Coefficient variable for crypto assets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coefficient variable for crypto assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import json\n",
    "import datetime\n",
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "headers = {'Content-Type': 'application/json'}\n",
    "api_url_base_coingecko = 'https://api.coingecko.com/api/v3/coins/'\n",
    "vs_currency = 'usd'\n",
    "days = '30'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCoinData(ids, vs, days):\n",
    "    api_url_coingecko = '{0}/{1}/ohlc?vs_currency={2}&days={3}'.format(api_url_base_coingecko, ids, vs, days)\n",
    "    res = requests.get(api_url_coingecko, headers=headers)\n",
    "\n",
    "    if res.status_code == 200:\n",
    "        df = pd.read_json(res.content.decode('utf-8'))\n",
    "        df.columns = ['timestamp', 'Open', 'High', 'Low', 'Close']\n",
    "        df['timestamp'] = df['timestamp']/1000\n",
    "        df['timestamp'] = df['timestamp'].astype(int)\n",
    "        df['timestamp'] = df['timestamp'].map(datetime.datetime.utcfromtimestamp)\n",
    "        df.index = df['timestamp']\n",
    "        feature_name = ['Close']\n",
    "        df = df[feature_name]\n",
    "        df.interpolate(limit_direction='both', inplace=True)\n",
    "        df.columns = [ids]\n",
    "        return df\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "coins = ['bitcoin', 'ethereum', 'chainlink', 'ripple', 'litecoin', 'polkadot']\n",
    "df = pd.DataFrame()\n",
    "for coin in coins:\n",
    "    df = pd.concat([df, getCoinData(coin, vs_currency, days)], axis=1, sort=True, join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bitcoin</th>\n",
       "      <th>ethereum</th>\n",
       "      <th>chainlink</th>\n",
       "      <th>ripple</th>\n",
       "      <th>litecoin</th>\n",
       "      <th>polkadot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-07 12:00:00</th>\n",
       "      <td>10616.74</td>\n",
       "      <td>339.77</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.245568</td>\n",
       "      <td>45.68</td>\n",
       "      <td>3.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07 16:00:00</th>\n",
       "      <td>10633.94</td>\n",
       "      <td>340.68</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0.248428</td>\n",
       "      <td>46.13</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07 20:00:00</th>\n",
       "      <td>10663.21</td>\n",
       "      <td>341.73</td>\n",
       "      <td>8.92</td>\n",
       "      <td>0.249665</td>\n",
       "      <td>46.76</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-08 00:00:00</th>\n",
       "      <td>10662.60</td>\n",
       "      <td>340.32</td>\n",
       "      <td>8.88</td>\n",
       "      <td>0.248464</td>\n",
       "      <td>46.59</td>\n",
       "      <td>3.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-08 04:00:00</th>\n",
       "      <td>10643.29</td>\n",
       "      <td>339.17</td>\n",
       "      <td>8.83</td>\n",
       "      <td>0.247561</td>\n",
       "      <td>46.28</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      bitcoin  ethereum  chainlink    ripple  litecoin  \\\n",
       "timestamp                                                                \n",
       "2020-10-07 12:00:00  10616.74    339.77       8.67  0.245568     45.68   \n",
       "2020-10-07 16:00:00  10633.94    340.68       8.83  0.248428     46.13   \n",
       "2020-10-07 20:00:00  10663.21    341.73       8.92  0.249665     46.76   \n",
       "2020-10-08 00:00:00  10662.60    340.32       8.88  0.248464     46.59   \n",
       "2020-10-08 04:00:00  10643.29    339.17       8.83  0.247561     46.28   \n",
       "\n",
       "                     polkadot  \n",
       "timestamp                      \n",
       "2020-10-07 12:00:00      3.75  \n",
       "2020-10-07 16:00:00      3.80  \n",
       "2020-10-07 20:00:00      3.83  \n",
       "2020-10-08 00:00:00      3.84  \n",
       "2020-10-08 04:00:00      3.83  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bitcoin</th>\n",
       "      <th>ethereum</th>\n",
       "      <th>chainlink</th>\n",
       "      <th>ripple</th>\n",
       "      <th>litecoin</th>\n",
       "      <th>polkadot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-10-07 12:00:00</th>\n",
       "      <td>17.20</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.002860</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07 16:00:00</th>\n",
       "      <td>17.20</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.002860</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-07 20:00:00</th>\n",
       "      <td>29.27</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-08 00:00:00</th>\n",
       "      <td>-0.61</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.001201</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-08 04:00:00</th>\n",
       "      <td>-19.31</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.000903</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bitcoin  ethereum  chainlink    ripple  litecoin  \\\n",
       "timestamp                                                               \n",
       "2020-10-07 12:00:00    17.20      0.91       0.16  0.002860      0.45   \n",
       "2020-10-07 16:00:00    17.20      0.91       0.16  0.002860      0.45   \n",
       "2020-10-07 20:00:00    29.27      1.05       0.09  0.001237      0.63   \n",
       "2020-10-08 00:00:00    -0.61     -1.41      -0.04 -0.001201     -0.17   \n",
       "2020-10-08 04:00:00   -19.31     -1.15      -0.05 -0.000903     -0.31   \n",
       "\n",
       "                     polkadot  \n",
       "timestamp                      \n",
       "2020-10-07 12:00:00      0.05  \n",
       "2020-10-07 16:00:00      0.05  \n",
       "2020-10-07 20:00:00      0.03  \n",
       "2020-10-08 00:00:00      0.01  \n",
       "2020-10-08 04:00:00     -0.01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = df.copy()\n",
    "for coin in coins:\n",
    "    df_[coin] = df[coin].diff().fillna(method='bfill')\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bitcoin</th>\n",
       "      <th>ethereum</th>\n",
       "      <th>chainlink</th>\n",
       "      <th>ripple</th>\n",
       "      <th>litecoin</th>\n",
       "      <th>polkadot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>181.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.598453</td>\n",
       "      <td>0.538840</td>\n",
       "      <td>0.016022</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.088729</td>\n",
       "      <td>0.003978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.819809</td>\n",
       "      <td>4.415849</td>\n",
       "      <td>0.205809</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.743924</td>\n",
       "      <td>0.071645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-484.820000</td>\n",
       "      <td>-13.150000</td>\n",
       "      <td>-0.420000</td>\n",
       "      <td>-0.005985</td>\n",
       "      <td>-2.610000</td>\n",
       "      <td>-0.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-17.230000</td>\n",
       "      <td>-1.410000</td>\n",
       "      <td>-0.120000</td>\n",
       "      <td>-0.001182</td>\n",
       "      <td>-0.270000</td>\n",
       "      <td>-0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.200000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>-0.000128</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.760000</td>\n",
       "      <td>2.330000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>550.540000</td>\n",
       "      <td>19.860000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.008927</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bitcoin    ethereum   chainlink      ripple    litecoin    polkadot\n",
       "count  181.000000  181.000000  181.000000  181.000000  181.000000  181.000000\n",
       "mean    26.598453    0.538840    0.016022    0.000064    0.088729    0.003978\n",
       "std    115.819809    4.415849    0.205809    0.002174    0.743924    0.071645\n",
       "min   -484.820000  -13.150000   -0.420000   -0.005985   -2.610000   -0.190000\n",
       "25%    -17.230000   -1.410000   -0.120000   -0.001182   -0.270000   -0.040000\n",
       "50%     17.200000    0.650000    0.010000   -0.000128    0.040000    0.000000\n",
       "75%     70.760000    2.330000    0.130000    0.001140    0.420000    0.040000\n",
       "max    550.540000   19.860000    0.730000    0.008927    3.220000    0.330000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistics\n",
    "df_.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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2rXx9fYsco0mTJmrcuLFDe2hoqI4eParjx4/f9UZGN27c0Pnz5/XQQw8V+ZMa4eHh2r59u1JTU+86pdJisWjlypVatWqVTp48qRs3bsh6260HCm8eJEmRkZF677339MorrygmJkadO3dWaGioQ73O9it8zX/44YciX/O8vDxlZmba1skBMDZCHACnNGjQQKdPn1ZaWprDF/M7uXHjhm3fOx3zp59+sv197do1mc1mxcXF3fGYOTk5JajaOYVXI3x8fEq1/7Vr1yTduiqxffv2O/b75c8a1K5d26FP4RUWi8VSqlpud+PGDTVv3rzIbYWvSeFrVFoPPfSQWrZsqS+++EJvvfWWvL299cUXXygvL89h/dLBgwf1wgsvyN3dXY8++qiaN2+uGjVqyM3N7Y43o5FUoi+cpR3DWc6c09Ktm2TcHuKKeq2rVasmqWSvdd26dR3C4O3jFtZX2nOyfv36Ttcile69U9wYAwYM0P/7f/9PW7ZsUd++ffXll1/q0qVLeuWVV4p83Hc6NwrHcOb8Luxzp7oK12A6c6zJkyfrs88+k5+fn7p3766GDRvK09NTWVlZio+Pl8lksvVt2rSpEhISNGfOHG3cuFGrVq2SdOsfLcaPH28LjM72K3zNb5/dUJSy/LwKgHsHIQ6AUzp06KD9+/dr37596tSpk1P7FH6JLZyK+UtXrlyx+6Jbq1YteXt7a9euXWUvuAQKF/Knp6eXav/Cx/Df//3ftilh94JatWrpypUrRW4rfE1Kc+Xxl3r37q1Zs2Zp586dio6O1rp161StWjX17NnTrt+CBQuUn59vu1vl7TZu3HjH4zs7ta8sYzjLmXNaku67774yj1WUzMxMWSwWh0Dzy9eztOdkSZ5r6T/htCTvneLG6NGjh959910lJiaqb9++SkxMlJub2x3vkHr16tUi2wtfB2fO78I+ZX2vXL58WcuWLVNQUJCWL19uN/3xyJEjio+Pd9gnKChI8+fPl8lkUnJysnbs2KFPP/1Uw4cP18aNG+Xr6+t0v8L6Fi9ebLsqDuDXizVxAJzSt29fubu7a8WKFcrIyCi2b+G/Nhfe+e3gwYMOfY4fP66srCy7qX5t2rRRWlqa/u///s+Fld9dixYtdN999+nw4cPKzs4u8f6F09sOHz7s6tJs3N3dVVBQUKJ9goKCdO3aNZ04ccJh24EDB2x9yqpXr15yc3PT+vXrlZ6err1796pTp04OV6t+/PFH1a1b1yFcXb582e6KbFmUdIzCMOTs1bDizukrV67o9OnTatq0qUvCcVHy8/OVnJzs0F64Fq7w9ayIc1L6z1TXb775xiXHu++++9SzZ0/t379fBw8e1K5du9SpU6c73vL+4sWL+vnnnx3aC9ev3X5+3+k9VKtWLfn7++vUqVNFhsLC98rd7gZ7/vx5Wa1Wde7c2WH92t3W03l5eenhhx/WuHHjNHLkSOXk5DisM71bv5K+5oVXgkv6uQLg3kCIA+CUFi1a6IUXXtCVK1c0atSoIr84ZWVlaerUqbYrHt26dVOtWrW0fPlynTt3ztavoKBAM2bMkHQrHBaKjY2VJE2cONHhhguS9NNPP+n8+fMufVzSrSmMAwYMUEZGhqZPn263hkW69eW8cE1eUUJCQtS2bVutWrVKO3bscNien59f5Jf+krj//vuVkZFhNx3rbgqf2/fee8/ui1rhb1nVrl3b7tbipVX4MwJfffWVEhISZLFYHKZSSrfWL127dk2nTp2ytZlMJr399tt269bKoqRjFK7/SktLc+r4oaGhatq0qbZu3erwxXzWrFnKy8uzO6fLw5w5c+zOx+TkZG3dulV+fn4KDQ2VVDHnpHTrPe7j46PExMQij+fs83q7AQMGSJL+8pe/yGw2F3lDk0Jms1lz5syxa/viiy+UnJyshx9+2G7dWHHvob59+yovL0+zZ8+2az948KC2bdumZs2aqUOHDsXWXXjV7Ntvv7X7DDlz5ow+/PBDh/7JyclFhsbCK4KFN3dytl9UVJR8fX310UcfFRn0c3NzbevmpP/MQCjNawSg8jGdEoDTXnvtNd28eVPLli1T9+7d9cgjj9jWx507d0579uzRzZs3bb+FVLt2bU2aNEmvv/66+vXrp549e6p27dratWuXTpw4ocjISLsvvF26dNHIkSP14Ycfqnv37nr00UfVuHFjXb16VadPn9bhw4c1c+bMcvkh2rFjx+rQoUNKSEhQUlKSHnnkEbm7u+vMmTP65ptvtGfPnmJ/P2nmzJkaOnSo7Ue0g4ODVa1aNV28eFEHDx7U/fffr82bN5e6voiICKWkpGjUqFHq0KGDPD091aVLl2J/hLhv377asmWLdu7cqb59++rxxx+3/U5cbm6uZsyY4bIrRr1799ahQ4e0YMEC1ahRo8hwOHjwYO3evVvPP/+8YmJi5OHhoT179shsNisoKKjMN1kpzRhhYWFyc3PT+++/rzNnzui+++6Tr6+v3V01b+fu7q4pU6Zo+PDhGjZsmGJiYtSoUSMdOHBA3377rVq1aqWXX365zI/jTho2bKiMjAw9/fTTevzxx22/E+fm5qa3337bbppleZ+T0q0A8d5772nEiBEaOnSo7XfiMjMzlZycrCZNmtj9Npoz2rRpo+DgYKWmpqpu3brq3r37HfsGBgZq9+7dGjhwoMLCwnTx4kVt3rxZNWvW1JtvvmnXt7j30PDhw7Vz504lJCTo+++/V1hYmNLS0rRx40Z5e3trypQpRa7Ju12jRo3UvXt3bd26Vc8884wiIiKUnp6uHTt26LHHHtOWLVvs+q9bt07Lli1TRESEbe1mamqqdu/erRYtWigyMrJE/by8vDR79mwNHz5cAwYM0COPPKLf/va3Kigo0Pnz53XgwAG1a9dOCxculHTrqmebNm104MABTZw4Uc2aNbPdSbOoNZwA7i2EOABO8/Dw0FtvvaXevXtr+fLlOnjwoPbs2SPp1hWQ3r17a+DAgXrooYds+/Tq1UsNGzbUggULtHHjRuXl5alZs2Z67bXX9OKLLzqsj3n11VcVGhqqTz/9VF9//bWys7NVr149NW/eXOPGjXN6PV5J1ahRQ/Hx8VqyZInWr1+vZcuWydPTU/7+/ho+fLhq1KhR7P7NmjXT6tWrtWjRIm3fvl0rVqyQh4eHGjVqpMjIyDuGAmf913/9lzIzM/XVV19pz549slgsatCgQbEhzt3dXXFxcVq8eLHWrl2r+Ph4eXt7q127dho5cmSRd1gsrZiYGE2ZMkX5+fl68skni1wT1q1bN73//vtasGCB1qxZo1q1aumxxx7TX//6V/3lL39xSR0lHSMgIECTJ0/W//zP/2jhwoXKz89XeHh4sa9XeHi4EhISNG/ePH311VfKzs6Wr6+vRo4cqZEjRzr9w92l4eXlpUWLFmn69OlKTExUTk6OHnroIf35z39Wx44d7fqW9zlZKCwsTImJibYfO//6669Vt25d2w+bl0afPn2Umpqq3r17O9yy/3b333+/5s+fr6lTpyohIUH5+fmKiIjQ66+/7jBVuLj3UPXq1RUfH68FCxZo06ZNWrx4se677z79/ve/1+jRo52edjx16lT5+vpq27Zt+uSTT9SsWTP95S9/UWRkpEOI69Wrl/Ly8pSUlKTDhw/LYrHI19dXf/zjH/XCCy/YPnOc7SfdugK7Zs0affzxx9q1a5f27dunGjVqqHHjxurbt6/69OljV8O0adM0ZcoUbd682Xbjlh49ehDiAANws/5y3hAAALjndO3aVZKKnB75azNu3DitXbtWa9euvWOACgwMVHh4uJYuXVrB1QFA5WNNHAAAuGekpaVp06ZNat++vUtuvAMAv0ZMpwQAAJXuyy+/VEpKijZt2iSTyaRRo0ZVdkkAcM8ixAEAgEq3efNmrV69Wo0bN9aECRPUpUuXyi4JAO5ZrIkDAAAAAANhTRwAAAAAGMg9O50yKSmpsksAAAAAgEoVGhrq0HbPhjip6IIBAAAAoCq404UtplMCAAAAgIE4dSXu9OnTiouL07Fjx3Tp0iVZrVb5+/srJiZGQ4cOVa1atWx9AwMDizxGixYttHnzZtdUDQAAAABVlFMhLi0tTVeuXFF0dLQaNWokNzc3paSkaP78+dq2bZsSEhLk5eVl69+mTRvFxsbaHaN27dqurRwAAAAAqiCnQlynTp3UqVMnh/YWLVron//8p77++mt169bN1t6kSRP16dPHdVUCAAAAACSVcU2cn5+fJCkrK8thm8lkUnZ2dlkODwAAAAD4hRLdnTI3N1c5OTnKy8vTd999pxkzZsjLy0vh4eF2/Xbt2qV27dqpoKBADRo0UO/evTVmzBjVrFnTpcUDAAAAQFVTohAXHx+vmTNn2v5u2bKl5s2bZ7siJ0khISGKjo5Ws2bNlJWVpa1bt2rx4sU6fPiwli5dKk9PT9dVDwAAAABVTIlCXM+ePdW6dWtlZWXp0KFD2r9/v3Jycuz6rFixwu7vfv366Z133lF8fLw+//xzPfPMM06Pl5qaWpLyAAAAAOBXz81qtVpLu/P69ev12muvafHixercufMd+125ckWdO3dWz5499d577zl17KSkJH7sGwAAAECVdadMVKYbmzz55JPy9PRUYmJisf3q16+v6tWrKzMzsyzDAQAAAECVV6YQZzabVVBQUOTdKW+Xlpam3Nxc1a9fvyzDAQAAAECV51SIu3z5cpHtCQkJslgsCgkJkSRlZGQ49LFarZo1a5YkKTIysrR1AgAAAADk5I1N/vGPf+jKlSvq2LGjmjRpohs3bmj//v3auXOnWrZsqWHDhkmSPvjgAyUnJysiIkK+vr66fv26tm/frm+//VaRkZGKjo4u1wcDAAAAAL92ToW4nj17avXq1UpMTFRGRoY8PDzUvHlzjR49Wi+++KJq1aolSQoPD9fJkyeVmJiozMxMeXp6qkWLFpo4caIGDx4sd/cyzd4EAAAAgCqvTHenLE/cnRIAAABAVVYud6cEAAAAAFSsEv3YNwAAKF6uyayCAktll4FSqlbNXdW9+HoE4N7GpxQAAC5UUGDR/2xNruwyUEovdG9b2SUAwF0xnRIAAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgXg40+n06dOKi4vTsWPHdOnSJVmtVvn7+ysmJkZDhw5VrVq17PqvWLFC8fHxOnfunOrVq6cePXpozJgxqlmzZrk8CAAAAACoKpwKcWlpabpy5Yqio6PVqFEjubm5KSUlRfPnz9e2bduUkJAgLy8vSdJHH32kGTNmqHPnzho6dKjOnj2r+Ph4ff/991q0aJHc3NzK9QEBAAAAwK+ZUyGuU6dO6tSpk0N7ixYt9M9//lNff/21unXrpqtXryouLk7t27fXwoUL5e5+a7amv7+/Jk2apC1btig6Otq1jwAAAAAAqpAyrYnz8/OTJGVlZUmStm3bptzcXA0ePNgW4CTp2WefVc2aNbV+/fqyDAcAAAAAVZ5TV+IK5ebmKicnR3l5efruu+80Y8YMeXl5KTw8XJKUkpIiSQoJCbHbz8vLS0FBQTp69KiLygYAAACAqqlEIS4+Pl4zZ860/d2yZUvNmzfPdkUuPT1dkuTj4+Owr4+Pjw4fPiyr1cq6OAAAAAAopRKFuJ49e6p169bKysrSoUOHtH//fuXk5Ni237x5U5JsNzm5nbe3tywWi0wmk7y9vZ0aLzU1tSTlAQBQ6Xz9m1d2CSiDfLNZqamnKrsMAChWiUKcn5+f7apbdHS01q9fr7Fjx2rx4sXq3LmzLZyZTCZVrzI5AasAACAASURBVF7dbt+8vDy5ubkVGfDuJDg4uCTlAQBQ6bJvmiq7BJSBp4cH3z8A3DOSkpKKbC/TjU2efPJJeXp6KjExUZLUuHFjSf+ZVnm79PR0+fj4MJUSAAAAAMqgTCHObDaroKDAdnfKVq1aSZKOHDli189kMun48eNq3bp1WYYDAAAAgCrPqRB3+fLlItsTEhJksVhsd6OMioqSt7e3Pv30U1ksFlu/lStXKicnR7169XJByQAAAABQdTm1Ju4f//iHrly5oo4dO6pJkya6ceOG9u/fr507d6ply5YaNmyYJKl+/foaPXq0Zs6cqZdfflkxMTE6e/aslixZooiICMXExJTrgwEAAACAXzunQlzPnj21evVqJSYmKiMjQx4eHmrevLlGjx6tF198UbVq1bL1HTFihOrUqaOlS5dq0qRJqlevngYNGqSxY8eyHg4AAAAAysipENejRw/16NHD6YMOHDhQAwcOLHVRAAAAAICilenGJgAAAACAikWIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYiMfdOhw7dkzr1q3T3r17df78eeXn56tly5bq37+/Bg4cKDc3N1vfwMDAIo/RokULbd682XVVAwAAAEAVddcQ9/HHH2vPnj3q3r27nnvuOZlMJm3atElvvfWWvvvuO02ePNmuf5s2bRQbG2vXVrt2bddWDQAAAABV1F1D3JAhQzR16lR5e3vb2mJjYzVs2DCtWLFCQ4cO1e9+9zvbtiZNmqhPnz7lUy0AAAAAVHF3XRMXGhpqF+Akyd3dXU888YQk6cSJEw77mEwmZWdnu6hEAAAAAEChu16Ju5P09HRJ0gMPPGDXvmvXLrVr104FBQVq0KCBevfurTFjxqhmzZplqxQAAAAAULoQd/nyZS1fvlx+fn4KDQ21tYeEhCg6OlrNmjVTVlaWtm7dqsWLF+vw4cNaunSpPD09XVY4AAAAAFRFJQ5xJpNJY8aM0fXr1zVr1ix5eXnZtq1YscKub79+/fTOO+8oPj5en3/+uZ555pkSjZWamlrS8gAAqFS+/s0ruwSUQb7ZrNTUU5VdBgAUq0Qhzmw2a+zYsTp06JDefvttde7c+a77/PGPf1R8fLx2795d4hAXHBxcov4AAFS27Jumyi4BZeDp4cH3DwD3jKSkpCLbnf6x74KCAr322mvasWOHJk6cqAEDBji1X/369VW9enVlZmY6OxQAAAAA4A6cCnEWi0Xjxo3T5s2b9cYbbzj8Dlxx0tLSlJubq/r165e6SAAAAADALXcNcRaLRRMmTND69ev16quv6qWXXiqyX0ZGhkOb1WrVrFmzJEmRkZFlLBUAAAAAcNc1cdOnT9eaNWvUpk0bNW7cWGvXrrXb3qFDBzVt2lQffPCBkpOTFRERIV9fX12/fl3bt2/Xt99+q8jISEVHR5fbgwAAAACAquKuIe7YsWOSpKNHj2rcuHEO29999101bdpU4eHhOnnypBITE5WZmSlPT0+1aNFCEydO1ODBg+Xu7vTyOwAAAADAHdw1xC1dutSpA0VFRSkqKqrMBQEAAAAA7ozLYwAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBeNytw7Fjx7Ru3Trt3btX58+fV35+vlq2bKn+/ftr4MCBcnNzs+u/YsUKxcfH69y5c6pXr5569OihMWPGqGbNmuX2IAAAAACgqrhriPv444+1Z88ede/eXc8995xMJpM2bdqkt956S999950mT55s6/vRRx9pxowZ6ty5s4YOHaqzZ88qPj5e33//vRYtWuQQ+AAAAAAAJXPXEDdkyBBNnTpV3t7etrbY2FgNGzZMK1as0NChQ/W73/1OV69eVVxcnNq3b6+FCxfK3f3WTE1/f39NmjRJW7ZsUXR0dPk9EgAAAACoAu66Ji40NNQuwEmSu7u7nnjiCUnSiRMnJEnbtm1Tbm6uBg8ebAtwkvTss8+qZs2aWr9+vSvrBgAAAIAqqdQ3NklPT5ckPfDAA5KklJQUSVJISIhdPy8vLwUFBeno0aOlHQoAAAAA8P8rVYi7fPmyli9fLj8/P4WGhkr6T6jz8fFx6O/j46P09HRZrdYylAoAAAAAuOuauF8ymUwaM2aMrl+/rlmzZsnLy0uSdPPmTUmy/X07b29vWSwWmUwmh6mZxUlNTS1peQAAVCpf/+aVXQLKIN9sVmrqqcouAwCKVaIQZzabNXbsWB06dEhvv/22OnfubNtWGM5MJpOqV69ut19eXp7c3NyKDHjFCQ4OLlF/AAAqW/ZNU2WXgDLw9PDg+weAe0ZSUlKR7U5PpywoKNBrr72mHTt2aOLEiRowYIDd9saNG0v6z7TK26Wnp8vHx4efGAAAAACAMnIqxFksFo0bN06bN2/WG2+8odjYWIc+rVq1kiQdOXLErt1kMun48eNq3bq1C8oFAAAAgKrtriHOYrFowoQJWr9+vV599VW99NJLRfaLioqSt7e3Pv30U1ksFlv7ypUrlZOTo169ermuagAAAACoou66Jm769Olas2aN2rRpo8aNG2vt2rV22zt06KCmTZuqfv36Gj16tGbOnKmXX35ZMTExOnv2rJYsWaKIiAjFxMSU24MAAAAAgKririHu2LFjkqSjR49q3LhxDtvfffddNW3aVJI0YsQI1alTR0uXLtWkSZNUr149DRo0SGPHjmU9HAAAAAC4wF1D3NKlS0t0wIEDB2rgwIGlLggAAAAAcGel+rFvAAAAAEDlIMQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBCPyi4AAAAAQPGu3cyTyWyp7DJQSl4e7rq/hrfLjkeIAwAAAO5xJrNFT733eWWXgVL6/NWnXHo8plMCAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAADIcQBAAAAgIEQ4gAAAADAQAhxAAAAAGAghDgAAAAAMBBCHAAAAAAYCCEOAAAAAAyEEAcAAAAABkKIAwAAAAAD8XCmU3Z2thYvXqyUlBSlpKTo0qVLevLJJzVnzhyHvoGBgUUeo0WLFtq8eXPZqgUAAACAKs6pEJeRkaG5c+eqYcOGat26tXbu3Fls/zZt2ig2NtaurXbt2qWvEgAAAAAgyckQ5+Pjo127dqlRo0aS7ny1rVCTJk3Up0+fslcHAAAAALDj1Jo4Ly8vW4BzlslkUnZ2dqmKAgAAAAAUzakrcSW1a9cutWvXTgUFBWrQoIF69+6tMWPGqGbNmuUxHAAAAABUGS4PcSEhIYqOjlazZs2UlZWlrVu3avHixTp8+LCWLl0qT09PVw8JAAAAAFWGy0PcihUr7P7u16+f3nnnHcXHx+vzzz/XM8884/SxUlNTXV0eAADlyte/eWWXgDLIN5uVmnqqsssAHNRt5FfZJaAMzPn5Sk0967Ljlct0yl/64x//qPj4eO3evbtEIS44OLgcqwIAwPWyb5oquwSUgaeHB98/cE+6dP1mZZeAMvDw9CzVZ0tSUlKR7RXyY9/169dX9erVlZmZWRHDAQAAAMCvVoWEuLS0NOXm5qp+/foVMRwAAAAA/Gq5NMRlZGQ4tFmtVs2aNUuSFBkZ6crhAAAAAKDKcXpN3CeffKKsrCzb36dPn9b8+fMlSWFhYQoLC9MHH3yg5ORkRUREyNfXV9evX9f27dv17bffKjIyUtHR0a5/BAAAAABQhTgd4hYtWqQLFy7Y/v7hhx80e/ZsSdLo0aMVFham8PBwnTx5UomJicrMzJSnp6datGihiRMnavDgwXJ3r5DZmwAAAADwq+V0iNuxY8dd+0RFRSkqKqpMBQEAAAAA7oxLYwAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBeFR2AQBgBKb8AlkslsouA6Xg7u4uL89qlV0GAAAuQ4gDACdYLBbtSjpT2WWgFB4PbSGJEAcA+PVgOiUAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEA8nOmUnZ2txYsXKyUlRSkpKbp06ZKefPJJzZkzp8j+K1asUHx8vM6dO6d69eqpR48eGjNmjGrWrOnS4gEAAACgqnHqSlxGRobmzp2rlJQUtW7duti+H330kd588001bNhQb775pnr16qVPPvlEr7zyiqxWq0uKBgAAAICqyqkrcT4+Ptq1a5caNWokSQoMDCyy39WrVxUXF6f27dtr4cKFcne/lRH9/f01adIkbdmyRdHR0S4qHQAAAACqHqeuxHl5edkCXHG2bdum3NxcDR482BbgJOnZZ59VzZo1tX79+tJXCgAAAABw7Y1NUlJSJEkhISF27V5eXgoKCtLRo0ddORwAAAAAVDkuDXHp6emSbk2//CUfHx+lp6ezLg4AAAAAysCpNXHOunnzpqRbV95+ydvbWxaLRSaTSd7e3k4dLzU11ZXlAUCpNW32m8ouAaVkNpuVevpkhY3n69+8wsaC6+WbzUpNPVXZZQAO6jbyq+wSUAbm/Hylpp512fFcGuIKw5nJZFL16tXttuXl5cnNza3IgHcnwcHBriwPAEotNy+/sktAKXl4eFTo/0+yb5oqbCy4nmcFny+Asy5dv1nZJaAMPDw9S/XZkpSUVGS7S6dTNm7cWNJ/plXeLj09XT4+PnJzc3PlkAAAAABQpbg0xLVq1UqSdOTIEbt2k8mk48eP3/U35gAAAAAAxXNpiIuKipK3t7c+/fRTWSwWW/vKlSuVk5OjXr16uXI4AAAAAKhynF4T98knnygrK8v29+nTpzV//nxJUlhYmMLCwlS/fn2NHj1aM2fO1Msvv6yYmBidPXtWS5YsUUREhGJiYlz/CAAAAACgCnE6xC1atEgXLlyw/f3DDz9o9uzZkqTRo0crLCxMkjRixAjVqVNHS5cu1aRJk1SvXj0NGjRIY8eOZT0cAAAAAJSR0yFux44dTh904MCBGjhwYKkKAgAAAADcmUvXxAEAAAAAyhchDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGIhHZRcAAABQVWXnmpRfYKnsMlAKntXcdV91r8ouA1UUIQ4AAKCS5BdY9MZnX1d2GSiFaYMeq+wSUIUR4lBlFRRYZLFYK7sMlJK7u5uqVWNGOAAAqHoIcaiyLBarzpy5VNlloJRatGioatUquwoAAICKxz9jAwAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIIQ4AAAAADAQQhwAAAAAGAghDgAAAAAMhBAHAAAAAAZCiAMAAAAAAyHEAQAAAICBEOIAAAAAwEAIcQAAAABgIB6uPuD58+fVrVu3Irc9+uijWrhwoauHBAAAAIAqw+UhrlCXLl3Us2dPuzYfH5/yGg4AAAAAqoRyC3EtW7ZUnz59yuvwAAAAAFAlleuauLy8PN28ebM8hwAAAACAKqXcQlxCQoJCQkLUrl07RUZG6oMPPpDZbC6v4QAAAACgSnD5dEp3d3d17NhR3bt3l6+vr65cuaK1a9dq1qxZSk1N1Zw5c5w+VmpqqqvLA2x+85sHK7sElIHZbNbJkycqbLymzX5TYWPBtcxms1JPn6yw8Xz9m1fYWHC9fLNZqamnKmy8hk2aVthYcC2z2Vyh31XrNvKrsLHgeub8fKWmnnXZ8Vwe4po0aaIlS5bYtfXv31+jR4/Wli1btHfvXnXs2NGpYwUHB7u6PMAmP7+gsktAGXh4eFToZ0RuXn6FjQXXquhzJfumqcLGgut5VvD5kpmdW2FjwbUq+rPl0nWWKBmZh6dnqc6XpKSkItsr5Hfi3NzcNHLkSEnS7t27K2JIAAAAAPhVqrAf+/bzu3UJODMzs6KGBAAAAIBfnQoLcefOnZMk1a9fv6KGBAAAAIBfHZeHuIyMDIc2s9msuLg4SVJkZKSrhwQAAACAKsPlNzZ58803lZOTo3bt2qlx48a6cuWKNmzYoB9++EGDBg1SSEiIq4cEAAAAgCrD5SGuS5cuWrt2rRISEpSVlSVvb28FBgZq2rRp6tu3r6uHc2CxWCRruQ+D8uB26ycqAAAAANyZy0Nc//791b9/f1cf1nlW6drV7MobH6V2/wP3VXYJAAAAwD2Pyx4AAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgRDiAAAAAMBACHEAAAAAYCCEOAAAAAAwEEIcAAAAABgIIQ4AAAAADIQQBwAAAAAGQogDAAAAAAMhxAEAAACAgbg8xBUUFGjBggXq3r27WrduraioKM2fP19ms9nVQwEAAABAlePh6gNOnjxZy5YtU0xMjEaMGKGjR49qzpw5On/+vKZMmeLq4QAAAACgSnFpiPv++++VkJCgHj166P3335ck9e/fX7Vr19bHH3+sgQMHqm3btq4cEgAAAACqFJdOp9y4caOsVqtiY2Pt2ocOHSpJ2rBhgyuHAwAAAIAqx6UhLiUlRe7u7mrdurVde6NGjdSoUSMdPXrUlcMBAAAAQJXj0hCXnp6uevXqycvLy2Gbj4+P0tLSXDkcAAAAAFQ5blar1eqqg0VFRclsNuvLL7902DZ48GCdPXtWu3fvdupYSUlJrioLAAAAAAwpNDTUoc2lNzbx9vZWTk5Okdvy8vLk7e3t9LGKKhYAAAAAqjqXTqds3LixMjIyZDKZHLalp6erUaNGrhwOAAAAAKocl4a4Vq1ayWKxKCUlxa49LS1NaWlpDjc8AQAAAACUjEtDXI8ePeTm5qalS5fatcfHx0uSevXq5crhAAAAAKDKcemauKCgIA0YMEDLly+X1WrVI488opSUFC1fvlxPP/20QkJCXDkcAAAAAFQ5Lr07pSSZzWYtXLhQ//73v/Xzzz/Lx8dH/fr108iRI+Xp6enKoQAAAACgynF5iAMAAAAAlB+XromralatWqXAwEDt3Lmz2H7nz59XYGCg5s6dW0GVOeratatiY2MrbXw4io2NVURERGWXgXvQ+PHjFRgYqOzs7HI5bmnNnTtXgYGBOn/+vMuPeeLEiVIfA84LDAzU+PHjK2Sssp4bcJ3C7yv79u2TVPR7+V5lpFqrul+eZ1LFfsbfC9+3KxIhrpKcP39ec+fOVWpqamWXgnK0bdu2KvNhAgAwLv5/hapu3759mjt3rrKysiq7FKcQ4iqAn5+fkpOTNWrUKFvbhQsXFBcXV2EhbvPmzVq4cGGFjIX/2LZtm+Li4iq7DECSNHnyZCUnJ9/zx0T5SU5O1uTJkyu7DFSyUaNGKTk5WX5+fra2e/X/V0XVCpSH/fv3Ky4uzjAhzqV3p0TR3Nzc5O3tXak1eHl5Ver4qFw5OTmqWbNmZZeBSlYeN5fihlXGUFBQoIKCgkr/fxHuDR4eHvLwMMZXQCPVClQkrsS5QEFBgWbPnq0uXbqoTZs2evbZZ7V3717b9l/O0V21apWGDh0qSZowYYICAwMd1ink5eVp3rx56tmzp9q0aaOIiAgNGTJE27Ztsxv76NGjGj58uB5++GG1bdtW/fr105o1axxqLGpNXGBgoMaMGaPk5GQNGTJEISEh6tSpk6ZMmaL8/HyXPT+/Rjdv3tTs2bP15JNPqnXr1urUqZPGjx+vtLQ0W5/Y2FitXr1akmyv8S/niuv/a+/+o2JKHz+Av6MZFflRfqQTQntDakRbYT9RSS3TJjpCaaolDovyY9Mu53TyIz8OonZ3lo2iYqN2kfEj2Vhr5cdh149lqU2m3ZNfhcjM0PP9w7n3222m6Q7Daj2vc5yj5z7z3GfmPj/ufe59ngvg7t27WLBgAdzc3ODq6opZs2bx0mFVV1djxYoV8Pb2xsCBA+Hl5YVly5bh8ePHvHjsXDulUonZs2fDzc2N945GIXln0/Hx8dHKR0lJCRwdHZGfn8+Fsc/BFxUV4ZtvvoG3tzckEgmmTJnCPQevUCgQGBgIZ2dn+Pv7o6ioSOjP/Z8itG4DwJMnT5CYmAhPT0+4uLhAJpOhtLSUF6eqqgorV65EYGAgBg8eDIlEgpCQECgUCq30dM1RYsuL0HL4NtJ8/vw5Fi1aBCcnJ145o4Rh6yN7Z8XHxwfOzs64cOGCVl/D9k+rV69GQUEBpFIpnJ2d4efnh+zsbK20fXx8MH78eFy9ehVTp07FoEGDMHToUCxfvhx1dXWC8nfjxg3MnTsXHh4eGDhwID7++GNkZGSArrP29jSeZyakvzLkuJ04cQKRkZFwc3ODRCJBQEAAkpOTeXFqa2uRnJzM9WkjR47E8uXLte6C6JuLe+fOnWbbSEq3hu2EvnNYAFCr1UhLS+POG4YPH46EhARB7bkuutr42tpabNiwAePHj4e7uzucnZ0RGBiosx0CgGvXrkEmk3HnromJiXj69KnOuELK2uLFi7k70b6+vlwdeJf7IDq0YQSpqalQq9WIiIiASqXCrl27MG3aNGzfvh2DBw/Wiv/hhx9i5syZkMvlCA0NxZAhQwAAPXv2BPCysshkMly4cAG+vr6YOHEiCCG4fPkyTp48iVGjRgEALly4AJlMBgsLC8hkMlhaWqKgoADx8fG4c+cOYmJims17WVkZYmJiEBwcjLFjx6K4uBiZmZno0KEDZs+ebcRf6b9DrVYjKioK165dQ0hICD744ANUVlYiKysLZ86cQX5+Pjp27IiZM2eivr4e586dw5o1a7jP9+3bl/u/RqOBTCbDgAEDMH/+fJSWliInJwdPnjxBZmYmF+/hw4eYNGkSqqurMXHiRPTo0QM3btzArl27cPHiRezcuZN3t5UtQ66urliwYAGePXtmUN5f1VdffQUTExPIZDI8fvwY3333HaZNm4a4uDhs3LgRkydPRps2bbB161bMmzcPR48ehY2NzSvvr6URWrdZs2bNgrW1NebMmYOqqipkZGRg1qxZUCgUaN26NQDg+vXr+OmnnzBq1Cj07NkTT548wb59+xAXFweNRoOgoKBm8yW0HBriVdOsq6vDvHnzuLkJugYSKGE2btwIExMTTJkyBWKxGF26dGky7qlTp7Bnzx6EhYWhU6dOOHDgAJKSkqBSqRAdHc2LW11djaioKIwePRoBAQE4e/YsduzYAaVSCblcrjdPFy9eRFRUFLp164bo6GhYWlri1KlTSE5OhlKpxJIlS4zy3SnDNNdfGXLcduzYgeXLl6NXr16QyWTo2rUrbt26hUOHDiEhIQHAy7YwMjISly5dQlBQECQSCa5evYqsrCycPXsWubm5gu4aC2kjKf2EnMPGxsaiqKgI3t7eiIiIwK1bt5CTk4NTp07hhx9+gJWVleD9L2azEgAADiVJREFUNdXGV1VVIS8vDwEBAZgwYQI0Gg0KCwuRlJSEhw8fYtasWVwaFRUVCA8PByEEkZGRsLKywsGDBxEfH6+1P6FlLTQ0FLW1tSgsLERCQgI6deoEADrP498ZhHpleXl5hGEY8tFHH5FHjx5x4ZWVlcTFxYVMnDiREELI7du3CcMwZNOmTVyc06dPE4ZhSF5enla6crmcMAxDvv32W61t9fX13P9DQkKIk5MTKSsr48JUKhUJDg4mTk5OpKqqigv39vYm4eHhvLQYhiEMw5CSkhJe+lKplAwbNsyQn+K9smXLFtK/f39y/vx5Xvj58+eJo6MjSUlJ4cLi4+MJwzA60wkPDycMw5DU1FReeFJSEmEYhty8eZMLS0xMJK6urqS8vJwXd//+/YRhGLJ7926tdDds2PBaeQ8PDyfe3t5aaegqu2xdCAwMJCqVirc/hmHIkCFDeOWxpKSEMAxD0tLSdP42/1VC6zZbbhYvXsyLk56eThiGIcePH+fC6urqeJ8l5GU7EBAQQAICAnjhusqjIeVw06ZNhGEYcvv2baOnef36dVJTU0NCQ0OJm5sbOXv2rNZvRAnD1sfRo0eTuro63jaGYUh8fDz3N9s/MQxDfvvtNy5cpVKRcePGERcXF1JTU8OFe3t7E4ZhyJYtW3jpJiYmEoZhyIkTJ7iwxmWD7V+CgoJ47QQhhCxdupQ4OjpqtXGUcbBl4vTp04QQ4XWZEMOOW2VlJXFyciLjx48nT5484cV98eIF9//s7GydbQTbxqWnp3Nh+vIqpI2kdBN6DltcXKzztz506BBhGIYkJSVppcmWM0KEt/EqlYpoNBpeWH19PYmIiCCDBw8marWaC4+NjdVqs9RqNZk4caLW+fbrlrV3GX2c0ghCQkJgaWnJ/W1rawt/f39cvHgR9+/fNzi9goICdO7cWWv0E3g5vw4A7t27h99//x3+/v7o3bs3t10sFiMqKgoajQYnTpxodl/9+/eHu7s7L30PDw/cu3fP6Mub/1ccOHAAjo6OsLe3x4MHD7h/9vb2sLGxwalTpwxKj320lsW+dqCiogIAQAiBQqGAp6cnLC0tefv08PCAqampzn2GhYW98bw3FhISwrsjOGjQIAAvH8Hq2rUrF+7q6goTExPuO74vhNTthmQyGe/vxmUDAMzMzLjPqlQqVFdXo7a2Fu7u7igrK0Ntba2gvDVXDl+FIWlWVVUhLCwMSqUSWVlZcHNze+X9Ui9NmDABZmZmguKyj+SzxGIxwsLC8OzZM/zyyy+8uOy2htgyre8x6evXr+PPP/+EVCpFbW0trw3y8vICIQS//vqr0K9HvSWGHLfDhw9Do9Hgs88+05qH3arV/59yHjt2DCKRCFFRUbw44eHhaNu2reDH7YW0kZR+zZ3DHjt2DAAwY8YM3uf8/f1hb28v+Fg118aLxWJu7qNarUZNTQ2qq6sxbNgw1NbWoqysDMDLKUzFxcVabZZIJNLqcwDjlbV3EX2c0gj69OmjFcZeWCmVSlhbWxuU3q1bt+Dq6qp3Ii/7bLiDg4PWNjZMyDtVdK321KFDBwBATU0N2rZtKyjP75OysjI8e/YMQ4cO1bndkAnY7du3R/v27XlhDX9/AHjw4AFqampQVFTUZGPTeLDA3Nxc56NTxsy7Lra2try/2e/WOFwkEsHc3BwPHz58rf21NELqdkON62fjsgG8fGzx66+/xt69e1FZWamVxqNHj9CuXTu9+xFSDg1laJrz5s1DfX099u3bxz1aTr2eHj16CI7bXD/WkK2tLczNzXlhdnZ2EIlEevsd9iRs7dq1WLt2rc44rzLwSb1Zhhy38vJyAEC/fv30pqlUKmFra6t1jiEWi9GjRw/B74QT0kZS+jVX95VKJUQiEXr16qUVr2/fvigqKsLz58+b7deaa+MJIcjMzMSuXbtQXl6uNdeSnb/24MEDPH36VGe+G05XYRmrrL2L6EVcC6dr9F7INpa+Z8YbVyDqpfr6ejg5OWHhwoU6txuy+lvDkcnG2N+/vr4eADBixAhERkbqjNv4ZLmpPBgj72x+dGmqPDUVTsuYfk2Vj4a/28qVK5GTk4NPPvkEsbGx6NSpE1q3bo28vDwUFBToPV7N7afxvgxhaJpjxozB7t27IZfLsWLFCkHtF6Wf0Ltwbwt73GfMmAFPT0+dcQy58KTejjd13F73/AUQ1kZSb56Q49VcG5+eno61a9di5MiRiImJgbW1NUQiEY4fP46MjAyuL9N3bJvaZoyy9i6iF3FGwI5SNfTXX38BeDk6qWvFLn2Fxt7eHqWlpXpHNuzs7AAAN2/e1NrGrsxE36nyZtjb26OmpgbDhg1rNq4xGgcrKytYWlqirq5O0D71MSTvHTp0wNWrV7XCb9++/Vp5eJ8JqduGKigogLu7u9YIeV5enlHSf1siIiLg4OCA5ORkmJiYYPny5S26c21pmuvHGvrnn39QV1fHuxunVCqh0Wi04jbEjuSLxeLXbsso42uqvhly3Ng7ONevX0f37t2bjGdnZ4eSkhKt19+o1Wrcvn0b/fv3NzT71Ctqru7b2dnh5MmTqKio0LobV1paiu7duwtaRKa5Nr6goAB2dnaQy+W88MYrZVpbW8PCwkJnvnWFGVLWWlqfQ+fEGcGePXt4y7z//fffOHz4MCQSSZOPUrIFSdcLBceOHYu7d+/qXMGNHWXo3LkzXFxccOTIEd5JtVqtRkZGBkQiEby8vF7re1G6SaVSVFZWIjc3V2sbIQQPHjzg/maP8+s8Nti6dWuMGTMGZ86cwcmTJ7W2P3/+XHD6huS9V69eqK2txeXLl7kwtVqNnJycV/gWFCCsbhuqVatWWp8tLy9HYWHhK6X3b4qMjERCQgL27NmDpUuX0tH0t+jcuXO8l7ar1WpkZ2fDzMwMw4cP58VVqVRay35v3boVAPSuJjpgwAD06dMHWVlZuHPnjtb2x48fQ61Wv87XoF5DU/2VIcdt9OjRMDU1RVpamtYAdsP67OPjA7VajR07dvDisCvY+vr6GuU7Uc1r7hyWrdPp6em8zxUWFqK8vNygY6WvjW/VqhVMTEx4T49UV1drDUi2bt0aI0aM0GqzNBoNtm/frrVPQ8qavnPzdxG9E2cEVlZWCA0NxYQJE6BWq7Fz5068ePECn3/+eZOfcXBwgIWFBXJycmBubo527drBzs4OEokEkZGRKCoqwpo1a3DhwgVu4ZErV67A3NwciYmJAIAvvvgCMpkMkyZNwuTJk9GuXTscOHAAly5dwvz583kLSVDGExUVhRMnTmDp0qU4efIkhgwZAlNTUyiVShw9ehTBwcHcUrjspNukpCT873//g0gkgqenp8HzJOfPn4+zZ88iJiYGQUFBGDhwIJ4/f46KigocPnwYixYtErSUvCF5Dw0NRUZGBmbPns1NHt+3bx99ufNrEFq3DeHn54fdu3djwYIF8PDwQGVlJXbu3Im+ffvqvJP6rmMfGWZHa5OSklrc6GhL5OjoiE8//ZT3ioGrV69i4cKF3Dwjlq2tLbZs2YKKigr069cPZ86cwcGDB+Hl5aV38LBVq1ZYtWoVoqKiIJVKMWHCBPTu3Rs1NTW4ceMGCgsLcejQoffqtSPvEn39ldDjZmtri0WLFiE5ORnjxo1DYGAgunbtCqVSCYVCwb0PMyQkBPn5+Vi/fj3Ky8vh7OyMP/74A7t370a/fv0wZcqUf/OneK80dw47YsQI+Pr64vvvv8f9+/cxbNgwVFRUIDs7GzY2Nryl/4Voqo338/NDSkoKZsyYgVGjRuH+/fvIzc1Ft27dcO/ePV4asbGx+PnnnxEdHY2wsDBYW1tDoVDoHAQypKyxdWDdunWQSqUQi8VwcXF5Zx/zphdxRjBnzhxcuXIFmZmZqK6uBsMwWL16td7V1czMzLBu3TqkpKRg2bJl0Gg0CA4OhkQiQZs2bZCZmYnNmzdDoVCguLgYbdu2BcMwvNV1XF1dkZWVhdTUVGzbtg1qtRoODg5YvXo1xo0b9za++ntJLBZj27ZtyMjIwP79+1FcXAxTU1N0794dI0aMgL+/PxdXKpXiypUrUCgUUCgUqK+vx/bt2w2+iOvYsSNyc3OxZcsWHD58GPv374eFhQVsbW0RHBzMrchlzLz37NkTmzZtQkpKCtavX48uXbogNDSUG2igDCe0bhsiISEBbdq0wZEjR1BYWIg+ffogMTERpaWlLfIiDuB38gDohdxbMHz4cDg5OUEul6O8vBw2NjZYsmQJpk6dqhW3U6dOSEtLw6pVq7Bv3z6YmZkhPDwcCxYsaHY/EokE+fn5kMvlKCgoQHV1NTp06AB7e3vMnTvXoPdNUcalr78y5LhFRkaiZ8+e2LZtG9LT00EIQffu3eHn58fFEYvFyMjIQGpqKtenWVlZITw8HHPnzn3n5nP+lwk5h01JScHmzZuxd+9eHD9+HO3bt4dUKkVcXJzB5zOA7jZ++vTp0Gg0+PHHH7FixQrY2dlh+vTpsLCw4N4vyLK3t0dWVhaSk5Oxbds2WFhYwN/fH2FhYQgMDOTFNaSsubm5IS4uDrm5ufjyyy/x4sULJCcnv7MXcSaEPq9CURRFUe8lpVIJX19fREdH63xRbmM+Pj7o2LEj8vPz30LuKIp6U/Lz85GQkAC5XA5vb+9/OzvUK6Bz4iiKoiiKoiiKoloQehFHURRFURRFURTVgtCLOIqiKIqiKIqiqBaEzomjKIqiKIqiKIpqQeidOIqiKIqiKIqiqBaEXsRRFEVRFEVRFEW1IPQijqIoiqIoiqIoqgWhF3EURVEURVEURVEtCL2IoyiKoiiKoiiKakHoRRxFURRFURRFUVQL8n8evPupLBzG7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "now = datetime.datetime.utcnow()\n",
    "path = 'data/' + 'coefficientVariable_' + now.strftime('%Y%m%d_%H%M%S') + '.png'\n",
    "\n",
    "# seaborn drawing\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_palette(\"PuBu\", 8)\n",
    "sns.set_context(context='paper', font_scale=2, rc=None)\n",
    "\n",
    "# Coefficient of variation per asset\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# calculate coefficient variance,\n",
    "# represents the ratio of the standard deviation to the mean\n",
    "cv = pd.DataFrame(df_.std()/df_.mean())\n",
    "cv.columns = ['Coef']\n",
    "ax.set_title('Coefficient of variation per crypto asset')\n",
    "sns_plot = sns.barplot(data=cv.T)\n",
    "figure = sns_plot.get_figure()\n",
    "figure.savefig(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}