Chapter 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import standard libs\n",
    "\n",
    "from pathlib import PurePath, Path\n",
    "import sys\n",
    "import time\n",
    "from collections import OrderedDict as od\n",
    "import re\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get project dir\n",
    "\n",
    "pp = PurePath(Path.cwd()).parts[:]\n",
    "pdir = PurePath(*pp)\n",
    "script_dir = pdir / 'src'\n",
    "viz_dir = pdir / 'report'/'figures'\n",
    "data_dir = pdir / 'data'\n",
    "script_dirs = Path(script_dir).glob('./*/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for sdir in script_dirs:\n",
    "    print(sdir.as_posix())\n",
    "    sys.path.append(sdir.as_posix())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import python scientific stack\n",
    "\n",
    "import pandas as pd\n",
    "# import pandas_datareader.data as web\n",
    "pd.set_option('display.max_rows',100)\n",
    "\n",
    "from dask import dataframe as dd\n",
    "from dask.diagnostics import ProgressBar\n",
    "pbar = ProgressBar()\n",
    "pbar.register()\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "from numba import jit\n",
    "import math\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import visual tools\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('seaborn-talk')\n",
    "plt.style.use('bmh')\n",
    "plt.rcParams['font.weight'] = 'medium'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "blue,green,red,purple,gold,teal = sns.color_palette('colorblind',6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import util libs\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "RANDOM_STATE = 777"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Ticks, volume,or dollar volume traded as the sampling interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and Clean Data\n",
    "\n",
    "A sample of equity tick data, symbol IVE, provided by kibot.com\n",
    "Download this data to the ./data/raw directory in your local repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_kibot_ticks(fp):\n",
    "    # read tick data\n",
    "    cols = list(map(str.lower,['Date','Time','Price','Bid','Ask','Size']))\n",
    "    df = (pd.read_csv(fp, header = None)\n",
    "          .rename(columns = dict(zip(range(len(cols)),cols)))\n",
    "          .assign(dates = lambda df : (pd.to_datetime(df['date']+df['time'],\n",
    "                                                      format = '%m/%d/%Y%H:%M:%S')))\n",
    "          \n",
    "          .assign(v = lambda df: df['size']) # volume\n",
    "          .assign(dv = lambda df: df['price'] * df['size']) # dollar volume\n",
    "          .drop(['date','time'],axis = 1)\n",
    "          .set_index('dates')\n",
    "          .drop_duplicates())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infp = PurePath(data_dir/'raw'/'IVE_tickbidask.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = read_kibot_ticks(infp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  size    v        dv\n",
       "dates                                                        \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79   100  100   5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79   638  638  32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76   100  100   5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75   100  100   5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76   300  300  15225.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outfp = PurePath(data_dir/'interim'/'IVE_tickbidask.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infp = PurePath(data_dir/'interim'/'IVE_tickbidask.parq')\n",
    "df = pd.read_parquet(infp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Obvious Price Errors in Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65f6363518>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "def mad_outlier(y, thresh=3.):\n",
    "    '''\n",
    "    compute outliers based on mad\n",
    "    # args\n",
    "        y: assumed to be array with shape (N,1)\n",
    "        thresh: float()\n",
    "    # returns\n",
    "        array index of outliers\n",
    "    '''\n",
    "    # This function is an approximation of MAD Approach\n",
    "    # @jit can speed up the calculation but may not be applicable to the orginal MAD Approach\n",
    "    median = np.median(y)\n",
    "    print(median)\n",
    "    diff = np.sum((y - median)**2, axis=-1) # this line is complicated\n",
    "    diff = np.sqrt(diff)\n",
    "    print(diff)\n",
    "    med_abs_deviation = np.median(diff)\n",
    "\n",
    "    modified_z_score = 0.6745 * diff / med_abs_deviation\n",
    "    print(modified_z_score)\n",
    "    \n",
    "    return modified_z_score > thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.96\n",
      "[36.17 36.25 36.21 ... 29.26 29.26 29.26]\n",
      "[1.41225268 1.41537627 1.41381447 ... 1.14245268 1.14245268 1.14245268]\n"
     ]
    }
   ],
   "source": [
    "mad = mad_outlier(df.price.values.reshape(-1,1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2010-05-06 14:49:07</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.10</td>\n",
       "      <td>44.03</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:53:30</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>30.28</td>\n",
       "      <td>2600</td>\n",
       "      <td>2600</td>\n",
       "      <td>2860.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>50.57</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>50.57</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>50.57</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-06 14:55:32</th>\n",
       "      <td>1.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>50.57</td>\n",
       "      <td>800</td>\n",
       "      <td>800</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price   bid    ask  size     v      dv\n",
       "dates                                                      \n",
       "2010-05-06 14:49:07   0.11  0.10  44.03   500   500    55.0\n",
       "2010-05-06 14:53:30   1.10  1.10  30.28  2600  2600  2860.0\n",
       "2010-05-06 14:55:32   1.10  1.10  50.57   300   300   330.0\n",
       "2010-05-06 14:55:32   1.10  1.10  50.57   100   100   110.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57   200   200   220.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57   700   700   770.0\n",
       "2010-05-06 14:55:32   1.10  1.00  50.57  1200  1200  1320.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   500   500   550.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   100   100   110.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   200   200   220.0\n",
       "2010-05-06 14:55:32   1.10  0.55  50.57   800   800   880.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mad]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65f1781128>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.loc[~mad].price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:30:00</th>\n",
       "      <td>50.79</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5079.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:30:00</th>\n",
       "      <td>50.71</td>\n",
       "      <td>50.70</td>\n",
       "      <td>50.79</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.75</td>\n",
       "      <td>50.76</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.72</td>\n",
       "      <td>50.75</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5075.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>50.75</td>\n",
       "      <td>50.73</td>\n",
       "      <td>50.76</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>15225.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  size    v        dv\n",
       "dates                                                        \n",
       "2009-09-28 09:30:00  50.79  50.70  50.79   100  100   5079.00\n",
       "2009-09-28 09:30:00  50.71  50.70  50.79   638  638  32352.98\n",
       "2009-09-28 09:31:32  50.75  50.75  50.76   100  100   5075.00\n",
       "2009-09-28 09:31:33  50.75  50.72  50.75   100  100   5075.00\n",
       "2009-09-28 09:31:50  50.75  50.73  50.76   300  300  15225.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[~mad] # drop the outliner\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outfp = PurePath(data_dir/'processed'/'clean_IVE_fut_prices.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infp = PurePath(data_dir/'processed'/'clean_IVE_fut_prices.parq')\n",
    "df = pd.read_parquet(infp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def general_bars(df,column,m,tick = False):\n",
    "    \"\"\"\n",
    "    Compute tick bars\n",
    "    \n",
    "    # Args:\n",
    "        df: pd.DataFrame()\n",
    "        column: name for price data\n",
    "        m: int(), threshold value for ticks\n",
    "    # Returns:\n",
    "        idx: list of indices\n",
    "    \"\"\"\n",
    "    t = df[column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    # for i, x in enumerate(tqdm(t)):\n",
    "    if tick: # if tick bar\n",
    "        for i,x in enumerate(t):\n",
    "            ts += 1 # each data plus 1\n",
    "            if ts >= m:\n",
    "                idx.append(i)\n",
    "                ts = 0\n",
    "                # continue\n",
    "    else: # if not tick bar\n",
    "        for i,x in enumerate(t):\n",
    "            ts += x # each data plus volume/dollar volume\n",
    "            if ts >= m:\n",
    "                idx.append(i)\n",
    "                ts = 0\n",
    "                # continue\n",
    "    return idx\n",
    "\n",
    "def general_bar_df(df,column,m, tick = False):\n",
    "    idx = general_bars(df, column, m, tick)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tick_bars(df,price_column,m):\n",
    "    return general_bars(df,price_column,m, tick = True)\n",
    "\n",
    "def volume_bars(df,volume_column,m):\n",
    "    return general_bars(df,volume_column,m)\n",
    "\n",
    "def dollar_bars(df,dollar_column,m):\n",
    "    return general_bars(df,dollar_column,m)\n",
    "\n",
    "\n",
    "\n",
    "def tick_bar_df(df,tick_column,m):\n",
    "    return general_bar_df(df,tick_column,m,tick = True)\n",
    "\n",
    "def volume_bar_df(df,volume_column,m):\n",
    "    return general_bar_df(df,volume_column,m)\n",
    "\n",
    "def dollar_bar_df(df,dollar_column,m):\n",
    "    return general_bar_df(df,dollar_column,m)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tick Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to choose M, or the threshold value for smapling prices\n",
    "One way is based on ratios of total dollar value/volume traded vs. number of ticks\n",
    "\n",
    "The rest of the notebook uses an arbitrary but sensible M value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num ticks: 1,416,200\n",
      "volume ratio: 548.0\n",
      "volume ratio: 46524.0\n"
     ]
    }
   ],
   "source": [
    "n_ticks = df.shape[0]\n",
    "\n",
    "volume_ratio = (df.v.sum()/n_ticks).round()\n",
    "dollar_ratio = (df.dv.sum()/n_ticks).round()\n",
    "\n",
    "print(f'num ticks: {n_ticks:,}')\n",
    "print(f'volume ratio: {volume_ratio}')\n",
    "print(f'volume ratio: {dollar_ratio}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tick threshold: 100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[99, 199, 299, 399, 499, 599, 699, 799, 899, 999]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_M = 100 # arbitrary\n",
    "print(f'tick threshold: {tick_M:,}')\n",
    "tidx = tick_bars(df,'price',tick_M)\n",
    "tidx[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14162, 6), (1416200, 6))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[tidx].shape, df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset is large so select smaller example for quick exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tick_df = tick_bar_df(df,'price',tick_M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14162, 6)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def select_sample_data(ref,sub,price_col,date):\n",
    "    \"\"\"\n",
    "    select a sample of data based on data, assumes datatimeindex\n",
    "    \n",
    "    # args\n",
    "        ref: pd.DataFrame containing all ticks\n",
    "        sub: subordinated pd.DataFrame of prices\n",
    "        price_col: str(), price colume\n",
    "        date: str(), date to select\n",
    "    \n",
    "    # returns\n",
    "        xdf: ref pd.Series\n",
    "        xtdf: subordinated pd.Series\n",
    "    \"\"\"\n",
    "    \n",
    "    xdf = ref[price_col].loc[date]\n",
    "    xtdf = sub[price_col].loc[date]\n",
    "    \n",
    "    return xdf, xtdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1466,), (15,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## try different dates to see how the quantity of tick bars changes\n",
    "\n",
    "xDate = '2009-10-01'\n",
    "\n",
    "xdf, xtdf = select_sample_data(df,tick_df,'price',xDate)\n",
    "\n",
    "xdf.shape, xtdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_sample_data(ref, sub, bar_type, *args, **kwds):\n",
    "    f, axes = plt.subplots(3,sharex=True, sharey=True, figsize=(10,7))\n",
    "    \n",
    "    ref.plot(*args, **kwds, ax=axes[0], label='price')\n",
    "    sub.plot(*args, **kwds, ax=axes[0], marker='X', ls='', label=bar_type)\n",
    "    axes[0].legend()\n",
    "    \n",
    "    ref.plot(*args, **kwds, ax=axes[1], marker='o', label='price')\n",
    "    sub.plot(*args, **kwds, ax=axes[2], marker='X', ls='', \n",
    "             color = 'r', label=bar_type)\n",
    "    \n",
    "    for ax in axes[1:]:ax.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'Tick Bar', alpha = 0.5, markersize = 7)\n",
    "        \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus Exercise: Make OHLC Bars from Custom Bars\n",
    "\n",
    "Extract tick_df.price and df.price into two pandas series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sub = tick_df.price\n",
    "ref = df.price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below creates the OHLC dataframe by:\n",
    "\n",
    "1. Iterating over the subordinated series' indexes extracting idx and idx+1 period\n",
    "2. Selecting the same date period from the reference series\n",
    "3. Extracting the max, min prices from the reference series\n",
    "4. Combining the o,h,l,c and start and end timestamps into a row\n",
    "5. Returning the aggregated rows as a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_ohlc(ref,sub):\n",
    "    \"\"\"\n",
    "    fn: get ohlc from custom\n",
    "    \n",
    "    # args\n",
    "        ref: reference pandas series with all prices\n",
    "        sub: custom tick pandas series\n",
    "    \n",
    "    # returns\n",
    "        tick_df: dataframe with ohlc values\n",
    "    \"\"\"\n",
    "    ohlc = []\n",
    "    for i in tqdm(range(sub.index.shape[0]-1)):\n",
    "        start, end = sub.index[i], sub.index[i+1]\n",
    "        tmp_ref = ref.loc[start:end]\n",
    "        max_px, min_px = tmp_ref.max(), tmp_ref.min()\n",
    "        o, h, l, c = sub.iloc[i], max_px, min_px, sub.iloc[i+1]\n",
    "        ohlc.append((end, start, o, h, l,c))\n",
    "    cols = ['end','start','open','high','low','close']\n",
    "    return (pd.DataFrame(ohlc,columns=cols))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 14161/14161 [00:03<00:00, 4251.83it/s]\n"
     ]
    },
    {
     "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>end</th>\n",
       "      <th>start</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>2009-09-28 10:02:52</td>\n",
       "      <td>2009-09-28 09:53:14</td>\n",
       "      <td>51.1500</td>\n",
       "      <td>51.260</td>\n",
       "      <td>51.0800</td>\n",
       "      <td>51.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-09-28 10:22:24</td>\n",
       "      <td>2009-09-28 10:02:52</td>\n",
       "      <td>51.2500</td>\n",
       "      <td>51.290</td>\n",
       "      <td>51.1873</td>\n",
       "      <td>51.1961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-09-28 10:38:44</td>\n",
       "      <td>2009-09-28 10:22:24</td>\n",
       "      <td>51.1961</td>\n",
       "      <td>51.380</td>\n",
       "      <td>51.1961</td>\n",
       "      <td>51.3650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-09-28 11:24:29</td>\n",
       "      <td>2009-09-28 10:38:44</td>\n",
       "      <td>51.3650</td>\n",
       "      <td>51.365</td>\n",
       "      <td>51.2700</td>\n",
       "      <td>51.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-09-28 11:52:17</td>\n",
       "      <td>2009-09-28 11:24:29</td>\n",
       "      <td>51.3200</td>\n",
       "      <td>51.436</td>\n",
       "      <td>51.3200</td>\n",
       "      <td>51.4300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  end               start     open    high      low    close\n",
       "0 2009-09-28 10:02:52 2009-09-28 09:53:14  51.1500  51.260  51.0800  51.2500\n",
       "1 2009-09-28 10:22:24 2009-09-28 10:02:52  51.2500  51.290  51.1873  51.1961\n",
       "2 2009-09-28 10:38:44 2009-09-28 10:22:24  51.1961  51.380  51.1961  51.3650\n",
       "3 2009-09-28 11:24:29 2009-09-28 10:38:44  51.3650  51.365  51.2700  51.3200\n",
       "4 2009-09-28 11:52:17 2009-09-28 11:24:29  51.3200  51.436  51.3200  51.4300"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## uncomment below to run (takes about 5-6min)\n",
    "\n",
    "tick_bars_ohlc = get_ohlc(ref,sub)\n",
    "tick_bars_ohlc.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outfp = PurePath(data_dir/'processed'/'tick_bars_ohlc.parq')\n",
    "tick_bars_ohlc.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volume Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volume threshold: 10,000\n"
     ]
    }
   ],
   "source": [
    "volume_M = 10000 # arbitrary\n",
    "print(f\"Volume threshold: {volume_M:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v_bar_df = volume_bar_df(df,'v',volume_M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:44:09</th>\n",
       "      <td>50.91</td>\n",
       "      <td>50.91</td>\n",
       "      <td>50.92</td>\n",
       "      <td>1100</td>\n",
       "      <td>1100</td>\n",
       "      <td>56001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:46:35</th>\n",
       "      <td>51.07</td>\n",
       "      <td>51.05</td>\n",
       "      <td>51.07</td>\n",
       "      <td>3300</td>\n",
       "      <td>3300</td>\n",
       "      <td>168531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:52:47</th>\n",
       "      <td>51.13</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.14</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>102260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:54:36</th>\n",
       "      <td>51.13</td>\n",
       "      <td>51.11</td>\n",
       "      <td>51.13</td>\n",
       "      <td>400</td>\n",
       "      <td>400</td>\n",
       "      <td>20452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:54:41</th>\n",
       "      <td>51.14</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.14</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>35798.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  size     v        dv\n",
       "dates                                                         \n",
       "2009-09-28 09:44:09  50.91  50.91  50.92  1100  1100   56001.0\n",
       "2009-09-28 09:46:35  51.07  51.05  51.07  3300  3300  168531.0\n",
       "2009-09-28 09:52:47  51.13  51.13  51.14  2000  2000  102260.0\n",
       "2009-09-28 09:54:36  51.13  51.11  51.13   400   400   20452.0\n",
       "2009-09-28 09:54:41  51.14  51.13  51.14   700   700   35798.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v_bar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (1466,),xtdf shape: (48,)\n"
     ]
    }
   ],
   "source": [
    "xDate = '2009-10-1'\n",
    "\n",
    "xdf, xtdf = select_sample_data(df, v_bar_df, 'price', xDate)\n",
    "print(f'xdf shape: {xdf.shape},xtdf shape: {xtdf.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample_data(xdf,xtdf,'volume bar', alpha = 0.5, markersize = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dollar Value Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dollar threshold: 1,000,000\n"
     ]
    },
    {
     "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>price</th>\n",
       "      <th>bid</th>\n",
       "      <th>ask</th>\n",
       "      <th>size</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:46:35</th>\n",
       "      <td>51.07</td>\n",
       "      <td>51.05</td>\n",
       "      <td>51.07</td>\n",
       "      <td>900</td>\n",
       "      <td>900</td>\n",
       "      <td>45963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:53:49</th>\n",
       "      <td>51.14</td>\n",
       "      <td>51.13</td>\n",
       "      <td>51.14</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>102280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:55:26</th>\n",
       "      <td>51.14</td>\n",
       "      <td>51.11</td>\n",
       "      <td>51.14</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>5114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:02:52</th>\n",
       "      <td>51.25</td>\n",
       "      <td>51.24</td>\n",
       "      <td>51.26</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>220375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 10:10:21</th>\n",
       "      <td>51.29</td>\n",
       "      <td>51.28</td>\n",
       "      <td>51.29</td>\n",
       "      <td>4500</td>\n",
       "      <td>4500</td>\n",
       "      <td>230805.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    bid    ask  size     v        dv\n",
       "dates                                                         \n",
       "2009-09-28 09:46:35  51.07  51.05  51.07   900   900   45963.0\n",
       "2009-09-28 09:53:49  51.14  51.13  51.14  2000  2000  102280.0\n",
       "2009-09-28 09:55:26  51.14  51.11  51.14   100   100    5114.0\n",
       "2009-09-28 10:02:52  51.25  51.24  51.26  4300  4300  220375.0\n",
       "2009-09-28 10:10:21  51.29  51.28  51.29  4500  4500  230805.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dollar_M = 1000000 # arbitrary\n",
    "print(f'dollar threshold: {dollar_M:,}')\n",
    "dv_bar_df = dollar_bar_df(df, 'dv', dollar_M)\n",
    "dv_bar_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (1466,), xtdf shape: (26,)\n"
     ]
    }
   ],
   "source": [
    "xDate = '2009-10-01'\n",
    "xdf, xtdf = select_sample_data(df,dv_bar_df,'price',xDate)\n",
    "print(f\"xdf shape: {xdf.shape}, xtdf shape: {xtdf.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sample_data(xdf,xtdf,'dollar bar',alpha=0.5,markersize=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing the Bars\n",
    "\n",
    "### Count Quantity of Bars by Each Bar Type (Weekly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count_bars(df, price_col = 'price'):\n",
    "    return df.groupby(pd.TimeGrouper('1W'))[price_col].count()\n",
    "\n",
    "def scale(s):\n",
    "    return (s - s.min())/(s.max()-s.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# count series\n",
    "# scale to compare `apples to apples`\n",
    "\n",
    "tc = scale(count_bars(tick_df))\n",
    "vc = scale(count_bars(v_bar_df))\n",
    "dc = scale(count_bars(dv_bar_df))\n",
    "dfc = scale(count_bars(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f65f1001320>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot time series of count\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10,7))\n",
    "\n",
    "tc.plot(ax=ax, ls='-', label = 'tick count')\n",
    "vc.plot(ax=ax, ls='--',label = 'volume count')\n",
    "dc.plot(ax=ax, ls='-.',label = 'dollar count')\n",
    "\n",
    "ax.set_title('Scaled Bar Counts')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which Bar Type has the most stable counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tc std:8.00%,         vc std:11.52%,         dc std:10.96%\n"
     ]
    }
   ],
   "source": [
    "print(f'tc std:{tc.std():.2%}, \\\n",
    "        vc std:{vc.std():.2%}, \\\n",
    "        dc std:{dc.std():.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tick      0.080032\n",
       "df        0.080290\n",
       "dollar    0.109594\n",
       "volume    0.115203\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_types = ['tick','volume','dollar','df']\n",
    "bar_std = [tc.std(), vc.std(), dc.std(), dfc.std()]\n",
    "counts = (pd.Series(bar_std, index = bar_types))\n",
    "counts.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, it does not make sense....\n",
    "\n",
    "What I understand is:\n",
    "1. time bar is almost fixed\n",
    "2. tick only reflects the change of the price but nothing more\n",
    "3. dollar and volume reflect not only the price change but also the strength of trading activity\n",
    "\n",
    "Therefore, time bars and tick bars are more stable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Which Bar Type has the lowest serial correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def returns(s):\n",
    "    arr = np.diff(np.log(s))\n",
    "    return (pd.Series(arr, index = s.index[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tr = returns(tick_df.price)\n",
    "vr = returns(v_bar_df.price)\n",
    "dr = returns(dv_bar_df.price)\n",
    "df_ret = returns(df.price)\n",
    "\n",
    "bar_returns = [tr, vr, dr, df_ret]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_test_stats(bar_types, bar_returns, test_func, \n",
    "                   *args, **kwds):\n",
    "    dct = {bar:(int(bar_ret.shape[0]),test_func(bar_ret, *args, **kwds))\n",
    "           for bar, bar_ret in zip(bar_types, bar_returns)}\n",
    "    df = (pd.DataFrame.from_dict(dct)\n",
    "          .rename(index={0:'sample size',1:f'{test_func.__name__}_stat'}).T)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>sample size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>50765.0</td>\n",
       "      <td>-0.123141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1416199.0</td>\n",
       "      <td>-0.092242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>-0.017485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>0.061935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample size  autocorr_stat\n",
       "dollar      50765.0      -0.123141\n",
       "df        1416199.0      -0.092242\n",
       "volume      60264.0      -0.017485\n",
       "tick        14161.0       0.061935"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sample size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>0.017485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>0.061935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1416199.0</td>\n",
       "      <td>0.092242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>50765.0</td>\n",
       "      <td>0.123141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample size  autocorr_stat\n",
       "volume      60264.0       0.017485\n",
       "tick        14161.0       0.061935\n",
       "df        1416199.0       0.092242\n",
       "dollar      50765.0       0.123141"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "autocorrs = get_test_stats(bar_types, bar_returns, pd.Series.autocorr)\n",
    "display(autocorrs.sort_values('autocorr_stat'),\n",
    "        autocorrs.abs().sort_values('autocorr_stat'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_autocorr(bar_types, bar_returns):\n",
    "    f, axes = plt.subplots(len(bar_types),figsize=(10,7))\n",
    "    \n",
    "    for i, (bar,typ) in enumerate(zip(bar_returns, bar_types)):\n",
    "        sm.graphics.tsa.plot_acf(bar, lags=120, ax = axes[i],\n",
    "                                 alpha = 0.05, unbiased = True, fft = True,\n",
    "                                 zero = False,\n",
    "                                 title = f'{typ} AutoCorr')\n",
    "    plt.tight_layout()\n",
    "        \n",
    "def plot_hist(bar_types, bar_ret):\n",
    "    f, axes = plt.subplots(len(bar_types), figsize=(10,6))\n",
    "    for i, (bar, typ) in enumerate(zip(bar_returns, bar_types)):\n",
    "        g = sns.distplot(bar, ax=axes[i], kde = False, label = typ)\n",
    "        g.set(yscale='log')\n",
    "        axes[i].legend()\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorr(bar_types, bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " plot_hist(bar_types, bar_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partition Bar Series into Monthly, Compute Variance of Returns, and Variance of Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def partition_monthly(s):\n",
    "    return s.resample('1M').var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tr_rs = partition_monthly(tr)\n",
    "vr_rs = partition_monthly(vr)\n",
    "dr_rs = partition_monthly(dr)\n",
    "df_ret_rs = partition_monthly(df_ret)\n",
    "\n",
    "monthly_vars = [tr_rs, vr_rs, dr_rs, df_ret_rs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>sample size</th>\n",
       "      <th>var_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>110.0</td>\n",
       "      <td>5.295761e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>110.0</td>\n",
       "      <td>1.898539e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>110.0</td>\n",
       "      <td>2.099271e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>110.0</td>\n",
       "      <td>2.710183e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample size      var_stat\n",
       "df            110.0  5.295761e-12\n",
       "tick          110.0  1.898539e-09\n",
       "dollar        110.0  2.099271e-09\n",
       "volume        110.0  2.710183e-09"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_test_stats(bar_types, monthly_vars, np.var).sort_values('var_stat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Jarque-Bera Test, which has the lowest test statistics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>sample size</th>\n",
       "      <th>jb_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>1.302361e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>50765.0</td>\n",
       "      <td>5.404986e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>3.277633e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1416199.0</td>\n",
       "      <td>2.067849e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample size       jb_stat\n",
       "tick        14161.0  1.302361e+08\n",
       "dollar      50765.0  5.404986e+10\n",
       "volume      60264.0  3.277633e+11\n",
       "df        1416199.0  2.067849e+14"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def jb(x, test=True):\n",
    "    #np.random.seed(12345678)\n",
    "    if test: return stats.jarque_bera(x)[0]\n",
    "    return stats.jarque_bera(x)[1]\n",
    "\n",
    "get_test_stats(bar_types, bar_returns, jb).sort_values('jb_stat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Shapiro-Wilk Test\n",
    "\n",
    "Shapiro-Wilk test statistic - the larger the better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>sample size</th>\n",
       "      <th>shapiro_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>0.655457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>50765.0</td>\n",
       "      <td>0.406283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>0.282896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1416199.0</td>\n",
       "      <td>0.176869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample size  shapiro_stat\n",
       "tick        14161.0      0.655457\n",
       "dollar      50765.0      0.406283\n",
       "volume      60264.0      0.282896\n",
       "df        1416199.0      0.176869"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def shapiro(x, test = True):\n",
    "    if test: return stats.shapiro(x)[0]\n",
    "    return stats.shapiro(x)[1]\n",
    "\n",
    "get_test_stats(bar_types, bar_returns, shapiro).sort_values('shapiro_stat')[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare Serial Correlation between Dollar and Dollan Imbalance Bars\n",
    "\n",
    "1. Compute the sequence $b_{t=1,...,T}$\n",
    "2. Compute the imbalance $\\theta$ at $T$: $\\theta_T = \\sum_{t=1}^T b_t v_t$\n",
    "3. $E_0[T]$: the expected size of the tick bar. Compute the expected value of  as expoential weighted moving average of previous $E_0[T]$ values\n",
    "4. Compute the expected value of $\\theta_T$ as exponential weighted moving average of $b_t v_t$ values\n",
    "5. For each index:\n",
    "    - compute $ \\lvert \\theta_t \\rvert \\ge E_0[T] \\lvert 2 v^+ - E_0[v_t] \\rvert $\n",
    "    - if the condition is met, capture the quantity of ticks\n",
    "    - reset tick count\n",
    "    - continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit(nopython = True)\n",
    "def numba_isclose(a,b,rel_tol = 1e-09, abs_tol=0.0):\n",
    "    # rel_tol: relative tolerance\n",
    "    # abs_tol: absolute tolerance\n",
    "    return np.fabs(a-b) <= np.fmax(rel_tol*np.fmax(np.fabs(a),np.fabs(b)),abs_tol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@jit(nopython=True)\n",
    "\n",
    "def bt(p0,p1,bs):\n",
    "    if numba_isclose((p1-p0),0.0,abs_tol=0.001):\n",
    "        return bs[-1]\n",
    "    else: return np.abs(p1-p0)/(p1-p0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@jit(nopython=True)\n",
    "\n",
    "def get_imbalance(t):\n",
    "    \"\"\"Noted that this function return a list start from the 2nd obs\"\"\"\n",
    "    bs = np.zeros_like(t)\n",
    "    for i in np.arange(1,bs.shape[0]):\n",
    "        bs[i-1] = bt(t[i-1],t[i],bs[:i-1])\n",
    "    return bs[:-1] # remove the last value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dates\n",
       "2009-09-28 09:30:00   -32352.98\n",
       "2009-09-28 09:31:32     5075.00\n",
       "2009-09-28 09:31:33     5075.00\n",
       "2009-09-28 09:31:50    15225.00\n",
       "2009-09-28 09:31:51    15225.00\n",
       "Name: dv, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Noted that get_imbalance return the tick bars from the 2nd obs\n",
    "## amazingly the date, dv, and ticks are matched, lol\n",
    "tidx = get_imbalance(df.price.values)*df.dv.iloc[1:]\n",
    "tidx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "window size:1,416.00\n"
     ]
    }
   ],
   "source": [
    "# window size\n",
    "wndo = tidx.shape[0] // 1000\n",
    "print(f'window size:{wndo:,.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Expected value of bs approximated by ewm\n",
    "E_bs = tidx.ewm(wndo).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## What is E_T???\n",
    "## in this implementation E_T is ewm of index values\n",
    "E_T = pd.Series(range(tidx.shape[0]),index=tidx.index).ewm(wndo).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df0 = (pd.DataFrame().assign(bs=tidx)\n",
    "       .assign(E_T=E_T)\n",
    "       .assign(E_bs=E_bs)\n",
    "       .assign(absMul = lambda df: df.E_T*np.abs(df.E_bs))\n",
    "       .assign(absTheta = tidx.cumsum().abs()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>bs</th>\n",
       "      <th>E_T</th>\n",
       "      <th>E_bs</th>\n",
       "      <th>absMul</th>\n",
       "      <th>absTheta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dates</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>2009-09-28 09:30:00</th>\n",
       "      <td>-32352.98</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-32352.980000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>32352.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:32</th>\n",
       "      <td>5075.00</td>\n",
       "      <td>0.500176</td>\n",
       "      <td>-13632.384285</td>\n",
       "      <td>6818.598141</td>\n",
       "      <td>27277.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:33</th>\n",
       "      <td>5075.00</td>\n",
       "      <td>1.000471</td>\n",
       "      <td>-7392.186750</td>\n",
       "      <td>7395.665836</td>\n",
       "      <td>22202.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:50</th>\n",
       "      <td>15225.00</td>\n",
       "      <td>1.500882</td>\n",
       "      <td>-1731.901047</td>\n",
       "      <td>2599.379899</td>\n",
       "      <td>6977.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-09-28 09:31:51</th>\n",
       "      <td>15225.00</td>\n",
       "      <td>2.001412</td>\n",
       "      <td>1664.269246</td>\n",
       "      <td>3330.888324</td>\n",
       "      <td>8247.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           bs       E_T          E_bs       absMul  absTheta\n",
       "dates                                                                       \n",
       "2009-09-28 09:30:00 -32352.98  0.000000 -32352.980000     0.000000  32352.98\n",
       "2009-09-28 09:31:32   5075.00  0.500176 -13632.384285  6818.598141  27277.98\n",
       "2009-09-28 09:31:33   5075.00  1.000471  -7392.186750  7395.665836  22202.98\n",
       "2009-09-28 09:31:50  15225.00  1.500882  -1731.901047  2599.379899   6977.98\n",
       "2009-09-28 09:31:51  15225.00  2.001412   1664.269246  3330.888324   8247.02"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f65f3e66b00>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f65f52e1978>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df0[['E_T','E_bs']].plot(subplots=True,figsize=(10,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>bs</th>\n",
       "      <th>E_T</th>\n",
       "      <th>E_bs</th>\n",
       "      <th>absMul</th>\n",
       "      <th>absTheta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1416.199000</td>\n",
       "      <td>1416.199000</td>\n",
       "      <td>1416.199000</td>\n",
       "      <td>1.416199e+03</td>\n",
       "      <td>1.416199e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.868396</td>\n",
       "      <td>706.685330</td>\n",
       "      <td>-0.876148</td>\n",
       "      <td>5.471895e+06</td>\n",
       "      <td>3.240696e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>678.087064</td>\n",
       "      <td>408.817559</td>\n",
       "      <td>12.981937</td>\n",
       "      <td>1.418429e+07</td>\n",
       "      <td>3.164342e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-244194.944400</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-226.301289</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.537070e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-19.388268</td>\n",
       "      <td>352.633500</td>\n",
       "      <td>-2.780387</td>\n",
       "      <td>4.454746e+05</td>\n",
       "      <td>4.764046e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.959100</td>\n",
       "      <td>706.683000</td>\n",
       "      <td>-0.075123</td>\n",
       "      <td>1.636679e+06</td>\n",
       "      <td>1.295138e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.188733</td>\n",
       "      <td>1060.732500</td>\n",
       "      <td>2.665891</td>\n",
       "      <td>4.849096e+06</td>\n",
       "      <td>5.892219e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>289491.800000</td>\n",
       "      <td>1414.782000</td>\n",
       "      <td>185.312974</td>\n",
       "      <td>3.116753e+08</td>\n",
       "      <td>1.401899e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bs          E_T         E_bs        absMul      absTheta\n",
       "count    1416.199000  1416.199000  1416.199000  1.416199e+03  1.416199e+03\n",
       "mean       -0.868396   706.685330    -0.876148  5.471895e+06  3.240696e+05\n",
       "std       678.087064   408.817559    12.981937  1.418429e+07  3.164342e+05\n",
       "min   -244194.944400     0.000000  -226.301289  0.000000e+00  3.537070e-01\n",
       "25%       -19.388268   352.633500    -2.780387  4.454746e+05  4.764046e+04\n",
       "50%        -4.959100   706.683000    -0.075123  1.636679e+06  1.295138e+05\n",
       "75%        19.188733  1060.732500     2.665891  4.849096e+06  5.892219e+05\n",
       "max    289491.800000  1414.782000   185.312974  3.116753e+08  1.401899e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df0.describe()/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65f3e66ef0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df0.loc['2010-06',['absMul','absTheta']]\n",
    " .reset_index(drop=True)\n",
    " .plot(figsize=(10,5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_t_abs(absTheta,t,E_bs):\n",
    "    \"\"\"\n",
    "    Bool function to test inequality\n",
    "    * row is assumed to come from df.itertuples()\n",
    "    - absTheta: float(), row.absTheta\n",
    "    - t: pd.Timestamp\n",
    "    - E_bs: float, row.E_bs\n",
    "    \"\"\"\n",
    "    return (absTheta >= t*E_bs)\n",
    "\n",
    "def agg_imalance_bars(df):\n",
    "    \"\"\"\n",
    "    Implements the accumulation logic\n",
    "    \"\"\"\n",
    "    start = df.index[0]\n",
    "    bars = []\n",
    "    for row in df.itertuples():\n",
    "        t_abs = row.absTheta\n",
    "        rowIdx = row.Index\n",
    "        E_bs = row.E_bs\n",
    "        \n",
    "        t = df.loc[start:rowIdx].shape[0]\n",
    "        if t<1: t = 1 # if t less than 1, set equal to 1\n",
    "        if test_t_abs(t_abs,t,E_bs):\n",
    "            bars.append((start,rowIdx,t))\n",
    "            start = rowIdx\n",
    "    return bars\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>start</th>\n",
       "      <th>stop</th>\n",
       "      <th>Ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-09-28 09:30:00</td>\n",
       "      <td>2009-09-28 09:30:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-09-28 09:30:00</td>\n",
       "      <td>2009-09-28 09:31:32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-09-28 09:31:32</td>\n",
       "      <td>2009-09-28 09:31:33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009-09-28 09:31:33</td>\n",
       "      <td>2009-09-28 09:31:50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009-09-28 09:31:50</td>\n",
       "      <td>2009-09-28 09:31:51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                start                stop  Ts\n",
       "0 2009-09-28 09:30:00 2009-09-28 09:30:00   1\n",
       "1 2009-09-28 09:30:00 2009-09-28 09:31:32   2\n",
       "2 2009-09-28 09:31:32 2009-09-28 09:31:33   2\n",
       "3 2009-09-28 09:31:33 2009-09-28 09:31:50   2\n",
       "4 2009-09-28 09:31:50 2009-09-28 09:31:51   2"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars = agg_imalance_bars(df0)\n",
    "test_imb_bars = (pd.DataFrame(bars,columns=['start','stop','Ts'])\n",
    "                 .drop_duplicates())\n",
    "test_imb_bars.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x514.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_imb_bars.set_index('stop')['Ts'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dvImbBars = df.price.loc[test_imb_bars.stop].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dates\n",
       "2009-09-28 09:30:00    50.7900\n",
       "2009-09-28 09:30:00    50.7100\n",
       "2009-09-28 09:31:32    50.7500\n",
       "2009-09-28 09:32:06    50.7800\n",
       "2009-09-28 09:32:56    50.8226\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvImbBars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dates\n",
       "2009-09-28 09:46:35    51.07\n",
       "2009-09-28 09:53:49    51.14\n",
       "2009-09-28 09:55:26    51.14\n",
       "2009-09-28 10:02:52    51.25\n",
       "2009-09-28 10:10:21    51.29\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvBar = dv_bar_df.price\n",
    "dvBar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dr = returns(dv_bar_df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "drImb = returns(dvImbBars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>sample size</th>\n",
       "      <th>autocorr_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>50765.0</td>\n",
       "      <td>-0.123141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>264610.0</td>\n",
       "      <td>-0.075539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample size  autocorr_stat\n",
       "dvBar      50765.0      -0.123141\n",
       "dvImb     264610.0      -0.075539"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bar_types = ['dvBar','dvImb']\n",
    "bar_rets = [dr,drImb]\n",
    "\n",
    "get_test_stats(bar_types, bar_rets, pd.Series.autocorr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GdAY2b96cdxEkoSziUBaxKI84lEUcyiKOiFmoEZ2B/v7+vIsgCWURh7KIRXnEoSziUBZxRMxCjegMNDY25l0ESSiLOJRFLMojDmURh7KII2IWakRnoKOj9JfNJS/KIg5lEYvyiENZxKEs4oiYhRrRGRgaGsq7CJJQFnEoi1iURxzKIg5lEUfELNSIzkDET0/1SlnEoSxiUR5xKIs4lEUcEbNQIzoDk5OTeRdBEsoiDmURi/KIQ1nEoSziiJiFGtEZmJgI9SvkdU1ZxKEsYlEecSiLOJRFHBGzUCM6AxHHNqxXyiIOZRGL8ohDWcShLOKImIUa0RmIOLZhvVIWcdRzFrtGD3HZ9ffy25/6GZddfy+7Rg/lXaS6ziMaZRGHsogjYhZqRGegqakp7yJIIq0sIjaCVpp6Pi6uuvkh7ugfY8bhjv4xrrr5obyLVNd5RKMs4lAWcUTMQo3oDGzYsCHvIkgirSwiNoJWmno+Lu4eGFvwfh7qOY9olEUcyiKOiFmoEZ2BPXv25F0ESaSVRcRG0EpTz8fFKb1tC97PQz3nEY2yiENZxBExCzWiM9DZ2Zl3ESSRVhYRG0ErTT0fF5efcxz777+NwzPTPHVzG5efc1zeRarrPKJRFnFknYVOFZxfxONCjegMRByWpV6llUXERtBKU8/HxdHt67jnY29lxzt/iw+f90SObl+Xd5HqOo9olEUcWWehUwXnF/G4SL0RbWYNZvYhMxs0s/1m9jUz615g+ReY2V1mNmFmd5rZ84vmPcnMvmpmv0y2dZeZ/V7J+j8ws0NmNlZ0Oy/tei3HwYMH8y6CJNLKImIjaKXRcRGL8ohDWcSRdRY6VXB+EY+LWvREvwN4EfBM4Nhk2ufKLWhmJwLXAR8AOpL/v25mxyeLdALfB34NaAcuAa4yswtKNvV+d28rul2fXnWWL+LYhvVKWcShLGJRHnEoiziyzkKnCs4v4nFRi0b0xcAH3f1+dx8B3ga8oKhhXOwi4FZ3/7y7T7r7F4AdyXTc/cfu/n/d/Vde8EPgn4Hn1qDcNRNxbMN6pSziUBaxKI84lEUcWWehUwXnF/G4WJvmxsysAzgOuHV2mrvfZ2ajwGnAgyWrnF68bGJHMr3c9tcDvw78r5JZf2xmbwV2AZ8HrnL3qUrKPD09zc6dO+fud3V10dXVVcmqFWtubk51e7J0yiIOZRGL8ohDWcSRdRazpwoCfHd4ONN9RxfxuEi1EU3hlAuAkZLp+4rmFdswz7Knli5oZg0UTgt5ALi2aNY7gf8CRimc9vGFZF/vrKTAu3fv5sILL5y7f8kll7B9+3b6+/tpbW2loaGB0dFRenp6GB4ext3p6elhYGCAtrbC1yxjY2P09vYyODiImdHV1cXg4CDt7e3MzMwwMjLCxo0b6e/vp7Gx8dGK7tvH5OQkExMTbN68mf7+fpqamtiwYQN79uyhs7OTiYkJDh48ODe/ubmZlpYW9u7dy6ZNm9i/fz+Tk5Nz81taWmhqamJkZITu7m5GRkaYmpqam59WnQ4cODC3zcbGRjo6OhgaGqKjoyN0nVpbW3n44YdTqdOs0dFR5bSEOo2NjTE5Obmq6lRNTrOGhoZC1KmtrY2+vr66eO5Fr9PIyAhmtqrqtFJzcndGR0czrdOsvr4+5VRUJzOjr68v8zotxNx9sXZmxcxsI7AXOMPd/7No+gjwanf/Rsny/wA86O5/XDTtI8Dj3f2CommNFBrHxwK/nZwmMl8ZXglc6e6Pr6TMt9xyixd/uqlFT3RfXx9btmw5Yh8Aw/qUmbnSLJZDOS5PmlmsRNGeP/WeRyTKIo48soj22hBFXsfFjh07bt22bdtZ5eal2hPt7vvM7CHgTOA/Ye7iwXbg9jKr3AacWzLtDOCm2Ttm1gx8FWgDnu/ui12qehiwSsu8du1atm7dWuniS7Jp06aabl8qpyziUBaxKI84Fsti1+ghrrr5Ie4eGOOU3sJ5sxohqDZ0XCwuq+djxCxqcWHhJ4C3m9kJZtYOfBC4wd0fLLPstcBZZvYKM2s0s1cATwc+C2BmbcB3gCYKPdBHNKDNbKOZnWdmbVZwBrAd+HIN6rVk+/fvz7sIklAWR8pzYH9lEYvyiGOxLDSWcHZ0XCwuq+djxCxq0Yi+Evgm8FPgl0AD8CoonGphZnMNYXe/D7gAeA+Fc5rfA7y4qMH9EuB5wNnAYNE40B9L5jcm6/wyWf/LwBep8HzorExOTma6P/3i0fyyziK6PN+MlUUsyiOOxbLQWMLZ0XGxuKyejxGzSL0R7e4z7n65u3e7+wZ3v8Ddh5J5X3D3tpLl/8ndT3X3luT/G4vmfdbdzd3Xl4wD/YZk/qC7P8vdO5J9Pcnd3+fuoR7prMc2VC/F/CKOM5mnPN+MlUUsyiOOxbLQWMLZ0XGxuKyejxGz0M9+ZyDrsQ3VSzG/iONM5inPN2NlEYvyiGOxLDSWcHZ0XCwuq+djxCzSHuJOymhpacl0f6f0tnFH/9gR96Ug6yyiu/yc47jgyr+jdcupnP64jZm+GSuLWJRHHItlobGEs6PjYnFZPR8jZqFGdAaKxxReKnfnsIMnfx8xr2TZS88+lpf/xZdp3XIqpx3TwaVnH8vkzOEFNr7s4oVVWjVraOTQ9AKPRZXbAzhYtL00h4zMwsaWtXMvft/oL4xPOj45U9U2ims8W/9KHoUZa2D/oemq9rUU0SMZPbj4Y5BFFaZoYKSSskR/QJchSs0maWDvREW/F/bocjkVPspjViuHvIHh8cJjPDA2ycd+9Ai/GBznST3recOzjqW37dH397Qfiz3jRc+BFDfuZTY2MDbJx3/0S+4dGueJ3eu55FmP46i26tsuu8dqeDZtQ+Piy2RMjegMzP7YSqmdQ+OFRjGFf5xCQxkKb/zuzmEK/1f7vjXbMPrwnX3sHZ9m73jtGysrwaHhIdYdSvdpf8/uA6luLy/3Do1nur9Dw8Osm1r+B8xy9oxP8ZXbB3hw70GO72zmZaf1sml9vBdggAeGJ3Ld/0p6rOrFoeFh1k1Xdmw8tPdgjUtT3w4ND7NuppDFNT96hPuHC4/3z3eP85EfPsQbn3Vszfb9yL7ssi2u238NjvPRf314SXWr5UAGzRP7OGpTZ822vxQ6JzoD3d3dZacfmJxhfHKGickZJqZmODh1mMnpwm1q5jDTh53Dh6tvQMv81rZ15F0ESRRnsWd8imt+9Ahv/85OrvnRI0f2wCzBV24f4P7hgxx2uH/4IF+5fWC5xV219FjFo9epOIqzeLDkA0vp/ZVsJdStozPdH8JLgxrRGRgZmfcHFiVjMxPVX2SZdgNPCoqzSLshtxLeEKLQYxXPUl6npDaKszi+s/mIeaX3V7KVULcD+0fzLsJjqBGdgakpNbqi8JmFz/ct12BWT11tFGeRdkNuJbwhRKHHKp7FXqckO8VZvOy03rlRKE7sKpz6tFqshLpNT8c7LVWN6AxEHNuwXjW2L/yzoeUazOqpq43iLNJuyK2EN4Qo9FjFs9jrlGSnOItN6xu552NvZcc7f4s3PuvYVXXtwEqoW1f3UXkX4TF0YWEG+vv72bJlS97FEGBqdA/ruub/UFOuwXx8Z/PcBRegnrq0FGfxstN6efenv0HrllPZ2tO27Ibc7BsCFC6uzVP0C/ciPVZSsNjrlGRHWcQxPLSbno4T8i7GEdSIzkBra2veRchE9MYCwJqmhRvA5RrMaTfwpKA4i9XckJv9dgMe/Xaj3FXvK+H4kWws9jq1WkU8Buo1i4iaW9bnXYTH0OkcGWhoaMi7CJlYCecO25qFn/LlvtpeCV9zrUSLZRFdpRecVno60Eo4fiQbK/3YWKqIx0C9ZhHRmoBZqCc6A6Ojo3R2xhrbsBZWwrnDMwfHWbu+fd75y+kRjdiLshRZ1WOxLPJSaf0r7WGu9HSglXD81Iu8j+Wsj4286zsr4jEQ9XWqHo0fGAN68i7GEeI161ehnp5YodfKSrjKv7HtsT96k5aIvShLkVU9aplFOZX2HFda/0rf8Cu9cG8lHD9RrbZxxrM+NvKu76yIx0DWWcj8NnbFu+BWjegMDNfwt+QjqfVV/mm8UU6P126cySx6UZbzGKR9+sFy1TKLctJuHFf6hl/p6UBZjJKxWsc8X23jjGd9bORd31kRj4Gss4guz9eQ/SP7MttXpXQ6Rwa8Tn5ysNYXh1X69flCaplFFqN4LOcxSPv0g+XK+riopnFcSf1X4ogiaRxDEZXLdjmnKOQ9Ik/Wx0be9Z0V6Rg48vkzuexTXMo9H2fLk/dpNNWo5PGr1elBhw8fXvY20qae6AzUy+kctZZGb0ljW+3OTc+iF2U5j0Hapx8sVy2zKKfSnuNK678SLziN0uOYtnLZLqd3Ou+xs7M+NvKub5byutC33PainEZTjUoev1rVa2NXdyrbSZN6ojMwMDCw6saJzuNClDR6S6b2D9dszM8selGW8xhUum5Ww83VMotyKu05Xs3D7UXpcUxbuWz/4l+OzK6aDwyVPgdq9TqY9bGxmp/zpfK60LeS7a2ED7WVPH61+rC+d88gR21sS2VbaVFPdAba2mKFnoY8PkGn0VvSsK6lBiXLTqWPQbnz1qL1NmWdxXJ6jlfLucTRngNpKZdtFhep1ep1cKW/TkWW14W+5bYX8ULKxVTy+NWqXi3r4/3mhnqi5QiV9qzk8bVwPfWWzKfSx2C+89bqffi+UmkPZxddPR1DWfxIUtqvg0c+H/ev2PNmK5HXa0qlx0Daz5/5trfSfsirksevnn6gLPVGtJk1AFcCrwWagRuBS9x9aJ7lXwB8GDgRuA94q7vfWDR/K/Ax4NeBvcD/dvcPF81fD/wV8GLAgK8Bb3b3ibTrtlRjY2Ns2hRvaJZyol18lraZQxOsbe3IbH95vVGk/eZei0Zk1lmUU2m9op9LvFouWkpT9NOryin3fJz9u3hapA9wS32Ni/7BtBa/GVBue6vxQ22tjr2J8QOpbSsttTid4x3Ai4BnArNHxOfKLWhmJwLXAR8AOpL/v25mxyfzG4BvAj+nMML2+cDbzezlRZv5CHBycnsS8GTg6jQrtFy9vSvnU1i0i8/S1rihK9P95XXhSNpfp9WiEblYFlmcQpH2cHZ5WS0XLa00ab8Olns+LufYy+IYWurzLPoH0+XQsVcbnZviDdJgaQ+lY2Z9wBXu/qnk/hOAncAJ7v5gybLvA37D3Z9TNO0W4Lvu/j4zOxf4FnCUu48l898PnO3u55pZCzAMnOfuNyXzt1FoeHe5+6JH5We/cZP/1T227HqLiIiIyOpy5Zl+67Zt284qNy/Vnmgz6wCOA26dnebu9wGjwGllVjm9eNnEjmT67PxfzDagy8w/icIpI7eWzG+h0CstIiIiIpK6tM+Jnv2B+ZGS6fuK5hXbMM+ypy4yv71ofun+Zv+u6MfuT+xs4q+f9+hXs11dXXR1pfuV//j4OOvXr5+7P3PYubN/7Ihlzn1KYQi87xedP7ScaeUsdd23f2cnh4u+sFhj8MHf3rrgvqrdZ+m0a370yBHnGp7Y1Tzv+XJZPHZLNd+5cVlkm4VKylx6v9Ln03zPgTxyrEbaZamkvmmcE3148iBrmh59LUzzuEr7GF3OY7zU7WX1mgSPzaIS5Y6r0nO2FzqGllqPcs+9C5+xteK6VqLcPksvXCv3ujrfcqUWek2qJItKHpPlXo+Q1zGU5utZGuXN4xegd+zYMe+8tBvR+5P/S68W2kihN7rc8gstW8n82f3tK/qbefb3GGvXrmXr1soahEs1ODi4oseJzuMiwtVyvtym9Y2hLpaJoJpfBCzXMJTHmu95Vs1zb2psX6ZjE680Wb4mLSWLcsdVFsdQHq9xX7l9gA0nFr6QXuiixEqXW+g1aSlZ6HW/fqTaiHb3fWb2EHAm8J8wd/FgO3B7mVVuA84tmXYGcFPR/CeZWau7Hyiaf1vy9z3AwWR/3yuaPwH8YtkVSkl7e0Wd4mHl0ZhZqaN/yOIqfT7pjShbDc3rF19oCfaMT3HSG66mdcupXPOjR+byLp1WaU9due1lsW6Wr0lLyaLccbVaj6H5fua9NNtqLpSf7zWpVseFrA61GCf6ExRG0Pg+sAf4IHBD6UWFiWuBPzGzVwBfBS4Eng68JplDcyHwAAAgAElEQVR/M9AH/LmZvYPCOdCXAJcCuPuEmX0euMLM7kzWuQK4tpKLCrMyMzOTdxGWJY8X4iwa7st5Q5Wlm30+TY3to7FtY97FkYQfPlyT7ZbrDQQq6iGsdHtZrJtlZ8JSslitDeZyyn2gKZdtNb/SOt9jV6vjQlaHWjSirwQ6gZ8C64B/Bl4FYGavBD7u7m1QuOjQzC6gME70p4H7gRfPNrjdfcbMfgf4OIUG+T7gQ+7+paL9XUphnOjZnuevAW+pQb2W7MCBA3R3x/vN91pbTiM1izeE5byhSmUWeg4cnkznc64+DKWjVnmk/XPHyzmtYjnrZtlITSuL1aDc8V3uA025n3l/23O3LPuDT4QsKn2N02th9lIfJ9rdZ9z9cnfvdvcN7n7B7A+tuPsXZhvQRcv/k7uf6u4tyf83lszf6e7b3H29ux/j7leVzB9399e5+8bk9vpIP7QCsHlzfZ5nONtIXdOwdq6ROnuQn/mBG3L/+eTVct51ZOWeA7Ma29P5AaKF9iGVq1UeTQ1HDiG63J87zmvdLKWVRTl5vAYvZ5/lju/ZDzQf/O2tC/7Me7nlqlXLLCpV6Wvcan0tLH7+XHb9vewaPZR3kebU4sdWpER/f3/eRchFuUZqpIM80htquTeZSB84lmqhDypTo3tqvg+pXHEey3nulT7+h6adE7uaWWPM/SDJy07rfcy0SuW1bpbSOjbKyeM1eDn7rOa85lpkW8ssKlXpY7AaXgvLvfYUP3/u6B/jqpsfyruYc2pxOoeUaGysz69Typ2PFukgjzT6w3LOG430FV5pWR7Xvo6HRx7tNSj+oGINDanss94uQq1V3sV5LOdUp9I8TphnKLilnhqxnNMqsjglI4180jo2ysnjNXg5+0zjvOblqGUWlar0MYj8WljpcVHutaf0+XL3wNhj1suLeqIz0NFROkpffSjXMxCp9zeNr/rSspyf+43Uu19aFnhsL+Sshpa2+TdUhZXSu5iWWuVdnMdyGj1p5rESv42pNJ+F6pbWsVFOHq/By9ln3sd3LbOoVKWPQd6P1UIqPS7KvfaUPl9O6c0/k1nqic7A0NAQra2tuZYhj97Kcj0DefX+RuqtLWe+HoRKehUi9e6X7vuXo5Pz/jDP9NgIDV0ty95nPY1KALXLuziP5fRoLTWPcsfoSrz4t9oPv/DYuqV1bJSTx2vwcvaZ9/FdyywqVeljkPdjtZBKj4vFxjs/tbeNy885rqZlrYYa0RmI0BMd5c2o3EGeRQM3Sv3nM9+bTCVvPJG+wqumLBF6eFaiWuVdnEeljZ40j91KvsZdCed4VprPQnWr5bGRR0MrcuNuMVFfp7J430xzH8v5ka3Z58+mtZMce1T+F3oWUyM6A5OTkzXbdqVP8shvRlk0cCPXH5b3i3ORzu2upiw+E/+r+Wpl8cZWq7yL86i00ZPmsTvf17hRPiBWqtJ8Fqrbajw2VqqoWWTxvpnmPtL4ka3pqdq1pZZKjegMTEzUbsS9NH7WNG9ZNHAj13+5IvXyVFOWw5NxhilKSxZvbLXKeyl5pHns5vWz1WmrNJ+F6rYaj42VKmoWWbxvprmPNF63Dh2M1fkFakRnopbjRKfxs6Z5y6KBG7n+9SrC+Ktpi/6Nx0IWy6NcL3uax25eP1ud1/USC9VtNR4bK9VSjossnj9ZvG9G63zq6j4q1/2Xo9E5MlDLcaIrveq53EgUUa58X84VxZXWYbb+VzyzLfeROKRgKeOvRnnOzifS6DPVWiyPclfXpzkaQF6j5WQxuk21z9sIYxMvVfRjtFpLOS6ykMVIHNFG+xge2p3r/stRT3QGmpqaarbt5fSwRrnYbjm9TdXWwdaq8RzFUrKI8pydz0r+xmOxPMr1skc6lWipsvj2oJ5ep6Ifo9VaynGRhSyOvWjHd8Tf3FAjOgMbNmyo2baX8yRfyV89z6q2Dg3r8h2qSB61lCyiP2ejvelUY7E8on21m5Ys6lVPr1PRj9Fq1etxEVHL+ngjpeh0jgzs2fPYr4Oa1q6hsWENaxuMtWtsbrqZYfaYxWtiJX/1PKvaOkwfGK1lcaQKS8liNTxno1osj2hf7aYli3qtxNeppZ6WsdqO0Xo9LrJQ7XNsdGRvRiWrnHqiM9DZ2XnE/YY1xpOPKv/jK6cdXfik5e5z007pbcUdDhct98Tu9TjL8yfP3cJH/+0R7tl9gJOOauUPn30smzesW+ZWK1dcx6W6/Jwt/NW/PcI9gwc4qaeVNz/7WDZvePT0mSdsOrIX4UBTN+s31KaXZynVOaFkEP/lPyLpqSaf4zqb5wpf6Vrjazexvq2yN9jZbf7R2cfxsR89wr2D4zyxZz1veNax9LbV7nSpVFXwwPgCCz2u48hjM+3nykRDFy1t8x//x3Ss46lHL68nKIVDPnVHt6/jKZtr28P15mc/nk/8+JfcOzTOE7vXc/EzH8dRCzxvJxq6aGnN7rW4nE/99FdHnJbxD3cN8p5tJ8zNn++94g9+/fF84ie/ZOfQOFu713PxMxauazUWOj5qZWJNJy2t85e/d0MT2//7iTXbf5o1LpdDTwXZ1Oq4/ZufHPkc+/qdg7zj3C3zLt/U3DnvvLyoEZ2BiYkJ2tvbq1rHirqjGxse+4XB+qaGZZfrCZvW85e/86RlbydPW9et5S/Pn78ObeuOfIof2DfJhnVxnvbtzXHKshydLdWfqza1f4quKi8e27S+kf9z/klV72s16F7gjTwNA2PT9NR4H/XqqLYm/s+LKn/eDhyYTq3huVT3Do0/5n5xmXo3lC9f74YmPlpFXaMbGN9Hb4adS7V0dPtj63FMmWlZ2bln/DH3H9cxf8fKwMBIrYtUNZ3OkYGDAcc2rFfKIg5lEYvyiCNCFqf0tj3m/q7RQ3Nfv192/b3sGo05hnKaImSxWpV7ji0kYhZqRGegluNES3WURRzKYnFZNlqURxwRsrj8nON46uY2GgyeurmNy885jqtufmhuOLc7+se46uaH8i5mzUXIYrUq9xxbSMQsVsd3ycH19/ezZcv85/lIumYbHq1bTuWy6+/l8nOOm/saS1nEoSwWN9toAeYaLR8+74k12ZfyiCNCFke3r3vMc+3ugbEF769GEbJYrco9xxYSMQv1RGeguXllX5280izUW6Is4lAWi8uy0aI84oiaRbVfv68GUbOoRxGzSLURbWbrzezTZrbXzPaZ2afMbMGhEMzsNWZ2n5mNm9mPzezpRfOeZWbfMrMBMxsxs1vN7H+UrP+gmR00s7Gi21PTrNdytbSs3DE/V6KFGh7KIg5lsbgsGy3KI46oWVT79ftqEDWLehQxi7R7oj8CnJzcngQ8Gbh6voXN7GzgGuCNQCfwNeDbZjY7lEUX8GXgKcn89wN/Z2a/VrKp33P3tqLbHSnWadn27o03tuFqtlDDQ1nEoSwWl2WjRXnEETWL2a/fv/P6M/jweU8sO9rDahM1i3oUMYvUzolOepxfBZzn7gPJtPcC3zSzt7h7ucsqfx+4zt1vTJb/EPBm4MXAZ9392yXL/4OZ3QmcDfw0rbLX2qZNm/IuQl2ZvQDm7oExTuk9suGhLOJQFour9pzB5VAecSiLOJRFHBGzSPPCwpOAZuDWomk7gBYKvdK3l1nndOAzs3fc3c3sZ8n0xzCzzcCpZbZ1tZn9H+Ah4Bp3/3ilhZ6enmbnzp1z97u6uujq6qp09Yrs37+ftrbVf+5YFAs1PJRFHMoiFuURh7KIQ1nEETGLihrRZvYZ4KIFFvkz4Ibk7+LRsGf/nu+XRjaULA+wr9zyZtZK4XSPb7j7TUWzLqLQcD8EPA/4kplRaUN69+7dXHjhhXP3L7nkErZv305/fz+tra00NDQwOjpKT08Pw8PDuDs9PT0MDAzMhTk2NkZvby+Dg4OYGV1dXQwODtLe3s7MzAyDg4N0dXXR399PY2MjHR0dDA0N0dHRweTkJBMTE3P737VrFxs2bDjip8L7+vrYvHkz/f39R0zbtGkT+/fvZ3Jycm5+S0sLTU1NjIyM0N3dzcjICFNTU3Pz06rTgQMH5rY5X51m5zc1Nc3VqbOzk4mJCQ4ePDg3v7m5mZaWFvbu3VvzOk1PT/Pwww/nXqfiHFdyTsX1qDanoaEhpqenw9WpXo+nmZkZ+vr6VlWdVmpOg4ODNDQ0rKo6rdScDhw4QGtr64qu06xDhw7N5TRrbGxsxdRpbGyMvr6+zJ97C7FKftrXzNoo9DLPZ5xCb/PPgE5335es1wkMA6e7+2N6os3sP4HPuPtfFk37R+A+d39r0bQNwLeAPcDL3X1ygbK+G3iBuz9n0YoBt9xyixc/oWrRE33o0CHWrVv43LHZfQ4PD1c9TSpXSRZZWC05LqceUbKQAuURh7KIY6VnsWv0EBdc+Xe0bjmV0x+3cW7I15X4HpRXFjt27Lh127ZtZ5WbV9GFhe4+5u5DC9zGgXuAg8CZRaueAUwAv5hn07cVL2+F37p+WjJ9dloXcBPwK+ClCzWgE4cBW2SZOWvXrmXr1q1zt7Qb0MARPY+SL2URh7KIRXnEoSziWOlZrKYfyImYRWqjc7j7BPB54AozO8rMjgKuAK6d56JCgE8CF5jZNjNrAi6j0OP9dZg7B/pfgJ8Dr3T36eKVzWyLmZ1rZs1m1mBmzwXeQmFEjzDSGpalHn9yNW0Rh8ipV8oiFuURh7KIY6VnUW7I15XaloiYRdpD3F1Kodd59nYPhUYtAGb2LjO7a/a+u/8QeBOFxvQI8DLghe4+mixyCYXh7S4ERorGgX5XMr+VwhB6g8Be4P8CV7j7R1Ou17I0NTWlsp3V9IkyL2llIcunLGJRHnEoizhWehblhnxdqW2JiFmk2oh293F3f527b0xur096qGfn/7m7n1qyzrXufqK7t7j7M9z91qJ573N3c/fWknGg/zyZf7e7n+HuG9y93d2f4u5/lWad0jAyUnrt5NLU40+upi2tLGT5lEUsyiMOZRHHSs+i3FjzK7UtETGLNIe4k3l0d3ensp1Tetu4o3/siPtSnbSykOVTFrEojziURRwrPYtyQ76u1LZExCzSPp1Dykjr01M9/uRq2iJ+kq1XyiIW5RGHsohjNWaxUtsSEbNQT3QGpqamUtlOlr9etlqllcVyzF7U0brlVC67/t65IYfqTYQs5FHKIw5lEcdqzGKltiUiZqGe6Axs3rw57yJIIkIWK/WijrRFyEIepTziUBZxKIs4ImahRnQGIo5tWK8iZLFSL+pIW4Qs5FHKIw5lEYeyiCNiFmpEZ6C1tTXvIkgiQhblhhyqRxGykEcpjziURRzKIo6IWagRnYGGhoa8iyCJCFms1Is60hYhC3mU8ohDWcShLOKImIUuLMzA6OgonZ2deRdDiJHFSr2oI20RspBHKY84lEUcyiKOiFmoJzoDPT09eRdBEsoiDmURi/KIQ1nEoSziiJiFGtEZGB4ezrsIklAWcSiLWJRHHMoiDmURR8Qs1IjOgLvnXQRJKIs4lEUsyiMOZRGHsogjYhZqRGcg4lcQ9UpZxKEsYlEecSiLOJRFHBGzUCM6AwMDA3kXQRLKIg5lEYvyiENZxKEs4oiYhRrRGWhrq89xgCNSFnEoi1iURxzKIg5lEUfELNSIFhERERGpkhrRGRgbq8+fdY5IWcShLGJRHnEoiziURRwRs1AjOgO9vb15F0ESyiIOZRGL8ohDWcShLOKImIUa0RkYHBzMuwiSUBZxKItYlEccyiIOZRFHxCzUiM6AmeVdBEkoiziURSzKIw5lEYeyiCNiFqk2os1svZl92sz2mtk+M/uUmbUsss5rzOw+Mxs3sx+b2dOL5h1vZm5mB8xsLLk9UrL+UWZ2nZntN7NBM/ugmYX6cNDV1ZV3ESShLOJQFrEojziURRzKIo6IWaTd2PwIcHJyexLwZODq+RY2s7OBa4A3Ap3A14Bvm1l7yaInuXtbcju2ZN4Xkv+PBZ4JvBj4k+VWJE0Rv4KoV8oiDmURi/KIQ1nEoSziiJhFao3opMf5VcB73X3A3XcD7wUuMrPmeVb7feA6d7/R3Q8BHwIOUWgIV7LPE4DfBP7E3Ufc/X7gg8AbKi339PQ0O3funLvV4rfZ29tLPxNIXpRFHMoiFuURh7KIQ1nEETGLtSlu6ySgGbi1aNoOoIVCr/TtZdY5HfjM7B13dzP7WTK92I/NrAm4C9ju7j8oWn/E3e8r2efxZtbu7qOLFXr37t1ceOGFc/cvueQStm/fTn9/P62trTQ0NDA6OkpPTw/Dw8O4Oz09PQwMDMwN/D02NkZvby+Dg4OYGV1dXQwODtLe3s7MzAyDg4OsX7+e/v5+Ghsb6ejoYGhoiI6ODiYnJ5mYmJjb/65du9iwYQN79uyZm9bX18fmzZvp7++nubmZlpYW9u7dy6ZNm9i/fz+Tk5Nz81taWmhqamJkZITu7m5GRkaYmpqam59WnQ4cODC3zfnqNDu/qalprk6dnZ1MTExw8ODBXOrU2NjI2NjYqqpTnjkVP0errdPu3bsZGxsLV6fVmFMldWpqaqKvr29V1Wkl5zSbyWqq00rMaWpqioaGhlVVp5Wa08zMDKOjo5nXaSHm7ou1MzGzzwAXLbDInwE3ADcDazzZaHJu8gzwHHf/YZnt3gf8qbv/bdG0zwJT7v57ZtYGPIVCw7gReB3wF8Az3f12M3t1sv6WovVPAO4HHu/uR5w/Xc4tt9zizc2PdpR3dXWlft5NX18fW7ZsWXCZ2X0W94SXmybLU0kWUrnlPEeVRSzKIw5lEYeyiCOvLHbs2HHrtm3bzio3r9Ke6DcDly8wf5xCbzNAB7Cv6G+A+XqE9xctM2sjcB+Au48BP0qmTwIfNbPzgZdS6Nmeb/3ZbS9q7dq1bN26tZJFl2zz5s013b5UTlnEoSxiUR5xKIs4lEUcEbOo6Jxodx9z96EFbuPAPcBB4MyiVc8AJoBfzLPp24qXt8L4JU9Lps/nMDA7zsltQIeZnViyzwfdfaSSumWhv78/7yJIQlnEoSxiUR5xKIs4lEUcEbNI7cJCd58APg9ckQw7dxRwBXCtux+cZ7VPAheY2bbknOfLKJxX/XUAM3uWmT3FzNaaWbOZXQw8d3a+uz8AfBf4CzNrT07leDvw8bTqlYbGxsa8iyAJZRGHsohFecShLOJQFnFEzCLtIe4updDrPHu7B3jL7Ewze5eZ3TV7PzlP+k0UGtMjwMuAFxZdEHgC8A/JvF8CrwZ+x92LL158ZVKPXwI/Bf6RwnnTYXR0lJ5xInlRFunZNXqIk95wNWd+4AYuu/5edo0eqmp9ZRGL8ohDWcShLOKImEWqjWh3H3f317n7xuT2+qSHenb+n7v7qSXrXOvuJ7p7i7s/o7iB7O5/5+5b3b3V3Te5+3Pc/Z9L1t/t7he4+wZ373b3t7n74TTrtVxDQ0N5F0ESyiI9V938EBtOPJ01DWu5o3+Mq25+qKr1lUUsyiMOZRGHsogjYhahftlvtYr46aleKYv03D0wtuD9xSiLWJRHHMoiDmURR8Qs1IjOwOTkZN5FkISySM8pvW0L3l+MsohFecShLOJQFnFEzEKN6AwU/5iK5EtZpOfyc47jqZvbaDB46uY2Lj/nuKrWVxaxKI84lEUcyiKOiFmk+YuFMo+IYxvWK2WRnqPb1/Hh85645PWVRSzKIw5lEYeyiCNiFuqJzkDEsQ3rlbKIQ1nEojziUBZxKIs4ImahRnQGmpqa8i6CJJRFHMoiFuURh7KIQ1nEETELNaIzsGHDhryLIAllEYeyiEV5xKEs4lAWcUTMQo3oDOzZsyfvIkhCWcShLGJRHnEoiziURRwRs1AjOgOdnZ15F0ESyiIOZRGL8ohDWcShLOKImIUa0RmIOCxLvVIWcSiLWJRHHMoiDmURR8Qs1IjOwMGDB/MugiSURRzKIhblEYeyiENZxBExCzWiMxBxbMN6pSziUBaxKI84lEUcyiKOiFmoEZ2BiGMb1itlEYeyiEV5xKEs4lAWcUTMQo3oDDQ3N+ddBEkoiziURSzKIw5lEYeyiCNiFmpEZ6ClpSXvIkhCWcShLGJRHnEoiziURRwRs1AjOgN79+7NuwiSUBZxKItYlEccyiIOZRFHxCzUiM7Apk2b8i6CJJRFHMoiFuURh7KIQ1nEETELNaIzsH///ryLIAllEYeyiEV5xKEs4lAWcUTMItVGtJmtN7NPm9leM9tnZp8yswVPYjGz15jZfWY2bmY/NrOnF817pZmNldxmzOwbRcv8wMwOlSxzXpr1Wq7Jycm8iyAJZRGHsohFecShLOJQFnFEzCLtnuiPACcntycBTwaunm9hMzsbuAZ4I9AJfA34tpm1A7j7F9y9bfYGPA44CHy+ZFPvL17O3a9PuV7LstjYhrtGD3HSG67mzA/cwGXX38uu0UMZlaz+RBxnsl4pi1iURxzKIg5lEUfELFJrRCc9zq8C3uvuA+6+G3gvcJGZzTcuye8D17n7je5+CPgQcAh48TzLvxrYD3w9rXJnYbGxDa+6+SE2nHg6axrWckf/GFfd/FBGJas/EceZrFfKIhblEYeyiENZxBExizR7ok8CmoFbi6btAFoo9EqXc3rx8u7uwM+S6eVcAnza3adKpv+xmQ2b2V1m9k4za6y00NPT0+zcuXPuNjw8XOmqFVtsWJa7B8YWvC/piThETr1SFrEojziURRzKIo6IWaytZCEz+wxw0QKL/BlwQ/L3SNH02b/b51lvQ8nyAPvKLW9m/w04BTi/ZNY7gf8CRoFfA76QrP/OBco7Z/fu3Vx44YVz9y+55BK2b99Of38/ra2tNDQ0MDo6Sk9PD8PDw7g7PT09DAwM0NbWBsDY2Bi9vb0MDg5iZnR1dTE4OEh7ezszMzPs3buXjo4O+vv7aWxspKOjg6GhITo6OpicnOQJHWv5xb7puTKc1N1CX1/f3P2+vj42b95Mf38/zc3NtLS0sHfvXjZt2sT+/fuZnJycm9/S0kJTUxMjIyN0d3czMjLC1NTU3Py06nTgwIG5bZar08TExNz8pqYmNmzYwJ49e+js7GRiYoKDBw/mUqeWlhYefvjhVVWnlZrT6Ogohw4dWlV1Wsk5rV+/nr6+vlVVp5Wa0+xQXqupTis1p8OHDzM6Orqq6rRSc5ptD2Vdp4VYofN3YWbWRqGXeT7jFHqbfwZ0uvu+ZL1OYBg43d1vL7Pd/wQ+4+5/WTTtH4H73P2tJcteC/S4+28vUtZXAle6++MXrRhwyy23ePGv4HR1ddHV1VXJqhXr6+tjy5Yt887fNXqIq25+iLsHxjilt43LzzmOo9vXzZWjFr3j9WqxLCQ7yiIW5RGHsohDWcSRVxY7duy4ddu2bWeVm1dRT7S7jwELnmNgZvdQuOjvTOB7yeQzgAngF/Osdluy/Ow2DHgacF3JtruAlwK/W0FxDwNWwXIArF27lq1bt1a6+JJ0d3cvOP/o9nV8+Lwn1rQMUrBYFpIdZRGL8ohDWcShLOKImEVq50S7+wSFUTOuMLOjzOwo4ArgWnc/OM9qnwQuMLNtZtYEXEahx7v0wsGLgCHgiFE3zGyjmZ1nZm1WcAawHfhyWvVKw8hI6RkrkhdlEYeyiEV5xKEs4lAWcUTMIu0h7i6l0Os8e7sHeMvsTDN7l5ndNXvf3X8IvIlCY3oEeBnwQncfLdnuxcDfuPtMyfRG4D3ALymcE/1l4ItUeD50VqamSq+DlLwoiziURSzKIw5lEYeyiCNiFhWdE72a/fu//7uffPLJNd3HoUOHWLduXdXr6Zzo9C01C0mfsohFecShLOJQFnHklcVC50TrZ78zEHFsw3qlLOJQFrEojziURRzKIo6IWagRnYHW1ta8iyAJZRGHsohFecShLOJQFnFEzEKN6Aw0NDTkXQRJKIs4lEUsyiMOZRGHsogjYhZqRGdgdLT0OknJi7KIQ1nEojziUBZxKIs4ImahRnQGFvvFG8mOsohDWcSiPOJQFnEoizgiZqFGdAY0ukYcyiIOZRGL8ohDWcShLOKImIUa0Rmo92EEI1EWcSiLWJRHHMoiDmURR8Qs1IjOQMSvIOqVsohDWcSiPOJQFnEoizgiZqFGdAYGBgbyLoIklEUcyiIW5RGHsohDWcQRMQs1ojPQ1taWdxEkoSziUBaxKI84lEUcyiKOiFmoES0iIiIiUiU1ojMwNjaWdxEkoSziUBaxKI84lEUcyiKOiFmoEZ2B3t7evIsgCWURh7KIRXnEoSziUBZxRMxCjegMDA4O5l0ESSiLOJRFLMojDmURh7KII2IWakRnwMyqXmfX6CFOesPVnPmBG7js+nvZNXqoBiWrP0vJQmpDWcSiPOJQFnEoizgiZqFGdAa6urqqXueqmx9iw4mns6ZhLXf0j3HVzQ/VoGT1ZylZSG0oi1iURxzKIg5lEUfELNSIzsBSvoK4e2BswfuyNBG/DqpXyiIW5RGHsohDWcQRMQs1ojPQ3t5e9Tqn9LYteF+WZilZSG0oi1iURxzKIg5lEUfELNSIzsDMzEzV61x+znE8dXMbDQZP3dzG5eccV4OS1Z+lZCG1oSxiUR5xKIs4lEUcEbNItRFtZuvN7NNmttfM9pnZp8ysZYHlTzez75jZLjNzMzu7zDJbzey7ZnbAzB4xs8uWs888HDhwoOp1jm5fx4fPeyLfef0ZfPi8J3J0+7oalKz+LCULqQ1lEYvyiENZxKEs4oiYRdo90R8BTk5uTwKeDFy9wPKTwHXA+eVmmlkD8E3g50BPstzbzezly9hn5jZv3px3ESShLOJQFrEojziURRzKIo6IWaTWiE56f18FvNfdB9x9N/Be4CIzay63jrv/3N0/6e4/nWez5wBbgHe6+7i77wA+DrxhqfssNT09zc6dO+duw8PDVdS6Mv39/alvU5ZGWcShLGJRHnEoiziURRwRs1ib4rZOApqBW4um7QBaKPQQ376EbZ4O/MLdi4em2AH8QVr73EtHu2gAACAASURBVL17NxdeeOHc/UsuuYTt27fT399Pa2srDQ0NjI6O0tPTw/DwMO5OT08PAwMDtLUVLvYbGxujt7eXwcFBzIyuri4GBwdpb29nZGSE73//+7zwhS/k4MGDNDY20tHRwdDQEB0dHUxOTjIxMcHmzZvp7++nqamJDRs2sGfPHjo7O5mYmODgwYNz85ubm2lpaWHv3r1s2rSJ/fv3Mzk5OTe/paWFpqYmRkZG6O7uZmRkhKmpqbn5adRpZmaGAwcOzG1zpdRpcHCQr371q7z0pS+ltbV1VdRppebU1NTEjTfeyDnnnMOWLVtWRZ1Wck6jo6N85Stf4WUvexnd3d2rok4rNafm5ma+973v8bznPY/Ozs5VUaeVmtMDDzzA9773Pc4//3xmZmZWRZ1Wak7T09N89atf5UUvehHHHHNMpnVaiLn7Yu1MzOwzwEULLPJnwA3AzcAaTzZqZmuAGeA57v7DRfbhpcuZ2XuB33T35xZNOxf4Z3dfa2bPWc4+AW655RZvbn6007qrqyvVsQh37tzJM57xDH7yk5+wdevW1LYr1VMWcSiLWJRHHMoiDmURR55Z7Nix49Zt27adVW5epT3RbwYuX2D+OIWeX4AOYF/R3wCjFe6n1P6ibczaWLS9/cvd59q1a3VwiIiIiEhVKuqJrmhDhfOTh4H/x92/l0z7DeB6oMvdDy6yfrme6HOBbwE97n4gmXZFsty5y90nwE033TQI9FVd4QpNT083DA8P93Z1dQ2sXbs23vgsdURZxKEsYlEecSiLOJRFHDlnsWXbtm1lz+tIrRENYGafpDA6xgXJpOuAO939DfMsb8Ds2G0TwDbg34Apd59JRue4E7gReAeFc6BvAC519y8tZZ8iIiIiIsuV9hB3lwK/KLrdA7xldqaZvcvM7ipafguFxvNEcv+m5O9XA7j7DPA7wFOAPcC3gQ/NNqAr2aeIiIiISNpS7YkWEREREakH+tlvEREREZEqqREtIiIiIlIlNaJFRERERKqkRrSIiIiISJXUiBYRERERqZIa0SIiIiIiVVIjWkRERESkSmpE15CZNZjZh8xs0Mz2m9nXzKw773Ktdmb2QTO7y8xGzexXZvZJM+sqWeY1ZnafmY2b2Y/N7Ol5lbdemNkaM/s3M3MzO7ZourLIkJn9ppn9yMzGzGzIzP66aJ6yyJCZbTazLyfvEXvN7HtmdnrRfOVRA2b2u2Z2S/IeMV1m/guS95AJM7vTzJ5fMn+rmX3XzA6Y2SNmdll2pV9dFsrCzF6YHBNDyfFxi5k9p2SZXLNQI7q23gG8CHgmMNto+Fx+xakbM8CrgE3A6RQe+7+dnWlmZwPXAG8EOoGvAd82s/bsi1pX3gKMF09QFtkys+cBXwWuonB8HAv8TTJPWWTvr4Eu4CSgF/gP4HorUB61s5fCY//HpTPM7ETgOuADQEfy/9fN7PhkfgPwTeDnQA9wPvB2M3t5FgVfhebNgsLz/qPAVgqP9ReB75jZ4yFGFvrFwhoysz7gCnf/VHL/CcBO4AR3fzDPstUTM/t/gC+6e0dy/7PAGnd/dXLfgD7gve7+2fxKunqZ2ZOA7wAvAX4GPN7dH1EW2TKzfwf+xd3fUWaessiYmd0O/JW7fyK5fxLwXxQaBB9GedRU8qHyu+6+tmja+4DfcPfnFE27JVnufWZ2LvAt4Ch3H0vmvx84293PzbQCq0i5LOZZbhC42N2/HiEL9UTXiJl1AMcBt85Oc/f7gFHgtLzKVae2AbcX3T+dI3NxCg2705HUmdka4NPAnwD7SmYri4yYWSvwDOCgme1IviL9gZmdlSyiLLL3IeAlZtZtZs3AxcAP3X0I5ZGXIx73xA4efdxPB34x22grM19qxMxOo/AN2p3JpNyzUCO6dma/chspmb6vaJ7UmJm9BPh94NKiyRtQLlm6FOh39+vKzFMW2emk8Jr/+8BrgWOAGymcIrARZZGHfwUagEFgDLiAQj6gPPKy2OOuXHJgZkdROBXtL9z93mRy7lmoEV07+5P/O0qmb6TQGy01ZmYvBT4JnO/uO4pm7Ue5ZMLMtgKXAW+eZxFlkZ3Z16S/dffb3X2SwvmejcCzURaZSr6h+S7wCwqP+3rgz4BbzKwX5ZGXxR535ZIxMzsG+D6FD/3vLJqVexZqRNeIu+8DHgLOnJ2WXLDQzpGnFkgNmNn/BD4O/I67f79k9m0cmYsBT0umS7rOpnB+551mNkThqzaA283sTSiLzLj7CPAgUO5CGEdZZK0LOAH4qLuPuvuku/8NhfflZ6E88nLE4544g0cf99uAJyWnR5WbLylKLui8BfiOu7/Zj7yQL/8s3F23Gt2AdwP3UHihbAf+HvinvMu12m/AHwF7gF+bZ/7ZFL463QY0AZcDA0B73mVfbTcKvWvHFt2eRaHBdhbQpiwyz+NPgEeAU4C1wNuAXRR6c5RF9nncQ2H0gdYkj9cBk8CJyqOmj3sD0Aw8H5hO/m4GDHgChVGEXkHhW5pXAAeA44vW/TnwEaCFwgebAeB3867XSrwtksXJyevVny6wbq5Z5P4AruZbEvBVwBCFrx2uA7rzLtdqvyWNtKnkDWjuVrLMa4D7gQngJ8DT8y53PdyA45N8jlUWuTz+BlwB9FM4d/D7wNOURW55PBm4PnmPGKFwQduLlEfNH/fXJq9Dpbfjk/kvAO5KHve7gOeXrL8VuClpbP8KuDzvOq3U20JZUBia1kvfy4FXRslCQ9yJiIiIiFRJ50SLiIiIiFRJjWgRERERkSqpES0iIiIiUiU1okVEREREqqRGtIiIiIhIldSIFhERERGpkhrRIiIiIiJVUiNaRERERKRKakSLiIiIiFRJjWgRERERkSqpES0iIiIiUiU1okVEREREqqRGtIiIiIhIldSIFhFJgZltN7OdOez3eDNzMzs7632LiNQzNaJFRHKSNH5fFaAcd5jZjJmdtsT132NmDy5j/79uZteZ2YCZHTSz+8zs82Z25lK3KSJSa2pEi4jUMTN7NnAU8Cng4hz2/z+BW4Ap4JXAk4GXAw8CH1nGds3MGstMX2NmDUvdrojILDWiRUSqZGbrzOwaMxsxs71mdg2wrmj+f096dh9fst7Lk57WjfNs183sD83sy2Z2wMweMrMLzazDzL5gZvvN7H4ze0mZ1U8ws5vMbMLMHjCzV1ZYnUuALwB/A7zKzNaXlOkzZvbdkmmvMjNP/n4t8H5gS1J+N7PtybwNZvZxMxtM6v0fZvb8ou0cA1wD/I27v9zdv+vuD7j7f7j7e4Dzi5Y9ycy+ZWZjye2bZra1aP5rzWzazM41s58Bh4Dfmj3NJnns/wuYpNBQFxFZFjWiRUSqdyXwEuA1wK8DB4A/KJp/E7ALKD1V49XAP7r7vgW2/W7g28DpwPXAtcCXgH8GzgC+BVxrZptK1vsg8Gngaf9/e3ceHtdd33v887WksWRt1hbJ4MSOY2LHaZLGCZS2JBR8G+i9ufCUhpQWSLjlQqDN05TaFApNG1K2QpLncqGkC0kTthbahi6hISFhsSGktBYXZ8OJ7UaJiUeWJVmbJWvJ7/5xzsjj0dHozPY7H2k+r+fRY8/+O/Oe5aejM2cQTIq/YGaX5lsIM2sD8EYAdzvnfgjgpwCuzneZCF8Jb/swgHXhzy3haXcCeA2C++FiAN8HcK+ZbQ1PvxrBLx8fjrpi59xwOM4GAA8AqAfwyvCnCcA3zCyVdZFVAD4BYCeArQD+PTz+RQB+G8DbAGwD0FfgMoqILKBJtIhIAcysEcC7AXzQOffPzrmfOOd2AXgqcx7n3AsAvohg0py53BkIJpSfX+Im/s45d7dz7gCAPwHQAOCAc+6u8Lg/BrAGweQ92x3OuS855/aHa3EfAfB7S9zWNQD2O+d+HB6+GwVu0uGcmwQwDmDOOZcOf8bDtcRXAfht59z9zrknnXM3AHgMwB+EFz8XwKhz7vASN/ObALoA/Lpzbq9zbi+ANwF4cfhvhgH4fefct5xzh5xzA+Hx9QDe6px7xDn3lHNurJBlFBGJokm0iEhhzkGw9vThnOO/l3P4bgDnmdlLw8O/AWAQwP1LXH9mQotwEjgHYF/WccMINkk4I+dyP8g5/H0Ea13zeWc4zowvAHiZmf3MEpeLI3Pbu3OO3w3g/PD/FvO6zgfwhHPuWOYI51w/gP1Z15XxHxGX73fOPRvztkREYtEkWkSkMJmJn8t3JufckwD+E8HaXoT/ftk5N7vE9c/EOM5h6dfvvBPUcJd42wDcGm5LPAvgOQA1OH1t9AsR17XgA3sFMJy67/YDaDGz9TEuF3V/W87xc865qYjzTRQ2RBGRpWkSLSJSmAMI1gT/Ys7xvxBx3s8DeJOZXQRgO05f61tuL885/PMAnsxz/usQbGd9EYLtqDM/NwB4a7gdMgAcRbBNcbbcXc9NI5h8Z3s8/PfynOMvyzrt7xF8APCPogYYbrOdua7zzawz67RuBJuDPB51WRGRStMkWkSkAM65CQB/AeDDZva6cK8Rn0DwQbZcfwugFcBdAPZlbXtcCW83s980s3PN7GYEk+j/E3VGM2tHsL3yF5xzj2X/INjV3WoEHzgEgAcBbDWz683sHDN7BxZ++PC/APSE+3vuNLM1zrmDCCbJnzWz15jZVjP7FICfAfBJAHDO/RTA9QDeYWZ/Z2Y7wi+P2W5mHwLwz+H1fxnAAICvhKddguDDlj9F8MFGERHvNIkWESnc+wH8E4JtiH8IYC2AP889U7gN79cRrOFd6gOF5RjTOxFsP30NgGudc1HbBwPAtQg2hfjn3BPCXxK+Hl4XnHMPIlhT/IcIttd+NYCbcy72TwgmzF9HMNnNfHDwfyPYBvyL4WV/EcCVzrmfZN3e5xDsbaMewS8d+wH8A4CzAfxueJ5JAFcgWGu9G8B3EWyi8Vrn3PRSd4yISCWYc3k36xMRERERkRxaEy0iIiIiUiBNokVERERECqRJtIiIiIhIgTSJFhEREREpUG3SA0jad77zHbd69eqK3sbs7Cxqa6v+rqagFjzUgot68FALHmrBI6kWJ06cOLZjx46uqNOq/pGxevVqbN0atXvX8hkfH0dTU1NFb0PiUQseasFFPXioBQ+14JFUi97e3r7FTtPmHB6MjY0lPQQJqQUPteCiHjzUgoda8GBsoUm0B9PT+i4AFmrBQy24qAcPteChFjwYW2gS7UFPT0/SQ5CQWvBQCy7qwUMteKgFD8YWmkR7kE6nkx6ChNSCh1pwUQ8easFDLXgwttAk2oOGhoakhyAhteChFlzUg4da8FALHowtqn7vHD6kUqmCL3Nk9CRu2f0snugfx7buJuy6/Cysa6nsrviqQTEtpDLUgot68FALHmrBg7GF1kR7MDIyUvBlbtn9LB5Nj2POAY+mx3HL7mcrMLLqU0wLqQy14KIePNSCh1rwYGyhSbQHnZ2dBV/mif7xvIelOMW0kMpQCy7qwUMteKgFD8YWmkR7UMxvT9u6m/IeluIw/iZbrdSCi3rwUAseasGDsYUm0R7MzMwUfJldl5+FsUM/xgtzs7igJ9gmWkpXTAupDLXgoh481IKHWvBgbKEPFnpQzL4N17Wsxv6/+H0AwINDQ+UeUtVi3M9ktVILLurBQy14qAUPxhZaE+0B474Nq5Va8FALLurBQy14qAUPxhaaRHvQ2NiY9BAkpBY81IKLevBQCx5qwYOxhSbRHtTU1CQ9BAmpBQ+14KIePNSCh1rwYGyhSbQHo6OjSQ9BQmrBQy24qAcPteChFjwYW+iDhR50dXUlPQQJqQUPteCiHjzUgke5WuhbiEvH+LzQmmgPhrR3DRpqwUMtuKgHD7XgUa4W+hbi0jE+LzSJ9sA5l/QQJKQWPNSCi3rwUAse5WqhbyEuHePzQpNoDxj/BFGt1IKHWnBRDx5qwaNcLfQtxKVjfF5oEu1Bf39/0kOQkFrwUAsu6sFDLXiUq4W+hbh0jM8LfbDQg6Ym/cbJQi14qAUX9eChFjzK1ULfQlw6xueF1kSLiIiIiBRIk2gPxsf1AQIWasFDLbioBw+14KEWPBhbaBLtQXd3d9JDkJBa8FALLurBQy14qAUPxhaaRHswMDCQ9BAkpBY81IKLevBQCx5qwYOxhT5Y6IGZJT0ECakFD7Xgslx6VMM3vy2XFtVALXgwttCaaA/a29u93t6R0ZPYee/T+JU7foSd9z6NI6Mnvd4+M98tZHFqwcVnj1Jeo6rhm9/03OChFjwYW2gS7YHvP0FUw5tMsRj/HFSt1IKLzx6lvEZVwze/6bnBQy14MLbQ5hwetLS0eL29aniTKZbvFrI4teDis0cpr1HbupvwaHr8tMMrjZ4bpyS9+U52i6THUu0YnxdaE+3B3Nyc19tj/nrRqD/j+tz8xHeLpCyHTXqqpcVy4bNHKa9R1fDNb3punJL0X1azWyQ9lmrH+LzQmmgPJiYm0NnZWZHrjvrNeNflZ+ENH/9bNG44Hxe9eC3Vm0zmRQg4/UUo97hbr3xJRW6/ki2YRN3PlbpPi1UtLaJEPW8BJLKW6/SxDHu53VJeo3x881spaxzLsbaymOdGUmtJK327Sf9lNbtF0mNZykpfU874nmHOuaTHkKgf/OAHbuvWrRW9jZMnT2L16ngP5LkXXPDjHF7c3QUAeOb5o4ue/48fOIQnjk7MH952RiNuvmITNr7ojCUvm4Rf/9KjmMt6yNWEH7bNPe4rb76gIrc/Mz2NulSqItfNJOp+rtR9WqzsFv1j0/jzHxzG/oEJbOlqxO/8/Hp0N5/q5PNVKjOWpwYmcG7EWMrhjx84hCeznrfnndEIAAuOu/mKTWW93bhj+Z2fX1/x++Ds8DXqv4p4jSr2snHbRt0ncVuUctmMYl6nynG7xaj07Sa1XBnZLUody2KP23LNw/7km4fw5NETWeNbgw/9sr/7qtJqMYemhnrvt9vb27t3x44dl0adpjXRHqTTaWzYsGH+8NwL7rQnYsYLziHqufRfQ5OLXvdPBiYWHM4+f77LJmFDWz0ODU2ddhjAguMqNe6TQ2msbu+pyHWX2+CJGXx1Xz+eGZ7CxrZ6XH1hNzrW1MW6bNT9/J+HR4u+vkrIbnH7I4fnx/vE0QncuqcP7375+ljXU8r9FCV7LE8encBtBYwlrv05z9vcw5njnvHw/I0ay217+ip+H2SUsoyFXjZu26j7JO5tlXLZUh7LpdxuKcp5u1HL//ptnfjhIw+jccP52NzVhNdv6yxpuQq9j7Nfp8o1lnJ0iVqO/QMnTjvP/oET6BueWuQayne7vt5H6iePYcs5Z3u5rbgoJ9FmVgPg4wDeBqAewAMArnPOHVvk/K8FcCuATQAOAvh959wDfka7tLq6hQ+wuRfK85vnxpzJ0sY2/7+lFeLqC7vxwTv/Zf5F6OoLg28gijquEqympuDLlPtFI+71fXVf/3zbQ0NT+Oq+flx9YfeCy2bOm31c1P0cdX3lnhgVcl9lt3gm54U+93A+cZcr7thKGUtciz1vk3guR43Fx32QhLjLVcrraimXLeU5WsrtlvIaV873oMWWP7P5zq2P9RV0fVHLVeh9nP061bGmLtZYfEw0o5bDx3wg7vtS7vKW4z6preWbsrJ+sPD9AF4P4OcAZB7dX4g6o5ltAnAPgI8BaA3//ZqZbaz4KGNqbW2t2HVffWH3/IdsNrXXV3QCWg6ZF6HeP3wN3v3y9ehYUxd5XKXUNOT/ANPgiRnc/shhvO++A7j9kcPzT/xDQ1N4wZ160Yg6X1xR1xcl6g0/6rJRx0Xdp+WeGMW9rxa73I0Pj8xfLvfFfrEX/6jbjFquYscWdduVeCOKet6W8lwu5fEYdbul9GARNba4y1VKi6jLxr2fSnmOlnK7cZ8bcW83l4/ljxK1XHFfLzKWes+Ie7vlFrUccR+3pTxv474v5SrH+2hjM9/eOSi3iTazPgA3O+fuCA+fA+AAgLOdc8/knPdDAF7tnLss67g9AB50zn1oqdu6+18ecp/Zz/ctOCIiIiKSrI9vd4tuE023JtrMWgGcBWBv5jjn3EEAowAujLjIRdnnDfWGx4uIiIiIlB3fBiZAZn39SM7xx7NOy9a8yHnPj3Njm9pS+OwvnfqTXnt7e9m/WvLo0aM444wzTrsNAPh21jZVr/qZDQuOixL3fHEuO3hiZsF2s5/4bh+yN9deZcCf/crmBZfN/oAOAGxqD7ajzL3sD9+7o6jxxr1+AAuOy90ubFP7wu1NF7u+qMtGbe+VvV1Y5nzvfvn6WPdT1PUtdr/nitquDFi4TXTcTWLed9+BWPdBKffVVS/bvOR9stg2zPm268532bj3e9zrAxY+f+I+RqOeP3Etdl8VO5aoZpnlnRkfRl1TW0FjARY+r3KPq681TM26084T9z5e7Lg4l437OIt7G1HPlbhtF3s85j6+M8/b7BZxn6NxnwdRj5/FXlfivj8sdZvA0o+TfH1yxX1tiLOtbiFjjvv4KWT737iP+WJfQwp5/BT7HMpd3qjXnj945YZYr79Rz4vcx12NAfe9/eKC7odS9Pb2Lnoa4yR6LPw3d0PitQjWRkedP+55F6itrcXmzQsnK+U0Ocm1h4yMr+7rR/OmYIV9oR9MiNouKuqyPyxybHGvPzP27OPibFe32PUt9qKb+8Kx2Af84ixH1PXFvd+jLgug6A8Ixv1QWSn3Va64913U4zPuZeOOrZRtMAt5jBZrse03t7zrNjRuOB+3P3J4fnvqpd6glnpuvDCd/wt5oi4b9cb4ie+e/iafPYGOup58opY17i+IcR8rcW+jlLaL9cl9fGcen9ktSvngZ9z7IOq5cfsjh4t6fyj2cVLoh4lzxxb3tTbKYis24oxvscdPpfZmU6i4r91RyxH3cZa7vLmT741t9bHfR+PMS5i+QI5uEu2cO25mzwLYDuD/AfMfHmwBsC/iIj8G8Kqc4y4G8FAlx1mInh7OXarFfbGLEveJ+dUixxb3+oGFa2GjJg/Awsl2sZNAIPpNJ+pFKO4bb9w3u3KLO/kq9r7K9wbzwuwMVtUuPiEq5U0x7vnKvReGuG9Opez5YLHJV9w3qMWWt66lo+CxxPmFMHdNdKF7q1hsormUuI+BuLdRynO00F/gslsU2nGp2437eCz2/aHYx0khj4ty/wJbyvhKeYz6EPe1u9hfmuLeZpS4z4vM465veOq0L6diwPrBwg8CuAbAawEMArgDQLNz7rUR5z0HwKMA3g7gHwBcBeBzAM7P/RBiFB9fttLX13fafqIzm3Nk73A9sxP2p55LY+4FYM45zL7g5r9lwoX/uficFwMAfnTwp6fdRpyK28PL9oaX/eR3+/D0sVNryV/S2YD3vnJDrMsOjE/jrr1HcHBwEud0NOBtl6xDV9PCLwfIvVxcca8/7mUBFH19cUXdn2+7ZF3Fb7fcSrnvc+V7jI0cfR6tZ7yoqMuWS9xlHRifxu/e/jU0bjgfW7qbC3pMlbIcUeO78YFDC/5U+hdviPcalm95l+pRyH2Vfb7XndeJf3nyWKzHU+7rxbvu+cmCZb39Vxcu68DENG7I6nPtJevQ1RjvMfvur8W7jcVcsjkY894Dhb3G3bJ74eNi1+XB42L06PNoyddiYhp3Z93HhSxvvtst5nzFjq3cy3DtJeuKvr5SlqMSj5+4x5VT1HLcfMWmst6nceR73M0d78fmTRsrevtRluOXrXwcQBuA/wCwGsA3AbwFAMzszQD+0jnXBAQfOjSzNyDYT/SdAA4B+NU4E2hfUot881Rr/cK7vzPmA3RDW0PR49kYXvaPXn12wV8RmrnsxrYGvPTM+Lvu21jgeAu9/jiXfemZrThy5AjWrVtX1PUu5dDg5ILDLz2ztejlSEop932uqPsk81g4MrUG6/I8Lop5fBYq7rJ++vun1tI8fWwSf/fjo7j1ypfEumy++6CY8Z3f3TT/le6Zw6VcX8ZSPeLeV1Hne82Wwr6qN7M8Uct6dvvCMX7m4dP7fCXsE0fc24hyZPTk/Frdzzx8uKDHaL7H95GTa7AuzxjObm/Ay4p8jkY9HqOWt9jnX9yxlbIMi42t2OvLN76l3jMq+fiJup64112oqOV42ZmtZb1P48j/vOD7CnPKSbRzbg7ArvAn97QvAfhSznHfAPANP6MrXHNzc9JDiLSuZXWsN5rsJ/rOe5+uyGTGl0q22JbzIsS03VZS8t0nS7WI+/j04Yn+8byH8yn342LX5WcteJMph6Rfp6JeZ+Iuayl9Srk/b9n97Pzk/dH0OG7Z/Wzsx2y+xzfD6xTT8y+Xz7Et1cLH48fHe3ClXlcKldTzoliUk+iVZnBwEE1NwQvVcpyQFvtGwbis2S3KjeVFiEm++6SSLcqtlIlwuR8XlZpAJN1jsdeZOMtaSp9S7s9SJu/56HWKx1ItfDx+SvllLS7mX5oykn6NiqJJtAdtbad2G+XjyVBuxb5RMC5rdotyWw4vQr7lu08q2aLcSpl4LJfHRdI9klqbXIpK/fVJr1M8Ktki6vETtfKpUr+sLTdJv0ZF0STag8nJSbS0BLu4Xo5PhmLfKBiXNbuFJGs5taiGiUfSPZJam1yKSk3ek24hp1SyRdTjJ2rlkzYVDDA+LzSJ9mBq6vT9Gy63J0OxbxSMy5rdQpKlFlyS7rEcNzOo1OQ96RZySiVbRD1+olY+3fnGbcvuuVEJjM8LTaI9yN5PdDW9UTAuK+s+u6uRWnBJukc1rO2PK+kWcorvFlErn/TcCDA+LzSJ9iCdTs/vJ7qangyMy5rdQpKlFlzUg4da8PDdgnHlEwvG54Um0R7U15f21b9SPmrBQy24qAcPteDhuwXjyicWjM+LVUkPoBo0NFRm5+hSOLXgoRZc1IOHWvBQCx6MLTSJ9mB4eDjpIUhILXioBRf14KEWPNSCB2MLTaI96OjoSHoI8/ue3P6x+7Hz3qdxZPRk0kNKBEMLCagFF/XgoRY81IIHYwtNoj0YXw4s/QAAHA5JREFUGxtLegjz+55cVVM7v+/JasTQQgJqwUU9eKgFD7XgwdhCk2gPpqenkx4C5RefJIGhhQTUgot68FALHmrBg7GFJtEeMOzbMPeLThi++CQJDC0koBZc1IOHWvBQCx6MLTSJ9iCdTic9BOy6/Cxc0NOEGgMu6KnefU8ytJCAWnBRDx5qwUMteDC20H6iPSjXblkyHw5s3HA+dt77NHZdfhbWtayOdVntezLAuIucaqUWXNSDh1rwUAsejC20JtqDVCpVluvRhwNLV64WUjq14KIePNSCh1rwYGyhSbQHIyMjZbkefTiwdOVqIaVTCy7qwUMteKgFD8YWmkR70NnZWZbr0YcDS1euFlI6teCiHjzUgoda8GBsoUm0B+X67UkfDiwd42+y1UotuKgHD7XgoRY8GFvog4UezMzMlOV69OHA0pWrhZROLbioBw+14KEWPBhbaE20B4z7NqxWasFDLbioBw+14KEWPBhbaBLtAeO+DauVWvBQCy7qwUMteKgFD8YWmkR70NjYmPQQJKQWPNSCi3rwUAseasGDsYUm0R7U1NQkPQQJqQUPteCiHjzUgoda8GBsoUm0B6Ojo0kPQUJqwUMtuKgHD7XgoRY8GFtoEu1BV1dX0kOQkFrwUAsu6sFDLXioBQ/GFppEezA0NJT0ECSkFjzUgot68FALHmrBg7GFJtEeOOeSHoKE1IKHWnBRDx5qwUMteDC20CTaA8Y/QVQrteChFlzUg4da8FALHowtNIn2oL+/P+khSEgteKgFF/XgoRY81IIHYwtNoj1oampKeggSUgseasFFPXioBQ+14MHYQpNoEREREZECaRLtwfj4eNJDkJBa8FALLurBQy14qAUPxhaaRHvQ3d2d9BAkpBY81IKLevBQCx5qwYOxhSbRHgwMDCQ9BAmpBQ+14KIePNSCh1rwYGyhSbQHZpb0ECSkFjzUgot68FALHmrBg7GFJtEetLe3Jz0ECakFD7Xgoh481IKHWvBgbKFJtAeMf4KoVmrBQy24qAcPteChFjwYW2gS7UFLS0vSQ5CQWvBQCy7qwUMteKgFD8YWmkR7MDc3l/QQJKQWPNSCi3rwUAseasGDsYUm0R5MTEwkPQQJqQUPteCiHjzUgoda8GBsoUm0Bz09PUkPQUJqwUMtuKgHD7XgoRY8GFtQTaLNbI2Z3Wlmw2Z23MzuMLOGPOe/yMzuM7MjZubM7BU+xxtXOp1OeggSUgseasFFPXioBQ+14MHYgmoSDeBTALaGP+cCOA/AbXnOPw3gHgCvq/zQildXV5f0ECSkFjzUgot68FALHmrBg7FFbdIDyAjXOL8FwJXOuf7wuBsB/KuZvcc5N5V7GefckwCeDM9b1O3Ozs7iwIED84fb29vLvi/C1tbWsl6fFE8teKgFF/XgoRY81IIHYwuaSTSALQDqAezNOq4XQAOCtdL7KnGjR48exVVXXTV/+LrrrsNNN92EdDqNxsZG1NTUYHR0FF1dXRgaGoJzDl1dXejv70dTUxMAYHx8HN3d3RgYGICZob29HQMDA2hpacHc3Byef/55bNmyBel0GnV1dWhtbcWxY8fQ2tqK6elpTE5Ozt/+kSNH0NzcjMHBQbS1tWFychJTU1Po6elBOp1GfX09GhoaMDw8jI6ODoyNjWF6enr+9IaGBqRSKYyMjKCzsxMjIyOYmZmZP71cyzQxMTF/nYstU+b0VCpFs0yzs7NIpVIrapmWa6dDhw5h7dq1K2qZlnOnubk51NTUrKhlWq6dDh48iJ6enhW1TMu108TEBNavX7+ilmm5dhofH0cqlfK+TPmYc66oyWchzOwuANfmOctHANwPYDeAVS4clJmtAjAH4DLn3PeWuA0X53y59uzZ4+rr6+cPV2JN9PHjx7F27dq858nc5tDQUFlvW04Xp4X4oRZc1IOHWvBQCx5Jtejt7d27Y8eOS6NO87Um+noAu/KcfgLB2mYAaAVwPOv/ADBaoXGhtrYWmzdvrtTVAwCmp6crev0Sn1rwUAsu6sFDLXioBQ/GFl4m0c65cQDj+c5jZvsBTAHYDuBb4dEXA5gE8FRFB1hh2ZtrSLLUgodacFEPHmrBQy14MLag2SbaOTdpZl8EcLOZPRYefTOAz0d9qBAALPg04eqso1JmVg9gxjlH89U2jPs2rFZqwUMtuKgHD7XgoRY8GFuw7eLuBgRrnTM/+wG8J3OimX3AzB7POv8GBGuqM7+ePBT+/61eRhsT474Nq5Va8FALLurBQy14qAUPxhY0a6IBwDl3AsBvhT9Rp38UwEezDj8DoLh923mUSqWSHoKE1IKHWnBRDx5qwUMteDC2YFsTvSI1NzcnPQQJqQUPteCiHjzUgoda8GBsoUm0B4ODg0kPQUJqwUMtuKgHD7XgoRY8GFtoEu1BW1tb0kOQkFrwUAsu6sFDLXioBQ/GFppEe8C4W5ZqpRY81IKLevBQCx5qwYOxhSbRHkxNRe6hTxKgFjzUgot68FALHmrBg7GFJtEeMO7bsFqpBQ+14KIePNSCh1rwYGyhSbQHjPs2rFZqwUMtuKgHD7XgoRY8GFtoEu1BfX190kOQkFrwUAsu6sFDLXioBQ/GFppEe9DQ0JD0ECSkFjzUgot68FALHmrBg7GFJtEeDA8PJz0ECakFD7Xgoh481IKHWvBgbKFJtAcdHR1JD0FCasFDLbioBw+14KEWPBhbaBLtwdjYWNJDkJBa8FALLurBQy14qAUPxhaaRHswPT2d9BAkpBY81IKLevBQCx5qwYOxhSbRHjDu27BaqQUPteCiHjzUgoda8GBsoUm0B4z7NqxWasFDLbioBw+14KEWPBhbaBLtAeNuWaqVWvBQCy7qwUMteKgFD8YWmkR7kEqlkh6ChNSCh1pwUQ8easFDLXgwttAk2oORkZGkhyAhteChFlzUg4da8FALHowtNIn2oLOzM+khSEgteKgFF/XgoRY81IIHYwtNoj1g/O2pWqkFD7Xgoh481IKHWvBgbKFJtAczMzNJD0FCasFDLbioBw+14KEWPBhbaBLtAeO+DauVWvBQCy7qwUMteKgFD8YWmkR7wLhvw2qlFjzUgot68FALHmrBg7GFJtEeNDY2Jj0ECakFD7Xgoh481IKHWvBgbKFJtAc1NTVJD0FCasFDLbioBw+14KEWPBhbaBLtwejoaNJDkJBa8FALLurBQy14qAUPxhaaRHvQ1dWV9BAkpBY81IKLevBQCx5qwYOxhSbRHgwNDSU9BAmpBQ+14KIePNSCh1rwYGyhSbQHzrmkhyAhteChFlzUg4da8FALHowtNIn2gPFPENVKLXioBRf14KEWPNSCB2MLTaI96O/vT3oIElILHmrBRT14qAUPteDB2EKTaA+ampqSHoKE1IKHWnBRDx5qwUMteDC20CRaRERERKRAmkR7MD4+nvQQJKQWPNSCi3rwUAseasGDsYUm0R50d3cnPQQJqQUPteCiHjzUgoda8GBsoUm0BwMDA0kPQUJqwUMtuKgHD7XgoRY8GFtoEu2BmSU9BAmpBQ+14KIePNSCh1rwYGyhSbQH7e3tSQ9BQmrBQy24qAcPteChFjwYW2gS7QHjnyCqlVrwUAsu6sFDLXioBQ/GFppEe9DS0pL0ECSkFjzUgot68FALHmrBg7GFJtEezM3NJT0ECakFD7Xgoh481IKHWvBgbEE1iTazNWZ2p5kNm9lxM7vDzBrynP8aM3s4PP8xM7vPzC7wOeY4JiYmkh6ChNSCh1pwUQ8easFDLXgwtqCaRAP4FICt4c+5AM4DcFue8zcD+BMA6wG8GEAvgAfyTbyT0NPTk/QQJKQWPNSCi3rwUAseasGDsQXNJDqc+L4FwI3OuX7n3FEANwK41szqoy7jnPtz59w3nXMTzrmTAD4KoAfBJJxGOp1OeggSUgseasFFPXioBQ+14MHYojbpAWTZAqAewN6s43oBNCBYK70vxnXsAHACwIG4Nzo7O4sDB06dvb29vey7Uamrqyvr9Unx1IKHWnBRDx5qwUMteDC28DKJNrO7AFyb5ywfAXB/+P+RrOMz/1/yI5lmdi6AzwHY6Zwbizu2o0eP4qqrrpo/fN111+Gmm25COp1GY2MjampqMDo6iq6uLgwNDcE5h66uLvT396OpqQlA8H3u3d3dGBgYgJmhvb0dAwMDaGlpwdzcHE6cOIGTJ08inU6jrq4Ora2tOHbsGFpbWzE9PY3Jycn52z9y5Aiam5sxODiItrY2TE5OYmpqCj09PUin06ivr0dDQwOGh4fR0dGBsbExTE9Pz5/e0NCAVCqFkZERdHZ2YmRkBDMzM/Onl2uZJiYm5q9zsWXKnJ5KpWiWqbm5Gc8999yKWqbl2mlqagrPPffcilqm5dyptbUVfX19K2qZlmunEydO4OjRoytqmZZrJzPD6Ojoilqm5dqprq4OfX193pcp79zTORd3vlk0M2tCsJZ5MScQrG3+EYA259zx8HJtAIYAXOScW3RNtJltA/BNAJ9yzn2ikLHt2bPH1defGlol1kT39fVhw4YNec+Tuc2hoaGy3racLk4L8UMtuKgHD7XgoRY8kmrR29u7d8eOHZdGneZlTbRzbhzAeL7zmNl+AFMAtgP4Vnj0xQAmATyV53LbAXwDwJ865z5d6Nhqa2uxefPmQi9WkNbW1opev8SnFjzUgot68FALHmrBg7EFzTbRzrlJM/sigJvN7LHw6JsBfN45NxV1GTP7RQD3Anifc+6vPA21YNPT00kPQUJqwUMtuKgHD7XgoRY8GFvQ7J0jdAOCtc6Zn/0A3pM50cw+YGaPZ53/wwBaAdxmZuNZP5f5HPRSsrd5lmSpBQ+14KIePNSCh1rwYGxBsyYaAJxzJwD8VvgTdfpHEezGLnP4VZ6GVhLGfRtWK7XgoRZc1IOHWvBQCx6MLdjWRK9IjPs2rFZqwUMtuKgHD7XgoRY8GFtoEu1BKpVKeggSUgseasFFPXioBQ+14MHYQpNoD5qbm5MegoTUgodacFEPHmrBQy14MLbQJNqDwcHBpIcgIbXgoRZc1IOHWvBQCx6MLTSJ9qCtrS3pIUhILXioBRf14KEWPNSCB2MLTaI9YNwtS7VSCx5qwUU9eKgFD7XgwdhCk2gPpqYivytGEqAWPNSCi3rwUAseasGDsYUm0R4w7tuwWqkFD7Xgoh481IKHWvBgbKFJtAeM+zasVmrBQy24qAcPteChFjwYW2gS7UF9fX3SQ5CQWvBQCy7qwUMteKgFD8YWmkR70NDQkPQQJKQWPNSCi3rwUAseasGDsYUm0R4MDw8nPQQJqQUPteCiHjzUgoda8GBsoUm0Bx0dHUkPQUJqwUMtuKgHD7XgoRY8GFtoEu3B2NhY0kOQkFrwUAsu6sFDLXioBQ/GFppEezA9PZ30ECSkFjzUgot68FALHmrBg7GFJtEeMO7bsFqpBQ+14KIePNSCh1rwYGyhSbQHjPs2rFZqwUMtuKgHD7XgoRY8GFtoEu0B425ZqpVa8FALLurBQy14qAUPxhaaRHuQSqWSHoKE1IKHWnBRDx5qwUMteDC20CTag5GRkaSHICG14KEWXNSDh1rwUAsejC00ifags7Mz6SFISC14qAUX9eChFjzUggdjC02iPWD87alaqQUPteCiHjzUgoda8GBsoUm0BzMzM0kPQUJqwUMtuKgHD7XgoRY8GFtoEu0B474Nq5Va8FALLurBQy14qAUPxhaaRHvAuG/DaqUWPNSCi3rwUAseasGDsYUm0R40NjYmPQQJqQUPteCiHjzUgoda8GBsoUm0BzU1NUkPQUJqwUMtuKgHD7XgoRY8GFtoEu3B6Oho0kOQkFrwUAsu6sFDLXioBQ/GFppEe9DV1ZX0ECSkFjzUgot68FALHmrBg7GFJtEeDA0NJT0ECakFD7Xgoh481IKHWvBgbKFJtAfOuaSHICG14KEWXNSDh1rwUAsejC00ifaA8U8Q1UoteKgFF/XgoRY81IIHYwtNoj3o7+9PeggSUgseasFFPXioBQ+14MHYQpNoD5qampIegoTUgodacFEPHmrBQy14MLbQJFpEREREpECaRHswPj6e9BAkpBY81IKLevBQCx5qwYOxhSbRHnR3dyc9BAmpBQ+14KIePNSCh1rwYGyhSbQHAwMDSQ9BQmrBQy24qAcPteChFjwYW2gS7YGZ5T39yOhJbHnXbdj+sfux896ncWT0pKeRVZ+lWog/asFFPXioBQ+14MHYQpNoD9rb2/OefsvuZ9G86SKsqqnFo+lx3LL7WU8jqz5LtRB/1IKLevBQCx5qwYOxhSbRHiz1J4gn+sfzHpbyYfxzULVSCy7qwUMteKgFD8YWmkR70NLSkvf0bd1NeQ9L+SzVQvxRCy7qwUMteKgFD8YWVJNoM1tjZnea2bCZHTezO8ysIc/532hmj4XnHzaz75nZK32OOY65ubm8p++6/Cxc0NOEGgMu6GnCrsvP8jSy6rNUC/FHLbioBw+14KEWPBhbUE2iAXwKwNbw51wA5wG4Lc/5HwHwy865NgAdAP4vgH8zs7WVHmghJiYm8p6+rmU1br3yJbjv7Rfj1itfgnUtqz2NrPos1UL8UQsu6sFDLXioBQ/GFrVJDyAjXOP8FgBXOuf6w+NuBPCvZvYe59xU7mWcc89lXwWAOQBrAJwJ4Hic252dncWBAwfmD7e3t5d94/Wenp6yXp8UTy14qAUX9eChFjzUggdjC5pJNIAtAOoB7M06rhdAA4K10vuiLmRmZ4WnNSNYs/4V59yjcW/06NGjuOqqq+YPX3fddbjpppuQTqfR2NiImpoajI6OoqurC0NDQ3DOoaurC/39/fPf4z4+Po7u7m4MDAzAzNDe3o6BgQG0tLRgbm4Ozz//PLZs2YJ0Oo26ujq0trbi2LFjaG1txfT0NCYnJ9HT04N0Oo1UKoXm5mYMDg6ira0Nk5OTmJqamj+9vr4eDQ0NGB4eRkdHB8bGxjA9PT1/ekNDA1KpFEZGRtDZ2YmRkRHMzMzMn16uZZqYmJi/zuW0TLOzs0ilUitqmZZrp4MHD2Lt2rUrapmWc6e5uTnU1NSsqGVarp3279+Pnp6eFbVMy7XTxMQE1q9fv6KWabl2Gh8fRyqV8r5M+ZhzLu58s2hmdheAa/Oc5SMA7gewG8AqFw7KzFYhWLt8mXPue0vcRiOANwJY7Zz7y7hj27Nnj6uvr58/XO410UNDQ7j11luxc+dOyt2zVBO14KEWXNSDh1rwUAseSbbo7e3du2PHjkujTvO1TfT1ALry/HwUwFh43tasy2X+P7rUDTjnJpxzdwG4wcxeE3dgtbW12Lx58/xPueMMDQ3h9ttvx9DQUFmvVwqnFjzUgot68FALHmrBg7WFl805nHPjAPLu/NjM9gOYArAdwLfCoy8GMAngqQJurhbASxCs2RYRERERKTsvm3PEZWZ/jWCPHG8Ij7oHwGPOuXctcv5rADwM4BCARgDvAfABANudc0/Euc2HHnpoAEBfiUNf1OzsbM3Q0FB3e3t7f21tLd/+WaqIWvBQCy7qwUMteKgFj4RbbNixY0fkxtFsk+g1AD6DU5PofwRwvXNuMjz9AwDe7Jw7Pzz8YQBvBdAJ4ASCDxh+1Dn3kO+xi4iIiEj1oJpEi4iIiIgsB2xftiIiIiIiQk+TaBERERGRAmkSLSIiIiJSIE2iRUREREQKpEm0iIiIiEiBNIkWERERESmQJtEiIiIiIgXSJFpEREREpECaRFeQmdWY2SfNbMDMxszsH82sM+lxrXRm9mdm9riZjZrZ82b212bWnnOea8zsoJmdMLN/N7NLkhpvtTCzVWb2sJk5M1ufdbxaeGRm/83MHjGzcTM7ZmafzTpNLTwysx4z+0r4HjFsZt8ys4uyTlePCjCzN5nZnvA9Yjbi9NeG7yGTZvaYmV2Rc/pmM3vQzCbM7LCZ7fQ3+pUlXwsz++/hc+JY+PzYY2aX5Zwn0RaaRFfW+wG8HsDPAchMGr6Q3HCqxhyAtwDoAHARgvv+bzInmtkrANwO4N0A2hB8vfy/mVmL/6FWlfcAOJF9hFr4ZWa/BOAfANyC4PmxHsDnwtPUwr/PAmgHsAVAN4D/BHCvBdSjcoYR3Pe/l3uCmW0CcA+AjwFoDf/9mpltDE+vAfCvAJ4E0AXgdQDeZ2a/7mPgK9CiLRA87j8NYDOC+/rLAO4zszMBjhb62u8KMrM+ADc75+4ID58D4ACAs51zzyQ5tmpiZv8DwJedc63h4bsBrHLOvTU8bAD6ANzonLs7uZGuXGZ2LoD7APwagB8BONM5d1gt/DKzHwD4rnPu/RGnqYVnZrYPwGecc38VHt4C4CcIJgS3Qj0qKvyl8kHnXG3WcR8C8Grn3GVZx+0Jz/chM3sVgK8DOMM5Nx6e/qcAXuGce5XXBVhBoloscr4BAO90zn2NoYXWRFeImbUCOAvA3sxxzrmDAEYBXJjUuKrUDgD7sg5fhNO7OAQTu4sgZWdmqwDcCeC9AI7nnKwWnphZI4CXAZgys97wT6TfMbNLw7OohX+fBPBrZtZpZvUA3gnge865Y1CPpJx2v4d6cep+vwjAU5lJW8TpUiFmdiGCv6A9Fh6VeAtNoisn8ye3kZzjj2edJhVmZr8G4B0Absg6uhnq4tMNANLOuXsiTlMLf9oQvOa/A8DbALwIwAMINhFYC7VIwvcB1AAYADAO4A0I+gDqkZSl7nd1SYCZnYFgU7RPOOeeDo9OvIUm0ZUzFv7bmnP8WgRro6XCzOyNAP4awOucc71ZJ41BXbwws80AdgK4fpGzqIU/mdekv3HO7XPOTSPY3rMOwC9ALbwK/0LzIICnENzvawB8BMAeM+uGeiRlqftdXTwzsxcB+DaCX/r/MOukxFtoEl0hzrnjAJ4FsD1zXPiBhRacvmmBVICZ/S8Afwngfzrnvp1z8o9xehcD8LPh8VJer0CwfedjZnYMwZ/aAGCfmf021MIb59wIgGcARH0QxkEtfGsHcDaATzvnRp1z0865zyF4X3451CMpp93voYtx6n7/MYBzw82jok6XMgo/0LkHwH3Ouevd6R/kS76Fc04/FfoB8EEA+xG8ULYA+HsA30h6XCv9B8DvAhgE8NJFTn8Fgj+d7gCQArALQD+AlqTHvtJ+EKxdW5/183IEE7ZLATSphfce7wVwGMA2ALUA/gDAEQRrc9TCf4/9CPY+0Bj2+C0A0wA2qUdF7/caAPUArgAwG/6/HoABOAfBXoR+A8FfaX4DwASAjVmXfRLApwA0IPjFph/Am5JeruX4s0SLreHr1YfzXDbRFonfgSv5Jwx8C4BjCP7scA+AzqTHtdJ/wknaTPgGNP+Tc55rABwCMAnghwAuSXrc1fADYGPYZ71aJHL/G4CbAaQRbDv4bQA/qxaJ9TgPwL3he8QIgg+0vV49Kn6/vy18Hcr92Rie/loAj4f3++MArsi5/GYAD4WT7ecB7Ep6mZbrT74WCHZN63LfywG8maWFdnEnIiIiIlIgbRMtIiIiIlIgTaJFRERERAqkSbSIiIiISIE0iRYRERERKZAm0SIiIiIiBdIkWkRERESkQJpEi4iIiIgUSJNoEREREZEC/X/S/nVk7rD5dQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorr(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>sample size</th>\n",
       "      <th>jb_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>1.302361e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>3.277633e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample size       jb_stat\n",
       "dvBar      14161.0  1.302361e+08\n",
       "dvImb      60264.0  3.277633e+11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>sample size</th>\n",
       "      <th>shapiro_stat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dvBar</th>\n",
       "      <td>14161.0</td>\n",
       "      <td>0.655457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>60264.0</td>\n",
       "      <td>0.282896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample size  shapiro_stat\n",
       "dvBar      14161.0      0.655457\n",
       "dvImb      60264.0      0.282896"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jbs = get_test_stats(bar_types,bar_returns,jb).sort_values('jb_stat')\n",
    "shaps = (get_test_stats(bar_types,bar_returns,shapiro)\n",
    "         .sort_values('shapiro_stat')[::-1])\n",
    "\n",
    "display(jbs,shaps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": 2
}