Snufkin0866/Sample_StockStats.ipynb

## Sample_StockStats.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 要旨\n",
    "・約定履歴を突っ込めば，勝手にローソク足を作ってくれるライブラリだと思っていた．\n",
    "\n",
    "→違う．約定履歴からローソク足を作るには，自分で処理する必要がある．\n",
    "\n",
    "・ではstockstatsは何で，どういうときに使えるのか．\n",
    "\n",
    "→pandasDFの金融データ用拡張版．テクニカル指標を簡単に計算できるようになっている．また，pandasDFなので表計算ソフト（Excel）感覚でデータを扱えて便利．"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 環境構築\n",
    "・stockstatsは金融指標を簡単に取得できる改造pandas.DataFrameクラス（Qiitaより）\n",
    "\n",
    "・pip install stockstats\n",
    "でインストール\n",
    "\n",
    "# 軽く使ってみる\n",
    "\n",
    "まずは軽く仕様確認のために乱数でStockDataFrameを作ってみます．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.416758</td>\n",
       "      <td>-0.056267</td>\n",
       "      <td>-2.136196</td>\n",
       "      <td>1.640271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.793436</td>\n",
       "      <td>-0.841747</td>\n",
       "      <td>0.502881</td>\n",
       "      <td>-1.245288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.057952</td>\n",
       "      <td>-0.909008</td>\n",
       "      <td>0.551454</td>\n",
       "      <td>2.292208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.041539</td>\n",
       "      <td>-1.117925</td>\n",
       "      <td>0.539058</td>\n",
       "      <td>-0.596160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.019130</td>\n",
       "      <td>1.175001</td>\n",
       "      <td>-0.747871</td>\n",
       "      <td>0.009025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.878108</td>\n",
       "      <td>-0.156434</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>-0.988779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.338822</td>\n",
       "      <td>-0.236184</td>\n",
       "      <td>-0.637655</td>\n",
       "      <td>-1.187612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.421217</td>\n",
       "      <td>-0.153495</td>\n",
       "      <td>-0.269057</td>\n",
       "      <td>2.231367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.434768</td>\n",
       "      <td>0.112727</td>\n",
       "      <td>0.370445</td>\n",
       "      <td>1.359634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.501857</td>\n",
       "      <td>-0.844214</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.542353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       open      high       low     close\n",
       "0 -0.416758 -0.056267 -2.136196  1.640271\n",
       "1 -1.793436 -0.841747  0.502881 -1.245288\n",
       "2 -1.057952 -0.909008  0.551454  2.292208\n",
       "3  0.041539 -1.117925  0.539058 -0.596160\n",
       "4 -0.019130  1.175001 -0.747871  0.009025\n",
       "5 -0.878108 -0.156434  0.256570 -0.988779\n",
       "6 -0.338822 -0.236184 -0.637655 -1.187612\n",
       "7 -1.421217 -0.153495 -0.269057  2.231367\n",
       "8 -2.434768  0.112727  0.370445  1.359634\n",
       "9  0.501857 -0.844214  0.000010  0.542353"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from stockstats import StockDataFrame\n",
    "#乱数のシードを固定\n",
    "np.random.seed(2)\n",
    "#pandas df作成．（乱数で生成したOHLCデータ（10行4列）\n",
    "df = pd.DataFrame(np.random.randn(10,4), columns=['open', 'high', 'low', 'close'])\n",
    "sdf = StockDataFrame(df)  # pandasデータフレームをStockDataFrame（以下SDF，SFDじゃないよｗ）に入れる\n",
    "sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "StockDataFrameクラスのコンストラクタにdfを引数として与えるとStockDataFrame(pandas dfの改造版)を作れます．"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 約定履歴（executions）からローソク足の作成\n",
    "\n",
    "では，BitFlyerFXの約定履歴からローソク足を作ってみましょう．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybitflyer\n",
    "api = pybitflyer.API()\n",
    "#最新の約定履歴を500件取得します．\"FX_BTC_JPY\"だと約定データが多すぎて500本では2分ぐらいしかローソク足を作れないので\"BTC_JPY\"にしています．\n",
    "executions = api.executions(product_code=\"BTC_JPY\", count=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            price      size\n",
      "timestamp                                  \n",
      "2018-04-01T12:48:49.57   716690.0  0.010933\n",
      "2018-04-01T12:48:41.303  716700.0  0.100000\n",
      "2018-04-01T12:48:34.477  716877.0  0.001000\n",
      "2018-04-01T12:48:32.82   716879.0  0.005000\n",
      "2018-04-01T12:48:31.38   716700.0  0.521000\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-01T12:34:23.89</th>\n",
       "      <td>716998.0</td>\n",
       "      <td>0.2590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01T12:34:16.857</th>\n",
       "      <td>717040.0</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01T12:34:16.793</th>\n",
       "      <td>717040.0</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01T12:34:13.933</th>\n",
       "      <td>716889.0</td>\n",
       "      <td>0.0036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01T12:34:09.043</th>\n",
       "      <td>717041.0</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            price    size\n",
       "timestamp                                \n",
       "2018-04-01T12:34:23.89   716998.0  0.2590\n",
       "2018-04-01T12:34:16.857  717040.0  0.0100\n",
       "2018-04-01T12:34:16.793  717040.0  0.0100\n",
       "2018-04-01T12:34:13.933  716889.0  0.0036\n",
       "2018-04-01T12:34:09.043  717041.0  0.0050"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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KLI1mL9wfP5JXaNUzQ0IlKe06KsUzNfxAW3IZSaJSo0cFlQqIoayrw2PFTp/O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3V9TQ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\nm4wxOnByXB/992dlJ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2TbX7vj/rq77XlJN0r6D3LmHZYX86aymfOm1mOTJX8X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\nUAMAAAAAADSl/w/wMB9/Z3LwrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x232f83f11d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#SDFに約定履歴のtimestampとpriceとsizeを突っ込んで，indexをtimestampにする（ローソク足作るだけなので他はいらない．）プロットもしてみる．\n",
    "sdf_executions = StockDataFrame({\"timestamp\":[i[\"exec_date\"] for i in executions], \"price\": [i[\"price\"] for i in executions], \n",
    "                                 \"size\":[i[\"size\"] for i in executions]}).set_index(\"timestamp\")\n",
    "sdf_executions[\"price\"].plot(figsize= (18,9))\n",
    "print(sdf_executions.head())\n",
    "sdf_executions.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "↑先ほどいったんpandasDFにデータを入れてからSDFに変換しましたが，辞書型を直接入れても良いようです．\n",
    "\n",
    "また，お気づきかもしれませんが，データは新しい順に並んでいます．（普段見てるチャートの逆）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "#indexはstringなのでdatetimeに変換\n",
    "def iso_to_jstdt(iso_str):\n",
    "    dt = None\n",
    "    try:\n",
    "        dt = datetime.strptime(iso_str, '%Y-%m-%dT%H:%M:%S.%f')\n",
    "        dt = pytz.utc.localize(dt).astimezone(pytz.timezone(\"Asia/Tokyo\"))\n",
    "    except ValueError:\n",
    "        try:\n",
    "            dt = datetime.strptime(iso_str, '%Y-%m-%dT%H:%M:%S.%f')\n",
    "            dt = dt.astimezone(pytz.timezone(\"Asia/Tokyo\"))\n",
    "        except ValueError:\n",
    "            pass\n",
    "    return dt\n",
    "def make_candle_sticks(df):\n",
    "    \"\"\"\n",
    "    executionsのdfを突っ込めば，1分足を返してくれる関数．\n",
    "    :params: df:約定履歴が入ったdf(index:timestamp, price, sizeの順)\n",
    "    :return: 1分足が入ったsdf（ohlcの順）\n",
    "    \"\"\"\n",
    "    #timestampの秒とマイクロ秒をゼロにする．ここを少し変えれば5秒足なども作成可．ex. second=x.second//5*5\n",
    "    df.index = df.index.map(lambda x: iso_to_jstdt(x).replace(second=0, microsecond=0) if iso_to_jstdt(x)!=None else x)\n",
    "    # 1分毎の安値と高値(low, high)、始値と終値(open, close)、および取引量(size) を集計\n",
    "    summary = df['price'].groupby(level=0).min().to_frame().rename(columns={'price': 'low'})           \n",
    "    \n",
    "    summary = summary.merge(\n",
    "        df['price'].groupby(level=0).max().to_frame().rename(columns={'price': 'high'}),\n",
    "        left_index=True, right_index=True)\n",
    "    summary = summary.merge(\n",
    "        df['price'].groupby(level=0).last().to_frame().rename(columns={'price': 'open'}),\n",
    "        left_index=True, right_index=True)\n",
    "    summary = summary.merge(\n",
    "        df['price'].groupby(level=0).first().to_frame().rename(columns={'price': 'close'}),\n",
    "        left_index=True, right_index=True)\n",
    "    summary = summary.merge(\n",
    "        df['size'].groupby(level=0).sum().to_frame(),\n",
    "        left_index=True, right_index=True)\n",
    "    summary = StockDataFrame(summary)\n",
    "    return summary\n",
    "sdf_candle_sticks = make_candle_sticks(sdf_executions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1分足ができました．\n",
    "ここからテクニカル指標を算出してみます．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>size</th>\n",
       "      <th>close_5_sma</th>\n",
       "      <th>close_12_ema</th>\n",
       "      <th>close_26_ema</th>\n",
       "      <th>macd</th>\n",
       "      <th>macds</th>\n",
       "      <th>macdh</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:34:00+09:00</th>\n",
       "      <td>716669.0</td>\n",
       "      <td>717041.0</td>\n",
       "      <td>717041.0</td>\n",
       "      <td>716765.0</td>\n",
       "      <td>2.643166</td>\n",
       "      <td>716765.000000</td>\n",
       "      <td>716765.000000</td>\n",
       "      <td>716765.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:35:00+09:00</th>\n",
       "      <td>716768.0</td>\n",
       "      <td>717218.0</td>\n",
       "      <td>716773.0</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>0.706224</td>\n",
       "      <td>716882.500000</td>\n",
       "      <td>716892.291667</td>\n",
       "      <td>716887.019231</td>\n",
       "      <td>5.272436</td>\n",
       "      <td>2.929131</td>\n",
       "      <td>4.686610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:36:00+09:00</th>\n",
       "      <td>717000.0</td>\n",
       "      <td>718032.0</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>717930.0</td>\n",
       "      <td>6.241428</td>\n",
       "      <td>717231.666667</td>\n",
       "      <td>717297.309469</td>\n",
       "      <td>717261.752095</td>\n",
       "      <td>35.557374</td>\n",
       "      <td>16.301362</td>\n",
       "      <td>38.512025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:37:00+09:00</th>\n",
       "      <td>717421.0</td>\n",
       "      <td>718561.0</td>\n",
       "      <td>717939.0</td>\n",
       "      <td>718259.0</td>\n",
       "      <td>10.671194</td>\n",
       "      <td>717488.500000</td>\n",
       "      <td>717600.877586</td>\n",
       "      <td>717540.539030</td>\n",
       "      <td>60.338557</td>\n",
       "      <td>31.219111</td>\n",
       "      <td>58.238892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:38:00+09:00</th>\n",
       "      <td>718100.0</td>\n",
       "      <td>718999.0</td>\n",
       "      <td>718273.0</td>\n",
       "      <td>718527.0</td>\n",
       "      <td>6.419696</td>\n",
       "      <td>717696.200000</td>\n",
       "      <td>717852.501736</td>\n",
       "      <td>717769.303397</td>\n",
       "      <td>83.198339</td>\n",
       "      <td>46.681756</td>\n",
       "      <td>73.033166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:39:00+09:00</th>\n",
       "      <td>718105.0</td>\n",
       "      <td>718999.0</td>\n",
       "      <td>718532.0</td>\n",
       "      <td>718999.0</td>\n",
       "      <td>1.140435</td>\n",
       "      <td>718143.000000</td>\n",
       "      <td>718131.161131</td>\n",
       "      <td>718015.601819</td>\n",
       "      <td>115.559312</td>\n",
       "      <td>65.351403</td>\n",
       "      <td>100.415818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:40:00+09:00</th>\n",
       "      <td>717600.0</td>\n",
       "      <td>718594.0</td>\n",
       "      <td>718125.0</td>\n",
       "      <td>718594.0</td>\n",
       "      <td>10.455272</td>\n",
       "      <td>718461.800000</td>\n",
       "      <td>718234.442005</td>\n",
       "      <td>718118.466937</td>\n",
       "      <td>115.975068</td>\n",
       "      <td>78.162902</td>\n",
       "      <td>75.624332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:41:00+09:00</th>\n",
       "      <td>718508.0</td>\n",
       "      <td>719000.0</td>\n",
       "      <td>718527.0</td>\n",
       "      <td>719000.0</td>\n",
       "      <td>3.310675</td>\n",
       "      <td>718675.800000</td>\n",
       "      <td>718394.202159</td>\n",
       "      <td>718260.503757</td>\n",
       "      <td>133.698403</td>\n",
       "      <td>91.509127</td>\n",
       "      <td>84.378551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:42:00+09:00</th>\n",
       "      <td>718600.0</td>\n",
       "      <td>719017.0</td>\n",
       "      <td>719017.0</td>\n",
       "      <td>718869.0</td>\n",
       "      <td>5.629181</td>\n",
       "      <td>718797.800000</td>\n",
       "      <td>718488.134072</td>\n",
       "      <td>718350.696258</td>\n",
       "      <td>137.437813</td>\n",
       "      <td>102.118882</td>\n",
       "      <td>70.637864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:43:00+09:00</th>\n",
       "      <td>718545.0</td>\n",
       "      <td>719558.0</td>\n",
       "      <td>718869.0</td>\n",
       "      <td>718545.0</td>\n",
       "      <td>13.644253</td>\n",
       "      <td>718801.400000</td>\n",
       "      <td>718498.910143</td>\n",
       "      <td>718377.508283</td>\n",
       "      <td>121.401860</td>\n",
       "      <td>106.439388</td>\n",
       "      <td>29.924943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:44:00+09:00</th>\n",
       "      <td>717600.0</td>\n",
       "      <td>718503.0</td>\n",
       "      <td>718503.0</td>\n",
       "      <td>718219.0</td>\n",
       "      <td>1.925141</td>\n",
       "      <td>718645.400000</td>\n",
       "      <td>718447.693336</td>\n",
       "      <td>718356.949709</td>\n",
       "      <td>90.743627</td>\n",
       "      <td>103.005245</td>\n",
       "      <td>-24.523236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:45:00+09:00</th>\n",
       "      <td>717621.0</td>\n",
       "      <td>718189.0</td>\n",
       "      <td>717621.0</td>\n",
       "      <td>717894.0</td>\n",
       "      <td>0.422865</td>\n",
       "      <td>718505.400000</td>\n",
       "      <td>718349.248478</td>\n",
       "      <td>718300.069043</td>\n",
       "      <td>49.179436</td>\n",
       "      <td>91.445719</td>\n",
       "      <td>-84.532566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:46:00+09:00</th>\n",
       "      <td>717450.0</td>\n",
       "      <td>718000.0</td>\n",
       "      <td>717894.0</td>\n",
       "      <td>717940.0</td>\n",
       "      <td>12.104700</td>\n",
       "      <td>718293.400000</td>\n",
       "      <td>718278.187395</td>\n",
       "      <td>718257.887019</td>\n",
       "      <td>20.300376</td>\n",
       "      <td>76.388892</td>\n",
       "      <td>-112.177033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:47:00+09:00</th>\n",
       "      <td>717211.0</td>\n",
       "      <td>717922.0</td>\n",
       "      <td>717482.0</td>\n",
       "      <td>717211.0</td>\n",
       "      <td>1.268523</td>\n",
       "      <td>717961.800000</td>\n",
       "      <td>718096.479455</td>\n",
       "      <td>718140.309148</td>\n",
       "      <td>-43.829693</td>\n",
       "      <td>51.239075</td>\n",
       "      <td>-190.137535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01 21:48:00+09:00</th>\n",
       "      <td>716690.0</td>\n",
       "      <td>717167.0</td>\n",
       "      <td>717167.0</td>\n",
       "      <td>716690.0</td>\n",
       "      <td>2.803797</td>\n",
       "      <td>717590.800000</td>\n",
       "      <td>717860.870069</td>\n",
       "      <td>717983.421224</td>\n",
       "      <td>-122.551156</td>\n",
       "      <td>15.213491</td>\n",
       "      <td>-275.529294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                low      high      open     close       size  \\\n",
       "timestamp                                                                      \n",
       "2018-04-01 21:34:00+09:00  716669.0  717041.0  717041.0  716765.0   2.643166   \n",
       "2018-04-01 21:35:00+09:00  716768.0  717218.0  716773.0  717000.0   0.706224   \n",
       "2018-04-01 21:36:00+09:00  717000.0  718032.0  717000.0  717930.0   6.241428   \n",
       "2018-04-01 21:37:00+09:00  717421.0  718561.0  717939.0  718259.0  10.671194   \n",
       "2018-04-01 21:38:00+09:00  718100.0  718999.0  718273.0  718527.0   6.419696   \n",
       "2018-04-01 21:39:00+09:00  718105.0  718999.0  718532.0  718999.0   1.140435   \n",
       "2018-04-01 21:40:00+09:00  717600.0  718594.0  718125.0  718594.0  10.455272   \n",
       "2018-04-01 21:41:00+09:00  718508.0  719000.0  718527.0  719000.0   3.310675   \n",
       "2018-04-01 21:42:00+09:00  718600.0  719017.0  719017.0  718869.0   5.629181   \n",
       "2018-04-01 21:43:00+09:00  718545.0  719558.0  718869.0  718545.0  13.644253   \n",
       "2018-04-01 21:44:00+09:00  717600.0  718503.0  718503.0  718219.0   1.925141   \n",
       "2018-04-01 21:45:00+09:00  717621.0  718189.0  717621.0  717894.0   0.422865   \n",
       "2018-04-01 21:46:00+09:00  717450.0  718000.0  717894.0  717940.0  12.104700   \n",
       "2018-04-01 21:47:00+09:00  717211.0  717922.0  717482.0  717211.0   1.268523   \n",
       "2018-04-01 21:48:00+09:00  716690.0  717167.0  717167.0  716690.0   2.803797   \n",
       "\n",
       "                             close_5_sma   close_12_ema   close_26_ema  \\\n",
       "timestamp                                                                \n",
       "2018-04-01 21:34:00+09:00  716765.000000  716765.000000  716765.000000   \n",
       "2018-04-01 21:35:00+09:00  716882.500000  716892.291667  716887.019231   \n",
       "2018-04-01 21:36:00+09:00  717231.666667  717297.309469  717261.752095   \n",
       "2018-04-01 21:37:00+09:00  717488.500000  717600.877586  717540.539030   \n",
       "2018-04-01 21:38:00+09:00  717696.200000  717852.501736  717769.303397   \n",
       "2018-04-01 21:39:00+09:00  718143.000000  718131.161131  718015.601819   \n",
       "2018-04-01 21:40:00+09:00  718461.800000  718234.442005  718118.466937   \n",
       "2018-04-01 21:41:00+09:00  718675.800000  718394.202159  718260.503757   \n",
       "2018-04-01 21:42:00+09:00  718797.800000  718488.134072  718350.696258   \n",
       "2018-04-01 21:43:00+09:00  718801.400000  718498.910143  718377.508283   \n",
       "2018-04-01 21:44:00+09:00  718645.400000  718447.693336  718356.949709   \n",
       "2018-04-01 21:45:00+09:00  718505.400000  718349.248478  718300.069043   \n",
       "2018-04-01 21:46:00+09:00  718293.400000  718278.187395  718257.887019   \n",
       "2018-04-01 21:47:00+09:00  717961.800000  718096.479455  718140.309148   \n",
       "2018-04-01 21:48:00+09:00  717590.800000  717860.870069  717983.421224   \n",
       "\n",
       "                                 macd       macds       macdh  \n",
       "timestamp                                                      \n",
       "2018-04-01 21:34:00+09:00    0.000000    0.000000    0.000000  \n",
       "2018-04-01 21:35:00+09:00    5.272436    2.929131    4.686610  \n",
       "2018-04-01 21:36:00+09:00   35.557374   16.301362   38.512025  \n",
       "2018-04-01 21:37:00+09:00   60.338557   31.219111   58.238892  \n",
       "2018-04-01 21:38:00+09:00   83.198339   46.681756   73.033166  \n",
       "2018-04-01 21:39:00+09:00  115.559312   65.351403  100.415818  \n",
       "2018-04-01 21:40:00+09:00  115.975068   78.162902   75.624332  \n",
       "2018-04-01 21:41:00+09:00  133.698403   91.509127   84.378551  \n",
       "2018-04-01 21:42:00+09:00  137.437813  102.118882   70.637864  \n",
       "2018-04-01 21:43:00+09:00  121.401860  106.439388   29.924943  \n",
       "2018-04-01 21:44:00+09:00   90.743627  103.005245  -24.523236  \n",
       "2018-04-01 21:45:00+09:00   49.179436   91.445719  -84.532566  \n",
       "2018-04-01 21:46:00+09:00   20.300376   76.388892 -112.177033  \n",
       "2018-04-01 21:47:00+09:00  -43.829693   51.239075 -190.137535  \n",
       "2018-04-01 21:48:00+09:00 -122.551156   15.213491 -275.529294  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#macdと5期間移動平均．\n",
    "sdf_candle_sticks.get(\"macd\")\n",
    "sdf_candle_sticks['close_5_sma']\n",
    "sdf_candle_sticks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "↑テクニカル指標を計算できました．macdを算出するのにemaを使うので，それらも生成されています．\n",
    "これ以外にも様々なテクニカル指標を使えるようです．詳細はpypiが分かりやすいです．"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 結論\n",
    "・stockstatsでローソク足を作るには約定履歴が膨大に必要だが，取引所公式ページが落ちなければデータをとれるので最も確実な方法ではある．\n",
    "・stockstats自体には約定履歴からローソク足を作る機能はなく，テクニカル指標を算出するのに便利というだけ．\n",
    "・データベースに約定履歴を大量に保存しておいて，pandasでデータ処理する場合に使えそう．"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 参考文献\n",
    "Pypi: https://pypi.python.org/pypi/stockstats\n",
    "\n",
    "Qiita: https://qiita.com/u1and0/items/0ebcf097a1d61c636eb9\n",
    "\n",
    "https://qiita.com/kazetof/items/44561332d12693e57936\n",
    "\n",
    "Github: https://github.com/jealous/stockstats\n",
    "\n",
    "今日も窓辺でプログラム： http://www.madopro.net/entry/bitcoin_chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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