "def updateChart():\n",
    "    global bar\n",
    "    global chart_bars\n",
    "    global df_chart\n",
    "    global fig\n",
    "    global ax1\n",
    "    global ax2\n",
    "    global ax3\n",
    "    global bg1\n",
    "\n",
    "    # date追加\n",
    "    df_chart[\"date\"] = pd.to_datetime(df_chart.index, unit=\"s\")\n",
    "    # チャート表示件数にトリミング\n",
    "    shift = (chart_bars * -1) - 1\n",
    "    df_chart = df_chart.iloc[shift:]\n",
    "    # index解除\n",
    "    df_chart.reset_index(inplace=True)\n",
    "\n",
    "    # 描画クリア\n",
    "    ax1.cla()\n",
    "    ax2.cla()\n",
    "    ax3.cla()\n",
    "    clear_output(wait=True)\n",
    "\n",
    "    # ax1:メインチャート\n",
    "    ax1.patch.set_facecolor(\"#131722\")        # subplotの背景色\n",
    "    ax1.patch.set_alpha(1.0)                  # subplotの背景透明度\n",
    "    ax1.xaxis.label.set_color(\"white\")        # x軸ラベルのテキスト色\n",
    "    ax1.yaxis.label.set_color(\"white\")        # y軸ラベルのテキスト色\n",
    "    ax1.spines[\"top\"].set_color(\"#555555\")    # プロットエリアを囲む枠線の色Top\n",
    "    ax1.spines[\"bottom\"].set_color(\"#555555\") # プロットエリアを囲む枠線の色Bottom\n",
    "    ax1.spines[\"left\"].set_color(\"#555555\")   # プロットエリアを囲む枠線の色Left\n",
    "    ax1.spines[\"right\"].set_color(\"#555555\")  # プロットエリアを囲む枠線の色Right\n",
    "    ax1.tick_params(labelbottom=False, bottom=False)         # x軸非表示\n",
    "    #ax1.tick_params(axis=\"x\", labelsize=16, colors=\"white\") # x 座標目盛り色\n",
    "    ax1.tick_params(axis=\"y\", labelsize=8, colors=\"white\")   # y 座標目盛り色\n",
    "    ax1.xaxis.grid(True, which=\"major\", linestyle=\"dotted\", color=\"#CFCFCF\") # x軸に垂直なグリッドメソッド\n",
    "    ax1.yaxis.grid(True, which=\"major\", linestyle=\"dotted\", color=\"#CFCFCF\") # y軸に垂直なグリッドメソッド\n",
    "    ax1.set_axisbelow(True)                 # グリッドがプロットした点や線の下に隠れる\n",
    "    #ax1.set_title(\"BTC/USD\", fontsize=20)  # タイトル設定\n",
    "    #ax1.set_xlabel(\"Date\", fontsize=20)    # xラベル設定\n",
    "    ax1.set_ylabel(\"USD\", fontsize=10)      # yラベル設定\n",
    "    ax1.set_xlim(-1, len(df_chart[\"date\"])) # x軸の範囲\n",
    "\n",
    "    # 出来高チャートは下側25%に収める\n",
    "    ax2.set_ylim([0, df_chart[\"volume\"].max() * 4])\n",
    "    ax2.set_ylabel(\"Volume\", fontsize=10)\n",
    "    ax2.tick_params(axis=\"y\", labelsize=8, colors=\"white\") # y座標目盛り\n",
    "    ax2.yaxis.label.set_color(\"white\") # y軸ラベルのテキスト色\n",
    "\n",
    "    # ax3:サブチャート\n",
    "    ax3.patch.set_facecolor(\"#131722\")        # subplotの背景色\n",
    "    ax3.patch.set_alpha(1.0)                  # subplotの背景透明度\n",
    "    ax3.xaxis.label.set_color(\"white\")        # x軸ラベルのテキスト色\n",
    "    ax3.yaxis.label.set_color(\"white\")        # y軸ラベルのテキスト色\n",
    "    ax3.spines[\"top\"].set_color(\"#555555\")    # プロットエリアを囲む枠線の色Top\n",
    "    ax3.spines[\"bottom\"].set_color(\"#555555\") # プロットエリアを囲む枠線の色Bottom\n",
    "    ax3.spines[\"left\"].set_color(\"#555555\")   # プロットエリアを囲む枠線の色Left\n",
    "    ax3.spines[\"right\"].set_color(\"#555555\")  # プロットエリアを囲む枠線の色Right\n",
    "    ax3.tick_params(axis=\"x\", labelsize=8, colors=\"white\") # x 座標目盛り色\n",
    "    ax3.tick_params(axis=\"y\", labelsize=8, colors=\"white\") # y 座標目盛り色\n",
    "    ax3.xaxis.grid(True, which=\"major\", linestyle=\"dotted\", color=\"#CFCFCF\") # x軸に垂直なグリッドメソッド\n",
    "    ax3.yaxis.grid(True, which=\"major\", linestyle=\"dotted\", color=\"#CFCFCF\") # y軸に垂直なグリッドメソッド\n",
    "    ax3.set_axisbelow(True)               # グリッドがプロットした点や線の下に隠れる\n",
    "    ax3.set_xlabel(\"Date\", fontsize=10)   # xラベル設定\n",
    "    ax3.set_ylabel(\"RCI\", fontsize=10)    # yラベル設定\n",
    "\n",
    "    # 最初の区切り目盛りのインデックス\n",
    "    unit_x = 12 # X軸目盛り区切り間隔\n",
    "    xtick0 = (unit_x - df_chart[\"date\"][0].hour % unit_x)\n",
    "    ax3.set_xticks(range(xtick0, len(df_chart[\"date\"]), unit_x))\n",
    "    ax3.set_xticklabels([x.strftime(\"%m/%d %H:%M\") for x in df_chart[\"date\"]][xtick0::unit_x], rotation=30)\n",
    "\n",
    "    # ロウソクチャート描画\n",
    "    sr_open    = df_chart[\"open\"]  if bar == \"Candle\" else df_chart[\"heikin_open\"]\n",
    "    sr_close   = df_chart[\"close\"] if bar == \"Candle\" else df_chart[\"heikin_close\"]\n",
    "    color_up   = \"#53B987\" if bar == \"Candle\" else \"cyan\"\n",
    "    color_down = \"#EB4D5C\" if bar == \"Candle\" else \"red\"\n",
    "    mpf.candlestick2_ohlc(ax1,\n",
    "                          opens     = sr_open,          # 始値\n",
    "                          highs     = df_chart[\"high\"], # 高値\n",
    "                          lows      = df_chart[\"low\"],  # 安値\n",
    "                          closes    = sr_close,         # 終値\n",
    "                          width     = 0.8,              # バー横幅\n",
    "                          colorup   = color_up,         # 陽線色\n",
    "                          colordown = color_down)       # 陰線色\n",
    "\n",
    "    # 出来高グラフ描画\n",
    "    mpf.volume_overlay(ax2,\n",
    "                       opens     = sr_open,\n",
    "                       closes    = sr_close,\n",
    "                       volumes   = df_chart[\"volume\"],\n",
    "                       width     = 1.0,\n",
    "                       colorup   = color_up,\n",
    "                       colordown = color_down,\n",
    "                       alpha     = 0.3)\n",
    "\n",
    "    # SMA描画\n",
    "    date_x = pd.Series(range(len(df_chart.index)))\n",
    "    ax1.plot(date_x, df_chart[\"SMA1\"], color=\"Red\",   linewidth=\"0.5\")\n",
    "    ax1.plot(date_x, df_chart[\"SMA2\"], color=\"green\", linewidth=\"0.5\")\n",
    "    ax1.plot(date_x, df_chart[\"SMA3\"], color=\"cyan\",  linewidth=\"0.5\")\n",
    "\n",
    "    # y軸調整\n",
    "    ymin = df_chart[\"low\"].min()\n",
    "    ymax = df_chart[\"high\"].max()\n",
    "    slide = (ymax - ymin) / 5\n",
    "    ax1.set_ylim([ymin-slide, ymax+10])\n",
    "\n",
    "    # RCI描画\n",
    "    ax3.plot(date_x, df_chart[\"RCI9\"],  color=\"Red\",   linewidth=\"1.0\")\n",
    "    ax3.plot(date_x, df_chart[\"RCI36\"], color=\"Green\", linewidth=\"1.0\")\n",
    "    ax3.plot(date_x, df_chart[\"RCI52\"], color=\"cyan\",  linewidth=\"1.0\")\n",
    "    ax3.set_ylim([-105, 105])\n",
    "    left, right = ax3.get_xlim()\n",
    "    ax3.hlines([-80, 80], left, right, \"white\", alpha=0.5, linestyles=\"dashed\") \n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# 指定された時間足のOHLCVデータをBitMEX REST APIより取得\n",
    "#---------------------------------------------------------------------\n",
    "# [@param]\n",
    "#     period  =\"1m\"     時間足(1m, 3m, 5m, 15m, 30m, 1h, 2h, 3h, 4h, 6h, 12h, 1d)\n",
    "#     symbol  =\"XBTUSD\" 通貨ペア\n",
    "#     count   =1000     取得期間 ※\n",
    "#     reverse =True     並び順(True:新->古, False:古->新)\n",
    "#     partial =False    最新未確定足を取得するか(True:含む, False:含まない)\n",
    "#     tstype  =\"UTMS\"   時刻形式(以下の5パターン)\n",
    "#              \"UTMS\":UnixTime(ミリ秒), \"UTS\":UnixTime(秒), \"DT\":datetime,\n",
    "#              \"STMS\":日付文字列(%Y-%m-%dT%H:%M:%S.%fZ), \"STS\":日付文字列(%Y-%m-%dT%H:%M:%S)\n",
    "#     heikin  =False    平均足(始値/終値)を取得するか(True:含む, False:含まない)\n",
    "# [return]\n",
    "#     DataFrame : [[timestamp, open, high, low, close, volume], ・・・]\n",
    "#     List      : [[timestamp, open, high, low, close, volume], ・・・]\n",
    "#     ※戻り値データ型 (List or DataFrame) によって取得関数を2パターンに分けてある\n",
    "#---------------------------------------------------------------------\n",
    "# ※ 1m, 5m, 1h, 1dを基準にmergeしているため、それ以外を取得する場合は件数に注意\n",
    "#    (3m : 1m * 3期間より、3mを10期間取得するなら、APIでは 10 * 3 = 30件になる)\n",
    "#    APIは1回でMAX10000件までの取得としている\n",
    "#    30mや4h、6h、12hなどを取得する場合、件数を大きくするとAPI制限にかかる可能性がある\n",
    "#---------------------------------------------------------------------\n",
    "# [usage] lst_ohlcv = fetch_ohlcv_lst(period=\"15m\", count=1000, tstype=\"DT\", heikin=True)\n",
    "#         df_ohlcv = fetch_ohlcv_df(period=\"5m\", count=1000, partial=True)\n",
    "#---------------------------------------------------------------------\n",
    "# ListでOHLCVを取得\n",
    "def fetch_ohlcv_lst(period=\"1m\", symbol=\"XBTUSD\", count=1000, reverse=True, partial=False, tstype=\"UTMS\", heikin=False):\n",
    "    df = fetch_ohlcv_df(period, symbol, count, reverse, partial, tstype, heikin)\n",
    "    if df is None:\n",
    "        return []\n",
    "    if heikin:\n",
    "        return [[int(x[0]) if tstype==\"UTS\" or tstype==\"UTMS\" else x[0], x[1], x[2], x[3], x[4], int(x[5]), x[6], x[7]] for x in df.values.tolist()]\n",
    "    else:\n",
    "        return [[int(x[0]) if tstype==\"UTS\" or tstype==\"UTMS\" else x[0], x[1], x[2], x[3], x[4], int(x[5])] for x in df.values.tolist()]\n",
    "\n",
    "# DataFrameでOHLCVを取得\n",
    "def fetch_ohlcv_df(period=\"1m\", symbol=\"XBTUSD\", count=1000, reverse=True, partial=False, tstype=\"UTMS\", heikin=False):\n",
    "    if period not in ALLOWED_PERIOD:\n",
    "        return pd.DataFrame(index=[], columns=self.OHLCV_COLUMNS)\n",
    "    period_params = ALLOWED_PERIOD[period]\n",
    "    need_count = (count + 2) * period_params[1] # マージ状況により、不足が発生する可能性があるため、多めに取得\n",
    "\n",
    "    # REST APIリクエストでOHLCVデータ取得\n",
    "    df_ohlcv = __get_ohlcv_paged(symbol=symbol, period=period_params[0], count=need_count)\n",
    "\n",
    "    # DataFrame化して指定時間にリサンプリング\n",
    "    if period_params[1] > 1:\n",
    "        minutes = ALLOWED_PERIOD[period][2]\n",
    "        offset = str(minutes) + \"T\"\n",
    "        if 60 <= minutes < 1440:\n",
    "            offset = str(minutes / 60) + \"H\"\n",
    "        elif 1440 <= minutes:\n",
    "            offset = str(minutes / 1440) + \"D\"\n",
    "        df_ohlcv = df_ohlcv.resample(offset).agg({\n",
    "                        \"timestamp\": \"first\",\n",
    "                        \"open\":      \"first\",\n",
    "                        \"high\":      \"max\",\n",
    "                        \"low\":       \"min\",\n",
    "                        \"close\":     \"last\",\n",
    "                        \"volume\":    \"sum\",\n",
    "                    })\n",
    "    # 未確定の最新足を除去\n",
    "    if partial == False:\n",
    "        df_ohlcv = df_ohlcv.iloc[:-1]\n",
    "    # マージした結果、余分に取得している場合、古い足から除去\n",
    "    if len(df_ohlcv) > count:\n",
    "        df_ohlcv = df_ohlcv.iloc[len(df_ohlcv)-count:]\n",
    "    # index解除\n",
    "    df_ohlcv.reset_index(inplace=True)\n",
    "    # 平均足付加\n",
    "    if heikin == True:\n",
    "        __attach_ohlcv_heiken_oc(df_ohlcv)\n",
    "    # timestampを期間終わり時刻にするため、datetimeをシフト\n",
    "    df_ohlcv[\"datetime\"] += timedelta(minutes=ALLOWED_PERIOD[period][2])\n",
    "    # timestamp変換\n",
    "    __convert_timestamp(df_ohlcv, tstype)\n",
    "    # datetime列を削除\n",
    "    df_ohlcv.drop(\"datetime\", axis=1, inplace=True)\n",
    "    # 並び順を反転\n",
    "    if reverse == True:\n",
    "        df_ohlcv = df_ohlcv.iloc[::-1]\n",
    "    # indexリセット\n",
    "    df_ohlcv.reset_index(inplace=True, drop=True)\n",
    "    return df_ohlcv\n",
    "\n",
    "# private\n",
    "def __convert_timestamp(df_ohlcv, timestamp=\"UTMS\"):\n",
    "    if timestamp == \"UTS\":\n",
    "        df_ohlcv[\"timestamp\"] = pd.Series([int(dt.timestamp()) for dt in df_ohlcv[\"datetime\"]])\n",
    "    elif timestamp == \"UTMS\":\n",
    "        df_ohlcv[\"timestamp\"] = pd.Series([int(dt.timestamp()) * 1000 for dt in df_ohlcv[\"datetime\"]])\n",
    "    elif timestamp == \"DT\":\n",
    "        df_ohlcv[\"timestamp\"] = df_ohlcv[\"datetime\"]\n",
    "    elif timestamp == \"STS\":\n",
    "        df_ohlcv[\"timestamp\"] = pd.Series([dt.strftime(\"%Y-%m-%dT%H:%M:%S\") for dt in df_ohlcv[\"datetime\"]])\n",
    "    elif timestamp == \"STMS\":\n",
    "        df_ohlcv[\"timestamp\"] = pd.Series([dt.strftime(\"%Y-%m-%dT%H:%M:%S.%fZ\") for dt in df_ohlcv[\"datetime\"]])\n",
    "    else:\n",
    "        df_ohlcv[\"timestamp\"] = df_ohlcv[\"datetime\"]\n",
    "\n",
    "def __get_ohlcv_paged(symbol=\"XBTUSD\", period=\"1m\", count=1000):\n",
    "    ohlcv_list = []\n",
    "    utc_now = datetime.now(pytz.utc)\n",
    "    to_time = int(utc_now.timestamp())\n",
    "    from_time = to_time - ALLOWED_PERIOD[period][2] * 60 * count\n",
    "    start = from_time\n",
    "    end = to_time\n",
    "    if count > 10000:\n",
    "        end = from_time + ALLOWED_PERIOD[period][2] * 60 * 10000\n",
    "    while start <= to_time:\n",
    "        ohlcv_list += __fetch_ohlcv_list(symbol=symbol, period=period, start=start, end=end)\n",
    "        start = end + ALLOWED_PERIOD[period][2] * 60\n",
    "        end = start + ALLOWED_PERIOD[period][2] * 60 * 10000\n",
    "        if end > to_time:\n",
    "            end = to_time\n",
    "    df_ohlcv = pd.DataFrame(ohlcv_list,\n",
    "                            columns=[\"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n",
    "    df_ohlcv[\"datetime\"] = pd.to_datetime(df_ohlcv[\"timestamp\"], unit=\"s\")\n",
    "    df_ohlcv = df_ohlcv.set_index(\"datetime\")\n",
    "    df_ohlcv.index = df_ohlcv.index.tz_localize(\"UTC\")\n",
    "    return df_ohlcv\n",
    "\n",
    "def __fetch_ohlcv_list(symbol=\"XBTUSD\", period=\"1m\", start=0, end=0):\n",
    "    param = {\"period\": ALLOWED_PERIOD[period][2], \"from\": start, \"to\": end}\n",
    "    url = \"https://www.bitmex.com/api/udf/history?symbol=XBTUSD&resolution={period}&from={from}&to={to}\".format(**param)\n",
    "    res = requests.get(url)\n",
    "    data = res.json()\n",
    "    return [list(ohlcv) for ohlcv in zip(data[\"t\"], data[\"o\"], data[\"h\"], data[\"l\"], data[\"c\"], data[\"v\"])]\n",
    "\n",
    "def __attach_ohlcv_heiken_oc(df_ohlcv):\n",
    "    # 始値 = (一本前の始値（平均足) + 一本前の終値(平均足)) / 2\n",
    "    # 終値 = (始値(ローソク足) + 高値(ローソク足) + 安値(ローソク足) + 終値(ローソク足)) / 4\n",
    "    d,t,o,h,l,c,v,ho,hc = range(9)\n",
    "    df_ohlcv[\"heikin_open\"] = float(0.0)\n",
    "    df_ohlcv[\"heikin_close\"] = float(0.0)\n",
    "\n",
    "    df_ohlcv.iloc[0, ho] = df_ohlcv.iloc[0, o]\n",
    "    df_ohlcv.iloc[0, hc] = df_ohlcv.iloc[0, c]\n",
    "\n",
    "    df_ohlcv.iloc[1, ho] = df_ohlcv.iloc[0, o:v].mean()\n",
    "    df_ohlcv.iloc[1, hc] = df_ohlcv.iloc[1, o:v].mean()\n",
    "\n",
    "    for i, ohlcv in df_ohlcv.iloc[2:].iterrows():\n",
    "        df_ohlcv.iloc[i, ho] = (df_ohlcv.iloc[i-1, ho] + df_ohlcv.iloc[i-1, hc]) / 2.0\n",
    "        df_ohlcv.iloc[i, hc] = ohlcv[o:v].mean()\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# closeリストからRCI3Lineを期間分取得\n",
    "#---------------------------------------------------------------------\n",
    "# [@param]\n",
    "#     lst_ts      timestamp(UnitxTime[秒])リスト(新->古)\n",
    "#     lst_close   終値リスト(新->古)\n",
    "#     term        計算する期間\n",
    "# [return]\n",
    "#     RCI3Line(直近3期間) DataFrame\n",
    "#---------------------------------------------------------------------\n",
    "# [usage] rci = get_rci_df(lst_close, 100)\n",
    "#---------------------------------------------------------------------\n",
    "def get_rci_df(lst_ts, lst_close, term):\n",
    "    # 空のDataFrame作成\n",
    "    df = pd.DataFrame(index=[], columns=[\"timestamp\", \"RCI9\", \"RCI36\", \"RCI52\"])\n",
    "    for i in range(term):\n",
    "        # RCIリスト作成\n",
    "        series = pd.Series([lst_ts[i],\n",
    "                            get_rci(lst_close[i:],  9),\n",
    "                            get_rci(lst_close[i:], 36),\n",
    "                            get_rci(lst_close[i:], 52)], index=df.columns)\n",
    "        # RCIのDataFrameに追加\n",
    "        df = df.append(series, ignore_index=True)\n",
    "    # index反転(古->新)\n",
    "    df_rci = df.iloc[::-1]\n",
    "    # indexリセット\n",
    "    df_rci.reset_index(inplace=True, drop=True)\n",
    "    return df_rci\n",
    "\n",
    "#---------------------------------------------------------------------\n",
    "# RCI取得\n",
    "#---------------------------------------------------------------------\n",
    "# [@param]\n",
    "#     lst_close   終値リスト(新->古)\n",
    "#     period      計算する期間\n",
    "# [return]\n",
    "#     RCI\n",
    "#---------------------------------------------------------------------\n",
    "# [usage] rci = get_rci(lst_close, period=9)\n",
    "#---------------------------------------------------------------------\n",
    "def get_rci(lst_close, period):\n",
    "    close_prices_sorted = lst_close[:period]\n",
    "    close_prices_sorted.sort(reverse=True)\n",
    "    d = 0.0\n",
    "    for i in range(period):\n",
    "        price = lst_close[i]\n",
    "        index = close_prices_sorted.index(price)\n",
    "        count = close_prices_sorted.count(price)\n",
    "        price_index = index + 1 + (count - 1) / 2.0 # if prices are duplicated\n",
    "        d += ( i + 1 - price_index ) ** 2\n",
    "    return round((1.0 - 6.0 * d / (period * (period ** 2 - 1.0))) * 100.0, 1)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n",
    "\n",
    "# interact object\n",
    "barDropdown      = Dropdown(options=[\"Candle\", \"Heikin\"], value=\"Candle\")\n",
    "periodDropdown   = Dropdown(options=ALLOWED_PERIOD.keys(), value=\"1h\")\n",
    "barsSlider       = IntSlider(min=60, max=300, step=20, value=100)\n",
    "sma1Slider       = IntSlider(min=1,  max=100, step=1, value=9)\n",
    "sma2Slider       = IntSlider(min=1,  max=100, step=1, value=36)\n",
    "sma3Slider       = IntSlider(min=1,  max=100, step=1, value=52)\n",
    "\n",
    "# interactイベント\n",
    "@interact(Bar=barDropdown,\n",
    "          Period=periodDropdown, \n",
    "          Bars=barsSlider,\n",
    "          SMA1=sma1Slider,\n",
    "          SMA2=sma2Slider,\n",
    "          SMA3=sma3Slider)\n",
    "def interact_event(Bar, Period, Bars, SMA1, SMA2, SMA3):\n",
    "    global bar\n",
    "    global period\n",
    "    global chart_bars\n",
    "    global sma1_term\n",
    "    global sma2_term\n",
    "    global sma3_term\n",
    "    global df_base\n",
    "    global fig\n",
    "\n",
    "    update_mode = 0\n",
    "    if sma1_term != SMA1:\n",
    "        sma1_term = SMA1\n",
    "        update_mode = 1\n",
    "    if sma2_term != SMA2:\n",
    "        sma2_term = SMA2\n",
    "        update_mode = 1\n",
    "    if sma3_term != SMA3:\n",
    "        sma3_term = SMA3\n",
    "        update_mode = 1\n",
    "    if bar != Bar:\n",
    "        bar = Bar\n",
    "        update_mode = 2\n",
    "    if period != Period:\n",
    "        period = Period\n",
    "        update_mode = 2\n",
    "    if chart_bars != Bars:\n",
    "        chart_bars = Bars\n",
    "        update_mode = 2\n",
    "\n",
    "    if update_mode == 0:\n",
    "        return\n",
    "    if update_mode > 1:\n",
    "        # OHLCVデータ+平均足取得\n",
    "        df_base = fetch_ohlcv_df(period=period, count=360, reverse=False, partial=True, \\\n",
    "                                 tstype=\"UTS\", heikin=True)\n",
    "        # index設定\n",
    "        df_base = df_base.set_index(\"timestamp\")\n",
    "        # RCI取得\n",
    "        attachRCI()\n",
    "    if update_mode > 0:\n",
    "        # SMA取得\n",
    "        attachSMA()\n",
    "        # チャート更新\n",
    "        updateChart()\n",
    "    # チャート表示\n",
    "    display(fig)\n"
   ]
  },
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