reouno/estimate-probability-distribution-of-finantial-data.ipynb

## estimate-probability-distribution-of-finantial-data.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 株式の対数差収益率（対数差分）が従う分布を推定する\n",
    "\n",
    "正規分布に比べてt分布の方が裾野が広いため、株式データにフィットしやすいという仮説。どちらも最適とは言えないかもしれないが、t分布が正規分布より適していそうであれば、とりあえずt分布を仮定しておく。\n",
    "\n",
    "## 結論\n",
    "\n",
    "先に結論だけ書いておくと、**正規分布よりは明らかにt分布の方が当てはまりが良い。**パラメータが一つ多いことも関係していると思われるが、少なくとも正規分布よりはt分布を仮定した方が株式データ分布の説明にも予測にも良いだろう。\n",
    "\n",
    "ただし予測に関しては、t分布はパラメータが一つ多いため、正規分布よりデータに過適合しやすい。パラメータ$ \\nu $に関しては固定しておくという手もあるのだろうか。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'en_US.UTF-8'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic', 'IPAPGothic', 'VL PGothic', 'Noto Sans CJK JP']\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import datetime as dt\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import locale\n",
    "\n",
    "# 月や曜日を英語で取得するためこの設定をしておく\n",
    "locale.setlocale(locale.LC_TIME, 'en_US.UTF-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## E-Mini S&P500先物と金先物データ読み込み\n",
    "\n",
    "データは、[TradeStationのDesktop Platform](https://www.tradestation.com/platforms-and-tools/desktop/)を使って出力したCSVファイル。公開は禁止されているので公開できないが、Quandlからも[E-mini S&P500先物](https://www.quandl.com/data/CHRIS/CME_ES1-E-mini-S-P-500-Futures-Continuous-Contract-1-ES1-Front-Month)と[金先物](https://www.quandl.com/data/CHRIS/CME_GC1-Gold-Futures-Continuous-Contract-1-GC1-Front-Month)の最新の日足データ（今回の分析で使っているのと同じ）をAPI経由で取得できるので、そのデータを使用可能。ただしquandl経由で取得したDFは若干構造が違うから、以下のセルは多少修正が必要。\n",
    "- quandl経由で取得したDFはすでにDatetimeindexになっている。また、終値は'Close'ではなく'Last'。おそらくこの2点だけ違う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leo/src/pyproject/py-envs/py383env/lib/python3.6/site-packages/pandas/core/series.py:679: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "dfsp_tmp = pd.read_csv('../data/e-mini-sp500-200530/e-mini-sp500-daily.csv')\n",
    "dfg_tmp = pd.read_csv(f'../data/gold-200626/gold-daily.csv')\n",
    "dfs = [dfsp_tmp, dfg_tmp]\n",
    "\n",
    "prods = ['S&P500先物', '金先物']\n",
    "\n",
    "# datetime indexに変換\n",
    "def to_datetime_index(df):\n",
    "    # DateTime列を追加\n",
    "    df['datetime'] = (df['Date'] + '-' + df['Time']).map(lambda s: dt.datetime.strptime(s, '%m/%d/%Y-%H:%M'))\n",
    "    df = df.set_index('datetime', drop=True)\n",
    "    df = df.drop(columns=['Date', 'Time'])\n",
    "    return df\n",
    "\n",
    "dfs = [to_datetime_index(df) for df in dfs]\n",
    "\n",
    "# 対数変換した列を追加\n",
    "def add_log_values(df):\n",
    "    df['logO'] = np.log(df['Open'])\n",
    "    df['logH'] = np.log(df['High'])\n",
    "    df['logL'] = np.log(df['Low'])\n",
    "    df['logC'] = np.log(df['Close'])\n",
    "    df['logV'] = np.log(df['Vol'])\n",
    "    df['logOI'] = np.log(df['OI'])\n",
    "\n",
    "_ = [add_log_values(df) for df in dfs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Vol</th>\n",
       "      <th>OI</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/11/1997</td>\n",
       "      <td>17:00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>1082.25</td>\n",
       "      <td>1062.75</td>\n",
       "      <td>1068.50</td>\n",
       "      <td>11825</td>\n",
       "      <td>2909</td>\n",
       "      <td>1997-09-11 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/12/1997</td>\n",
       "      <td>17:00</td>\n",
       "      <td>1070.50</td>\n",
       "      <td>1089.00</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>9759</td>\n",
       "      <td>4059</td>\n",
       "      <td>1997-09-12 17:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Time     Open     High      Low    Close    Vol    OI  \\\n",
       "0  09/11/1997  17:00  1071.25  1082.25  1062.75  1068.50  11825  2909   \n",
       "1  09/12/1997  17:00  1070.50  1089.00  1066.00  1071.25   9759  4059   \n",
       "\n",
       "             datetime  \n",
       "0 1997-09-11 17:00:00  \n",
       "1 1997-09-12 17:00:00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：読み込み直後の生データ（E-mini S&P500）\n",
    "dfsp_tmp.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "      <th>Vol</th>\n",
       "      <th>OI</th>\n",
       "      <th>logO</th>\n",
       "      <th>logH</th>\n",
       "      <th>logL</th>\n",
       "      <th>logC</th>\n",
       "      <th>logV</th>\n",
       "      <th>logOI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-11 17:00:00</th>\n",
       "      <td>1071.25</td>\n",
       "      <td>1082.25</td>\n",
       "      <td>1062.75</td>\n",
       "      <td>1068.50</td>\n",
       "      <td>11825</td>\n",
       "      <td>2909</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>6.986797</td>\n",
       "      <td>6.968615</td>\n",
       "      <td>6.974011</td>\n",
       "      <td>9.377971</td>\n",
       "      <td>7.975565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-12 17:00:00</th>\n",
       "      <td>1070.50</td>\n",
       "      <td>1089.00</td>\n",
       "      <td>1066.00</td>\n",
       "      <td>1071.25</td>\n",
       "      <td>9759</td>\n",
       "      <td>4059</td>\n",
       "      <td>6.975881</td>\n",
       "      <td>6.993015</td>\n",
       "      <td>6.971669</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>9.185945</td>\n",
       "      <td>8.308692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Open     High      Low    Close    Vol    OI  \\\n",
       "datetime                                                               \n",
       "1997-09-11 17:00:00  1071.25  1082.25  1062.75  1068.50  11825  2909   \n",
       "1997-09-12 17:00:00  1070.50  1089.00  1066.00  1071.25   9759  4059   \n",
       "\n",
       "                         logO      logH      logL      logC      logV  \\\n",
       "datetime                                                                \n",
       "1997-09-11 17:00:00  6.976581  6.986797  6.968615  6.974011  9.377971   \n",
       "1997-09-12 17:00:00  6.975881  6.993015  6.971669  6.976581  9.185945   \n",
       "\n",
       "                        logOI  \n",
       "datetime                       \n",
       "1997-09-11 17:00:00  7.975565  \n",
       "1997-09-12 17:00:00  8.308692  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：datetimeindexにし、対数変換した列を追加後（E-mini S&P)\n",
    "dfs[0].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 価格、対数価格、価格階差、対数差収益率（100倍）のDFを作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leo/src/pyproject/py-envs/py383env/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "def to_log_return_ratio_df(df):\n",
    "    diff_df = df.diff()\n",
    "    close_df = df[['Close', 'logC']]\n",
    "    diff_df = diff_df.rename(columns={'Close': 'CloseDiff', 'logC': 'logCDiff'})\n",
    "    close_diff_df = diff_df[['CloseDiff', 'logCDiff']]\n",
    "    close_diff_df['logCDiff'] = close_diff_df['logCDiff'] * 100\n",
    "    rr_df = pd.concat([close_df, close_diff_df], axis=1)\n",
    "    rr_df = rr_df.dropna()\n",
    "    return rr_df\n",
    "\n",
    "rr_dfs = [to_log_return_ratio_df(df) for df in dfs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>logC</th>\n",
       "      <th>CloseDiff</th>\n",
       "      <th>logCDiff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-09-12 17:00:00</th>\n",
       "      <td>1071.25</td>\n",
       "      <td>6.976581</td>\n",
       "      <td>2.75</td>\n",
       "      <td>0.257040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-09-15 17:00:00</th>\n",
       "      <td>1083.75</td>\n",
       "      <td>6.988183</td>\n",
       "      <td>12.50</td>\n",
       "      <td>1.160106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Close      logC  CloseDiff  logCDiff\n",
       "datetime                                                   \n",
       "1997-09-12 17:00:00  1071.25  6.976581       2.75  0.257040\n",
       "1997-09-15 17:00:00  1083.75  6.988183      12.50  1.160106"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考：上記セル実行で差分をとっているため、初日（1997-9-11）のデータは消える（初日は差分を取れないから）\n",
    "# また、終値だけに注目しているため、それ以外の列は除外\n",
    "# E-mini S&P500\n",
    "rr_dfs[0].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 対数差収益率をプロットしてみて、どんな分布に従うかみてみる"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### まずは終値の対数差収益率分布をプロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAGKCAYAAADzD6z1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3de5ycdX3o8c83u5uQJQE2EMBaLoJU29TaQ1O1gm2qaNXW6qkXwEsAjTkg2kprL0fOOfbUQ+3FtmLTBhAxQq2otdJjSasSiihWC94PVsXG2AsFIjsBciObzff88TyzPJnsbmaTnXlmdj/v12tfM/NcZr6zO/t7vvN7vr/fE5mJJEmSpINbUHcAkiRJUr8weZYkSZLaZPIsSZIktcnkWZIkSWqTybMkSZLUJpNnSZIkqU0mz5IkSVKbTJ6lWRIRb4+Iy8v7T4yIjIgL29jvtIhYGBFbIuK3K8ufEBEPRsQzOhe1JHVfRDw/IvZFxNaIOL2N7Z8dEe+LiB8sH382Im5rY78TIuKYiPjtiNjSsu4TEfHOQ30Pmr9MntVREfFjEXFTRNwXEd+PiFsi4vyIiMo2PxURX4iIHeXtU6d4rg1lQtr8eTgi7i4bxWWV7Va1bNf82dLyfEvLxvjeiNgcEX8UEUMt2wxFxB+X6++NiOsiYskksS0Ffh04KSJOBJaXq46OiBNbfo6s7DcAfBN43iRv+VxgCPjy9L9lSeofEfFE4C+A7wDjwN9GxPLp9+K1wIvK/U8EFgILJ2lfj2vZ73eBayeJ4fHAc4EvHt670Xw0WHcAmrsiYgXwOSCBTwAPAD8D/CVFw/f+iDgWuBnYC9wIvAx4L7Bymqe+AfgucAzw08DbgOdGxLMz89HKdrcD/1B5vK0SWwB/D/wURSN+FPCrwInAqyr7vB84H/gb4GHgIuCHIuJZuf/lOX8eWAT8t/Kn6V3lT9VllWU/SJEgHw8cAZwaET+TmZ8GzgMC2FT5rtG0JTNffcBvRpJ6WEQ8CbiVom17IXA08Gngjoh4fmZunmSfIygS56OAf2tZ/Z8tj78K/Hjl8WnAVuBU4IiIWAX8M/DyMoa3RMSlk4T6+sz85xm9Oc0bJs/qpDcBRwK/kJk3w0RP6yuAD5fbnA2MAL+YmR+PiN3A6oM87/WZeUv5fAH8HfBzFIn5Jyvb3ZSZfzLFczwbeCbwjsx8a/k8HwReGRFvy8zvRMQZFInzB4FXZWZGxL3AbwI/S3EAoOyt/p/AHcC68vlPBP4EuIriwFBV7Ul+cnn73vL2AuDVZQP/VODdFF8oNgM7y5i/Bzxx2t+QJPWYiHg+RWdFAs/PzO+Uy18A3AR8PiJem5l/27LrZcAwRe/zrnLZ/6botf6dlm1bO0meDKyqrP+H8nkupmjDx4BnULTBvwp8vnxc7RyR9mPZhjrpceXtxJe0zBzPzA9m5ni5qNlTvCsihoFnUfQYt6Xs/f1E+fApLavvn2bX88vb91We533lsleUt+c2t6n0Mje3Oa/yXG8EfgR4S2bemJk3As3G/wvNZZWfb1X2fR7wWeCHgXuBPwPOBK6k6PX+Q+AEioPHvwG3AfcAo9O8N0nqGRGxJCJ+H9hIcTx4LfBIRDw5Ip5M0TN8EcVZuI+X5XHN2uaTgP8BXJuZ76u0sVuBByZpX/++8tI/StGR8dMUbeu9FG3tEyhK615BkST/BfCxcp9mR4ZtrKZk8qxOavYC31DWJR87yTZ3AnuANwBfoOipfuMMX+fk8vZfW5a/uayLvresbT6hZZ8xipq7pm+Ut6e0PO/dlW2+TdHbcUrLsj/LzM/PMG4oEuW/zsxvlvF8n6JX+4kUPdwXl3F9kSLRvhU4Fht2SX2gTI6/RdGreyVFmcb/pSidqP78DUXp2hUUnRvfiYifoEhuP14un6kzga9l5mco2tYx4CGK8SlvpWhLnw9cR9G+fg3YV+7bOITX0zxh2YY66SrgJIqG6m3Ab0TE9cDvZmYz0d0GfB34rxTlDasy80GAcgT2i4BPZWY1gW2ejltGUTN3cfk8nylXf5/i1N6/l8ueC1wIPCUinpGZe4HHA42WuuUHy9vHt9xONKJl6cZoc11E/AhwBkVD/+bKczUHvzwvIo6Z5Hfzjcz8ZPn+FlWWDwB3UdTjbQPWUhw0fozid/kpYA3wlUmeU5J6zbeBa4APlzXElzVXRMR3gG9m5i9Ud4iI9wAvBr4E/ApFKcXLWsZ+PB4Yb2l3J2Tmu4Dr2X/cCxTjbd4IbAD+mKIt/TJFj/OnKBLqRzJz7BDeq+YJk2d1TFma8VsR8SHgt4CXUgymO7+scfsqRT3xfwF2UCSc+ypP8evA69m/lxeKBq7qPuCCzLyvfN3/FxHHZ+Z2mKhJ/jzwExS1zp+k6D1u1WyZx1tuJ9uuue5pFLXNUzmfx0pEqt4PfDIzt0fE0RHxGora7/9R/rwIeAtFT8wHgLcDXy7f27E8luhLUs/KzH3A/46IJ5W90FULgSWTLB/PzHcDRMR07StM3f6+q+zs2BoR51CMrzkF2AJ8lKI88PUUyfnTKAYZXgC8EttXHYRlG+q4zPxyZp5LMXDj4xQjpq+kGGT3IuAS4JeAJwEbI+K4ss7t1cDfZOa/tzzlDRSDRX6Nosf6tLIXt/qa2yv3x4CPlA9XlLf3Asti/66MYyvrqrfVafAWlI/vLZ97Q2ZG84fHBq+MlI9/uHz8mup2mXlh+XzrKXrI3w8sLX8/P0xRz/e28ne0B3gNcG0Zr2UbkvrNVzmwVOMkioHercv/sblTS/t6Wrn4zZVlXwA2tbSvARARP01x5vBT5etsoxhc/lqK48g3KEo23gDcmZlfw/ZVbbDnWV1TzmDxUoqG6SkUvQ7bgesyc29EXECRRH6eIqEMigS51cRsGzPQ/Kw3G8XvlcueTNFYw2MDDrdUtoGiZOI/yvs/TPGls7lNq2cA/5KZ26ZY3+r/Av9EMV3fP1GcptxB8Tv4B4pBLldQTOX3lxQJ9iA27pL6SGYeEREvBk7KzHWwf9lG2THwx8DftXaGVDQvGNXu3Mxfp2g//xZ4CcUZyk9GxEcp2vKzKDpUXklRDgcmz2qDPc/qmIh4W0Rc3NK7ewKwmGLGiAeBJRQ1w2TmByhmsTidopfgc8AjM3zNKAcnPr6ybDFF4whFLwUUiSgUI7ybNdQXUgxO+VC57sby8YWV93Bhy/7V134SRX31Xx8kxueW9dxk5t+VI8gfaK7PzH8rX+dlFKPFfw14W5mQP6Hc7AEkqb/8EPAH5UVOWr0IeDPFgMIDlG3wGyk6Mv5pqheIiB+MiBdGxILMbGTm2zPzy+w/9dyvAy+g6Dh5D8VAwb8o1z0B21cdhD3P6oiyLvdVFInxmyLisxQjnV9KMSjurRTTEv0sxeT4N1J8mXsxRd3zN4BzgH+LiLWZeUObL/1Eigb4zRFxK0Xv8S+Uy68sZ7WAot7t08CvR8TjKEpJfhF4f2ZuAcjM70bEDRTzTi+KiEcoSkk+TTG9XPX9nkhRGrKbYm7m6byW4kvEs6faIDNviojjKeqdvwX8ebnqJ8vbuyfdUZJ619XA5RRjYCYG+pXz//8uxXRxf9W6U5k4/x7FPPe/mpl7pnmNFRRn8k7hwBmYAMjMzRHxXeAPKOqdz87M8SiuHvsjPFbmJ03K5FkdkZkPRnGZ7V+lSIJfTpE8fxU4NzNvB4iIlwD/naKm90GK3ua3l9u9gmIwx3TzNbe+7j0R8WMUtcfPpZiN4x7gUorZP5rbZUT8PEWi+wKK+ab/iKJRr3odRf3xSylmxbgO+OXmLB3ll4TXlXEeB7yopUZ7d3n78nLWjQUUA1c2Tvc+yoS+OS3ds4Dzyqn2LgX+hQOvqiVJPSkiTqa4yAkUM2BsKwcJLqQ4+7iSYhzJu4EnlSf6dlL0Mr+A4jjysxQzYlzZ8vS7gR8uZ93YW263k2nayEqJyJvL594ZEb9KUcaxkBlca0DzU+w/U5ekmYiIn6O4zPcm4PLM/ELL+gUUvSnPp2iUoTgg/EJmfqVl2y3Ahsz87Yg4kqKn5X2Z+aWIeCfFwMoHgYszc9rkW5J6RUTcAjxnhrt9Gvh5iqnkhoF3AOsrF9hqPvdq4F0UsxVBMU3pOzLz7S3b/TZwYWaeWj5+BXB6Zr6j7Oj5R4rB2R8E3pAmR5qGybN0mCLihzLz23XHIUlzTdlr/UBm7j7oxlKXmDxLkiRJbXK2DUmSJKlNJs+SJElSm/pqto3jjjsuTz311LrDkKQZ++IXv/j9zFxedxzdZJstqV9N12b3VfJ86qmnctddd9UdhiTNWER87+BbzS222ZL61XRttmUbkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8ixJkiS1yeRZkiRJapPJsyRJktQmk2dJkiSpTSbPkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8qy+l5mMjo4yOjpKZtYdjiSpDzSPHR43NFMmz+p7jUaD1es3sXr9JhqNRt3hSJL6QKPR4Lx33uRxQzM2WHcA0mwYGj6q7hAkSX1maHhp3SGoD9nzLEmSJLXJ5FmSJElqk8mzJEmS1CaTZ0mSJKlNJs+SJElSm0yeJUmSpDaZPEuSJEltMnmWJEmS2mTyLEmSJLXJ5FmSJElqk8mzJEmS1KbBbrxIRFwEJDAMXJWZ+8rlw8Aa4BFgQWa+txvxSJIkSYei48lzRJwG3J+ZGyNiBXA2cHu5+jzgvZm5IyKi07FIkiRJh6MbZRtnAZsAMvNu4Izq62fmjnJddiEWSZIk6ZB1o2xjIDMfrTwer66LiFdSJPFfz8yvtu4cEWuBtQAnn3xyRwOVJEmSptOVmudpPAO4JDN3R8RvRMTXm/XQTZl5DXANwMqVK+2dlqQOm2ycSkSMAC+ubLYKeH1mjtUQoiTVphvJ83hELMrMR8u65oHKulsyc3d5fzNwHPBAF2KSJE1iqnEqmdkANpTbPB74DxNn9bvMpNFoADAyMoLDr9SObtQ83wGcU95fAdxTWTdeGSh4CjDahXgkSVObbpxK0yrgli7GJHXE2K4dXHrDnaxev2kiiZYOpuPJc2ZuBk6IiAspGtyvRMQV5erbgDURcQHwjczc2+l4JEnTmm6cChExAOybapB3RKyNiLsi4q6tW7d2Mk5pViwcXsrQ8FF1h6E+0pWa58y8rmXR5eXy+4D3dCMGSdKsOAP41lQrHaciaa7zCoOSpKrxiFgEMMk4FYAfBf6561FJUo8weZYkVU03TgVgcWbu6m5IktQ7TJ4lSRMOMk4FPG5ImufqnudZktRjphqnUq57f5fDkaSeYg+CJEmS1CaTZ0mSJKlNJs+SJElSm0yeJUmSpDaZPEuSJEltMnmWJEmS2mTyLEmSJLXJ5FmSJElqk8mzJEmS1CaTZ0mSJKlNJs+SJElSm0yeJUmSpDaZPEuSJEltMnmWJEmS2mTyLEmSJLXJ5FmSJElqk8mzJEmS1CaTZ0mSJKlNJs+SJElSm0yeJUnSvJaZNBoNMrPuUNQHTJ4lSdK8kZmMjo7SaDQmlo3t3M7aq2/db5k0lcG6A5AkSeqWRqPB6vWb2LNzO+PjeyeWDy4+kkajwcjICBFRY4TqdfY8S5KkeWVo+CiGFi/db9nY7h32PqstJs+SJEnA0PCSukNQHzB5liRJktpk8ixJkiS1yeRZfa05vZAkSVI3mDyrrzUaDdasu5nxsb0H31iSJOkwmTyr7w22jJiWJEnqFJNnSZIkqU0mz5IkSVKbTJ4lSZKkNpk8S5IkSW0arDsASVJviYiLgASGgasyc1+5fBhYAzwCLMjM99YXpSTVw+RZkjQhIk4D7s/MjRGxAjgbuL1cfR7w3szcERFRW5CSVCPLNiRJVWcBmwAy827gjMq6BZm5o1yXNcQmHRYvrKXZ0JWe52lOAf4y8HC52ebMvH2Kp5AkdcdAZj5aeTxeXRcRr6ToePl6Zn61deeIWAusBTj55JM7Gqg0U80Lay193Ol1h6I+1vHk+SCnALdm5gc7HYMkaVY8A7gkM3dHxG9ExNebnSFNmXkNcA3AypUr7Z1Wz/HCWjpc3SjbmO4U4OkRcW5EXBwRR3chFknS9MYjYhFAWdc8UFl3S2buLu9vBo7rdnCSVLduJM/TnQK8LjM/BGwAXjvZzhGxNiLuioi7tm7d2sEwJUnAHcA55f0VwD2VdeOVgYKnAKPdDEySekGtAwYz897ydjcwaQV/Zl6TmSszc+Xy5cu7Gp8kzTeZuRk4ISIuBFYBX4mIK8rVtwFrIuIC4BuZubeWICWpRt0YMDgeEYsy89FJTgFWWRsnST0gM69rWXR5ufw+4D3dj0iSekc3ep4nPQUYESeVE+43HdGFWCRJkqRD1vGe58zcHBGrylOAS4Dry1OAVwCrI2IncCTwiU7HIkmSJB2OrszzPNUpQOCqbry+JEmSNBu8wqAkSZLUJpNnSZIkqU0mz5ozMpNGo0GmE7dIkvbXPEZIh8vkWXPG2M7trL36VhtHSdIBGo0Ga9bdzPi405Pr8Jg8a04ZGl5SdwiSpB41uHhp3SFoDjB5liRJktpk8ixJkiS1yeRZkiRJapPJsyRJktQmk2dJkiSpTSbPkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8ixJkiS1abDuAKTZlJk0Gg0ARkZGiIiaI5IkSXOJPc+aU8Z27+DSG+5k9fpNE0m0JEnSbLHnWXPOwuGlDAz50ZYkSbPPnmdJkiSpTSbPkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8ixJkiS1yeRZkiRJapPJsyRJktQmk2dJkiSpTSbPkiRJUptMniVJkqQ2DdYdgCSpt0TERUACw8BVmbmvXP7LwMPlZpsz8/aaQpSk2pg8S5ImRMRpwP2ZuTEiVgBnA80keWtmfrC+6CSpfpZtSJKqzgI2AWTm3cAZlXWnR8S5EXFxRBxdS3SSVDN7niVJVQOZ+Wjl8Xjl/nWZeW9EHAFcAvxJ684RsRZYC3DyySd3NFBJqoM9z5KktmTmveXtbqAxxTbXZObKzFy5fPnyrsYnSd1g8ixJqhqPiEUAERHAwBTbZfdCkrorMxkdHSXTj7kOZPIsSaq6AzinvL8CuAcgIk6KiOHKdkd0OzCpWxqNBue98yYajUlPsGies+ZZkjQhMzdHxKqIuBBYAlwfEVcAVwCrI2IncCTwiRrDlDpuaHhp3SGoR5k8S5L2k5nXtSy6vLy9qtuxSFKvsWxDkiRJapPJsyRJktSmrpRtTHWp18r6XwM+mplbuhGPJEmSdCg63vNcudTrBuDTFJd6ra4/BTi103FIkqT5pznt3ExnzshMGo2GU9bpAN3oeT4L+DAUl3qNiNcBt1fWvxz4WBfikCRJ80yj0WD1+k3s2bmd8fG9be83tmsHl95wJ4NDA1x/yXNYtmxZB6NUP+lGzfOUl3qNiOcBG4F9B+z12DZrI+KuiLhr69atHQxTkiTNRUPDRzG0eOZTzy0cXsrQ8FEdiEj9rLYBg+Vk+4/PzG9Mt52XepUkSVKv6EbyPNWlXs8CMiLOA54NvCgiPCciSZKkntWNmufmpV5vpnKp18z8VHODiFgFbMnM0S7EI0mSJB2Sjvc8Z+Zm4ITyUq+rgK+Ul3qVDktzJLQkSbOheVzx2KLpdGWe52ku9dpcf1s34tDc0mg0WLPuZmLh4rpDkSTNAWO7ixk29o3tmtHMHJpfvMKg+trgIYyeliRpKguHlx7SzByaP0yeJUmSpDaZPEuSJEltMnmWJEmS2mTyLEmSJLXJ5FmSJElqk8mzJEmS1CaTZ0mSJKlNJs+ak5pXicrMukORJElziMmz5qSxndtZe/WtXmJVkiTNKpNnzVlDw0vqDkGSJM0xJs+SJElTsAxQrUyeJUmSpmAZoFqZPEuSJE3DMkBVmTxLkiRJbWo7eY6IZ0fEUCeDkSTNHtttSZp9gzPY9m7gxRGxEHgUuD0zt3YmLEnSLLDdlqRZ1nbynJn3A38FUDbEz4qInwe+BvxDZn6vMyFKkg6F7bYkzb6Z9DwDEBHPBE4B9gLvzMx7I+LsiHhuZl476xFKkg6L7bYkzZ62k+eIeD2wB/hMZn6uui4zPxsRJ812cJKkQ2e7LUmzbyY9z1/KzC9WF0TET2XmPwJk5gdnNTJJ0uGy3ZakWTaTqeqeVH0QEYuB581uOJKkWWS7LUmz7KA9zxHxVODHgWdGxCAQ5apHgPd0MDZJ0iE43HY7Ii4CEhgGrsrMfS3rfw34aGZumc24JakfHDR5zsyvAl+NiI1OcSRJve9w2u2IOA24PzM3RsQK4Gzg9sr6U4BTZzNeSeon7fQ8n1/Wxb0wIrK6CsjMvL5j0UmSZuww2+2zgA9TbHh3RLyOSvIMvBz42GzHLEn9op0Bgx8DyMz3dzgWSdLsOJx2eyAzH608Hm/eiYjnARuB46faOSLWAmsBTj755EN4eUnqbQcdMJiZuwGqUxpFxKqIeH1EPGnqPaXOyExGR0dpNBp1hyL1pE602xExDDw+M79xkNe+JjNXZubK5cuXH8pLSVJPm8lsGz8HEBEjwGmZ+R7gBR2JSppGo9Fg9fpNXHLd7YyP7607HKmXHUq7PR4Ri8r9Ahgol58FZEScBzwbeFFELOtM2NLsyEw7WjTrZjLP85Hl7UuBDeX9B2c1GqlNQ8NHkRnsfcRGUZrGobTbdwDnADcDK4B7ADLzU80NImIVsCUzR2cxVmnWNRoN1qy7maWPO73uUDSHzKTn+YsRcTHwnczcGxELgEUdikuSdPhm3G5n5mbghIi4EFgFfCUiruh4pFKHDC5eWncImmPa7nnOzM8Cn6083gdc24mgJEmH71Db7cy8rmXR5S3rb5uN+CSpH7WdPEfEDwFPp+itTpyqTpJ6mu225jPrndUpM6l5fn5mvrtjkUizrNlwjoyMUIx7kuYd223NW81651i4uO5QNMfMpOZ5W8eikDpgbPcO1l59qz0Pms9stzWvWe+sTphJz/NQRLwW+E51YWbePsX2Uu2GhpfUHYJUJ9ttSZplM0meb+lYFJKkTrDdlqRZNpOyjf8Engb8aGZ+LzO/B+zsTFiSpFlguy1Js2wmyfMlwCeAYyvLfml2w5EkzSLbbUmaZTNJnkcz82GKqY6ads1yPJKk2WO7LUmzbCbJ83hEDFLMFUpEnMD+vRmSpN5iuy1Js2wmAwY/AjwbOD4i1gJ7gXXt7BgRF1E03sPAVeVVroiI83nsUrHfzszPzSAeSdL0DrndliRNrq3kOSJeSZH4jgFbKRLeHZk51sa+pwH3Z+bGiFgBnA00p0n6SGbuLbd7I2DyLEmz4HDabUnS1A6aPEfEauC2zPzXluWnRsRrMvOGgzzFWcCHATLz7oh4HWXyXEmcjwb+4xDilyS1mIV2W5I0hXZqnodaG2CAzNzS5v4Dmflo5fF4dWVEvAn4Q+Djk+0cEWsj4q6IuGvr1q1tvJwkzXuH225LkqbQTiO69xDXtSUz/xR4C8WUSpOtvyYzV2bmyuXLlx/uy0nSfNDRdluabzKTRqNBZtYdinpAOzXPPx4RSTHVUfVTE8BTgA8cZP/xiFiUmY9GRAADrRtk5sMRsaPdoCVJ0zrcdltSxdjuHay9+lY+8lsjLFu2rO5wVLODJs+ZedlhvsYdwDnAzcAK4B6AiDi2fP4Hy+08lShJs2AW2m1JLYaGl9QdgnrETKaqOySZuTkiVkXEhcAS4PqIuAL4X8BrImIfMATc3elYJEmSpMPR8eQZIDOva1l0eXm7oRuvL0mSJM0GSyUkSZKkNnWl51mqS3OENMDIyAjFmFVJkqRDY8+z5rSx3Tu49IY7Wb1+00QSLUmSdKjsedact3B4KQNDftQlSdLhs+dZkiRJapPJsyRJktQmk2dJkiSpTSbPkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8ixJkiS1yeRZkiRJapPJsyRJktQmr1msvpKZNBqNusOQ5rSIuAhIYBi4KjP3lcvPBxaVm307Mz9XU4iSVBt7ntVXGo0Ga9bdzPjY3rpDkeakiDgNuD8zNwCfBs6urP5IZm4o151ZQ3iSVDuTZ/WdwcVL6w5BmsvOAjYBZObdwBnNFZm5FyAijgb+o5bopIPITEZHRz1LqY6xbEOSVDWQmY9WHo9XV0bEm4CnAG+YbOeIWAusBTj55JM7FaM0pUajwer1m9izczvj456l1Oyz51mS1LbM/FPgLcAlU6y/JjNXZubK5cuXdzc4qTQ0fBRDnqVUh5g8S5KqxiNiEUBEBDDQukFmPgzs6HZgktQLTJ4lSVV3AOeU91cA9wBExLERcWxlO48fkuYla54lSRMyc3NErIqIC4ElwPURcQXwv4DXRMQ+YAi4u8YwJak2Js/qC835nR09LXVeZl7Xsujy8nZDl0ORpJ5j8qy+4OhpSZLUC6xZU99w9LQkSaqbybMkSZLUJpNnSZIkqU0mz5IkSVKbHDAoSZJ0EM1ZnwBGRkYoriGk+cieZ0mSpIMY272DS2+4k9XrNzlt6jxnz7MkSVIbFg4vZWDI1Gm+s+dZkiRJapPJs+aFZq1aZtYdiiRJ6mMmz5oXxnZuZ+3Vt1qnJkmSDovJs+aNoeEldYcgSZL6nMmzJEmaE6rTyUmdYvIsSZLmhEajwZp1NzM+trfuUDSHmTxLkqQ5Y3Dx0o4+vwPQZfIsSZLUJgegy+RZkiRpBhyAPr95mRxJktTXmqUU9garG7qSPEfERUACw8BVmbkvIgJ4ObAYWAJsysxvdiMeSZI0dzQaDVav38SendsZH3ewoDqr48lzRJwG3J+ZGyNiBXA2cHv52p/LzH8vt/tNwORZkiTN2NDwUWQGex+x91md1Y2a57OATQCZeTdwRnl/rJk4l+6fbOeIWBsRd0XEXVu3bu14sJIkSdJUupE8D2Tmo5XH460bRMSUPeCZeU1mrszMlcuXL+9IgJIkSVI7emW2jV8ENtYdhCRJkjSdbiTP4xGxCKAcJDhQXRkRpwCjmflAF2KRJEk6LF4oZX7rRvJ8B3BOeX8FcE9zRUQsB340M2/rQhySJEmHbWz3Di+UMo91PHnOzM3ACRFxIbAK+EpEXBERRwJ/ChwTEa8uf5hu9BwAABcQSURBVI7sdDySJEmHywulzF9dmec5M69rWXR5eXteN15fkiRJmg29MmBQkiRJ6nkmz5IkSVKbTJ4lSZKkNpk8a95waiFJknS4TJ7V0zKT0dHRWUl4nVpIkiQdLpNn9bRGo8G5f/gxtmzZMivP59RCkiTpcHRlqjrpsMQCLtvwGY456Yy6I5HmhYi4CEhgGLgqM/eVV4h9ObAYWAJsysxv1himJNXC5Fl9YXDY6+dI3RARpwH3Z+bGiFgBnA3cTnG8+Fxm/nu53W8CJs+S5h3LNiRJVWcBmwAy827gjPL+WDNxLt1fQ2ySVDuTZ0lS1UBmPlp5PN66QURMedYyItZGxF0RcdfWrVs7EqAk1cnkWZI0U78IbJxsRWZek5krM3Pl8uXLuxyW5qPmNKRSt5g8S5KqxiNiEUA5SHCgujIiTgFGM/OBOoKTWjUaDdasu5nxsb11h6J5wuRZklR1B3BOeX8FcE9zRUQsB340M2+rIS5pSoOLl9YdguYRk2dJ0oTM3AycEBEXAquAr0TEFRFxJPCnwDER8eryx2lwJM07TlUnSdpPZl7Xsujy8va8bsciSb3GnmdJkiSpTfY8S5IkzVB1lo+RkRGK8bWaD+x5liRJmqGx3Tu49IY7Wb1+k1PlzTP2PGtesadAkjRbFg4vZWDIVGq+sedZ84o9BZIk6XD4dUnzjj0FkiTpUNnzLEmSJLXJ5FmSJOkQNcfSZGbdoahLTJ4lSVJfyUxGR0cZHR2tPWkd27mdtVff6jiaecTCT0mS1FcajQar128C4Mrzzqw5GhgaXlJ3COoik2dJktR3hoaPqjsEzVOWbUiSJEltMnnWvOQAD0mSdChMnjUvOcBDkiQdCmue1ZOaPcOdTG4d4CFJkmbK5Fk9JzPZvHkzv3Ljl9izczvj43vrDkmSJAmwbEM9qNFosGbdzSwYGmZo8dK6w5EkSZpg8qyeNGjSLEmSepDJsyRJktQmk2dJktSXMpNt27bVHYbmGZNnSZLUl8Z2bueyDZ+pfWB5c4ao0dFR9u3bx+joqNcRmMNMniVJUt8aHD6y7hAY272DS2+4k9XrN/Hd736X8955k9cRmMOcqk6SJOkwLRxeysBQkVYNDTvofS4zeZYkSZpFzTIOgJGRESKi5og0myzbkCRJmkVjux4r47B8Y+7pSs9zRFwEJDAMXJWZ+8rlJwJvBb6UmRu6EYskSeovzZ7cfurFrZZxaG7peM9zRJwG3F8mx58Gzm6uy8z7MvOXOx2DJEnqX41GY2IQXrUkQqpDN8o2zgI2AWTm3cAZXXhNSZI0hzQH4TUaDdasu5nxsXqnp9P81Y3keSAzH608Hp/JzhGxNiLuioi7tm7dOsuhSZKkfjO42NksVJ+eL8bJzGuAawBWrlzpjONzVPM0nKfjJEmT8figXtGN5Hk8IhZl5qNRVPkPdOE11WcajQar129iz87t7H74oZ6Y9F6S1DuaM1jsG9tV+xUFNb91o2zjDuCc8v4K4J4uvKb60NDwUQwtXmriLEma1MLhpQxZsqGadTx5zszNwAkRcSGwCvhKRFwBEBE/HRGvBp4ZES/vdCySpIOLiIsi4sKIeENELKgsPzEi3l2255I0L3Wl5jkzr2tZdHm5/Pby8V90Iw5J0vQq04tujIgVFNOL3g7F9KLAL5s8S5rPvMKgJKnK6UUlaRomz5q3miO3R0dHyXQiF6nk9KLSIcpMtm3bVncY6jCTZ81bY7uLkduv+fNb2Lx5swm0NAsy85rMXJmZK5cvX153OFJXje3czmUbPuNsIHOcybPmtYXDS4EFrL36VucPlQrjEbEIwOlFpZlzxqi5z+RZAoaGl9QdgtQrnF5UkqZh8ixJmuD0opI0vZ6/PLfmPi+5KvUWpxeVpKnZ86zaNRoN1qy7mfExB1hIkgqZyejoqDMiqefY86yeMFjz5Vabvd8jIyMUY6QkSXVqNBqsXr8JgCvPO7PmaKTH2POsrmv2JvRST8LY7h3OuCFJPWZo+CiGho+qOwxpPybP6rpGo8F577yp5xJVZ9yQJM2m5lnNXuos0uEzeVYthobrLdOQJKnTxnZu96zmHGTyLEmSatWL5XyzxbOac4/JsyRJqtV05XyZybZt22qIavbN5S8J84nJs2phHZgkqWpw8RIajcYByeXYzu1ctuEzjI/3/3SmvTrmRzNj8qxajO1ydgtJ0mPGdu3g0hvuZPX6TYyOju53fBgcPrLGyA5Ps7Oo+aXAMT/9z3meVRvrwCRJVQuHlzIwNMi2bdtYs+5mlj7u9LpDOmxju4svBYNDA85XPUeYPEuSpJ5T98WzZlPzS4HmBss2pBYO6JCk+sylAYKTaS3jUP8xeZZaOKBDkuozlwYITqZa2+1xpj95DkGahAM6JKk+/TxAsB2WcfQ3/3KqTfPUlSRJUr8weVZtmiOQ943t6onTcybzkiTpYEyeVauFw0sZ3zPI3kfqT1qryfyCoSPqDkeS+l6zU2JkZISIqDscaVY4YFBd05zFopd7dxcOL2VoDk2PJEl1OtgA7H44LsyWuT6LyHxiz7O6ptFosHr9Jvbs3N4TZRqSpM6bagB2ZrJ582Z+5cYvzYvjQjGLyDfn/GDI+cCeZ82qZi/CVPNXDg0fZc+uJIlGo8GadTezYGh43hwXqolz83j54IMPOt9znzF51qxq9i5X56/sx4uOOIm9JHXeXLqK4EyN7dzOa6/8W17x+389L8pW5hKTZ826oeGjGBo+aiJp3rx5M+e986a+qvVqDh50EntJUqcMDh/J4OIj7azpM9Y8q2O2bds2UcsWQ0f0VfIMTmIvSeq8ZmfN4NAA11/yHJYtW1Z3SDoIe57VUc0a57HdO4rLrY7134CQfiw7kaS6OXd++xYOL2Vo+Ki6w1CbTJ7VEZNNydOPI4wzky1btkw71ZIk6UDNAYHjex/rNDnYoHKpH3hOWh3RnJLnmJPOqDuUw9J8H8PHnlB3KJLUd6oDAqtT02Umv/OC02uMTDp09jyrY/qxp3kyc+V9SFI3THXhk+rUdLCgKOWb43M7z5Q98/3BnmfpMFXr+rwEraT5rvWCWAOVddWeaDsmDtQcaJ+ZvPv8n+C0007zmNKD7HmWZmCywYOTzW0tSfNNtX30gliHrhg4uIC1V9/a19dLmMtMnqU2NHuXm3NWtybJQ8NHMbh4KY1Gw8ZN0pzWmshV5/Q/9w8/xpYtWw7YvtFo2LkwQ835n5u/Pweu9w7LNjSt5j9ttRxhsjKF5rK5mjg25+HcN7aLGDpivwNH83cxtnM7a6++lY/81ojzdEqaU6rtfmZy3jtv4qo1qxgZGSEzueCqW4syjX37uGzDZ/YbLF5tP61xnlrrLFVju3dMHFMAhobtye8VJs+aVvPb7o1veQnLli07YLT0leedycjICNu2bePia2/j917243WH3DELh5cyvmeQXY80eO2Vf8vwsSeyb2wXux9+aOJA0ewpGBkpGrvR0dH9nmPZsmUHfAmxTlpSr2uWpwFced6ZEAsmLuxx5XlnlleVDfY+0pi0lrnZfu59xJ7TqUw2S9XQ8JIaI9JUTJ51QBLX2sPQ/LbbTJzXrLuZpY87nfE9u/ZLIifrcZirBoePfOxgUJnDdGz3Dl5/1Sbec3HxeM26m1l09PHF72dsLx/+zV/aL3k+/4/+hhvf8hJGRkZMpCX1jOpx4JhjjqHRaBxwEQ+vwjr7Wr94THehmeoZ3+aZ0IggIjyWdFhXPvURcRGQwDBwVWbuK5evBJ4GLARuyswt3YhnrjrYrA+tjWHz9FA1iVu2bNlED0NzHs5qvdqadTcTCxdPPOd+SeQUPQ7zzoJ4rMRj4eKJ38+CobGJkdRA2VtTfDFp7eGX6mSb3dtmMsNPu9s265aBiU6UC666FSjaqmanSWuyPNkFsTR79isZHFx0QMngmz/0Zfbs3M7uhx8CYPjYExkYXDBxVhiKv1FE7HfmcyrVzjRgv8+ECfljOp48R8RpwP2ZuTEiVgBnA7eXq5+emX8WxV/jYmD9bL9+O6fGp2tcJvv2Xb6vKZ9zutecqpe3+UGdrL54sg9vdXnzH6J6Wu39Fz/7gNfdtm0bb/7QlyeS4rf9/WagaBgHFy/Zb2DC0PBR7NnxCJdt+AyDw0fulwzq4CY7Rdn8GxSnN4v71c9X828AB37Wml92mj0M7TSErZ8ROPDzNdX2rZ/vuVRicqhTC86l38F0+qHN7hcHey/TjR+Z7v+v2tZff8lz9vvCPdn4k2YSXD0utLYrjUaD1//ZRhYdfTwDgwv4nRecvl9bNTjFzBlz5YJYvexgJYNDi2PiDGix7a4Dths8Ypj3XPwcjjnmGGDyEsLm7Ruuu50b3/ISAF72fz7AoqOPZ3Bo4IDPz6H+j7Y7lmq6fQ+23Uy3nalu9DyfBXwYIDPvjojXAbdHxBCwvVyeEbGnEy/eaDR46ds/wLVvfOFEgjrZNpe899MArH/dz+y3XXXdO176VH7l2k+xaOmxDAwOHLBtdZ816zZO+pqt66qPgQP2azQaXPDOjxzwms3lC4aO4H2/8qKJZU1btmyZiHXf2C5273gEgKN/4HT2je3ijVf/PUf/wOkMDg2wbds2do3ex9qrb53YtrkdwN6dO1hYNpx7d+4AYM/iR9g3tou9O3fsd38m6zq1ba8+z+6HH+KNV3+38jf4IkOLjmTt1Q8VZR1797H26ocYGBw44LP2jpc+lf/+0a8ytms7u3c8wtARR0783afS+hmZ7PM12faTfb6n+0z3m+n+3w+235p1G/no/3zVXD870PNtdr842P/NZJ/FyfaZ7LhRfY7JnnNs13bGx/fxJxc8a2Jd9bgwabuyqDhzOLZrx8QxotpWDSxcPHHM2LvrEfYsXDxxnNizszfb3bn2PK2m+r232rt758Qxfnx8337Hj+pnZveORxg+5vhJS0VaPz8zaT+rpvqMt9Muz6T9rm4722d1o9OzI0TEhZm5ofVxRJwAnJmZfzfZdpXt1wJry4dPAr7V0YAP33HA9+sO4hD1c+zQ3/Ebe326Ff8pmbm8C69zWOZAm91Ln8deigV6Kx5jmZyxTK6OWKZss+uu9D9oH3pmXgNc04VYZkVE3JWZK+uO41D0c+zQ3/Ebe336Pf4u6/k2u5f+nr0UC/RWPMYyOWOZXC/FAt25SMp4RCwCKOvkmlfqHAWOr2w30LqjJKnrbLMlaRrdSJ7vAM4p768A7gHIzDFgCUw00Au7EIskaXq22ZI0jY4nz5m5GTghIi4EVgFfiYgrytWfj4g3AG8G/q7TsXRJ35SYTKKfY4f+jt/Y69Pv8c+qOdBm99Lfs5digd6Kx1gmZyyT66VYOj9gUJIkSZorulG2IUmSJM0JJs+SJElSm+qeqm5OiIgzgN8G3pOZt5XLngC8BNgNfCEzv1RbgG2KiLcC95YPv9YPMTdNdTnhfhAR7waav+tPZua9023fCyLiHOB3MvOZ5ePnAScBxwDXZeaBM+z3kEni79vPvvYXEauAn6S4oMv/y8zP1BzPccCrgYeAnZn5oTrjAYiIP83MN9X4+gMUv5OgaDNuzMz7aoqlJ44dEbEMeAXwKLAUWFfncay8KNIfZOZldcVQieVM4KeAXcA9df9Pg8nzrMjMeyLicuDUyuIXAu8qr8R1CY8lR73su5n5wbqDmKmDXE64p0XEkcCmzPybumOZicy8JSJ+ECZmXjijvGzzQmA1cG2tAR5ENf5SX372NamnZeYfwMSXoroPtC+jSIT2Rg9cbzwingYsrjmMpcDHM3O0TNLeBPxxt4PosWPHUooOuPHyb/QzwD/UFAvAa4Dv1fj6AETEAuAZmfln5ePa/4fAso1O2p2PjcbcVX7T7nVPiYjzI+J1zXle+8RZwCYoLicMnFFvODOyHPiRiLggIv5rrzQMM/QE4J8BMnMPRS9Ov+nXz74O9N3K/X+pLYrH7M3MvVBc1rzOQMrj0LOAz9YZR2Zuy8zR8v4YxRzideiZY0dmfi8zx8uH3wJquzZ9RJxE0cu7ra4YKn4MuLP5oO7/oSaT584Zr9x/ADi2rkBmYF3Z+/bXwEV1BzMDA5n5aOXx+JRb9p7/AK7NzPeX959dczyHYjnwn5XH/fT7b+rXz74OdFdEvDUi/guwuc5AImIpkBHx6vIL8ql1xgOcC9xQcwz7iYhjKEps6tCrx46fpN6z1b8E1F5eVDoJWBgRryr/h46sOyCwbGPGIuK3gOdXFv1rZq4+2G70WG/cdO8jMxsRsbueyOaXstdla3n/n8q5dftRP/aYT2jWmfvZ7y+TtWMUF3n5c+A8unzaeZJ4xoCvAr9BcQz4DeD3a4zlqsx8oNsnuA5y3HwJYMlUqTz7eFpm3lLT6z+HYuzNvh45EboEIDM/EBGLgbXAlfWGZPI8Y5n5e8DvtbFp9Xe7nPpOS02qjffRU8n+QYxHxKLMfLTlcsL9qJ9+700PAKcB3ygf9/PvH/rzbzAvtbZj5enmJ2bmNuCqiHhVzfEMAi9tDvyKiEdqjOVcih6884Cnl18SP9bS89qVWCoxPYVigG7HY5hCLx47Xgh8tMbXXwEsj4inUnxO7s/MOi+IdB9FGQuZuSsixmqMZYJlG52zqFK/OlypZepJEXF6sy67LNDvp0vvTno54X4QET9Uuf944MEawzlUW4AnA5QDBnuiu6Jdff7Z1/6Gyp+mk+oKBKCsda7+PyytMZYPZeYHM/NGihmgbqwxaSUiTgdGap7ZpqeOHRHx08A3M7O240Bmvrv8bDQ/J3VfSfQLwEqY6JVfUm84BXueZ0FEvBB4EnBcRAxn5kZgI/DmiHiU4o/f6x4C/ltEbKdo4Hul3umgMnNzRKwqSx6WUJyy7RdHRsQaYA9wBD0+SwVMTGF0LvDMiNhLMVjwnvJ9HANcV2d8BzNJ/PcAP9ePn33tr2wLnh4RF1D0In6x7piAL0fEa8v7dc/80RMi4geA3wE2RsSrKcaBfaDbcfTSsaMsl/hF4M6I+CngvrpKN3pJZu6MiB0R8RqKv1FPlPh4eW5JkiSpTZZtSJIkSW0yeZYkSZLaZPIsSZIktcnkWZIkSWqTybMkSZLUJpNnSZIkqU0mz5oz6ri0dUScExG/VN5/cUT8XLdjkKR+ZJutfuVFUjTvRcSZwNOBMYqrk92Qmdsj4lqKK1ANANsy86/K7S8A/jIzx4AfyMzrI+IoinnTP1HPu5Ck+cE2W3Uzeda8FhHHAD+WmevLx0M8djndz2bmhnL5yoh4bmZ+KjPfX3mK5tmbZcC2LoUtSfOSbbZ6gcmz5pyIGAEuAB6muJzn1Zn5aEScArwAeAJwIvAuYAVwY3PfsmfiAJl5V0SsLZ//wszcEBHnUFzi+dvAjwBPiIjRzPxaB9+eJM0pttnqNybPmovOBdZl5t6yl+LlwF+Ut3+UmRkRb8/ML0fEUzNzd5vPu6f6IDNviYgfzMzPRcS9wKk2wpI0Y7bZ6isOGNRctCsz9wJk5jYe+5K4PTOzvH9veZutO0uSuso2W33F5FlzUUyxPCNiSUQMAgvLZXsj4og2n3fR4YcmSWphm62+YvKsuWhRRAwAlCOq95bL7wVeBbwSeG+57ObyMeX2gxFxUusTRsTTgO90MmhJmqdss9VXrHnWXHJ8efsR4A0R8RBwFHBtuXwpRe9FAE+PiM9m5raI+FpEvIGiPm4xcH25/TMjYh/F/8lDmfnRbr0RSZoHbLPVl+KxciJpbivn+mw2sk8AXpyZf1JjSJKkKdhmq1fZ86z5ZLA5+KQcaf39muORJE3NNls9yZ5nzRsR8RLgicB9wJEUV6XaWW9UkqTJ2GarV5k8S5IkSW1ytg1JkiSpTSbPkiRJUptMniVJkqQ2mTxLkiRJbTJ5liRJktpk8ixJkiS16f8DvM3hAS85erEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
    "for i, prod in enumerate(prods):\n",
    "    sns.histplot(rr_dfs[i]['logCDiff'], bins=200, kde=False, stat='density', ax=ax[i])\n",
    "    ax[i].set_title(prod, fontweight='semibold', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正規分布とt分布をそれぞれ仮定してパラメータを最尤推定し、その分布を重ねてプロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(-10, 10, 300)\n",
    "fig, ax = plt.subplots(2, 1, figsize=(20, 12))\n",
    "for i, prod in enumerate(prods):\n",
    "    sns.histplot(rr_dfs[i]['logCDiff'], bins=200, kde=False, stat='density', ax=ax[i], color='lightblue')\n",
    "    ax[i].set_title(prod, fontweight='semibold', fontsize=16)\n",
    "\n",
    "    # 正規分布（オレンジ）\n",
    "    mu, sigma = stats.norm.fit(rr_dfs[0]['logCDiff'])\n",
    "    norm_ys = stats.norm.pdf(xs, loc=mu, scale=sigma)\n",
    "    ax[i].plot(xs, norm_ys, color='tab:orange')\n",
    "\n",
    "    # t分布（ピンク）\n",
    "    df, loc, scale = stats.t.fit(rr_dfs[0]['logCDiff'])\n",
    "    t_ys = stats.t.pdf(xs, df=df, loc=loc, scale=scale)\n",
    "    ax[i].plot(xs, t_ys, color='tab:pink')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 結果\n",
    "どちらの銘柄についても同様に、明らかにt分布の方が当てはまりが良い。中心付近の密度が大きいところを見ると当てはまりの違いが一目瞭然だが、重要なところはおそらく裾野の厚み。こちらはこの図だと非常に見づらいが、**両端の方に行くとピンクの線の方がオレンジより上にあるのが辛うじてわかる。**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}