{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Chapter-1---Exploring-Tick,-Volume,-DV-Bars\" data-toc-modified-id=\"Chapter-1---Exploring-Tick,-Volume,-DV-Bars-1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Chapter 1 - Exploring Tick, Volume, DV Bars</a></span><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Read-and-Clean-Data\" data-toc-modified-id=\"Read-and-Clean-Data-1.2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Read and Clean Data</a></span></li><li><span><a href=\"#Remove-Obvious-Price-Errors-in-Tick-Data\" data-toc-modified-id=\"Remove-Obvious-Price-Errors-in-Tick-Data-1.3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Remove Obvious Price Errors in Tick Data</a></span></li></ul></li><li><span><a href=\"#Tick-Bars\" data-toc-modified-id=\"Tick-Bars-2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Tick Bars</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Bonus-Exercise:-Make-OHLC-Bars-from-Custom-Bars\" data-toc-modified-id=\"Bonus-Exercise:-Make-OHLC-Bars-from-Custom-Bars-2.0.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">2.0.1&nbsp;&nbsp;</span>Bonus Exercise: Make OHLC Bars from Custom Bars</a></span></li></ul></li></ul></li><li><span><a href=\"#Volume-Bars\" data-toc-modified-id=\"Volume-Bars-3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Volume Bars</a></span></li><li><span><a href=\"#Dollar-Value-Bars\" data-toc-modified-id=\"Dollar-Value-Bars-4\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Dollar Value Bars</a></span></li><li><span><a href=\"#Analyzing-the-Bars\" data-toc-modified-id=\"Analyzing-the-Bars-5\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Analyzing the Bars</a></span><ul class=\"toc-item\"><li><span><a href=\"#Count-Quantity-of-Bars-By-Each-Bar-Type-(Weekly)\" data-toc-modified-id=\"Count-Quantity-of-Bars-By-Each-Bar-Type-(Weekly)-5.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Count Quantity of Bars By Each Bar Type (Weekly)</a></span></li><li><span><a href=\"#Which-Bar-Type-Has-Most-Stable-Counts?\" data-toc-modified-id=\"Which-Bar-Type-Has-Most-Stable-Counts?-5.2\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Which Bar Type Has Most Stable Counts?</a></span></li><li><span><a href=\"#Which-Bar-Type-Has-the-Lowest-Serial-Correlation?\" data-toc-modified-id=\"Which-Bar-Type-Has-the-Lowest-Serial-Correlation?-5.3\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Which Bar Type Has the Lowest Serial Correlation?</a></span></li><li><span><a href=\"#Partition-Bar-Series-into-Monthly,-Compute-Variance-of-Returns,-and-Variance-of-Variance\" data-toc-modified-id=\"Partition-Bar-Series-into-Monthly,-Compute-Variance-of-Returns,-and-Variance-of-Variance-5.4\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Partition Bar Series into Monthly, Compute Variance of Returns, and Variance of Variance</a></span></li><li><span><a href=\"#Compute-Jarque-Bera-Test,-Which-Has-Lowest-Test-Statistic?\" data-toc-modified-id=\"Compute-Jarque-Bera-Test,-Which-Has-Lowest-Test-Statistic?-5.5\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>Compute Jarque-Bera Test, Which Has Lowest Test Statistic?</a></span></li><li><span><a href=\"#Compute-Shapiro-Wilk-Test\" data-toc-modified-id=\"Compute-Shapiro-Wilk-Test-5.6\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">5.6&nbsp;&nbsp;</span>Compute Shapiro-Wilk Test</a></span></li></ul></li><li><span><a href=\"#Compare-Serial-Correlation-between-Dollar-and-Dollar-Imbalance-Bars\" data-toc-modified-id=\"Compare-Serial-Correlation-between-Dollar-and-Dollar-Imbalance-Bars-6\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Compare Serial Correlation between Dollar and Dollar Imbalance Bars</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Update-[05.04.18]\" data-toc-modified-id=\"Update-[05.04.18]-6.0.1\" data-vivaldi-spatnav-clickable=\"1\"><span class=\"toc-item-num\">6.0.1&nbsp;&nbsp;</span>Update [05.04.18]</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Advances in Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 1 - Exploring Tick, Volume, DV Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:06.523447Z",
     "start_time": "2018-05-05T00:36:02.825808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-05-04T18:36:02-06:00\n",
      "\n",
      "CPython 3.6.4\n",
      "IPython 6.2.1\n",
      "\n",
      "compiler   : GCC 4.8.2 20140120 (Red Hat 4.8.2-15)\n",
      "system     : Linux\n",
      "release    : 4.13.0-39-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 12\n",
      "interpreter: 64bit\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/media/bcr/HDD/anaconda3/envs/bayes_dash/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n",
      "/media/bcr/HDD/anaconda3/envs/bayes_dash/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pandas 0.22.0\n",
      "pandas_datareader 0.6.0\n",
      "dask 0.17.0\n",
      "numpy 1.14.0\n",
      "pymc3 3.3\n",
      "theano 1.0.1\n",
      "sklearn 0.19.1\n",
      "statsmodels 0.8.0\n",
      "scipy 1.0.0\n",
      "matplotlib 2.1.2\n",
      "seaborn 0.8.1\n",
      "pyarrow 0.8.0\n",
      "fastparquet 0.1.5\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# import standard libs\n",
    "from IPython.display import display\n",
    "from IPython.core.debugger import set_trace as bp\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",
    "import json\n",
    "os.environ['THEANO_FLAGS'] = 'device=cpu,floatX=float32'\n",
    "\n",
    "# import python scientific stack\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "pd.set_option('display.max_rows', 100)\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",
    "import pymc3 as pm\n",
    "from theano import shared, theano as tt\n",
    "\n",
    "# import visual tools\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",
    "\n",
    "#plt.rcParams['font.family'] = 'DejaVu Sans Mono'\n",
    "#plt.rcParams['font.size'] = 9.5\n",
    "plt.rcParams['font.weight'] = 'medium'\n",
    "#plt.rcParams['figure.figsize'] = 10,7\n",
    "blue, green, red, purple, gold, teal = sns.color_palette('colorblind', 6)\n",
    "\n",
    "# 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",
    "import missingno as msno\n",
    "\n",
    "from src.utils.utils import *\n",
    "from src.features.bars import get_imbalance\n",
    "import src.features.bars as brs\n",
    "import src.features.snippets as snp\n",
    "\n",
    "RANDOM_STATE = 777\n",
    "\n",
    "print()\n",
    "%watermark -p pandas,pandas_datareader,dask,numpy,pymc3,theano,sklearn,statsmodels,scipy,matplotlib,seaborn,pyarrow,fastparquet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This notebook explores the idea of sampling prices as a function of something other than fixed time intervals. For example using the number of ticks, volume or dollar volume traded as the sampling interval. The rest of this notebook works through some of the exercises found in chapters 1 and 2 of the book. \n",
    "\n",
    "This notebook makes use of the following script found here: `./src/features/bars.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and Clean Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set used in this example is too large to be hosted on github. It is a sample of equity tick data, symbol `IVE`, provided by [kibot.com (caution: download link)](http://api.kibot.com/?action=history&symbol=IVE&interval=tickbidask&bp=1&user=guest). Download this data to the `./data/raw/` directory in your local repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:29.025681Z",
     "start_time": "2018-05-05T00:36:06.525588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask    size       v          dv\n",
      "dates                                                                  \n",
      "2018-02-26 15:59:59  115.35  115.34  115.36     700     700     80745.0\n",
      "2018-02-26 16:00:00  115.35  115.34  115.35    5362    5362    618506.7\n",
      "2018-02-26 16:10:00  115.35  115.22  115.58       0       0         0.0\n",
      "2018-02-26 16:16:14  115.30  114.72  115.62  778677  778677  89781458.1\n",
      "2018-02-26 18:30:00  115.35  114.72  117.38       0       0         0.0\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293589 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 6 columns):\n",
      "price    1293589 non-null float64\n",
      "bid      1293589 non-null float64\n",
      "ask      1293589 non-null float64\n",
      "size     1293589 non-null int64\n",
      "v        1293589 non-null int64\n",
      "dv       1293589 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 69.1 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def read_kibot_ticks(fp):\n",
    "    # read tick data from http://www.kibot.com/support.aspx#data_format\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",
    "          .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\n",
    "    \n",
    "infp = PurePath(data_dir/'raw'/'IVE_tickbidask.txt')\n",
    "\n",
    "df = read_kibot_ticks(infp)\n",
    "cprint(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save initial processed data as parquet in the `./data/interim/` folder and reload."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:29.843915Z",
     "start_time": "2018-05-05T00:36:29.029886Z"
    }
   },
   "outputs": [],
   "source": [
    "outfp = PurePath(data_dir/'interim'/'IVE_tickbidask.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:30.438528Z",
     "start_time": "2018-05-05T00:36:29.845701Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask    size       v          dv\n",
      "dates                                                                  \n",
      "2018-02-26 15:59:59  115.35  115.34  115.36     700     700     80745.0\n",
      "2018-02-26 16:00:00  115.35  115.34  115.35    5362    5362    618506.7\n",
      "2018-02-26 16:10:00  115.35  115.22  115.58       0       0         0.0\n",
      "2018-02-26 16:16:14  115.30  114.72  115.62  778677  778677  89781458.1\n",
      "2018-02-26 18:30:00  115.35  114.72  117.38       0       0         0.0\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293589 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 6 columns):\n",
      "price    1293589 non-null float64\n",
      "bid      1293589 non-null float64\n",
      "ask      1293589 non-null float64\n",
      "size     1293589 non-null int64\n",
      "v        1293589 non-null int64\n",
      "dv       1293589 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 69.1 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "infp=PurePath(data_dir/'interim'/'IVE_tickbidask.parq')\n",
    "df = pd.read_parquet(infp)\n",
    "cprint(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:32.711789Z",
     "start_time": "2018-05-05T00:36:30.440842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc620350390>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc67ce29fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove Obvious Price Errors in Tick Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:33.141416Z",
     "start_time": "2018-05-05T00:36:32.713517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc62034ce80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qOYnwcCu6s7OzXqFDUfEAHRcVj31CJ0eo5CTCwy3rVaXxd+UqHqDjouLRoVNzZXIS4ZHLCy1Q8QAdFxWPx4/p3ElTyUmtlxcmJye9Qoei4gE6Lioe1w1p3PEHnZxEeLgV3Z07d3qFDkXFA3RcVDyOrujMdFVyEuEhtKqUJElSPm5Fd2lpySt0KCoeoOOi4nFxr86cRyUnER5uWd+9e7dX6FBUPEDHRcXj8JxOn65KTiI83Iru1NSUV+hQPD3Gx8cZHx93i7+VzElZXLNLZ6arkpNTHp7vTbesm2nsFariATouKh4nde6jyeQkwsOt6A4PD3uFDkXFA3RcVDyOLOpUXZWcRHjk8kILVDxAx0XF4+pdOn26KjmJ8HArugMDA16hQ1HxAB0XFY+J4zozXZWcRHi4Fd2TJzXuzKp4gI6LikeXzn00mZxEeLilfXl52St0KCoeoOOi4jHSo1N1VXIS4ZEHU7ZAxQN0XFQ88mDK8siDKQtAxQN0XFQ88mDK8qj1wZRdXV1eoUNR8QAdFxWP1Q2NPWhBJycRHm5Fd3Bw0Ct0KCoeoOOi4vHcsk7RVclJhIdb0Z2envYKHYqKB+i4qHhcOahzI00lJxEeOdNtgYoH6LioeDy3rNOnq5KTWs9019bWvEKHouIBOi4qHn2dGvsVgE5OIjzciu7KyopX6FBUPEDHRcVj1w6doquSkwiP7NNtgYoH6LioeGSfbnlkn24BqHiAjouKR/bplket+3S7u7u9Qoei4gE6Lioey+s6LWMqOYnwcCu6/f39XqFDUfEAHRcVj6MrOkVXJScRHm5Fd2Zmxit0KCoeoOOi4rFvQKdPVyUnER5uWR8aGvIKHYqKB+i4qHg8I3RyhEpOIjyyZawFKh6g46LikS1j5VHrlrHV1VWv0KGoeICOi4rHQLdO0VXJSYRH9um2QMUDdFxUPLJPtzyyT7cAVDxAx0XFI/t0y6PWfbo9PT1eoUNR8QAdFxWPhTWdljGVnER4uBXd3t5er9ChqHiAjouKx7ETOkVXJScRHm5Fd25uzit0KCoeoOOi4nFZv06frkpOIjzcsj4yMuIVOhQVD9BxUfE4sqDTp6uSkwgPt6K7uLjoFToUFQ/QcVHxuLhXp2VMJScRHrmJeQtUPEDHRcWjr0un6KrkpNabmGffXnmouKh4ZJ9ueWSfbgGoeICOi4pH9umWR637dLOFpDxUXFQ8smWsPGrdMpabGpeHiouKx/KGTtFVyUmtNzGfn5/3Ch2KigfouKh47O3T6dNVyUmEh1vWR0dHvUKHouIBOi4qHk/O6/TpquQkwiNnui1Q8QAdFxWPvX06LWMqOan1THd9fd0rdCgqHqDjouLR06lTdFVyEuGRfbotUPEAHRcVj+zTLY/s0y0AFQ/QcVHxyD7d8qh1n25fX59X6FBUPEDHRcVjZlXnRppKTiI8Or0Cd3Ro/BRX8QAdl1YeHV/9YtBIXhwnL72UjmeffUHPKdXth+W11Q7cZroLCwteoUNR8QAdFxUPlXVQ0MlJhIfbTHdsbMwrdCgqHqDjcjaP8fHx4JG8OI4fP8773//+7R5GW1B/bbUTt5nu7OysV+hQVDxAxyU9ykPFJcLDrehWlcbflat4gI5LepSHikuEh1vRzV83ykPFJT3KQ8Wl1ssLk5OTXqFDUfEAHZf0KA8VlwgPt6K7c+dOr9ChqHiAjkt6lIeKS4SHzt5ySZIkNcCt6C4tLXmFDkXFA3Rc0qM8VFwiPNyK7u7du71Ch6LiATou6VEeKi4RHm5Fd2pqyit0KCoeoOOSHuWh4hLh4VZ0zTT2ClXxAB2X9CgPFZcID7eiOzw87BU6FBUP0HFJj/JQcYnwyOWFFqh4gI5LepSHikutlxcGBga8Qoei4gE6LulRHiouER5uRffkSY2jSFQ8QMclPcpDxSXCw63oLi8ve4UORcUDdFzSozxUXCI88mDKFqh4gI5LepSHikseTFkAKh6g45Ie5aHiUtuDKWdnZ7njjjtqv7GxigfouKRHeai4RHm4Fd3Pf/7zEklQ8AAdl/QoDxWXKI/cZSxJkiQQO9fxFPfdd98U8PQLDbqxsdExOzu7e3h4eLKzs7O2vSQqHqDjkh7loeLSZo/L9+/ff8ZjKM5ZdJMkSZL2kssLSZIkgWTRTZIkCSSLbpIkSSBZdJMkSQLJopskSRJIFt0kSZJAsugmSZIEkkU3SZIkkCy6SZIkgbS96JpZh5l92symzGzRzA6Z2Wi7v087MbNPmtmjZrZgZt8zsy+Y2fCWr3mHmT1pZsfN7D/N7DXbNd7zwcxeYmb/ZmaVmV2y6fG6efyEmf2HmS2Z2bSZ/cmma7VwMbM9Zvbl5ntizsz+xcxesel6kR5m9nYze7D5vtg4w/U3N983K2b2TTN705brV5nZvWa2bGbfNbMPxI3+eeM4q4eZ/XQzH9PN3DxoZm/Y8jXt9aiqqq0fwEeBJ4CXAYPAIeAr7f4+bR7zHwGvArqAMeArwN9tuv56YBl4E7AD+DAwCQxs99jP4fQB4F6gAi6powfwRuAY8IvN8fYAr66bC3A38M/AMNANfAp4FrCSPYCfBG4G3glsbLn2MuA48MtNp1uaHlc0r3cAh4HPAhcBrwaOAm8rzOMW4K3ALqAT+HVgCbjUy8ND8GngVzf9/8rmG/+K7X4RvQCHnwHmN/3/LuAvNv3fgGeAW7d7rGcZ/9XAk8ArtxTdunn8O/CJs1yrjQvwCPCeTf+/ppmX0Tp4NH/4bS1Wvw88uOWxB4Hfa37+482ivHPT9T8E7i/J4yxfNwW81cujrcsLZjYIXAb816nHqqp6ElgAXt7O7+XMfhpvlFO8guc7VcB/Nx8vCjN7CfDnwIdozBI3UyePPuAmYNXMHm7++veAmb22+SW1cQE+DRwws1Ez6wHeA3ytqqpp6uWxmeeNu8nD/GDcrwCeqKpq6SzXi8TMXg6MAN9sPtR2j3av6Z46v3h+y+PHNl0rGjM7ALwbeN+mh/upj9P7gImqqu4+w7U6eQzReH2+G/gV4KXAPwF/b2a7qJfLv9L4NXWKxq+uv0DDC+rlsZlW466dl5ldDPwN8Kmqqv6n+XDbPdpddBeb/w5ueXwXjdlu0ZjZLwFfAH62qqqHN11apAZOZnYVjbXc957lS2rh0eTUa+nOqqoeqapqDfg4jXX3H6EmLs3fPO6lcZ9jkMa64MeAB81sNzXxOAOtxl0rLzN7KXA/jR/sH9l0qe0ebS26VVUdo7Ee9epTj5nZy2j8VHjkbM8rATO7Dfgz4C1VVd2/5fI3eL6T0Vgv/UbcCM+L19O4EfhNM5um8WsQwCNm9hvUx4OqquaBp2isff6/y9THZRjYB3y2qqqFqqrWqqr6Io333uuoj8dWnjfuJq/iB+P+BnB1c5noTNeLwcyuoLEe/ZWqqt7bXOI5Rfs9HBarPwo8TuOFNgD8NfAP27V4fp5j/i1gBrjxLNdfT+PXwv007tR+kELuMG8Z50XAJZs+XkejQL0W2FkXj00+HwK+C1xP487yh4H/pTHzqI1L8/3wWaCv6fFOYI1GB0CxHjSWRHpodFZsND/voXGz70oaN5hupvHbx82cuXvhM0AvjR8kk8DbC/O4tvkau/0cz22rh5fgHwPTNKbmdwOj2/0CajHmClhvvvhPf2z5mncA3wFWgIeA12z3uM/D6wo2dS/UzaP5pvgDYILGOtr9wCvr5gJcB9zTfE/M07gB9XOle9BYS6/O8HFF8/qbgUeb434UeNOW518F3Ncszt8DPliaB3Bn8/OlLR+3eHnkcT1JkiSB5J8BJ0mSBJJFN0mSJJAsukmSJIFk0U2SJAkki26SJEkgWXSTJEkCyaKb1Aoze4OZPb7d40iSCyX7dJMkSQLJmW5SG8ysc7vHkCQvliy6ybZjZk+Z2UfM7LHmkSl3mlmPmb2xeTzKb5vZBHDnqcc2PfdSM7u7eRTOjJl9btO1d5rZ4WbMfzSzy7dFMEk2kUU3KYVbaByrciWNky9+t/n4Hho7dV1OY/Pv05hZB409DZ6m8Xf0e4EvNa/9PPA7NPauHaOxi9RfOTskSUuy6Cal8Lmqqp6tqmqWxn6zNzcf/z6NI2BOVFW1suU5N9HY3PxDVVUtV1W1WlXV15rXfg34eFVVh6uq2qBxDt4rc7abbDdZdJNSeHbT50/TKKYAU1VVrZ7lOZcCTzeL6lYuBz5jZsfM7BgwS2PXsr3tGnCSXAh5YyIphUs3fX4ZjS304MybmJ/iWeAyM+s8Q+F9FvhYVVV/2cYxJsmLJme6SSn8ppldYmbDNNZiv3wez3mIxqbmnzCzvubNtx9tXvtT4CNmdgM0Dk1tHseUJNtKFt2kFMZpnE/1nebH7a2eUFXVSeAtNDaZfobGCQBva177W+CTwJfMbIHG6a4/5TLyJHkB5B9HJNuOmT0FvKuqqnu3eyxJ4k3OdJMkSQLJopskSRJILi8kSZIEkjPdJEmSQLLoJkmSBJJFN0mSJJAsukmSJIFk0U2SJAnk/wD2LV/Im71MfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc620347278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:33.454840Z",
     "start_time": "2018-05-05T00:36:33.142719Z"
    },
    "code_folding": []
   },
   "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",
    "    median = np.median(y)\n",
    "    diff = np.sum((y - median)**2, axis=-1)\n",
    "    diff = np.sqrt(diff)\n",
    "    med_abs_deviation = np.median(diff)\n",
    "\n",
    "    modified_z_score = 0.6745 * diff / med_abs_deviation\n",
    "\n",
    "    return modified_z_score > thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.073189Z",
     "start_time": "2018-05-05T00:36:33.458178Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "mad = mad_outlier(df.price.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.379824Z",
     "start_time": "2018-05-05T00:36:35.076483Z"
    }
   },
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mad]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:35.827227Z",
     "start_time": "2018-05-05T00:36:35.381347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6241e3780>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6208863c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df.loc[~mad].price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop outliers from dataset and save cleaned data in the `./data/processed/` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:36.824195Z",
     "start_time": "2018-05-05T00:36:35.829852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask    size       v          dv\n",
      "dates                                                                  \n",
      "2018-02-26 15:59:59  115.35  115.34  115.36     700     700     80745.0\n",
      "2018-02-26 16:00:00  115.35  115.34  115.35    5362    5362    618506.7\n",
      "2018-02-26 16:10:00  115.35  115.22  115.58       0       0         0.0\n",
      "2018-02-26 16:16:14  115.30  114.72  115.62  778677  778677  89781458.1\n",
      "2018-02-26 18:30:00  115.35  114.72  117.38       0       0         0.0\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293578 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 6 columns):\n",
      "price    1293578 non-null float64\n",
      "bid      1293578 non-null float64\n",
      "ask      1293578 non-null float64\n",
      "size     1293578 non-null int64\n",
      "v        1293578 non-null int64\n",
      "dv       1293578 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 69.1 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = df.loc[~mad]\n",
    "cprint(df)\n",
    "\n",
    "outfp = PurePath(data_dir/'processed'/'clean_IVE_fut_prices.parq')\n",
    "df.to_parquet(outfp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:37.434928Z",
     "start_time": "2018-05-05T00:36:36.826278Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask    size       v          dv\n",
      "dates                                                                  \n",
      "2018-02-26 15:59:59  115.35  115.34  115.36     700     700     80745.0\n",
      "2018-02-26 16:00:00  115.35  115.34  115.35    5362    5362    618506.7\n",
      "2018-02-26 16:10:00  115.35  115.22  115.58       0       0         0.0\n",
      "2018-02-26 16:16:14  115.30  114.72  115.62  778677  778677  89781458.1\n",
      "2018-02-26 18:30:00  115.35  114.72  117.38       0       0         0.0\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293578 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 6 columns):\n",
      "price    1293578 non-null float64\n",
      "bid      1293578 non-null float64\n",
      "ask      1293578 non-null float64\n",
      "size     1293578 non-null int64\n",
      "v        1293578 non-null int64\n",
      "dv       1293578 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 69.1 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "infp=PurePath(data_dir/'processed'/'clean_IVE_fut_prices.parq')\n",
    "df = pd.read_parquet(infp)\n",
    "cprint(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tick Bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:37.778351Z",
     "start_time": "2018-05-05T00:36:37.437179Z"
    }
   },
   "outputs": [],
   "source": [
    "def tick_bars(df, price_column, m):\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[price_column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    for i, x in enumerate(tqdm(t)):\n",
    "        ts += 1\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def tick_bar_df(df, price_column, m):\n",
    "    idx = tick_bars(df, price_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many ways to choose `M`, or the threshold value for sampling prices. One way is based on ratios of total dollar value/volume traded vs number of ticks. The rest of the notebook uses an arbitrary but sensible `M` value. I leave it as an exercise for the reader to see how the results change based on different values of `M`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:38.140768Z",
     "start_time": "2018-05-05T00:36:37.782229Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num ticks: 1,293,578\n",
      "volume ratio: 536.0\n",
      "dollar ratio: 43767.0\n"
     ]
    }
   ],
   "source": [
    "n_ticks = df.shape[0]\n",
    "volume_ratio = (df.v.sum()/n_ticks).round()\n",
    "dollar_ratio = (df.dv.sum()/n_ticks).round()\n",
    "print(f'num ticks: {n_ticks:,}')\n",
    "print(f'volume ratio: {volume_ratio}')\n",
    "print(f'dollar ratio: {dollar_ratio}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:38.932231Z",
     "start_time": "2018-05-05T00:36:38.142830Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 201521/1293578 [00:00<00:00, 2014779.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tick threshold: 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1293578/1293578 [00:00<00:00, 2701348.96it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[99, 199, 299, 399, 499, 599, 699, 799, 899, 999]"
      ]
     },
     "execution_count": 15,
     "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": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:39.266920Z",
     "start_time": "2018-05-05T00:36:38.933579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12935, 6), (1293578, 6))"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:40.120356Z",
     "start_time": "2018-05-05T00:36:39.269233Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1293578/1293578 [00:00<00:00, 2596507.45it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(12935, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df = tick_bar_df(df, 'price', tick_M)\n",
    "tick_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:46.224121Z",
     "start_time": "2018-05-05T00:36:40.122783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1466,), (15,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def select_sample_data(ref, sub, price_col, date):\n",
    "    '''\n",
    "    select a sample of data based on date, assumes datetimeindex\n",
    "    \n",
    "    # args\n",
    "        ref: pd.DataFrame containing all ticks\n",
    "        sub: subordinated pd.DataFrame of prices\n",
    "        price_col: str(), price column\n",
    "        date: str(), date to select\n",
    "    # returns\n",
    "        xdf: ref pd.Series\n",
    "        xtdf: subordinated pd.Series\n",
    "    '''\n",
    "    xdf = ref[price_col].loc[date]\n",
    "    xtdf = sub[price_col].loc[date]\n",
    "    return xdf, xtdf\n",
    "\n",
    "## try different dates to see how the quantity of tick bars changes\n",
    "xDate ='2009-10-01' #'2017-10-4'\n",
    "xdf, xtdf = select_sample_data(df, tick_df, 'price', xDate)\n",
    "\n",
    "xdf.shape, xtdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.034945Z",
     "start_time": "2018-05-05T00:36:46.226037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc62041e940>"
      ]
     },
     "metadata": {},
     "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",
    "    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], label='price', marker='o')\n",
    "    sub.plot(*args, **kwds, ax=axes[2], ls='', marker='X',\n",
    "             color='r', label=bar_type)\n",
    "\n",
    "    for ax in axes[1:]: ax.legend()\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    return\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'tick bar', alpha=0.5, markersize=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus Exercise: Make OHLC Bars from Custom Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract `tick_df.price` and `df.price` into two pandas series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.302701Z",
     "start_time": "2018-05-05T00:36:47.036224Z"
    }
   },
   "outputs": [],
   "source": [
    "sub = tick_df.price\n",
    "ref = df.price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below creates the OHLC dataframe by:\n",
    "    1. Iterating over the subordinated series' index 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": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.600145Z",
     "start_time": "2018-05-05T00:36:47.304265Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_ohlc(ref, sub):\n",
    "    '''\n",
    "    fn: get ohlc from custom bars\n",
    "    \n",
    "    # args\n",
    "        ref : reference pandas series with all prices\n",
    "        sub : custom tick pandas series\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",
    "\n",
    "## uncomment below to run (takes about 5-6 mins on my machine)\n",
    "#tick_bars_ohlc = get_ohlc(ref, sub)\n",
    "#cprint(tick_bars_ohlc)\n",
    "\n",
    "#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": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:47.898221Z",
     "start_time": "2018-05-05T00:36:47.601572Z"
    }
   },
   "outputs": [],
   "source": [
    "def volume_bars(df, volume_column, m):\n",
    "    '''\n",
    "    compute volume bars\n",
    "\n",
    "    # args\n",
    "        df: pd.DataFrame()\n",
    "        volume_column: name for volume data\n",
    "        m: int(), threshold value for volume\n",
    "    # returns\n",
    "        idx: list of indices\n",
    "    '''\n",
    "    t = df[volume_column]\n",
    "    ts = 0\n",
    "    idx = []\n",
    "    for i, x in enumerate(tqdm(t)):\n",
    "        ts += x\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def volume_bar_df(df, volume_column, m):\n",
    "    idx = volume_bars(df, volume_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:48.774435Z",
     "start_time": "2018-05-05T00:36:47.900399Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 134661/1293578 [00:00<00:00, 1346315.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volume threshold: 10,000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1293578/1293578 [00:00<00:00, 2407460.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      price     bid     ask    size       v           dv\n",
      "dates                                                                   \n",
      "2018-02-26 15:49:42  115.20  115.17  115.18     800     800     92160.00\n",
      "2018-02-26 15:49:42  115.25  115.17  115.18   23923   23923   2757125.75\n",
      "2018-02-26 15:58:15  115.24  115.24  115.25    3900    3900    449436.00\n",
      "2018-02-26 16:00:00  115.35  115.34  115.35    5362    5362    618506.70\n",
      "2018-02-26 16:16:14  115.30  114.72  115.62  778677  778677  89781458.10\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 54903 entries, 2009-09-28 09:44:09 to 2018-02-26 16:16:14\n",
      "Data columns (total 6 columns):\n",
      "price    54903 non-null float64\n",
      "bid      54903 non-null float64\n",
      "ask      54903 non-null float64\n",
      "size     54903 non-null int64\n",
      "v        54903 non-null int64\n",
      "dv       54903 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 2.9 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "volume_M =  10_000 # arbitrary \n",
    "print(f'volume threshold: {volume_M:,}')\n",
    "v_bar_df = volume_bar_df(df, 'v', 'price', volume_M)\n",
    "cprint(v_bar_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:55.626890Z",
     "start_time": "2018-05-05T00:36:48.776371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (1466,), xtdf shape: (48,)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6242165f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xDate = '2009-10-1'\n",
    "xdf, xtdf = select_sample_data(df, v_bar_df, 'price', xDate)\n",
    "print(f'xdf shape: {xdf.shape}, xtdf shape: {xtdf.shape}')\n",
    "\n",
    "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": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:55.921723Z",
     "start_time": "2018-05-05T00:36:55.628100Z"
    }
   },
   "outputs": [],
   "source": [
    "def dollar_bars(df, dv_column, m):\n",
    "    '''\n",
    "    compute dollar bars\n",
    "\n",
    "    # args\n",
    "        df: pd.DataFrame()\n",
    "        dv_column: name for dollar volume data\n",
    "        m: int(), threshold value for dollars\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",
    "        ts += x\n",
    "        if ts >= m:\n",
    "            idx.append(i)\n",
    "            ts = 0\n",
    "            continue\n",
    "    return idx\n",
    "\n",
    "def dollar_bar_df(df, dv_column, m):\n",
    "    idx = dollar_bars(df, dv_column, m)\n",
    "    return df.iloc[idx].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:36:56.779280Z",
     "start_time": "2018-05-05T00:36:55.923791Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▎        | 176990/1293578 [00:00<00:00, 1769593.70it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dollar threshold: 1,000,000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1293578/1293578 [00:00<00:00, 2426948.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                        price     bid     ask    size       v            dv\n",
      "dates                                                                      \n",
      "2018-02-26 15:42:24  115.3199  115.31  115.32     290     290  3.344277e+04\n",
      "2018-02-26 15:49:42  115.2500  115.17  115.18   23923   23923  2.757126e+06\n",
      "2018-02-26 15:58:15  115.2400  115.24  115.25    3900    3900  4.494360e+05\n",
      "2018-02-26 16:00:00  115.3500  115.34  115.35    5362    5362  6.185067e+05\n",
      "2018-02-26 16:16:14  115.3000  114.72  115.62  778677  778677  8.978146e+07\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 44812 entries, 2009-09-28 09:46:35 to 2018-02-26 16:16:14\n",
      "Data columns (total 6 columns):\n",
      "price    44812 non-null float64\n",
      "bid      44812 non-null float64\n",
      "ask      44812 non-null float64\n",
      "size     44812 non-null int64\n",
      "v        44812 non-null int64\n",
      "dv       44812 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 2.4 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dollar_M = 1_000_000 # arbitrary \n",
    "print(f'dollar threshold: {dollar_M:,}')\n",
    "dv_bar_df = dollar_bar_df(df, 'dv', 'price', dollar_M)\n",
    "cprint(dv_bar_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:03.626421Z",
     "start_time": "2018-05-05T00:36:56.782573Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xdf shape: (1466,), xtdf shape: (26,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc624207cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xDate = '2009-10-1'\n",
    "xdf, xtdf = select_sample_data(df, dv_bar_df, 'price', xDate)\n",
    "print(f'xdf shape: {xdf.shape}, xtdf shape: {xtdf.shape}')\n",
    "\n",
    "plot_sample_data(xdf, xtdf, 'dollar bar', alpha=0.5, markersize=7)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count Quantity of Bars By Each Bar Type (Weekly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:03.903498Z",
     "start_time": "2018-05-05T00:37:03.627930Z"
    }
   },
   "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": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T00:37:04.239680Z",
     "start_time": "2018-05-05T00:37:03.906214Z"
    }
   },
   "outputs": [],
   "source": [
    "# count series\n",
    "# scale to compare 'apples to apples'\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": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:31.294452Z",
     "start_time": "2018-05-05T01:08:30.655597Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc603361828>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc603426080>"
      ]
     },
     "metadata": {},
     "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 Most Stable Counts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:15:38.670995Z",
     "start_time": "2018-05-05T01:15:38.332653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tc std: 7.84%, vc std: 10.99%, dc std: 10.16%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "df        0.078265\n",
       "tick      0.078445\n",
       "dollar    0.101649\n",
       "volume    0.109923\n",
       "dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'tc std: {tc.std():.2%}, vc std: {vc.std():.2%}, dc std: {dc.std():.2%}')\n",
    "\n",
    "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": [
    "## Which Bar Type Has the Lowest Serial Correlation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:31.845911Z",
     "start_time": "2018-05-05T01:08:31.582441Z"
    }
   },
   "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": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:15:49.217691Z",
     "start_time": "2018-05-05T01:15:48.886957Z"
    }
   },
   "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": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:17:45.890539Z",
     "start_time": "2018-05-05T01:17:45.516855Z"
    }
   },
   "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>44811.0</td>\n",
       "      <td>-0.125228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1293577.0</td>\n",
       "      <td>-0.091913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>54902.0</td>\n",
       "      <td>-0.017564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>12934.0</td>\n",
       "      <td>0.062736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  autocorr_stat\n",
       "dollar      44811.0      -0.125228\n",
       "df        1293577.0      -0.091913\n",
       "volume      54902.0      -0.017564\n",
       "tick        12934.0       0.062736"
      ]
     },
     "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>54902.0</td>\n",
       "      <td>0.017564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>12934.0</td>\n",
       "      <td>0.062736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1293577.0</td>\n",
       "      <td>0.091913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>44811.0</td>\n",
       "      <td>0.125228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  autocorr_stat\n",
       "volume      54902.0       0.017564\n",
       "tick        12934.0       0.062736\n",
       "df        1293577.0       0.091913\n",
       "dollar      44811.0       0.125228"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_test_stats(bar_types,bar_returns,test_func,*args,**kwds):\n",
    "    \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'})\n",
    "          .T)\n",
    "    return df\n",
    "\n",
    "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": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:32.817899Z",
     "start_time": "2018-05-05T01:08:32.477309Z"
    }
   },
   "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_rets):\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": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:34.001599Z",
     "start_time": "2018-05-05T01:08:32.819419Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6032ae588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " plot_autocorr(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:35.507998Z",
     "start_time": "2018-05-05T01:08:34.002850Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60312de48>"
      ]
     },
     "metadata": {},
     "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": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:35.783601Z",
     "start_time": "2018-05-05T01:08:35.509227Z"
    }
   },
   "outputs": [],
   "source": [
    "def partition_monthly(s):\n",
    "    return s.resample('1M').var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:16:13.455019Z",
     "start_time": "2018-05-05T01:16:13.021434Z"
    },
    "scrolled": 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",
    "monthly_vars = [tr_rs, vr_rs, dr_rs, df_ret_rs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:16:19.562679Z",
     "start_time": "2018-05-05T01:16:19.220785Z"
    }
   },
   "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>102.0</td>\n",
       "      <td>5.701116e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tick</th>\n",
       "      <td>102.0</td>\n",
       "      <td>2.033541e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>102.0</td>\n",
       "      <td>2.258333e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>102.0</td>\n",
       "      <td>2.918462e-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size      var_stat\n",
       "df            102.0  5.701116e-12\n",
       "tick          102.0  2.033541e-09\n",
       "dollar        102.0  2.258333e-09\n",
       "volume        102.0  2.918462e-09"
      ]
     },
     "execution_count": 119,
     "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 Lowest Test Statistic?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:36.802000Z",
     "start_time": "2018-05-05T01:08:36.406004Z"
    }
   },
   "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>12934.0</td>\n",
       "      <td>1.107317e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>44811.0</td>\n",
       "      <td>4.022110e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>54902.0</td>\n",
       "      <td>2.608531e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1293577.0</td>\n",
       "      <td>1.658044e+14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size       jb_stat\n",
       "tick        12934.0  1.107317e+08\n",
       "dollar      44811.0  4.022110e+10\n",
       "volume      54902.0  2.608531e+11\n",
       "df        1293577.0  1.658044e+14"
      ]
     },
     "execution_count": 98,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shapiro-Wilk test statistic > larger is better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:37.132288Z",
     "start_time": "2018-05-05T01:08:36.803870Z"
    }
   },
   "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>12934.0</td>\n",
       "      <td>0.650087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dollar</th>\n",
       "      <td>44811.0</td>\n",
       "      <td>0.401451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volume</th>\n",
       "      <td>54902.0</td>\n",
       "      <td>0.276730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>df</th>\n",
       "      <td>1293577.0</td>\n",
       "      <td>0.172775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sample_size  shapiro_stat\n",
       "tick        12934.0      0.650087\n",
       "dollar      44811.0      0.401451\n",
       "volume      54902.0      0.276730\n",
       "df        1293577.0      0.172775"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def shapiro(x,test=True): \n",
    "    np.random.seed(12345678)\n",
    "    if test: return stats.shapiro(x)[0]\n",
    "    return stats.shapiro(x)[1]\n",
    "\n",
    "(get_test_stats(bar_types,bar_returns,shapiro)\n",
    " .sort_values('shapiro_stat')[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Serial Correlation between Dollar and Dollar Imbalance Bars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update [05.04.18]\n",
    "\n",
    "Earlier version was missing some additional code. Before we can compare we must compute the Dollar Imbalance Bar. This is my initial implementation of this concept but is experimental and may need some adjustments. \n",
    "\n",
    "   1. Compute the sequence ${bt}_{t=1,...,T}$.\n",
    "   2. Compute the imbalance at time $T$ defined as $\\theta_T = \\sum_{t=1}^{T}b_tv_t$.\n",
    "   3. Compute the expected value of $T$ as ewma of previous $T$ values.\n",
    "   4. Compute the expected value of $\\theta_T$ as ewma of $b_tv_t$ values.\n",
    "   5. for each index:\n",
    "        - compute $\\lvert\\theta_t\\rvert >= E_0[T] * \\lvert2v^+-E_0[v_t]\\rvert$\n",
    "        - if the condition is met capture the quantity of ticks\n",
    "        - reset tick count\n",
    "        - continue\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:37.402694Z",
     "start_time": "2018-05-05T01:08:37.134093Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                             dv\n",
      "dates                          \n",
      "2018-02-26 15:59:59     80745.0\n",
      "2018-02-26 16:00:00    618506.7\n",
      "2018-02-26 16:10:00         0.0\n",
      "2018-02-26 16:16:14 -89781458.1\n",
      "2018-02-26 18:30:00         0.0\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293577 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 1 columns):\n",
      "dv    1293577 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 19.7 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tidx = get_imbalance(df.price.values)*df.dv.iloc[1:]\n",
    "cprint(tidx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:41.492275Z",
     "start_time": "2018-05-05T01:08:37.404111Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "window size: 1,293.00\n",
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                             bs        E_T          E_bs        absMul  \\\n",
      "dates                                                                    \n",
      "2018-02-26 15:59:59     80745.0  1292279.0  11492.246200  1.485119e+10   \n",
      "2018-02-26 16:00:00    618506.7  1292280.0  11961.345468  1.545741e+10   \n",
      "2018-02-26 16:10:00         0.0  1292281.0  11952.101770  1.544547e+10   \n",
      "2018-02-26 16:16:14 -89781458.1  1292282.0 -57440.023579  7.422871e+10   \n",
      "2018-02-26 18:30:00         0.0  1292283.0 -57395.634071  7.417140e+10   \n",
      "\n",
      "                         absTheta  \n",
      "dates                              \n",
      "2018-02-26 15:59:59  5.971441e+08  \n",
      "2018-02-26 16:00:00  5.965256e+08  \n",
      "2018-02-26 16:10:00  5.965256e+08  \n",
      "2018-02-26 16:16:14  6.863070e+08  \n",
      "2018-02-26 18:30:00  6.863070e+08  \n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1293577 entries, 2009-09-28 09:30:00 to 2018-02-26 18:30:00\n",
      "Data columns (total 5 columns):\n",
      "bs          1293577 non-null float64\n",
      "E_T         1293577 non-null float64\n",
      "E_bs        1293577 non-null float64\n",
      "absMul      1293577 non-null float64\n",
      "absTheta    1293577 non-null float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 99.2 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "wndo = tidx.shape[0]//1000\n",
    "print(f'window size: {wndo:,.2f}')\n",
    "\n",
    "## Expected value of bs approximated by ewm\n",
    "E_bs = tidx.ewm(wndo).mean() # expected `bs`\n",
    "\n",
    "## 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()\n",
    "\n",
    "df0 =(pd.DataFrame().assign(bs=tidx)\n",
    "      .assign(E_T=E_T).assign(E_bs=E_bs)\n",
    "      .assign(absMul=lambda df: df.E_T*np.abs(df.E_bs))\n",
    "      .assign(absTheta=tidx.cumsum().abs()))\n",
    "cprint(df0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:44.211934Z",
     "start_time": "2018-05-05T01:08:41.493878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q4r3AfwPXAK8Crssc8wTQA/ybEOImxucE3gB8AkBKGRFC/BS4RQixJ3POLcCPMwtFYHyl8m1CiHsZXzX8ReAosD1TvqQ6qEQkEsHjyWvE29IYX/XRzdn4qs1cvqFYkhdOhPjTYT/P9QVJTFnGu6zexoqGOs5bVs8FK+pZ22THNkPevkrDtHFlkm+P4AeA/8q5P5EkZ52UsksIcTXjOfx+CBwB3iml7AbIpHd5G/B9YIjxuXlflVL+Mud6n2A8j+BED+A9wKcmCqWUPxNCrAD+h1N5BN8upUxlygtRByWIRqPzH6QQxld9dHM2vmozk28oluSHz5/k9/sGJz2+vtnOhSs8bGp1cu4yN02OmrLnql0Mpo0rk0XNEdSdp59+Wp5xxhnlrsacxGIxrfIIGl/10c3Z+KpNru+RoQg/fcHL9u6RbPmmVievzuzTu6rRPttlLIXObVxu5pojWPl9yYZFMd8qIdUwvuqjm7PxVRuv10skkeK7T/fxkXv3Z4PAZfU2/u0vTuNb7zid6161TJkgEPRsYytg9hpWlNxVzTpgfNVHN2fjqx5SSnzhBAcHx3juSIw/Prw7W3bRynr+9qLlbGh1lrGGxUWHNs7FKr4mEFQUh8NR7iqUFOOrPro5G1916B+Ns717hAcODtEzPHneWIuzlr+5aBlv3NhsyXl/+aByG8+EVXxNIKgow8PDllitVCiMr/ro5mx8rUkwmuSV/jAHfGFe6Q9zeChCOJ6adMyZ7S5WOlL8+VkrOW+ZW/kAcAJV2nihWMXXBIKK0tLSUu4qlBTjqz66ORvfyieRSrNvIEzvSIyj/vH9e6f2+AHU1VSxsdXBleubuHJ9Ex57DaFQKLtDhC5YsY2XglV8TSCoKKOjo1p9yBhf9dHN2fhWJsFokid7Ajx62M++gTDxKTn+aqoEp7WMJ3fe3O5iY6uDdreNqim9flbxLSS6OVvF1wSCihKPT99ySGWMr/ro5mx8K4fnjgV56OAQh4YinAjGJpWtabSzsc3J2kb7eAC4zL2g5M6V7FssdHO2iq8JBBWls7Oz3FUoKcZXfXRzNr7lJy0l//nkMf6wf2jS42d1uFjuqeOvzu1gddPiVoZWom+x0c3ZKr4mEFQUr9dbMZtdlwLjqz66ORvf0pFMS3zhOD3DUXqGoxzxRzg2EuXwUCR7zOtPa+LPNzRz3vKF9fjNh27tC/o5W8XXBIKKYpVl64XC+KqPbs7GtzjEU2n6RmIc8Uc4PDTGvoEwXUORaXP9Jmiw13D12W289/zC9u7o1r6gn7NVfE0gqCg2m63cVSgpxld9dHM2vksnlkyzxxuiezhKlz9C1+AYvSNRZor5mhw1LPfUsbHVybomO2ubHXTW22i0F2dfX93aF/RztoqvCQQVJRAI0NjYWO5qlAzjqz66ORvf/EimJfsHwhz1Rzg8FOHQ4Nikod1clntsrGqws6nNyeZ2F2e0OXHXlfbrULf2Bf2creJrAkFFaW1tLXcVSorxVR/dnI3v7MSTafYOhOkLxDjgC9M9HOXQ4BjpKT19VQKW1dexud3JxlYnm1qdbGh1Ulez9Dl+S0W39gX9nK3iawJBRQkEArhcrnJXo2QYX/XRzdn4jhNPpjk4OEbPSJSDvjEODY5x1B+ZcXh3haeOtU12zu50s77FwRltThy11SWoff7o1r6gn7NVfE0gqCiJRKLcVSgpxld9dHPW1TeeSrO3P8wLx0d5+LCfwfD0v4MA1jTZcdRUccnqBja2OtjU6qTRUVviWi8e3doX9HO2iq8JBBXFKvmLCoXxVR/dnHXzHa7ycM8TvTx+dJhIIj2pbGVDXXZod1Obkw0tjort6VsourUv6OdsFV8TCCqKVfIXFQrjqz66Oevg+8SRYZ7uDfDC8VH8kWT28XZ3Lectq+eS1R7O7XRbqqdvoejQvlPRzdkqviYQVBQrzEsoJMZXfXRzVtE3lZYcHBxj5/FRHjnkn7Rdm61acNFKD391Xgeb29Vzn4qK7TsfujlbxdcEgopSXW3tYZN8Mb7qo5uzCr6ptKRrKMKLJ0fZfTLEHm+IsZxh32oBr13XyDvOaqelOkpHa0sZa1taVGjffNHN2Sq+JhBUlGAwSFNTU7mrUTKMr/ro5mxV31Ract9eH9u6RzgyFJkU+MF4Dr8zO9yc0+nmyvWN2bl+PT2DoFEgaNX2XQq6OVvF1wSCitLW1lbuKpQU46s+ujlbzTctJU92B/h/z52YNOS73GPj3M56zl3m5txlbtrdM++2YDXfpaKbL+jnbBVfEwgqit/vx+l0lrsaJcP4qo9uzpXum0il6QvEeKU/zN6BME91j0zq/bvq9Bb+6rwOlnvqFnS9SvctNLr5gn7OVvE1gaCiSDnzBuqqYnzVRzfnSvKNJtMc9UfYdnSEaCLNy/0heoejTK1hXU0Vr13XyLvObuO0lvy+ACvJtxTo5gv6OVvF1wSCimKVLulCYXzVRzfncvqOxVO80h/miD/CSydHeflkiNgMW3m0umpZ22TnjDYX5yxzc3aHi9rqxW3fZtpXfXRztoqvCQQVpb+/3xL5iwqF8VUf3ZxL5RuOpzjgC7N/ILN923AU72hs2r69qxvtbG4f373j4pX1nNbixGUr3KpI077qo5uzVXxNIKgobre73FUoKcZXfXRzLoavlBLvaJzDQxEOD43xdE+A7uHotOOqBJzR5mRDq5PTWhxctqaBpiIndTbtqz66OVvF1wSCBoPBoDDBaJJnegM81xfkZW8I/1hyUnmVgLVNds5dVs+GFgcbW50s89Rhr1ncEK/BYLAWJhBUlFAoREuLPjm5jK/66Oa8WN+0lOw6Psq2oyO87A3RF4hNKm+w17C+2cGaJjvnLnNz8UoPdRUQ9Jn2VR/dnK3iawJBReno6Ch3FUqK8VUf3Zzz9Y0l0+w4FuDfHu2eNL+vWsCmNieXrWnk3GVuTm9zUiVEgWu7dEz7qo9uzlbxNYGgovh8PlatWlXuapQM46s+ujnP5SulZCSS5JWBMM8dC7LjWIBgNEUyJwK86vQWrjytidNbnTgLuKijWJj2VR/dnK3iawJBRREV+Iu/mBhf9dHNOdd3LJ5i14lRth8doXs4whH/9AUeAJtanVy2poErT2tacCLnSkHn9tUF3Zyt4msCQUVpbm4udxVKivFVH12ch8cSHPFHeLEvzZE9XfSORBkMx5khjR+b252c2+nmnGVuzupwFzSdS6nRpX0n0M0X9HO2iq8JBBXF5/NZIn9RoTC+6qOiczyZpmckyvN9QY76Ixweikxb3AHjK3vPbHdxyWoP53S6WdNkp75OrY9vFdt3LnTzBf2creKr1ieJIYvH4yl3FUqK8VUfFZzH4imO+iPs9ob4/b5BhsYS0xI3O2qrWNNoZ4W7iletbmFjq4PO+rqKWNlbTFRo33zQzRf0c7aKrwkEFSWVSpW7CiXF+KqPFZ1DsSSHBiPs6Q/xx/1DDI4lph3T4bZxepuTszpcnNXpZl2TndrqKgYHB2lttcbQUiGwYvsuBd18QT9nq/iaQFBRwuEwra2t5a5GyTC+6mMF51RasncgzL6BMC+fDLHjWHDaMc2OGl69qoGzO11cvMoz644dVvAtJMZXfXRztoqvCQQVpbOzs9xVKCnGV30q0TkYTfJ8X5B9A2G2HhlhLJ4ikTPWWyXAbavminVNnL/CzfnL6vHYF/axW4m+xcT4qo9uzlbxLeikEyHEXUKIhBAilPPvo1OOuU4I0SWEGBNC7BBCvGpK+UVCiGcz5V1CiPdPKW8XQvxGCDEqhPAJIW4VQlTllFcLIb6aKRsVQtwjhGidco0566ACXq+33FUoKcZXfSrBeSAU54/7B/nK1m7+5td7ueanL/PlrT38bu8ggWiSRFriqavmLza18PHLVvKz95zNf3/gXP7+8lVcsa5pwUEgVIZvKTG+6qObs1V8i9Ej+CMp5d/NVCCEuBz4LvBO4HHgE8AfhBAbpZRBIUQD8Efga8BrgSuAe4UQXVLKpzOX+RkwCqwEWoAHAD9wa6b8JuAvgUuAIeCHwE+AqxZSh4L9FcpMbW1xN4ivNIyv+pTD2T+WYE9/iB29QV7pD3MiOH1F7wXL3ZzdOb5jx7pmB20uW0GeW7c2Nr7qo5uzVXxLPTT8IeA3UsqHAIQQXwU+znhQ9iPgaiACfEVKKYGHhRD3AtcDTwsh1gF/DmyQUgaAgBDiVuBznAoErwdukVIeyTzHPwKHhRBrpZTdC6iDEjQ0NJS7CiXF+KpPqZzH4il+suskzx4LcmxKKpe6asF5y+u5YHk9G1udbG53UltdnNW8urWx8VUf3Zyt4luMQPBdQoirgUHgd8AXpJShTNl5wF0TB0oppRDihczjE+W7MkHgBLuAD+SUB6SUXVPK1wohPIAAVgM7c56jSwgRBM4FuhdQh3lJJpMcPnw4e7+5ubniEkcODg7icrnKXY2SYXzVp1jOqbSkdyTKzr4gO4+P8sKJ0UkpXU5vc3LJKg8XrvCwqc1JTVVpdgvQrY2Nr/ro5mwV30IHgt8EPgv4gM3AfwE/AN6bKa8HAlPOGQE8Sywnc8zEJ/RSnmNeBgYGuOaaa7L3b7jhBm6++Wa8Xi8ul4vq6mqCwSBtbW34/X6klLS1tdHf34/b7QYgFArR0dGBz+dDCEFzczM+nw+Px0MqlSIcDtPZ2YnX66W2tpaGhgYGBwdpaGggHo8TiUSy5Tabjfr6eoaGhmhqaiISiRCNRonFYni9Xux2Ow6Hg+HhYVpaWhgdHSUej2fPdzgc2Gw2AoEAra2tBAIBEolEtrySnCbKpzqlUil6enqUcpqrnerq6jhx4oRSTvO1k91up6+vryBONQ43Tx7uZ+8IPN4Tyn17I4DTW+xsbhRcc14nMhEbd2qs4fix3pK9n6LRKNFo1HLttNjXHjDpPayC01ztVF1dzcDAgFJO87VTbW0t/f39SjnN1U7RaJRQKFQRTnMhJne+FRYhxJ8BWwG3lDImhHgRuEtK+Y2cY34HdEkpPy2E+AawVkr5jpzyTwEfkFJeKIR4R+b8xpzyCxjvFWxk/DN8GLhASvlizjGBzDXum68OC/Hatm2btNvt2fuV2CM4MDBAe3t7uatRMoyv+izFOZWWPNMbYOfxUfYPhDnij0xL5Hx2p4s3bWzh0tUeGmdJ6VJKdGtj46s+ujlXku+uXbt2btmy5aKZyoo9RzCd+X+ip+4l4MKJQjG+I/P5wG9yyt855RoXZB6fKG8QQqyfmAOYKe/OzBlECNGbeY4XM/fXM97bt3uBdZiXmpoaNmzYsNDDy0IkEil3FUqK8VWffJzjyTR7+kPs8YY5NDjGAd8YI9HkpGNOb3Py6lUeNre7OH95fcmGfBeKbm1sfNVHN2er+BY0EBRCvAd4QEo5IoTYCHwduE9KGc0c8gPgASHEj4BtwN8DduDeTPm9wFeEEJ8Bbmd85fDVwBsBpJRHhRCPZI75W8ZXDX8W+H5ONe4APiuEeIzxVcO3Ag9mFoospA5KYJX8RYXC+KrPfM6JVJrdJ0Ns6x7hj/uHmDrWsdxj4+KVDZzZ4eKSVR6cturiVbYA6NbGxld9dHO2im+hewQ/DHxHCFEHDDAeXN08USil3J7JK/gDYBnwMvCWibQtmQDyLcC3gVuAk8CHc1LHALwP+B5wHIgxnh7mKznlXwaagOeAOuBhIJuLcL46qILX67XEZteFwviqz1TncDzF3v4we/pD7D4Z4qg/wlginS1vd9dywfJ6LlzhYUOLgxUNdVSJyur1mwvd2tj4qo9uzlbxLWggKKW8cgHH/Bj48RzlzwGvnqN8gPFewtnKU8CNmX+LqoMK2GyFyWVmFYyv+thsNobHEjzTG+DFkyGe6gkQS6YnHbPcY+OilR7+bE0jF6yoL1NNC4NubWx81Uc3Z6v4mi3mFKW+3tpfgvlifNUkmZb0BaLs7BvloYPDHB2evPptU6uTze0uLl5Vz9omB22uWoSFev3mQpc2nsD4qo9uzlbxNYGgogwNDWWXmOuA8VWDZFpy1B/hlf4we7whdvQGiKVOzfarrRasaqjjDRuauXilh3XNjjLWtrio2sazYXzVRzdnq/iaQFBRmpqayl2FkmJ8rUUilWYglKB3JMoBX5jDqpg1AAAgAElEQVTDQxF6hqMMhOLTFnnYa6o4b5mby1Y4uPL0Dhy1lb3Io1BYvY3zxfiqj27OVvE1gaCiRCKRbJJWHTC+lUkqLekejtA9HOWoP8IRf4S+QIyBUHxaHr8JOuttnN7q5Jxlbs7qcLG+2YEQgv7+fm2CQLBOGxcK46s+ujlbxdcEgooSjUbnP0ghjG95kFISjKXwjsboH43jnfgXiuEdjdM3Za/eXFpdtSyrr+OMNicbW52sb3awzGObde/eSnEuFcZXbXTzBf2creJrAkFFsUr+okJhfItHOJ4J9EI5gd5E4BeKE0mk5zy/vq6aTa1OTm9zsr7FwdpGB+31Nuw1Mwd8s2HaWG2Mr/ro5mwVXxMIKopV8hcVCuO7NKSU+CNJjgeidA1FOBaIcWQowrFAlNFYas5znbVVdNbX0VFvo7PeRqfbRmd9HZ31NjrctoIlbjZtrDbGV310c7aKrwkEFSV3L2QdML4LI5FKc8Qf4Yg/yolgDO9ojP0DY/jCs8/Zq6sWdOQEdp31tlOBn9tGfV11SVK2mDZWG+OrPro5W8XXBIKK4nCom1ZjJozvdEKxJD0j4z18XUORTAAYIZGaJeJjfP/dtU121jQ5WNNoZ32zg2ZnTUXk5jNtrDbGV310c7aKrwkEFWV4eNgSq5UKhc6+wWiSLn+ErsExDg9F6B2J4h2NE4rPPKS7rN7G5nYXyz3jvXzLPXWsaKijyVFbSoW80bmNdcD4qo9uzlbxNYGgorS0tJS7CiVFB18pJb5wgq6hCPtOQu/BI3QNRegPxWc83lYtWNlgZ0OLg/UtDk5rHv+/vs6ab3sd2jgX46s2uvmCfs5W8bXmN4JhXkZHRy2R0bxQqOYbTaY5NjI+j+/Q4BiHBseHdQPR5LRj66oF65odbGgZX5W7rslOp6eOZkdlDOkWCtXaeD6Mr9ro5gv6OVvF1wSCihKPz9xLpCpW9ZVSMhxJ0j08vq3a4aEIR+bo5auvq+a0FgfttUkuWNfBhhYHKxvsVFepE/DNhlXbeLEYX7XRzRf0c7aKrwkEFcUq+YsKhVV846k0PcPjCzh2Hg+yozdINDk9D58AVjTUscJTx2ktDja2OtnQ4qTdXYsQglgsRl1dXekFyohV2rhQGF+10c0X9HO2iq8JBBXFKvmLCkWl+cZTafb2hznqj3AiGKMnM8w7GE7MmKZlZUMdr1rh4fQ2J5tanaxoqJuzl6/SfEuBbs7GV2108wX9nK3iawJBRbHKsvVCUQm+aSnpGY7y8CE/Dx4cmjERc5WA5Z46NrQ4WNfs4NLVHtY2OfIe2q0E31Kjm7PxVRvdfEE/Z6v4mkBQUWw2W7mrUFJK6TsQitMzPN7DNxCKczwYy6ZsSeZ097W6ajmr3cXGVidrmuysaKijwz37Xrr5oFv7gn7OxldtdPMF/Zyt4msCQUUJBAI0NjaWuxolo5C+Ukr6Q3GOB2L4wgl84TiD4QQ9w1F6R6Kz5ucDaHHWsrndxdvPbOW8Ze6irdrVrX1BP2fjqza6+YJ+zlbxNYGgorS2tpa7CiVlsb4jkfG8fH2BGH2BKCeCcfb7wnPur1tTJTij3cmqBjvtbhvLPTZWNdhZ5qnDVaB9dedDt/YF/ZyNr9ro5gv6OVvF1wSCihIIBHC5XOWuRslYqO/QWIInjgzz4skQhwfHGAwnmGnDtfq6atY1OWh319ListHqrGVlQx0rG+y0uWupKnN+Pt3aF/RzNr5qo5sv6OdsFV8TCCpKIpEodxVKymy+J0djPNMT4JX+MDuOBYlNSdVSUyU4o83JqkY7KxvqWOapY12Tg+UeW0UnY9atfUE/Z+OrNrr5gn7OVvE1gaCiWCV/UaHI9R0KJ3ji6DB/PDBE93B02rHnLXNz3jI3l69rZGWDnRoLJmPWrX1BP2fjqza6+YJ+zlbxNYGgolglf1EhSKUlD+zupS/h4LEuP8Gc+X22asHmdhcXr/Jw8UoPqxvV2IVDp/adQDdn46s2uvmCfs5W8TWBoKJYYV5CvsSTafpDcfoCMY74x3fmGIkk6QvEMkeEssduanVy1RktvHZtIx67ei9zFdt3PnRzNr5qo5sv6OdsFV/1viENAFRXl2b1arEYi6c44Bvj0NAYR/0RDvrGOJYN+KbT6aphy6ZWzul0cWaHG3vN0nP1VTJWb9/FoJuz8VUb3XxBP2er+JpAUFGCwSBNTU3lrsaCSKUlBwfH2OMN0ReIsW8gPOPcvmoBrS4byzw21jY5WNdkZ22zgzWNdnwn+1izZlkZal8erNS+hUI3Z+OrNrr5gn7OVvE1gaCitLW1lbsK00hLyclgnIODYQ4NRvCF4+wfGMMfSZBITU7iUlslWNVoZ32znXXNDs5od7G53TXrwo5K9C0muvmCfs7GV2108wX9nK3iawJBRfH7/TidzrI8t5SSYCzFHm+Io/4IxwIxTgRjHBuJMpZIz3hOi7OWV6/ysK7ZwYYWB5vanNjy2IqtnL7lQDdf0M/Z+KqNbr6gn7NVfE0gqChSzpQmuTjPMxxJ0j0cYY83zJ7+EEeGIpNW7ubSaK9hY6uTzR0uOt022t021jbZl7ygo1S+lYJuvqCfs/FVG918QT9nq/iaQFBRitUlHU+leeH4KAd8Y+wbCLN3IExkhl4+W7VgdaOd9c0OzupwsaJhPGFzs7O2KPWyShd8odDNF/RzNr5qo5sv6OdsFV8TCCpKf39/QfMXeUdjPHLIz72v+Kbtw+u2VbOioY4zO1yc0+lmU6uTVldpt2ErtG+lo5sv6OdsfNVGN1/Qz9kqviYQVBS3273ka0gpORGM8eBBP798qT/7eLWAd57dzpomO+d2ulnmqVvycy2VQvhaCd18QT9n46s2uvmCfs5W8TWBoCFLKJakezjK/syw78snQ4xEk9nyFmct7z63nbdubqU2j4UcBoPBYDAYKhMTCCpKKBSipaVl1vJIIkXPcJRDg2P0jkTZ0x/mqD9Cesrc1vq6as7qcHHRSg9Xnd5SsQHgfL6qoZsv6OdsfNVGN1/Qz9kqviYQVJSOjg5gYng3TtfQGF3+CPsHxjgejDIQSkw7p0rAuiY761scnNvp5swOFysbrLE374SvLujmC/o5G1+10c0X9HO2iq8JBBUikUrTF4jRPRzhiYNefLFqeoYjxFLTl7BXC1jRYGdTq4PVTXY2tjjZ3O7CabPGljhT8fl8rFq1qtzVKBm6+YJ+zsZXbXTzBf2creJrAkGLEo6nOOKPcHhwbHxP3sExjgdj04Z2YTx33+ltTtY22dnU5mJ9s52O+rpZd+mwIqKEK5QrAd18QT9n46s2uvmCfs5W8dUyEBRCVANfBj4I2IGHgBuklIPlrNdcpNKSJ7tHeLInwN7+MP2h+LRjqgQsq7exutHOak8t56xo4Mx215KTNVuB5ubmclehpOjmC/o5G1+10c0X9HO2iq/6EcLM3AT8JXAJMAT8EPgJcFU5KzWVYDTJzuNBdvaN8mRPgHD8VP6+agGrGu2c3ubk9DYXm9qcrG20Y6sZX8zR09PDmtUN5ap6yfH5fJbI11QodPMF/ZyNr9ro5gv6OVvFV9dA8HrgFinlEQAhxD8Ch4UQa6WU3eWsmJSS5/qC3PX8SbqGIuSO9LY6a3nr5lYuzuzJO9fQrsfjKX5lKwjjqz66ORtftdHNF/RztoqvdoGgEKIBWA3snHhMStklhAgC5wLd810jmUxy+PDh7P3m5uaCdQHHUpKvPt5LIJO/77xlbi5a6eHsTheb210L3q0jlZp5r19VMb7qo5uz8VUb3XxBP2er+GoXCAITIXpgyuMjOWVzMjAwwDXXXJO9f8MNN3DzzTfj9XpxuVxUV1cTDAZpa2vD7/cjpaStrY3+/v5spvFQKERHRwc+nw8hBM3Nzfh8PjweD1efXk9wLMq7X7Wa4JCP2toUDW7Bsd5eGhoaiMfjRCIROjs78Xq92Gw26uvrGRoaoqmpiUgkQn9/P/X19Xi9Xux2Ow6Hg+HhYVpaWhgdHSUej2fPdzgc2Gw2AoEAra2tBAIBEolEtrwQTqlUinA4nL1mbW0tDQ0NDA4OLtgpGo1my6c69ff3T7q+Ck5ztVM8HicejyvlNF87JRIJotGoUk5ztdOJEydwu91KOc3VTj6fTzmnudopGo2STqeVcpqvnWKxGKlUSimnudrpxIkT2O32inCaCyHlDMtMFUYI0QgMAxdIKV/MeTwAfEBKed9819i2bZu02+3Z+4XsESwUsViMurryb/1WKoyv+ujmbHzVRjdf0M+5knx37dq1c8uWLRfNVFaZ20QUESnlCNALXDjxmBBiPeO9gbsXco2amho2bNiQ/VdpQSAw7y8A1TC+6qObs/FVG918QT9nq/hqFwhmuAP4rBBinRDCA9wKPFjuhSKFwu/3c8cdd+D3+8tdlZJgfNVHN2fjqza6+YJ+zlby1TUQ/DJwP/AccByoBt5f1hoVEL/fz3e/+92KewHed9+8o+6LolJ9i4VuvlA852K9JpeKFdt4KX9LK/ouhbl8K/U1uVTK0cbl/Fta6TWtZSAopUxJKW+UUrZKKeullFdXcjJpVbj//vvLXQWDYRLmNVk4zN+yMJi/Y+Ewf8uFod1ikULwpz/9yQf0lLses5FMJqv9fn9Hc3Nzf01NTcWsXw8EAo0NDQ0jhb5upfoWi2QyWe31etd0dnb26OALxWvjYr0ml4oVX9NL+Vta0XcpzOVbqa/JpVKONi7n37ICX9NrtmzZ0jZTgQkEDQaDwWAwGDRFy6Fhg8FgMBgMBoMJBA0Gg8FgMBi0xQSCBoPBYDAYDJpiAkGDwWAwGAwGTTGBoMFgMBgMBoOmmEDQYDAYDAaDQVNMIGgwGAwGg8GgKSYQNBgMBoPBYNAUEwgaDAaDwWAwaIoJBA0Gg8FgMBg0xQSCBoPBYDAYDJpiAkGDwWAwGAwGTTGBoMFgMBgMBoOmmEDQYDAYDAaDQVNMIGgwGAwGg8GgKSYQNBgMBoPBYNAUEwgaDAaDwWAwaIoJBA0Gg8FgMBg0xQSCBoPBYDAYDJpiAkGDwWAwGAwGTTGBoMFgMBgMBoOmmEDQYDAYDAaDQVNMIGgwGAwGg8GgKSYQNBgMBoPBYNCUmnJXwIps3bpV1tXVlbsac5JMJqmp0ad5ja/66OZsfNVGN1/Qz7mSfMfGxga3bNnSNlNZZdTQYtTV1XHGGWeUuxpz0tPTw5o1a8pdjZJhfNVHN2fjqza6+YJ+zpXku2vXrp7ZyszQsKJ0dnaWuwolxfiqj27OxldtdPMF/Zyt4msCQUXxer3lrkJJMb7qo5uz8VUb3XxBP2er+JpAUFFcLle5q1BSjK/66OZsfNVGN1/Qz9kqviYQVJTq6upyV6GkGF/10c3Z+KqNbr5gXeexnhMcuOXbxHz+vM6ziq8JBBUlGAyWuwolxfiqj27OxldtdPMF6zo/9+6/5+h3fsbuj38hr/Os4mtWDReYWCxGNBpFCFHWetTX11f0i1BKid1up1BpeNraZlwVryy6+YJ+zsZXbXTzBes6R3pOABB8+VBe51nF1wSCBSQcDgPg8XjKHgjG43FsNltZ6zAXUkrGxsZIJpMFmUfh9/txOp0FqJk10M0X9HM2vmqjmy9Y3znhH8nreKv4mqHhApJKpXC5XGUPAq2AEAKXy0UqlSrI9aSUBbmOVdDNF/RzLqTv6N7DxP2Bgl2vGOjSvid/9ycCuw9o45uLbs5W8TU9gopSKdnMS4VVuuALhW6+oJ9zoXxDB47y5BuuQ9RU8+a+bQW5ZjHQoX1H9x7mpRs+D8CV3Y+WuTalx6ptXFVnIx2L532eVXxNj6CiJBKJclehpPT395e7CiVFN1/Qz7lQviMv7AVAJgvT+14sdGjfSN+pvHI6+E7Fqs6ianGhklV8TSCoKFZZtl4o3G53uatQUnTzBf2cC+Urqq3xMW/a15rk01OmivNCsYqvNT4hDBXB9u3bLdPVbTAYxllsb4bBMB+jew/z0Jor2fcvty/5Wul4gvDRvgLUqggoPu+/pJ8QQoi7hBAJIUQo599HpxxznRCiSwgxJoTYIYR41ZTyi4QQz2bKu4QQ759S3i6E+I0QYlQI4RNC3CqEqMoprxZCfDVTNiqEuEcI0Vpc89Iz3yKMt73tbXR2drJq1apJ//bu3VuiGhaWUChU7iqUFN18QT/ngvlWWeNLzLSv9Tj6vV8C0HPHrxZ0/FzOD61+Hdte826891fg3MlFBoJWaeNy/FT8kZTSnfPvOxMFQojLge8CHwGagHuAPwghPJnyBuCPmcebgA8D3xNCvCbn+j/L/L8SuAR4J/CZnPKbgL/MlK3MPPaTwiqWn9ra2nmPufHGGzl27Nikf2eeeWYJald4Ojo6yl2FkqKbL+jnXCjfnN/BFY1pX/WZzfnkbx/J3t5709dKVZ2Fs8jfUlZp40pbWvoh4DdSyocAhBBfBT7OeDD3I+BqIAJ8RY6vy35YCHEvcD3wtBBiHfDnwAYpZQAICCFuBT4H3Jp5juuBW6SURzLP8Y/AYSHEWilld6GFHui8rNCXnJO/8D4FQDKZLFoewV/84hf8+7//O6FQiKuuuopbb70Vt9uNlJIvfelL/PznPycUCtHU1MTHPvYxrr/++qLUIxefz8eqVauK/jyVgm6+oJ9zoXytMkdQi/bNSSeigu/Izj3Z275HnqKqzkbLay+a9fjZnPf+09ezt2U6XdhKlhGrtHE5PiHeJYTwCyEOZoZoc2dTngfsnLiTCfZeyDw+Ub5LTk7Os2tKeUBK2TWlfK0QwpPpUVw95Tm6gCBw7kIFkskkhw8fzv7z+/Pbf9DKpFIpHnjgAbZv384zzzzD4cOH+fznx9MhPPbYY/ziF7/goYceore3l4cffphLLrmkJPXSLXejbr6gn3PBfC0yR9C0r/UY6+rN3t75/ht57tq/n/P4hTjLtDVy7y0Eq7RxqXsEvwl8FvABm4H/An4AvDdTXg9MzXo6AniWWE7mmIlWmesa8zIwMMA111yTvX/DDTdw8803Ew6HcTgcCCFIpVLU1NTwht6twHhev0QikV3Nm0qlqK2tJZlMAuOrfJPJJNXV1UgpSafT1NbWkkgkEEIsuDyVSpFOp7P/EokEVVVVVFVVkUwmqampyR5z22238a1vfWuSW1dXF6lUCill9vpVVVUIIbIpaf75n/8Zu92O2+3mxhtv5LrrruPWW29FCEEsFmPPnj14PB5aWlpoaGggmUzOWudwOEw6nSYejxOJROjs7MTr9WKz2aivr2doaIimpiYikQjRaDRbbrfbcTgcDA8P09LSQiqVoqenJ1vucDiw2WwEAgFaW1sJBAIkEolsucvlorq6mmAwSFtbG36/HyklbW1t9Pf3Z1d7hUIhOjo68Pl8CCFobm7G5/Ph8XhIpVKEw+HsNWtra2loaGBwcJCGhoYlO42OjhKPx2d0crvdnDhxQimn+dqpvr6evr4+pZzmaqdoNEo0Gl2y05G7/5B9f/f09JTVaa52EkJMeg9bpZ3yee2N5XQa1NbWMjAwYGmn2b4fZ3NyOBz09/dPc8rtBUwnkxX3WZ4bmvb09Cy4naLRKKFQqOzt5PV6mQtRzszXQog/A7YCbillTAjxInCXlPIbOcf8DuiSUn5aCPENYK2U8h055Z8CPiClvFAI8Y7M+Y055Rcw3ivYyHggOAxcIKV8MeeYQOYa9y2k3tu2bZN2uz17v7m5mebmZoLBIB7PguPJohKLxebcx/dtb3sbr3vd67jxxhsXfM3t27fz9re/nYGBgWzC6iNHjnDRRRdx4MAB2trauOuuu/jVr37FK6+8wsUXX8znPvc5LrjgglmvWai/WU9PD2vWrFnydayCbr6gn3OhfHOnp0xMHalEdGjfgQe3seuvPwvA5h2/sLzvTFOf5nqN5baxTKXY+YF/pOG8M+j54X+TDIwCUO2w88ajlbVg5JFNbyIZHF/4kc97qJJe07t27dq5ZcuWGcftyz1mMPEzYKKn7iXgwolCMd6ven7m8YnyqVHFBVPKG4QQ66eUd0spA1LKEaB3ynOsZ7w3cPdCK11TU8OGDRuy/5qbmxd6askoZh7BY8eOZW/39vZSV1dHS0sLAB/84Af54x//yP79+zn77LO57rrrilaPXColAC8VuvmCfs7G1xrIVIpkeCzv86zquxRynUde2Mvgo0/T9R//NWnuZEXOEVzkEK9V2rjU6WPeI4RozNzeCHwduE9KGc0c8gPgaiHEFiGEDfgHwA7cmym/F3AKIT4jhLAJIbYwvoDkDgAp5VHgEeArmTmB6xgfiv5+TjXuAD4rhFiXWY18K/BgMRaKlJNi9vTecsstBINBfD4ft956K+9+97upqqpi165dPPPMM9neSLfbXbKt7gq1Z7FV0M0X9HM2vtZgxzs/xiOn/TnRk768zrOq71LIda52nBpVyw3+KjEQnOitzBertHGpewQ/DBwRQoSBh4BngL+ZKJRSbgc+ynhAGADeDbxFShnMlI8AbwGuzZT/APiwlPLpnOd4H+Nex4HngN8BX8kp/zJwf6bsOFANTMpFqALpBbyZvva1r03LI/jggw/OeU51dTVvetObuPzyy3n1q1/N2rVr+eIXvwiMz1e46aab2LhxI6eddhqPPfYYd955Z0F85iMcDpfkeSoF3XxBP2fjaw1Gnh0fTOr/w+PzH5zTs2RV36UQDofp+eE97P0/Xyc1Fj1VkNNvIVOVFwguFqu0cUkXi0gpr1zAMT8GfjxH+XPAq+coH2C8l3C28hRwY+afssyXR/D+++/P+5qXX345Pt/4r973vve908qvuOIKtm7dmvd1C8FsE5dVRTdf0M/Z+FoLKRcQwOSM1FjddzG0OFxsz6SKSUVjpwpyOy4qsEfQuXYFY93H8z7PKm1c7jmChiIxscJXVUKHunn5k18icnx8U+/5VkWphgq+iZEgR7/9M6L9gws6XgXnfDC+FiPP6TiW910E/X2ngql0TiC4oCC6jNQ2N85/0AzM1MZxf4BjP7svrz2ai02lJZQ2FIjF5i+aLfnlpZdeyt13372UKhWUZ9/xUeJDI4QP93Dp7+9Y0E4qKqGC755/+DL9/7OVk799mMsevmve40vpLKVkdM9BXBvXUm2fffX9Ugm+cointvw153zz86y49qpJZSq0cT5M9U0nkvT99He0XHkJrnUrZzmrgsgz/51u7QtQk+OcO4+90oeDxSK3aZypjR89c/x9ngyGWPeR/7WkehUKEwgqymJXDeeuCK5k4kPj6SHDR8br29DQUM7qlBwVfP1Pj2dwCr58cEHHl9L55G8eYvfHvkDzZRfy6t98a/4TFslTW/4agJf/9/9l2TvfSFXO4ioV2jgfpvr23nUP+z9/O1D4tDeRPi+RPi/Nl56/pOtMCmby7BHUrX1hyira3MC50pNILzIp+1xt7L3v0YoJBM3QsKJMJKpWnsx8ksHBhQ0vqoIKvvl2WpfSufeu3wDgf2pXyZ5Txie/Z1Vo43yY6ht8+VDRnuvxi67m2Xd8lNF9XfMfPAcnf/vwqTt5BjO6tS+Af2jo1J1KTxmTg4wvbqrVXG1c1145aedMIKgoxcwjWElMbEek269rFXxFbX4DEqV0Hnnu5ZI91wRTe5SK5ZuKxoqaXmqxTPUtxe5coQNHlnS+75FTPZWRYyfzOne+9k2OhjnyzZ8Q6VNnLmF9To/gpNdgBb4ec7G1LG6OoFU+p00gWEAmtkyrhA/ZSqjDXEgpCYfDSw5YJ7K9x+OVM/F2oaQX+SsTrOk7FVueE7BVcJ6bye/ZYvhGjvfz8NrX8+KHPpf3uVJKZBHzok3ztcA+rW2vvzR7u/e/7pn3+NzP5fnad9ff3MTBL32Xxy+aNQlG2RG2/OY5TlrEWMbvqGM/+S1D23cu+PjapsUlho7FYvT+6F6CLx+YVlZJX9FmjmABcblcxGIxgsFg2TebDofDuFyustZhLqSU2O32ObfBm4squ410dPyDNDESJBKJFLJ6Raf/gSd44YM3sfnf/oE1f/uuvM+3mu9MVDnya3sVnOdkytBiMXy99/0JgP7fP5b3uQ8u+zMAXvv0r4uyeGOqr1jkvKySsoQ6zte+/jwCFasQzXUuUyTk/f1jvPKZ8dTClz/xc07+7hHWffR91Lgcc5y1uO9z3yNP0ffZrwKVvb2jCQQLTF1d3aKDGxXrUSxqXE7imUBwdF8XnReeWeYa5ce+f/6P8f//6euLCgStkp9qLvL9olfBeS6m9uIXwzcxElzyNQ5/7U7O+/bNS6/MFKb5Vn6H4JJQ/vWcTvPs1R/HtWE1Z3/tJgDa29uZmPlZrlGrg/9+aqOx7VeML9bo/s7P597feJEBv2M0OnthBXUJWuAnl2ExqJ6jamLVMABCWM63xu1c0vlW852JcFd+K9RVcJ6TKV8MxfA9cvusufoXTP3mDQWoyXSm+VpgaHgpX+aqvp4nArzAC3sZfuZF+n56X7ZsYGDg1IFlWiBSNcPc5FRkjoCNyelj8pkeEQwtbmu6UmMCQUWx2WzlrkJRqT9rY/Z2XUer9XyX+CVnOd8M4aN9BF7aD0DD+ZvzOteqzgtFThkaLoavfUXH0i9SpJ6Mqb5VdWq3t+qv57g/MO2x2hznCuoQm5OBB7dx/Jf/k70f9U5eCZyOxdn3+W/gf/qFaefWzJUrsoL+ACYQVJT6+vpyV6GotFxxcfa2EBb0XWIgaDnfDNte826efvPfEh8OUtfWlNe5VnVeMFO+GIrh61i9fMnXKNaQ3lRfxwr1hk5z547n075PX/V3JMMVOEd2ptdC5rGZpn643e65z60wUtEYu/76s5Meq5qyQObo935Bzw9+zbPv/Ni08+12e/b2gVu+zQOdl00qT1dImjcTCCrKUG6+pgIQ8/nxPfJUReZ7krLwvpWOFX0TgVPDJJFjJxF5rhi3onM+JEdDk+4Xwndq0FaQRWxF+gyY6nbGKHwAACAASURBVCtqrZUCq+P/uzKv4/Np38ALe3nx7/55xrKTv32Ek7/7U17PXVQyr7ng7v3Zh+LD43NTh/3+U8eV67skj/fAkdt/NO2xqUPDyUBo2jEThMfGsrePfudnk8rGek7w0Mor2P+v/7ng+hQLEwgqSlNTfr0t8/Hk6z/AzvffyMl7H57/4FIjZcF9i81Sv5Ct5gsQHxzO3u667YeTFgMsZN5NrnM6kaTrG3cRfKV4SYdLTax/cmBQiDbOHdICJv3N8+lhyv0BmMrZI7aQTPXN3WXFClS75p/3mxuY59u+g489M+P1Xvrwv/DSDZ+vuB/ph279Qfb2nk99CYCGxlMpo1Kz7LXb8ZbXFbdis3Dkmz+Z9lhiePriqqnb4c013cI5R+aO8KFuALq//8sF1rB4mEBQUQqdemLiS7yUOy3kg+VSiywxELScL1BTf+pDMXSwe9KH7ODjz817fq5z7133cOjLd2S3aMuXSsyzKaYEPktt45Gde9jzqX+b9FjusFbCPzL1lFnp+/n92dtHvjG9l6QQTEsfk2fC8XKTb3LqgryHc17H/qemz1Gbj3BXb96JsGd7/lMPTX+s9Q2vASAaPbUoY7b0OMVu96lDuxMc/NJ3pz2WDIWnPTZtX+Q5PssTycXnii0lJhBUlNw3nOrIVEorXxhv35Fdr1h214GxI8fo/8Pj2fsTicHnIreNw4d7F/3c3vse5bFz3krgxX2LvkYxkOnJvaKLfU133/ErfI89wzP/3/XTC8XiPvK7bvuvRZ2XD1N9q+aaaF8glvyDIOf84Ev75zhwOoX4zMrtBTxx9x9nPa7nzrvZ8Y6Pkoqc6s2ND42w7c/ew+MX55++au5KTe819j85HvTFFuAcG/Bz5Js/Jubzz3vsYhjdM/sowtTP0xP//eC0YwYffXrS/alxYHI0nB3hSBYxAXshMYGghUnH4uz5zK2T97vMYKUcVf0PPMETr3n3ovf9DL60f0m+xRrqmpMlTtVqiKV45i0fquhdB/JByvmHtSa18QK/v9OJJN7fPzYp3dCL13+O+OAwu//3LflWkxP3PsRz7/kkydHpPQVLJTVlqHYxr+nA7gPs/5fb2fneT89Ynjtvq+f//feCr5sYKX4ajKm+VusRhPFe2LkYy0mZNFP75gZqCyLnfXD8V3+Y9bB9n/sPhp95ka2vekf2sdyh5kUHxDOcduQ/p6co8mbmMLa3t897yeGnX+Dgl77HY+e8dXF1mrjOjpd46k1/Q2D35F095pqGsvN9/zDvdaetDs6JBGM+P49sfCNPvflvAah3LC1NWKkwgWAFEh8OEjpwdN7j/Dteou8nv+OlD//rtDIr5ah64YM3MXa0b1FfzAC1zY2L9t33r7fz8NrXs+fGLy/q/MWy1DmCfTvyHwZaKslwhL5f/k924vd8pOOJhQdM6fm/iBbTxt3f+zkv/t0/z7iiL3yoJ+/r7f7IzQxtfbYo83r6fnb/pPuL8Y3P04uSO0+z+3u/IBkem+PoU6x491WT7hdjaH2q72LmCA4//zLDz5d+n+gJZuyFzRD1+jjwf7+dvT/Vd+jJXTy87vUcuvWOOZ8jemKAA7d8m+hJHzKZX49TIiely+6Pn/q8HV3kXNuZXgeHv3onQ48/O+Pxk/IILoAD//fbPPuujy9qa8Mdf/kRgrsPsOu6zyz4nIV879Y2TFntnfNZPjHKMdHr6O89vqDn7fvF7xdYw+JgAsEK5NGz3sL2172P8NG+OY9LBmb/lZ67bL2QDD9bvA9ZGV/8UvqpvulEksHHn+X4r/5AepZJyQA93/8VwKSkp6Ugcjy/D8SphB7Zkb2dWMCw6lzEfH76H3hi3snm+//1dvZ88ku88DefnfO4CZ54zbt5ZOMbs8HgnMPYCwgsJrfxwgKRw5khzdDB+T/g8yF0sHvGxwe37uCBzstmnHgOEO0fpPvOX88YgLW89lXA+Jfrvn+9neG7fpd/wJXn8cnQwgLBxovOnnT/mav+Dv8zL+b1XPMx7TOr+tTX00IWQgRe3MeOt97AjrfeMOPerrORGosWdNHRTPnzYHoy76m+h796JwBd/3HXnO2+9cJ3cPQ7P2PrBX/Jw+ten3f9kuExQpmFChNMTZEyG+nEwj6jT9zz0LTHXvzwv2DLc2rC0W//DP+Tuxh5fu6e1rmITcn7t1RSY1OGt3MCwQNf+Fb2tkylpr1vZqPnzrsLUrfFYgLBSiTzoTffnJOZ0m/IVIqRXa9QV1Oc+TXhKR8gSyX3TbXoL+t0Godj8j6Ruz/2BZ7/q0/y8ie+yO5PfHEpVSwKza85P3t7MUPiImfz9lf+4cvsfN8/EBtYXLqRJ99wHS988KY5h5YATvz3AwAMP/MSUa9v3utGj/cDMJqZRP/MWz4067ETyZST4cisv/6ntvFCSE8ZapvrR0H2mAXk9prYszeXoSd38fx7PgWMTzzfetHVnLh38hfiE5dey/7PfYO9/+e2aeeL6vEesMgxLz3f/xXe7/5yQT0UueQbOA4/vbhgLvDiPp59x0enBRSLJZ1MIo8PTKp/bq/5QoZMc39ojB1dWE8MwMPr38BTW/56xik28/09j/3kd9Mee+LSa8fPTaVIx0+9TwO7J3+eT389n3quQ1/+PjPR++PfzlmfhbDz/Z9h+2v/16THJt6rczG4dQcPrbqC3h/di5SSlz/xxVlTwMz0/vD+9hGGf/PIouospSzcdIwljsZMfA7OdLncz/WR5/cw+ugOFoK9s3VJdVoqJhCsYKbOm9r/hW/x7NWnuslzVz9FT4z3MD1x2V/xzFs+xEuzzBGqNJ57zycXfGzgpf2EDk8fzpNSMjw8PuSVjsUJvLR/0geR97ePTDseFtbLUCxy2+7J13+AdCzO0PadC04wWnvmadnb3vsfxfenp9n7T9ODi5mYGmhNDCcOzjKcM0E6eiqI2nr+X8557CuZjdYBnr/2E/P2JBz55k8IHermkdO28OCK1/JA52XZf8+89Xoifd5sG8PkL2jvfY9y7CeTvyADL+ydlry16/Yf8fBpW2Z8/v4HnqBrhrlNAC9/4ovs+5fb5w0in3vXxyfdj/Z52f2Rm7P308lkNjA98es/THv9Zd/vOYtGJnqJYHzKSN8v/4dkOELM55/xizF36HchzBZwBHYfoPeu32Tvx2a5bvTEwPjk+IzLQgLtuD9AuKuXRDDEoVt/wND2nbx0w7/w4ltuoO+n0wMrGO+Nno/cbRtfvP5zCwqKc+dl7vnUv08q6/v573lw2Z9x4t6H6H/giRmD0eEdL017LBkM4X/6BR496y08tPp1nLhnfMHB1N6h3NczTA5eZ9sKcO8/fmUeo5kRNac6DYZn2AFjJqSUBF85lF05O/EjZ+9nv8qJux+Y94fjTATy6KnNZeDB7Tyy8Y3zDpvPxqTPvHleF/n0JgOkY6eCfd+fTi0kOfDF79D/y4X9jVpff2lez1lotAwEhRDVQoivCiF8QohRIcQ9QojyhuQzMWXeVPd3f47/qVPd5NWuU78ofX96igc6LyPScwKAyO6DS356KeX4F/HbbsjrvN4f3YtvhpxXE4zs3MMjG9/I6L4uRp7dvaBrRo6d5Ok3/y3bL3/v9HQHElpaWgB4aM2VPJ2ZqDsT+/71dh5e93q2X/l+nv9fk4PlUmZ5d6xaNun+Q2uu5Llr/jcv//3k3sv40Eg2IJrg/2/vvMPjqK4+/B71LqtZcpVsy713GxvbYGOb3kwLvQRDEnov+eKEbiCUEFpCCR0S05NAIIAxGALBgVBDtQCD3OQqF9ny/f6Y2dVsX+3Otpn7Ps8+0sydmb2/vVPOnHvvOSue+DubXw78fbc2hfaCKKVQSrH5f9/wYo/def/UXwZs428w+9PvXN/f1ePt2LVjJ98+8LS3nqteXMJ3f3rKu1371m2sfT18eJjWL5YHeCk8rP/3Rywedwjrf/do0PL3T72cjy9YyA9PGt63HRs389bepwRs98U1dwWMqdq1YyfbV7fwnxMu5our72RtkJAWKx7/G013P84/6meE1RCKHxa9yOfX3OnzwAB4sftUn+WdQSZkrPzra97//3PixXx09lW83G8mrw7fz+h2b91K0z1/8T7oOutB7DJuGP8csg8v1O3GqyMP8K5/a/aJfHLxDaxZ/A7vHnEW/1vwu6D7//uIs3m5/1682H0q39zxCP+on8HqV4xzs2Xpf/jq1gd8utHbt23nlSF7s2TKkfxzwGy+uuk+3p13hlfndw8+Q/u27by9/3zWvNbhTYlm6IbHUPHw6vD9+O4h43gbP/xfUMPw4/Ov8/6/a4dv+3x0rhF657+nL+A/J1zM17dGHzbnnYN/7p1g89+f/5q2teupmjrOZxvPPcvD9pX2dmGCYZh/eM7VUY0l3PRZR/ibde/8lxe7TWHpzON5uXGvgG1bv+r8+FqA1reju9/7s/yORwCj2zwSu7a3BbwEhuquD8bSvU6MuI1qb0e1t7Px4y/49PKbgm6z/t1ODKMKkoUlmUg6xtNKNCJyGXA8MBdYC9wLFCml9g67o8lbb72lBg0alLD6+Z/E5aMGM/mFe7zrq6aNZ/CV57Dlm+86xnaIBLzpzPz8H+SWlbD65aW8d8z5gBH0tL11C0NvuIiambvR1rKesqH9UUp5u2I8///wlxd8BhR7qJoxgRG/+z/yayoDylq/+Z4lkw8HYM4PbwSkGVJK8WK3KSG1z21e6v1/++oWPr38JvpfPJ9dW7fx5p7HBd1nzAMLaR/ej9KN23hj+tEB5Vn5ecxueg0I/G2tTH93UYCBFoytK1ayeOzBlAzqS+/jD6b3iZ0Lv6B27eL9U3/JyudfDVpeNnwA4564lba163hj6lGdOrb19/Pw1c33892DzwR0/8xtXspXN93nE/i14bSjGLTgDJ/tdm5q5evbHiQrN5cvb7jHu76ge1evJzoS+V2rYu66tjJ18cPkd6vh34efZVv4l/pTDvMZo9P3rONCemSs5NdVM/zWX1I5eTRZuTlhzy0wAs9G6oKb27yUdw75RUC8zmnvLOL1CeHPs7nNS/nkkhv59r5FEese7hht6zbyyuC5MR8jFIOuPJvPLr857DYlg/uxOcRQidlNryG5OUFTl0H4axtg2G8voedP9ve5Hwb7jldHH0jNrCn88ESgN2du81Je6DaFLuOGMem5uyJ+ZzhG/mcR3boZ95tI98VoGb/oNnKKCmh+7lWyiwvZ9uOqTo1/nvavv1BU3z1A16wvXwpqEKaCcU/cQrWZYnTX9jba1m+koLaatnUbySkuZOnsE9n8mW9Mx26Hzmbk7xcAkc8TgMn/uI+35p6ctOwng68+j/qTbA7j48eyZcvemzlz5rhgZW41BJuA3yil7jGX+wFfAn2UUssj7Z9sQzDZZBXkMfT6i/jwjCui3mfCk7+ndGgj/xw4x7uuZEAfJjx5G3nVFYY36rOvKexVF/aGMrd5KS/1m0V76xYkJ9v7JlvU0IMty0N7vHrecD7dGnrz7rwzQx4Xwv+23Q6ZzcjbFwBGOjTr7LC2Nev47sGnafrjn31CkQT7DjC6Jr+9/0l6nzQvwENZMqhvwI3KLnZ/8zGK+/X2Lod76M1tXhr09+h/yXzWLn6XgQvOoHzEwJSfj4mm+2F7h43BFg2Rzs9oidYIdTNDb7iIXscEDk2IdJ6WDmlkyisP8Prkw9kSYiJez5/s7xM82x/ruTJwwRkhPaXRMPhfj9KrRw++f+T5mLt87WbMAwspqu8R9IU6najafRwF3bt6u6d7HLUfKx59HsnOJr+uOuCFq+7AmYy66wqUUiyZciRbvv7OpzyaF61Eog3BJCMi5cB6YLRS6n3L+g3AsUqpiK9PS5YsUdYZX5WVlVRWBnrHYsXpD95wjHvspoAunmip3mNS0DRMncFzQ/Ew9IaL2PD+p1G9VXc7eC/UznYGXP4zXp84L656xMPc5qUs/8PjfPbL8OOqZje9FrG7s+fR+weENbGD4v4Ntk88Shdq953h06XrNOI1gOxg9P3XUjt3mnd57ZvLAsZoBmPKaw/x5oxjElm1qBlw9Tl8fmnwbsVUMXXxw2lvBMZK4wWn+Iy5tTK76TU2ffJl0CElyUAbgklGRHoB3wJ9lVLfWNY3AZcppR6KdIxFixapefM6HvTz589nwYIFNDc3U1xcTHZ2Nhs3bqSmpoaWlhaUUtTU1LBy5UpKSkoA2Lx5M7W1taxevRoRobKyktWrV1NWVsa/Bu8bs77ioY20fvxlzPtr0pTsLPBPbQQUTxhG6zu+oRXqLjyJ5oX3Jqwqkp+L2h45dVLZrMlsfPmtoGV7NL3Kj99/z67Pv+XzEy+zu4oppXz6eLqfeDCfnnBpqqtiO92vOIN+PzmAJf1S301Yv/B8eh0ym1Xffs/ne4YeF+wEqvfajTUvBQ77iJfaX/yElbc94l0e9szv+ehA35ibOdUV7OzkJKRMY9xHz9La2sqnEzs3FMcuul90MuWHzKKurs42O6K9vZ3W1lbvMdeuXRvSEHTjZBHPiOxyv/VdgKgi5Xbt2pV33nnH+7ngggvIz8+nvr6e6upqKioqqK+vp6ioiJ49e9KrVy8KCgqor6+nqqqKqqoq6uvrKSgooFevXvTs2ZOioiLq6+upqKggt7IjMffM//mmuCkZ3I+5zUuZ/e1iht54MeMev5ncSkNKr+MPZvd/BnYrjb73Gmr3nUHFpJEADPp1YPdpw+nBB+o7nSmvPsjUxQ/HtG+w8XihGHx18Ij1eVVdmP3968z44FkqJo/2rs8uKaJ8zFAApr7xKHNXvMHc5qXM+vIl5vz4Jt3PPo4xDyxk92cDZ9El0ggsHzU4KiNwbvNSdnvoRspGDAxanp+fT0O/fvTdu/Nx0JKJZ8xofl01g6+KbiZ+70PmUNGrR8jyaW8/EfEYY/50HZUHpN9v02/PKRQXFzPzs44QGhIid2uiabrwBkpKStLWCOzz86MZeuPFcR2j/6WnMfvbxYx78AZ2X/q4TTUz6HHUfvSYMMpnXXm5/2ORsEbgxOfu8tHY67iD46pTdmFHT1vfs4KPCU8E1dXV1NfXx7x/7xPiy/BUXt6F+vp6W+0IjybPMcPhOkNQKbUewyM4xrNORPoCZUBUU5pycnJobGz0fuzsFgbDOADDTZ9bXsrc5qXez9RXjUC1WXm59Dr6AKqnT2DmJ39nbvNShl4XGEF9jw+fp3af6Yy+52omPn0Hc5uX0jD/SO/x5vz4JnOblzLoV7+g8YJAt/isr15m+ruLQISyEYMY9Oszmf3tYkbfew1TXnvIexwr2SVFURlJc354g6lLHmHPjwMHZY8wx+r5M2ThhUz6W4d7v/6nhwfdrqhvr4jf3/2wvSkd3I/iAQ0Rt/Vn5ufGDNVodFZMHk39SYcy58c3GXWPMROxfNRgykcPYewjvyUrJ4eC2momPvV75vzwBnN+fJO9vnyZyX/7A3Obl1LS2HER55QUIyL0Pu1Ius42Zp3WzJwcdb2H3RS7l2pu81Imv3AP4x73HfDf54xjA15YPIy+L3LGlkFXGiGEamZOZs6KJTHVbc+P/hrTfpGY/u4i5jYvZY/3n6X+5Oi6+yt3G83Wb38IWV7U0NP7f15NJXt+9FdG3+cbuqR8zFAGLLwg5Pm119evRFWXaBl97zWUjYxi3LM5USO3Sxkz/vMMo++/lr2+DowZlwx6HLFP0PX1px4R9TH6X3oac354g+nvPRV5407Q85gDGHDZ6fQ6+gB2fyuy4R+KPj/7iTfUVHHfXkx85g6G/fYSn21q950RdZ2stG/ZSqXl5RPgnYNO9/5fMrgf4Zi6+GEqxg+n19EHeJ8DQxdGn8UjGIOvsgwLijPeX/nYoWHLJzxpZHmZ+Mwd3nXT3gk/0aqgZ/C0j33POcFnuXRY/yhqmD5kXjJHe7gbuEhEXsWYNXwd8GI0E0UyAcnNQe3YSc+j9w86s9dnW8vFlt81cNuc4iJyiouY++ObPutr95nuszz8d7/0Ti6xegusjLhjAd0OmMnHFyykYvIoJCuLkv4NASnLJj53FxXjh7OrbQerX17KyDt/TduqFnLKS8gpNmKFTXz+Llq//JaeR+5L0x98b7QTn72Tigkj+GzB71h+Z/CQI6Z472/geeBu/mI5axa/Q9nQ/rR+9S1VU8fSvnU7b+5xrHe3QVeeTW5ZSejjWtjzk7+TZ3psRYS6fWeENR5DzYj0Z8OGDXTpYrwwjH34Rja8/6kxyy0C3Q4yuvQ+Oudq77qSAX06Fcy7evqEsBrKRw32/p9fW0V2USHZxYUh0581nHI4Dad0GPSzv3udFY//lS5jh/HmHsfS67iD2fzF8rDxz3Irytjjw+f5+PxrWfXiGz5lWYX5jH/sZv514Okh9obSof29abaGXHcBn1jiIAajfv4RbG36gVUvBBquhb26sfGj8OGbdnvpPta+/m8aTj8qaJvnlBazYWUzXbp0of/Fp/LFtR2e30FXnEV2UUFUE6DKRg6KGJi+8cKfUrvPdGr3mc7Gj79g1/YdZBfm8/GFCxlw6WlUTBjBiz12ByC7IN+7X0G3Ggq61QCGV1C1BXqKux+2N6VDG33GFHY/fJ+AGbmxjKss7t/gE/LEQ6+jD6Dp7g7v2bCbLvU53z3MWPY0Bd2N3LeFPWoZfPV5fHrpjZ2qQzDm/Pimz321uE9PZje9xlv7GgHVPenHIjHkugsC0uxVTBxJxcSRfHSu8eIw6Mqz6XXMgbwU5rezXqsrHv2rN9TQ4CvPIae02GccsDWndMNPj/CG0AlGycA+UenoDFXmbGCg01ly/Gk8/+SQObfBeGHzv48V9qwNf9AQdfJPGdow/0iK+/UOG0Q/nXCdR9DkWuA54F1gBZANpMcIYhuY8soD9DztSAb+6ozIG1vocdR+FPdviOk7ux8ym37nnMCEp28PmiN01lcv0/3g2Uh2NsN+ewk9DuuI1CNZvhdRxfjhAPQ8cl9G//Eqw2PWvavXCASoGDecnkcGH0vZxdzf/42y2yGzmfF+8IC1Hkr6N9BwyuFUTh5Nr2MOpKihp0/g37nNS32Mlkh4jEC7qa72C3sZ5dtzdmG+z2zonj/Zn20/Bg8BM/3dRez+5mOUDOzDtH9FToHUeOFPySktZvyijod+Vk4OMz97gT0i/O5WsnJz6HXMgZSawyCGLryAAZcExrLMKS+l/qeHM+Ta843ZgjWVjPmT7+zLuoNmMfubV6mYOJLR911DUZ+eTHn1QXodd5DPdiPv+DVTXn2QWV++5H3JqZoxIWQdc4oKGXP/dcz6wjcbhWe5ZmZw48zzO5YNH0ifnx8d1AgsGdiH7IJ8bxtbY6A1nn8yDT/19Xj5e8+t4YwG/+askBq8xzy3I25a2dD+dBkzhNLB/Zj03F1UTh6NZGdTvedkqqaPJz9EBoTZTa/R85gDKGrw7RIfcu35VE/v+B1r951BpTlExUq0RuCYBxbSfV5HaJsP/fKTF/XpSfGABqqmGwZFTnkpPY/aL+iLi8cI9ND7+IPod+5JTHzmDqa/9xRV08Yz5gHf86ls+AB6HR+++zNYHvGs/DymvPwnRv/xqvACLVSHOf+8x83LC1vuPwxoyuKH6H/xqez19SteJ8GwGy8J2C+7sIDC3qFDaWVb7sV2kmcZFlXQPYJRFoGs3Bj8XEHars/POibPRJvbWXKy6TImvEcynXClIaiUaldKna+UqlZKlSqlDlFK2R/NM0WU9G+g8tR5UXutPGTl5DDgstO8y5NfjH6smWRn0/+iU6mc1DHmxDreIys39DiiaL1gUdfFczH7vb2NvH0BBXU1nT5e6ZBG+pxxLMN/FxiE2Up+bfJikm/Y4BcgNcZulP6XzGfKKw8gebk0Xuj39ipCcb/eTF38MEX1oce8eWg890RmffGSj8EOxjAGazrEmMaUBdGXV1HG4CvODjs+p/vBHZMaaveezrS3nqB0cD/6/Lzjva/fuSdRMqCB0sH9yCkpJr+mkr2+eZVxj0ae0ZlTWuz9P79bjXc5Ky+XfueeRN1Bs7yG2si7rojqdyzs3R3oaOPtP3ak8+vzi8D31e6HzGZu81LqDjCypvQ90zK2KsJ5Ee0DfezDNzDusZuDGjnG1wjDbriYaW/7vjDkFPumURtw6WlBvXhWAzKct7lmrynkdzUCMW9fuYaNH/p6XhvPOwkRYfzjtzC76TVmfhI8s8O4JwJn1Et2Nv0vPIWKiSMp7FHL+Cdu8Q6/AGPIzthHfsvQ6y6Iq7s3Wvyvo2CUDRsQttw/DWlJYz39zj6B7KLwuej7nRc47rKfpfvT/+U9Fvb6+hV6+w23kNwcZn+7mMkv3EO3g2bFdfyKSaOomT018obW7/c7v3sctR+VU8d6l+tPCTE8xG+/UNeJlbEP30jPY8NnaEoWrjQE3cCOHZEH9Afdz9JNWxbnOIf2rR15hK0pjvyxplsLNpElEsFuWnYiIgy87HQfL6YVz4NpymsRJ5zbhn/7RmMHejxd1nh3uWUlFPbqxpxvF1O79zSf7VWQWcp2YA37ES3BHjwj74oizmWIl4yi+u4Mue4Cxjx4Pf0vDBwbm12YH9XN3Erp4Eaf5f4XnsKoO3/jNdS6HRg8vR3g04AerZ42bt/WkdrM2jXrz6i7r2DOj296u2sBiPDAzu1SGra8o3oS9e9R3N8Y0xrM2Cqs7077lq0B64ff9n9R18OD/5AQMLz+HrLy84LmYwe8AYk7Q79zTvR60Yr7dIzzrJwyJtQu8RHm956x7GnG/+V3dBkzJOwhQnn7/QkwyLIDr5vqPTvGItuR9ze7qIAhfhOwJCebrLxcn+ElwZj97WJG3vlrJv89eDgYMBwbQ68NHkO1Yf6RUdVxxaPPU73HRIbeeDFTXnkg7skwVmpmTiYrxPmZbLQh6FDq6oIPao2ENfdmqJtoLITz+mXl5zF+0W2MvPM3ISd/hCNkwm6LRzDYjLs4xyJ7mfHB87ezFwAAIABJREFUs8xZsYS8ijJ2+2f0aajiIaB9/cQM+23gpBBPvt8Wy1i7rPzQXUsFPbqGLIuF/peeRlGfngwJMqkpIn76dnvpPspDTG6wepeKw0wa6n38wXTdK/5sDh6y8uwZcr1z0xago409Xh+r9zEU/sZaJG+73d54gN2XPMpey18NamyJSNDegeww52E0DL76PGZ99c+o9ERrBHjod95JFDX0oPvhvi+CjRecAiIMuSa4sdFZ8vzGc+eE6dEp6N6VKounKhTL73wsqu/2D9DtGS9rxb/bPxTWqBedxXr+ql2hxwhm5eXS7aC9KB8d3hD27/730OfnoWMl+usUEXodfQClQxrJLS9l4vN3MfX1Rxh+y+XklJeG6DkL/3CxGtXpgDYEHUpzc3NM+6kkpdTxp2rKGLodNCumB1P3w/Ymz7x4p//7Se96RceNxGoQeN7g6+LsevAgIl6juWxohxe1xkYjw5+A9rXcQHufPI/uh82l/pTDfGb4em6KOzdtjuo7go31jId+Zx7HtLeeIK+iLK7jNJx2FGXDg4elAd9ZlNYsK4km0nitaPGkl/O0cd8zj2PQFWexW5DQUJEI5n2zUtAjvnFYoQjpuRQJeo8pHdqfkunj6HdO5DyvOzYG5mQuG9oY0A0dinAvP8Hof8EpTHv7zwFDbRrPO4k5379OiSXqQKgxlNFQf9KhdLV45WMa4+ZH/4tPjWk/f+82QH5NJTWzjLGv1iFE/uRV2TMuOlpvdSz4G91WxjwYfqJYxbjhlAxooMcR+zDzsxcoHzmo070Hdjkh7MKts4YdT3FxZO9BMKIdDBsNlVPH0vLGewHhDuwmuyCfEc/fETiBIgTjHruZbSuafcJ4JIJEGiH+7Wv13tbOnUZWbg6DrzRCMYz/y+/4YuEfGOSdPJRmd6EosN5oI4XLSVSXdiSq95hoy3Fq9zPiB3raOLswP2CCSCSK+zfQtnZdQHgQ/+/xzyudcILkRAfDMzno9/8X1TUcLMtPfm1V1FXYsSHQkIwVz3WXV11B25p1lAzqG2GH4Nfe5L//kbKRg2h5cxmr/v66bfXz92JGS3Fj76B1HX3vNWz69KuQ8UEBCupqaP2iKWB9n58fzTe/jz5mq4gw44NneW3kAZE37iRhDbdOTFYOeZxIll6aWYLaI+hQsmPs1rUjX6qH8X++lRnvP0PPn+xv2zFDEVRviAs6Kzcn4Uag8f2Jy9rjr7dkQAO9jj+YnsceSJdxw33KqqaOZdKzd3oHiEfbxZNWWG+cEW6iyfZqd50zleyiQnqEmMXeWXJNj2ms1zDA7kseYeYnfw87vGP0H6+iMERctEQhIoTKZhVJ75AQ470AyIr8W3niDg64NLQ3K1YmPH07vU+ax4hbw08o86fnT/Zn5v9epHz0ECQrK+RvEysxd/2HuMay8nIjesCsGjy9L1n5efS/6FSGPtG5lHoFtdU0nHYUAy7/Waf2K+oT+/3d5/4RrcHmt511iJWH0fdbYqqmmSGoPYIOZePGjVRUVHR+RxvPTxGJaZZuLATV6+D0if56JSsraEDxYFTPmMCKx/4a180y6fhMpgj/cGv98ttE18YH/5A1sWB9sK59/V0gjms4zWnfvCXo+kh6w3n9opnFOvyWyxl+y+WRKxgDJY31DLk6uswz3n2mjwvsLYnlnhVml85GjvASwnMbXX069suvrWbWFy+hlCIrL5fW1s5PMvF4rT+/8vao9+keYmLfkIUXBo2X64Ol/lEb0n6GnWrf6bPc46j9fCfJaUNQkwxqamIzwFI1RjBegup1sCEYa/sC1B04i7yaKsqGBo4DSlssN87tq8JHerJr0kaq6H7IHCC+Nk4r/K7Dtpb1QTeLqDfMw9POiW3JYuTvfxW4MoZ7ltoVejhPZ8dDepCsLG/g6c5XqOPfvKouPpOcKioqWB5iN2u4sXgJdar09osfGgzrMzBctIvOVCBgEld62YG6a9iptLQEz+IQkRSNr4qXmPUmAM+sxN4nHRphy9iJR6+IUDVlDLld4pu0kUysHp8d6yKkBE/CC4DdXXhWCuuNOILpdE7HQ8BvFWI2aFx60+zBGg3rNtkzVlEkAY9xEdSOnZG3C4K1vf0D6m/cGHjtemYZdyZubSS6Hx48/WA0WMOZxeoR9PRK1JxsxDht+NlPwm6farQh6FBifVDlxjmjM1UE05vIh3U4Bv36TGZ//3pUwYNjJdHaus+bk9DjdxrLjTMrP3QsPch8R7Dn4ZOq8zfRWD0udQfOZOSdRnaQSHrDPZSTGczdPuy5Z2UXFcQUfzUc21Y0s2tnbIagzwXo32UaRN/0d//CtLef8Jl9HS8+sTQ7SbElt3usHsFSs7el62lHMGfFEp988caBtSGoSQKxdiv1Pet46g6YydhHf2tzjRJL8K7h5NfDg92hV/xJeLdhIrwMNlHQPYL2TDegzGeEU7qGt69c67NcYWYfKuhRy6i7rvBmkIikt8KStcifzobvSAeqa4LEuIvx1O1sbMRIlAzux67tsSUlCGcIVlQGz2dv9+S9eOJjWs+lYIG1g+/ju+x5MampqQk6bCFSWKdkk753e01crFy5Mqb9cstKGHX3FdTsMcnmGiWWYHq3/RDbb5AJxNq+0WJHCik78emuiXXcTjoTZDJMots4Wezavt1nud+ZxzHspkuZ9Lc/+KwPpXf2d6+z1zevxj7xIU1ZtTpI1g8bXmIGXHYaedUVjIgyW0sw8msqo85K4o/ymWzhex9Zu3at/+bpTYxdw5Gu4bWL342rWnaT2aOqNSEpKXHWTTMSwfRu/S62oNqZQKLbN90G31u7a3LLIwSazUCPYPvWDmNpe7MxGcYp17B/hois/Dx6HrVfwHah9Gbl5oAluHLvk+bx7b1/sbeSKaCkNMh5bMO5Wz56KHt8+Hx8XlLJinm8uG+kCN86FEWZ2zptiHHypMeTGOqcLm5MXqD7aNAeQY1jCZl6ThORvOr0ClsiImSbsw8jhr1JhiFo83fssMyk7Xl04uNuJpO8qthTjgUjVGrBjMDqLQtiqNkxLlSysuLuKpcsYVeMs4Y9Oc2NA6VXz0JnyY7WcPXTGS4nOEDV9AmxVikhaEPQoWzeHF0aMacQTG8GOoaiJtHtW2tJdZUuNP71dmZ+9gI5JbFlzUlnrF4zz2xup1zDFRNH0v+inzL24RvDbucUvdGSML022F7ZxUVh83SHo7i/ZbKFn4G0dWt6jY2LxMi7fhPVdv46iwf0AUK3cbqNadVdw2lI7T7T2N48JGzS8YjHqE1MDtF0JaheB1uCiW7f7NL0M7a69e5FbkHkWGPdDt6L1f98y5vVICMIcq7a1cYVk0ax7u33bTlWLIhIVDmE3XDPKuxthAaq3G1MUL0VE0ZQ0KOW0sH9On3s+lMOY/2yT+gyfnjkjSOQV1Hmk7M7ZvwMnsrKKr6J/6hJI9bYhp681yHP6TgmsyQCbQimIQMuiT8F0urVq+nVK7Y3ukwkuF7nGoKJbt90e2OF6DV3O3QOJQP7UNy/IfGVsgkV5Fy1q42tE2081J/audzFycAN9yzJymLOj28iInz33XcBenPLS5n21hNIbucfzZ7c4nYhIhT3rw+aNzj6g/gurlsfPJi4XQxccAZd95pi2/GijyMYfHXIczrNbq/pZZZqbCMdH+SJJFHjbdKVhLdvGp4/0WoWEcqGD4w4Tice7MyCAAQNsmxXG1eb45HSbYC6P265Z3l0htKblZebPr9FvPfQCBk27KZmj0kU97PxPI93rGWI/SXNLEFtCDqUyiDxmpxMUL0ONgQT3r7p8iCykA7n9Ijb/o+qaeOpP+UwW4+7a3tbwDq79NbPP4JRf7yKic/c2bEyDa+NdGjfZOIGvf6GUHl5YhMW5HUNzEddP9/wfofKPxyOWDOLeAjZxmnWNZxetdHYxurVq1NdhaQSVG/6PetsI9Htm4Z2YFqc093nzWX8E7fYPmFlS9OKgHV26c3KyaFuvz1sn71rN7HqjSedWCpJh/M5EkMXXgQiDPvtJbEdwO9Gsm5dYruG84Jkxhq04ExmLHua4bde3vkDRh1PNfh2odo4bTy+JnqMoEMpK8vMVHGxElRvGno97MJt7QvO1pyVnxewLqF60/DaiEVv6dD+jIjlAZ8GZML5XLnbaOasWBJ7pg4/e6eioaftk0XKxwxlw7KPQ1dBhILuQbK4REFWnMHrQ7ZxetmByfUIishrIrJdRDZbPvv5bXOBiKwQkVYReVlE+vqVzxWRj0Vkq4h8JCKz/cobzf1aReR7ETnPr7xIRO4VkXUisl5E7hGRwsSpTg3tMcaAylSC6c31S3juJBLevmn2xgoOP6eD2GWO1huEWPRmcpaZTGnfThuBlpeM8hG+MR+zKsqY8PTtTF38sB1VA2D0fddQ3L+BoddfaNsxc7sYwb4LukVnQIby8IVsY901zBVKqRLL53lPgYgcDVwA7A/UAJ8Az4pItlneF3gSuAYoN/8+JSINZnk28Bzwqbn/AcBFImKdIncLMMj8DAAGA5mVWDcKWltbU12FpBJMb+N5J1M2cpA3qb2TSHz7pp8h6OhzOoiHztF6gxC1Xms6vjR8YYkWx7av5VyumDjSp6i1tZXKSaMoGdjHtq8rqK1m9yWP0OvYg2w75m4v3c+0f/05qKe+M4Rq43QbppFeZimcCtyllFqmlNoCXAr0Baaa5ccD7ymlHlJKtSmlHgaWmesBpgH1wCVKqS1KqWXAXcBpAKbn7xjgl0qplUqpVcAvgeNFxOZpgKmlrq4u1VVIKsH0lgxoYLcX7/UmtXcSCW/fNHzA6nPa2Wi9ziDcqINM0VzYqxtF9T2i3yHE/dJf79iHbqD74fvQ+4RD4qme7aTCEDxbRFrM7t1LRMQa5Gok8J5nQSm1GfjCXB9QbrLMr/xzc79g5QOBAr9jLAMKMbyDUbFz506+/PJL76elpSXaXZNGc7Nz8+wGQ+t1Po7WHOTp6Wi9QYhFbyaHiHJb+4KDNYd4b/bXWzNrN0bcenlCQ1vFgi2TRUTkfjq8csG4Sil1OXAJ8BmwERgPPAyUmesBSoENfvuuN7cJVz60E/vjt43n/6hH7q5atYp58+Z5l+fPn8+CBQtobm6muLiY7OxsNm7cSE1NDS0tLSilqKmpYeXKld4k1Js3b6a2tpbVq1cjIlRWVrJ69WrKyspob2+ntbWVuro6mpubyc3Npby8nDVr1lBeXk5bWxtbt271lufl5VFaWsratWupqKhg69atbNq0ie3bt9Pc3ExBQQGFhYWsW7eOqqoqNm3aRFtbm3f/wsJC8vLy2LBhA9XV1WzYsIEdO3Z4y9NF07Zt27zl/pq2bt1KU1OTozSFayelFD/88IOtmnI2bvSe0zt2tNHU1JRUTZHaSSnF999/n1HtFPW5N9QYCp1TW8XatWsB2LRpE9u2bbNVk4ctW7b4XC/pcI/Yvn27t05WmpqafDSxc4e3bOMHn9HU1JS8drLx3Nu5cyerVq1K/bmnFIXbtwf85rFeT+stjpGmpiYfTe3t7axcuTImTf71S6d7eet6X7Nj5cqVFBYWsmnTJjZv3hxS06ZNmwDYsGF9wjWFQ2xJci1SguFpC8UWs6vXf7+jgWuVUr3M5fXACUqppy3bfADco5S6VUSeBpYrpc62lN8C9FJKHSIiZ5v7j7KUH2zuXykio4D/ABVKqfVmeQXQAoxUSv03Gr1LlixRBZZUV5WVlWkXE6q1tZXi4vRLE5YotN742fTpV7y5x7EATP/3kxT2TK9uHCe3sVKKdW+/T8mgft4QGInQ+0LdbgD0PnkeQ64619Zjx4tVr6eeAHObl/pst+KJv/PhmVcARk7cvb56OXmVtJF0Op+t137ZyEHs9uK9MR9rwwef8dack4DAtotHc7hzItXs2t7GP+pneJc99Yuk95OLb+Db+59k8NXnUX/SoQmt47Jly96bOXPmuGBltnQNK6U2K6XWhPkEGIEmu/B1qn4AeBOEmgZmf3N9QLnJaL/yASJSHKL8f8A2v2OMBrYCn0ejFSAnJ4fGxkbvJ92MQIA1a9akugpJReu1mTQcI+jkNhYRKieP9omD5mS9wYhFb9c5UyNvlKaka/vGPRM7jHMpXTUnikzRm7QxgiLSRUT2E5ESMRgNLAAet2x2NzBfREabEzuuBL4B3jDLHwDGichRIpIrIkcBY4E/meWvA03A1SJSaHoA52NMGEEptRV4CPiNiHQVka7Ab4AHlFLbEig/6ZSXOzd0SjC0XufjNs1abxSk3/tK1KRr+8YcMzAK0lVz3IR4cY6kd8dGczpDise6JnOySC5wObACY4zg48AjdIwPxJwFfCPwV2AtMBw4QCnVbpZ/BRxiHmej+fdgpdRys7wdI/TMMHP/vwHXK6Ues9TjLAzvn+fzP8DebN1pQFtbYMoqJ6P1xo/atcv7f25Zie3Hjxfdxs4mWr071neMZRVJt8AX0ZO27Rtvb0AYoyZtNcdLiN8skt4d64xzefmdj9pepc6QtMwiSqnVwKQotlsILAxT/gLwQpjyL4GZYcq3ACeZH8eydevWVFchqWi9NmC5geeUpsfYJSu6jZ1NtHq/e/DpjoU0HMIQLenavhJ1WrXghHNupatmOxnz4PXe/yPpbTz/JCQ7i4ELzkh0tcKiU8w5lEyJ12QXWq8NpHkoDt3GziZavW1rO2Zoxmu0pJJ0bd+cstLIG8VIumqOG8tpWD1tvPf/SHq7jB3G2IduSFStoiZz/eqasDg2XlMItN74SXM7ULexw4ler+VETbNUXZ0hXdu38fyT4zxC6BtJumqOF58MN5b/M0Vv5l5FmrD4x11yOlqv83GbZq03BJY3lkxOMZeu7ZvbJeqQusEJ80aZrpptxXJOZopebQg6FGvwWDeg9dpAmrsEdRvbR0FddcKOHStR67WepxncNZxW57PlN83tkrh6pZVmOwnxQpIperUh6FA82QncgtZrA2luCOo2jp8JT99O75PmUX/KEbYfO16i1etzmmawRzCdzmdrYol4IwaES1KRTpoThuWUzBS9erKIQ6moqEh1FZKK1ut83KY5EXorJ42ictKoyBumgKj1+nQNZ64vw7Hnc5j3ScdqtryQWIcrZIrezL2KNGFxwzR9K1qvDaS5R1C3sbOJWq/1PM1ch2B6tW+Srv200mwjoSaLZIpebQg6lG3bHJUoJSJab/ykuR2o29jhRKu3oEet9/9EZsFINGnVvnZe/GGOlVaaE4XFEMwUvZl7FWnC4th4TSHQep2P2zRrvcEZ+Mufdyxk8BjBdGpfW18CwxwsnTTbzbjHb2bcYzf5eAczRa82BB1KpsQvsgut1w7S2yWo29jZRKs3t6Ijf2smB5R2W/uCszVXT59A9YyJPusyRa82BB1KQUFBqquQVLReG0jzvmHdxs4mJr0Z7BFMq/a18doPN2s4rTQngUzRqw1Bh1JYWJjqKiQVrdf5uE2z1hsFGewRTKv2TdIYwbTSnAQyRa82BB3KunXrUl2FpKL1xk+4N/l0QLexswmmt2RQ37D7SAZPG06r9k3StZ9WmpNApujVhqBDqaqqSnUVkorW63zcplnrheyC/PA7ZfCs4XRqX3sni4QuSifNySBT9GbuVaQJy6ZNm1JdhaSi9dpAejsEdRs7nFj0ZnKuYae2b7ieBadqDkWm6NWZRRxKW1tbqquQVLReG0jzrmHdxs4mJr0ZPEYwvdrXvmu/YuIIqvecTH5NYFaN9NKceDJFrzYEHUqmxC+yC63X+bhNs9YbBRnsEUyr9rXxJTArJ4dxj9wYtCytNCeBTNGru4YdSqbEL7ILrTd+ihp6INnZ5NdW235sO9Bt7Gxi0avjCNpDsiaKpZPmZJAperVH0KFkyrR1u9B64ye/axVTX3+YvJpK249tB7qNnY1Vb8Wkkax7+wO67jM9YLudGy3jrjLYI5hO7bujZWNSviedNCeDTNGrDUGHkpeXl+oqJBWt1x6K+/VOyHHtQLexs7HqHfvQDax790Oqdh8XsJ3VeyWSuZ1a6dS+OzdtTsr3pJPmZJApejP3KtKEZcOGDamuQlLRep2P2zS7WW9OSTE1e0wiKyeIr8Lai5nBHsG0at8kdbGnleYkkCl6bTUEReRMEfmXiGwRkS9DbHOciHxlbvMvERnrVz5ORN4xy78SkWP8yruKyJMisklEVovIdWJ5LRSRbBG53izbJCKLRKTa7xhh6+AEqqvTc5xXotB6nY/bNGu9kcnkMYJp1b5JChiQVpqTQKbotdsj+AOwELgqWKGITAXuAE4HKoBFwN9EpMwsLwf+bq6vAE4D7hSRyZbDPGz+7QlMBA4GLrCUXwwcaJb1NNc9GG0dnEKmvInYhdbrfNymWesNgXViQwYHlHZb+4L7NGeKXluvIqXUX5RSi4AVITb5KfCkUuofSqntwPXAdgxjDuAQYCuwUCm1XSn1EvAUcCqAiPQBZgEXKKU2KKW+Bq7DMBg9nApcp5T6Wim1AbgQmCsiDVHWwRHs2LEj1VVIKlqv83GbZq03ONtXrvX+n8E9w65rX3Cf5kzRm+zJIiOB+z0LSiklIv8x13vKlynfuezLgGMt5RuUUl/5lTeYHj0BegPvWb7jKxHZCIwAlkdRh4js3LmTL7/s6PmurKyksjK9ZlpmSvwiu9B6nY/bNGu9wWnftr1jIYM9gunUvpKdnZTvSSfNySBT9EZlCIrI/cDxYTa5Sil1eRSHKgX8faXrgbI4yzG38bwfxvMdEVm1ahXz5s3zLs+fP58FCxbQ3NxMcXEx2dnZbNy4kZqaGlpaWlBKUVNTw8qVKykpKQFg8+bN1NbWsnr1akSEyspKVq9eTVlZGe3t7bS2tlJXV0dzczO5ubmUl5ezZs0aysvLaWtrY+vWrd7yvLw8SktLWbt2LRUVFWzdupWVK1cycOBAmpubKSgooLCwkHXr1lFVVcWmTZtoa2vz7l9YWEheXh4bNmygurqaDRs2sGPHDm95umjatm2bt9xfU1NTEyUlJY7SFK6d2traKC4udpSmSO20Y8cOCgoKHKUpXDutWLGCAQMGOEpTuHZqbm6mqKgooqb29p3ee29bWxtNTU1pqylcO23bts1b71S3U9UeEygaM4TK6eNZu3Ztwq6n7du3U15eHpMmK01NTRlx31u+fDl9+/ZNi3MvHBJNIEkRKQEKwmyyRSm1xbL9CcDlSqlGv+O8D9yvlLrZsu4Z4Cul1LkicjPQoJQ6yFJ+DnCsUmqMiBxk7t/FUj4awyvYBcMQXAeMVkq9b9lmg3mMZyPVIeKPASxZskQVFHT8HOnoEVyzZk3GDFS1A63X+bhNs9YbnG/vW8QnlxiZKwZcdjp9zzg2wh7pidvaF+LT/M4hv6Bl6TIqJo9m4lO/t7lmiSGd2njZsmXvzZw5MzAeE1F6BJVSmwE7Ag19AIzxLIiRMXwU8KSl3H+s3mhzvae8XET6muMDPeXLzfGAiMi35ne8by73xfD2/TfKOkQkJyeHxsbGyBumkOwkufrTBa3X+bhNs9YbHOWQ8DFua1+IT/O4x25izWv/onLKmMgbpwmZ0sZ2h4/JEZECINdYlAJz2cMfgENEZKaI5AHnYXganzLLnwKKROQCEckTkZkYE0juBlBKfQO8DCwUkTJz8shFwF2W77gbuEhE+pjjBq8DXlRKLY+yDo5g48bkRIpPF7Re5+M2zVpvCKwBpTN4jKDb2hfi05yVl0vX2VPJKS6ysUaJJVPa2O6r6HKMWb93A33N/7d6CpVSbwA/wzDGNgCHA/sopTaa5euBfYDDzPI/AKcppd6yfMfRZr1XAO8Cz2CErPFwLfCcWbYCyAa8sQgj1cEp1NTUpLoKSUXrdT5u06z1RkHmOgRd177gPs2ZotfWWcNKqQXAggjbPAA8EKb8XWBCmPJVGF7CUOXtwPnmJ6Y6OIGWlhaKijLnzSletF7n4zbNWm9wlNrl/b+wd/dEVimhuK19wX2aM0Vv5vrVNWGJZhKQk9B6nY/bNGu9ITf0/lu7z/QE1SbxuK19wX2aM0WvNgQdSqa4pO1C63U+btOs9YbA8myVDJ4s4rb2BfdpzhS92hB0KCtXrkx1FZKK1ut83KZZ6w1BhnhZIuG29gX3ac4UvdoQdCiegJNuQet1Pm7TrPUGJ1O62yLhtvYF92nOFL3aENRoNBpN5rDLGYagRpMuaEPQoWzebEf878xB63U+btOs9Tobt+kF92nOFL3aEHQotbW1qa5CUtF6nY/bNGu9IXBI17Db2hfcpzlT9GpD0KGsXr061VVIKlqv83GbZq03OE4ZI+i29gX3ac4UvdoQdCiZHFYhFrRe5+M2zVpvcEoH90twTZKD29oX3Kc5U/RqQ9ChVFZWproKSUXrdT5u06z1BqdiwogE1yQ5uK19wX2aM0WvNgQdSqa4pO1C63U+btOs9QZHdw1nLm7TnCl6tSHoUMrKylJdhaSi9Toft2nWep2N2/SC+zRnil5tCDqU9vb2VFchqWi9zsdtmrXe4Ei2Mx5bbmtfcJ/mTNHrjCtKE0Bra2uqq5BUtF7n4zbNWm9wcoqLaLzwpwz81S8SXKPE4rb2BfdpzhS9OamugCYx1NXVpboKSUXrdT5u06z1hqbx3BMTWJPk4Lb2BfdpzhS92iPoUJqbm1NdhaSi9Toft2nWep2N2/SC+zRnil5tCDqQlpYW7r77blpaWlJdlaSg9Toft2nWep2N2/SC+zRnkl5tCDqQlpYW7rjjjrQ7AZ999tmEHDdd9SYKt+mFxGlO1DkZL5nYxvH8lpmoNx7C6U3XczJeUtHGqfwtM+mc1oagJmk899xzqa6CRuODPiftQ/+W9qB/R/vQv2V0iFOCcyaTf/7zn6uBplTXIxQ7d+7Mbmlpqa2srFyZk5OTNvPXN2zY0KW8vHy93cdNV72JYufOndnNzc31dXV1TW7QC4lr40Sdk/GSied0PL9lJuoEdtP4AAAN8ElEQVSNh3B60/WcjJdUtHEqf8s0PKfrZ86cWROsQBuCGo1Go9FoNC5Fdw1rNBqNRqPRuBRtCGo0Go1Go9G4FG0IajQajUaj0bgUbQhqNBqNRqPRuBRtCGo0Go1Go9G4FG0IajQajUaj0bgUbQhqNBqNRqPRuBRtCGo0Go1Go9G4FG0IZjAiIqmuQzJwi05/3KbbbXrBnZo1zkVEslNdB03n0YZgBiIi40WkBLP9RMTR7ajM9DdueWiKyDARKQRccVMVkd1EpArIN5cdfT4DiMjxIlKtlFJueHiKyDkicqyI5KS6LslARH4mIleJSHWq65IsRGQ+sEVEZpvLjr5fi8ieItJXRErN5Yy9b7nionQKIjIN+BOwBVgP/Bs4Sym1K6UVSxCm3puBR5RSN2AYvumQszEhiMhewE3AZmAH8DpwWUorlUBMvbcAG4HVwI/AqU49nz2IyBkYup8E5gGO1WsaBTcDxcD+SqmdKa5SQhGREcAjGC9xF+ICZ4t5Hf8ew55oBgYD/1AOzV8rIjOAuzDu0ZsxnsVzM/m+5fiT1CmISDfgBuB6YDhwN3CYiFyX0oolABHJMd8u/wi0ASeLSFelVHsmv3WFQkTyROQ3wJ0YBsIs4FngABHZN6WVSwAiUiAit2K0761KqUnAMmB3ERmU2tolDsu5q4C/AHuLyCzTK+iol3Kzje/BOI9vUkrVK6X+m+p6JYETgWeVUoOVUs8ppVZ5CpzkIRODLiLyb+B+4DqlVF/ga6DM3MZxnm4RGQncDlyrlBoGXAoMF5FDU1uz+HDUzcfhNAKFwHPmm8efRGQt8JSIvKCUejW11bOVdoyXlEsxbizXA1cA8zEeok4jG+gNnOJpRxF5EDggpbVKHDuAL4ExSqm15rpxGG27JmW1SjAWj8Ew4COM3+A2YJADPWU7gQHAg0qpPwCIyKkY3t9PlVIfpLJyiUBEajGu2WPM5auAUox2vksptT2F1bMV8+VlC/CSUuoSS9HnwHTgCqWUE3tvpmL0XjxqLr8LfAIsT1WF7MBx3hWnYXmLrDQ/6831WUqp54EXgEs861JSSRsRkWyzS+HPwJNKqWXAg8BsEZngtDFVpt6twELgbfNNWzDauR+wLaUVtBlTbzvGg3GtiEwTkVVANfB34EQRuczcNuPPZysWPa3A28DDQFcROc4sr0hV3exERHJNw3YhMEhEHhORFcDBwFnAwyLyfymtpI1Y7tF1GC8zhaanbATGdXwu8GcRyU1RFW1HRHKUUm0eI9DsxRGgBdguImWpraG9WK7dnRhd37uZy8cBI4F9RORnqaibHTjqRusERGSCiPxORKZ6Vpl/nwUKMN82gTzz7yXATBHpl4ljFILo3QWglFpj0fMq8Bbwa+s2mUgYvZ+YBqGYhvBYjPEnr2eyQRRGr8c70g7sq5SaqJQ6D2Pc6xUiMiITz2cIqhnw8QgOAboppT4GfgncLiLvAvtmorEQRG87gFLqOQyPfl/gOKXU3sCBwNXAWSLSKyUVjpMw7fsBRq/NpcCrSqn9lVL/h+Ht3hfDU5aRXcT+mq0ebFPPLvO+9QWGp39jiqpqC2Ha+C7gDmC+iHwPXIQxFnQzcJuIHGzun1FtrLuG0wTzAXA5RvdnIdAiIh8opTaZ3r9dInILcKWI/EEptc30jK3F6GYaCXyVMgGdJIJejzEEgFKqSUQeBm4SkSOUUo+LSJ1SqjlF1e800eq1GAtjgf8opXaY+xcrpVpTUfdY6ITeN83tszG8Ke9jdLPMBjJqTFk0mkUkH8Nr8lcxZhvuhfFCnquUeiiTHiAR9Oaa5+6lQIVS6iPTG7xKRP4LrMToIv8uZQI6SQS9Hk/39cBvgc/NF7h8pdQaEXkLY+zvy5k0iSKac9rU49H0IcbM4UlKqbdTVO2YiaA3Rym1Uyn1KxE5AEApdYRl370xJn89lUltDNojmG7UAScBv8F4e5wOPsbB3Rizsu4TkXLzxpOLMebqneRXN25C6fVeRJYH478wutIuEmOiwQ8iMiS51Y2bqPSamvfF6B5HRK4GvhCRvkmvcXxE1Gthl3me98R4uflbsippM2E1m57QWgwP/6cYEQAuBEaISGOmPUAIrXeH+XeFUuojc1vPfawcY6zgv5NbVVsIpdczHu7PGOPGhgD9lVJbxQgFtR14OvnVtYXOXMceMu08thJKr3Uc7zFAF8+C+YKXRaa2sVJKf1L0AfL8lusxuoLzgRcxZif19NtmOMZb9BLgWgzD8I8Y3caSak126Q2mBeNNe5e57fBU60mUXoyJIx9i3Ii+ARZjTChIuaYE6a3EuIkeZeq9JRPO585qNsuLMGaH/w3Y07LulxgPoLTWHEcbF5t/j8Toufg1Ro+UY/RathkKNGF4tG8y79ePAKWp1pPINrZsvx44PdU6EqQ3x/x7iPksuhg4A6MX4ykM73fKNXX6N0h1Bdz4AUYBL2PMPLoVKAiyzREY4+JOsKzLMv+OA07GMAAPTrWeROk11wvGzLu7gA0YschSrilRes0yT2y5ZcAhqdaT4PbNAQ4FPjANhLQ/n2PV7HloAr0813KmfOJs4yxgDsYQlq8zoY1tuEePxJhI8HvgwFTrSXQbW8prMF7Yx6VaT6L1AueYz6V/AAelWk9cv0WqK+C2DzADI3DuFcDhGB6QPwMTzPJsy7YPA08AQ8zlklTXP8l6iy1lE1KtJVnti/FG+vNUa0mC3iLzbxUZYODbqTmTPja1cTUwL9VakqA34+7RdrVxJn3samMy7IUu1MfzhqpJEiJyE4aBc6q53IjRVfQlcLFSar1noLWITAauAZ4BumFMIDhRKfVtiqrfaWzQe7JSanlqat95bNB7klKqKUXV7zRu0wv6GnZ6G7utfcF9mt2mNxJ6skjyycFItwSAUupLjPEjjcB+5jrPQOu3MCaC3AjMBS7IwJMvXr3Lk1zfeIlH74WZ9MA0cZte0New09vYbe0L7tPsNr1h0YZgghCR/iJyh4j8RkSOtRTtAApEpL9l3SJgKzBaRPLNiaNVIvI2MBqjS2WEMoIrpyVar5d49L6XRAmdwm16QZ/TliJHtrHb2hfcp9ltemNFG4I2IyJZInItRmiEzRihIq4REU8anicxBqlOEjN4rFJqA/AGMFMptV0ZrAXuVUpVK6WeTL6S6NB6tV4cpBfcp1nrdbZecJ9mt+mNFx1Q2n5+gpFaaKxS6ksx4gt9ARwqIr9VSr0hIq9hzCj7DnjN3K8ZIzZePrBTKdWulLo7+dXvNFqv1vsaztEL7tOs9TpbL7hPs9v0xoX2CNrP34DHzZMvRxkBZMuBZtWRVusyjByy14rIMSIyG/gV8KL5JpJJybq1Xq3XSXrBfZq1XmfrBfdpdpveuNCzhhOIiDet1A3ANqXU5WKmIhKRBoxYgOOAfsBNSqk7UljduNF6tV4cpBfcp1nrdbZecJ9mt+mNBW0IJgjpyA+cg5El4nKl1KIg25UqpTYlv4b2ovVqveZ2jtAL7tOs9TpbL7hPs9v0xoruGk4cHgt7IlDuOflE5Ociso93I+ecfFovWq+D9IL7NGu9OFovuE+z2/TGhDYEE4TqcLUOBe4XkREi8m+MnKJbUlezxKD1ar1Ow22atV5n6wX3aXab3ljRXcMJRESyMPIQ7omRiPsapdT1qa1V4tB6tV6n4TbNWq+z9YL7NLtNbyxoQzDBiMg/MKatn2uZreRYtF5n4za94D7NWq/zcZtmt+ntLNoQTDCe2Umprkey0Hqdjdv0gvs0a73Ox22a3aa3s2hDUKPRaDQajcal6MkiGo1Go9FoNC5FG4IajUaj0Wg0LkUbghqNRqPRaDQuRRuCGo1Go9FoNC5FG4IajUaj0Wg0LkUbghqNRpNAROR+Ebky1fXQaDSaYGhDUKPRaNIAEXlNRE5JdT00Go270IagRqPRaDQajUvRhqBGo9HYiIiMFpFlIrJJRB4HCsz1FSLyvIisFpF15v89zbKrgN2B20Rks4jcZq4fJCIviUiLiPxPRA63fM8+IvKJ+T0rROT8FMjVaDQZjjYENRqNxiZEJA94GngQqAT+DBxqFmcB9wH1QG9gK3AbgFLqMmAJ8AulVIlS6hciUgy8BDwCdAWOAm4XkaHm8e4B5iulSoFhwCuJV6jRaJyGNgQ1Go3GPiYBucDNSqkdSqm/AO8CKKXWKqUWKaW2KKU2AVcB08Mcaz9guVLqPqXUTqXUMmARMM8s3wEMEZEypdQ6s1yj0Wg6hTYENRqNxj66AyuUbxL3JgARKRKRu0SkSUQ2Aq8DXUQkO8Sx6oGJIrLe8wGOBurM8kOBfYAmEVksIpMTokij0TgabQhqNBqNffwI9BARsazrbf49DxgITFRKlQHTzPWeba3GI8B3wGKlVBfLp0QpdTqAUupdpdSBGN3GTwNPJECPRqNxONoQ1Gg0Gvt4C9gJnCkiOSJyCDDBLCvFGBe4XkQqgV/57bsS6GtZfh4YICLHikiu+RkvIoNFJE9EjhaRcqXUDmAj0J5QZRqNxpFoQ1Cj0WhsQinVBhwCnACsA44AnjSLbwYKgTXA28ALfrvfAswzZxTfao4jnA0cCfwANAPXAfnm9scCy81u5tOAYxIkS6PROBjxHcqi0Wg0Go1Go3EL2iOo0Wg0Go1G41K0IajRaDQajUbjUrQhqNFoNBqNRuNStCGo0Wg0Go1G41K0IajRaDQajUbjUrQhqNFoNBqNRuNStCGo0Wg0Go1G41K0IajRaDQajUbjUrQhqNFoNBqNRuNS/h+pUS9UbGGs5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc603094f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df0[['E_T','E_bs']].plot(subplots=True, figsize=(10,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:44.710274Z",
     "start_time": "2018-05-05T01:08:44.214754Z"
    }
   },
   "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>1293.577000</td>\n",
       "      <td>1293.577000</td>\n",
       "      <td>1293.577000</td>\n",
       "      <td>1.293577e+03</td>\n",
       "      <td>1293.577000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.530550</td>\n",
       "      <td>645.497127</td>\n",
       "      <td>-0.469406</td>\n",
       "      <td>4.069884e+06</td>\n",
       "      <td>268920.544491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>455.872429</td>\n",
       "      <td>373.419987</td>\n",
       "      <td>9.894720</td>\n",
       "      <td>8.029208e+06</td>\n",
       "      <td>258737.672345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-122720.979510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-131.586742</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.353707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-18.534000</td>\n",
       "      <td>322.101000</td>\n",
       "      <td>-2.595953</td>\n",
       "      <td>4.009306e+05</td>\n",
       "      <td>44783.389801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>645.495000</td>\n",
       "      <td>-0.022982</td>\n",
       "      <td>1.469284e+06</td>\n",
       "      <td>112420.917375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.519408</td>\n",
       "      <td>968.889000</td>\n",
       "      <td>2.665594</td>\n",
       "      <td>4.069666e+06</td>\n",
       "      <td>538502.123368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>103881.575980</td>\n",
       "      <td>1292.283000</td>\n",
       "      <td>68.755154</td>\n",
       "      <td>1.172180e+08</td>\n",
       "      <td>792922.568925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bs          E_T         E_bs        absMul       absTheta\n",
       "count    1293.577000  1293.577000  1293.577000  1.293577e+03    1293.577000\n",
       "mean       -0.530550   645.497127    -0.469406  4.069884e+06  268920.544491\n",
       "std       455.872429   373.419987     9.894720  8.029208e+06  258737.672345\n",
       "min   -122720.979510     0.000000  -131.586742  0.000000e+00       0.353707\n",
       "25%       -18.534000   322.101000    -2.595953  4.009306e+05   44783.389801\n",
       "50%        -0.000000   645.495000    -0.022982  1.469284e+06  112420.917375\n",
       "75%        18.519408   968.889000     2.665594  4.069666e+06  538502.123368\n",
       "max    103881.575980  1292.283000    68.755154  1.172180e+08  792922.568925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df0.describe()/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:28:13.856302Z",
     "start_time": "2018-05-05T01:28:07.710605Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc618573d30>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc618573198>"
      ]
     },
     "metadata": {},
     "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": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:08:51.342504Z",
     "start_time": "2018-05-05T01:08:51.034248Z"
    }
   },
   "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_imbalance_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 lt 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:26.553865Z",
     "start_time": "2018-05-05T01:08:51.343906Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                      start                stop  Ts\n",
      "1293543 2018-02-26 15:59:59 2018-02-26 15:59:59   2\n",
      "1293544 2018-02-26 15:59:59 2018-02-26 16:00:00   3\n",
      "1293545 2018-02-26 16:00:00 2018-02-26 16:10:00   2\n",
      "1293546 2018-02-26 16:10:00 2018-02-26 16:16:14   2\n",
      "1293547 2018-02-26 16:16:14 2018-02-26 18:30:00   2\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1112111 entries, 0 to 1293547\n",
      "Data columns (total 3 columns):\n",
      "start    1112111 non-null datetime64[ns]\n",
      "stop     1112111 non-null datetime64[ns]\n",
      "Ts       1112111 non-null int64\n",
      "dtypes: datetime64[ns](2), int64(1)\n",
      "memory usage: 33.9 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bars = agg_imbalance_bars(df0)\n",
    "test_imb_bars = (pd.DataFrame(bars,columns=['start','stop','Ts'])\n",
    "                 .drop_duplicates())\n",
    "cprint(test_imb_bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:26.900421Z",
     "start_time": "2018-05-05T01:11:26.555120Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1112111.0\n",
       "mean           3.0\n",
       "std            2.0\n",
       "min            1.0\n",
       "25%            2.0\n",
       "50%            2.0\n",
       "75%            3.0\n",
       "max          139.0\n",
       "Name: Ts, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_imb_bars.Ts.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:28.038734Z",
     "start_time": "2018-05-05T01:11:26.901749Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc6206019b0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6204f09b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_imb_bars.set_index('stop')['Ts'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.101336Z",
     "start_time": "2018-05-05T01:11:28.040300Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                        price\n",
      "dates                        \n",
      "2018-02-26 15:51:00  115.2148\n",
      "2018-02-26 15:51:51  115.2348\n",
      "2018-02-26 15:52:33  115.2068\n",
      "2018-02-26 15:53:03  115.1860\n",
      "2018-02-26 15:56:35  115.2448\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 245255 entries, 2009-09-28 09:30:00 to 2018-02-26 15:56:35\n",
      "Data columns (total 1 columns):\n",
      "price    245255 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.7 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dvImbBars = df.price.loc[test_imb_bars.stop].drop_duplicates()\n",
    "cprint(dvImbBars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.391117Z",
     "start_time": "2018-05-05T01:11:37.103211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------\n",
      "dataframe information\n",
      "-------------------------------------------------------------------------------\n",
      "                        price\n",
      "dates                        \n",
      "2018-02-26 15:42:24  115.3199\n",
      "2018-02-26 15:49:42  115.2500\n",
      "2018-02-26 15:58:15  115.2400\n",
      "2018-02-26 16:00:00  115.3500\n",
      "2018-02-26 16:16:14  115.3000\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 44812 entries, 2009-09-28 09:46:35 to 2018-02-26 16:16:14\n",
      "Data columns (total 1 columns):\n",
      "price    44812 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 1.9 MB\n",
      "None\n",
      "-------------------------------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dvBar = dv_bar_df.price\n",
    "cprint(dvBar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:37.706922Z",
     "start_time": "2018-05-05T01:11:37.392565Z"
    }
   },
   "outputs": [],
   "source": [
    "dr = returns(dv_bar_df.price)\n",
    "drImb = returns(dvImbBars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:38.000919Z",
     "start_time": "2018-05-05T01:11:37.708594Z"
    }
   },
   "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>44811.0</td>\n",
       "      <td>-0.125228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>245254.0</td>\n",
       "      <td>-0.075617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_size  autocorr_stat\n",
       "dvBar      44811.0      -0.125228\n",
       "dvImb     245254.0      -0.075617"
      ]
     },
     "execution_count": 112,
     "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": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:38.602438Z",
     "start_time": "2018-05-05T01:11:38.002187Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6243828d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorr(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:39.787193Z",
     "start_time": "2018-05-05T01:11:38.606456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc602d97a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist(bar_types,bar_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-05T01:11:40.146218Z",
     "start_time": "2018-05-05T01:11:39.789951Z"
    }
   },
   "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>12934.0</td>\n",
       "      <td>1.107317e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>54902.0</td>\n",
       "      <td>2.608531e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sample_size       jb_stat\n",
       "dvBar      12934.0  1.107317e+08\n",
       "dvImb      54902.0  2.608531e+11"
      ]
     },
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       "      <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>12934.0</td>\n",
       "      <td>0.650087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dvImb</th>\n",
       "      <td>54902.0</td>\n",
       "      <td>0.276730</td>\n",
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      "text/plain": [
       "       sample_size  shapiro_stat\n",
       "dvBar      12934.0      0.650087\n",
       "dvImb      54902.0      0.276730"
      ]
     },
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   ],
   "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)"
   ]
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