main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Event Driven Trading System\n",
    "\n",
    "Main notebook for testing and documenting component usage for my own reference. Created with reference to Successful Algorithmic Trading (Michael Halls Moore).\n",
    "\n",
    "To do: Figure out risk management component"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and setting up db connection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "\n",
    "from data import DataSource, EODDataHandler\n",
    "\n",
    "ds = DataSource()\n",
    "\n",
    "symbols = [\"SPY\", \"IEF\", \"TLT\", \"GLD\", \"VNQ\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EOD DataHandler implementation via DataSource abstraction layer\n",
    "DataHandler responsible for actually creating the mechanism that mimics a live event-driven system using historical data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "\n",
    "dataHandler = EODDataHandler(deque(), symbols, \"2019-12-01\", \"2020-01-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Run this cell a few times to get a few rounds of 'data feed updates'\n",
    "dataHandler.updateBars()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([311.0526, 313.7336, 313.9216])"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "dataHandler.getLatestBarsValues(\"SPY\", 'adj_close', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                             SPY                   IEF                   TLT  \\\ndatetime                                                                       \n2019-12-01  {'pos': 1, 'val': 0}  {'pos': 1, 'val': 0}  {'pos': 1, 'val': 0}   \n\n                             GLD                   VNQ     cash    total  \ndatetime                                                                  \n2019-12-01  {'pos': 1, 'val': 0}  {'pos': 1, 'val': 0}  10000.0  10000.0  ",
      "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>SPY</th>\n      <th>IEF</th>\n      <th>TLT</th>\n      <th>GLD</th>\n      <th>VNQ</th>\n      <th>cash</th>\n      <th>total</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2019-12-01</th>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>10000.0</td>\n      <td>10000.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "from portfolio import Portfolio\n",
    "\n",
    "p = Portfolio(0, dataHandler, '2019-12-01')\n",
    "p.positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                    SPY                          IEF  \\\ndatetime                                                               \n2019-12-01         {'pos': 1, 'val': 0}         {'pos': 1, 'val': 0}   \n2019-12-02  {'pos': 1, 'val': 308.3024}  {'pos': 1, 'val': 110.2145}   \n2019-12-03  {'pos': 1, 'val': 306.2348}  {'pos': 1, 'val': 111.1974}   \n2019-12-04  {'pos': 1, 'val': 308.1243}  {'pos': 1, 'val': 110.7705}   \n2019-12-05  {'pos': 1, 'val': 308.6783}  {'pos': 1, 'val': 110.5124}   \n2019-12-06  {'pos': 1, 'val': 311.4978}  {'pos': 1, 'val': 110.1748}   \n\n                                    TLT                        GLD  \\\ndatetime                                                             \n2019-12-01         {'pos': 1, 'val': 0}       {'pos': 1, 'val': 0}   \n2019-12-02  {'pos': 1, 'val': 137.1039}  {'pos': 1, 'val': 137.79}   \n2019-12-03  {'pos': 1, 'val': 139.9786}  {'pos': 1, 'val': 139.11}   \n2019-12-04  {'pos': 1, 'val': 138.5809}  {'pos': 1, 'val': 138.92}   \n2019-12-05  {'pos': 1, 'val': 137.9068}   {'pos': 1, 'val': 139.0}   \n2019-12-06  {'pos': 1, 'val': 137.1435}  {'pos': 1, 'val': 137.62}   \n\n                                   VNQ     cash       total  \ndatetime                                                     \n2019-12-01        {'pos': 1, 'val': 0}  10000.0  10000.0000  \n2019-12-02  {'pos': 1, 'val': 89.9846}  10000.0  10783.3954  \n2019-12-03  {'pos': 1, 'val': 90.5638}  10000.0  10787.0846  \n2019-12-04  {'pos': 1, 'val': 90.9369}  10000.0  10787.3326  \n2019-12-05   {'pos': 1, 'val': 90.986}  10000.0  10787.0835  \n2019-12-06  {'pos': 1, 'val': 91.1725}  10000.0  10787.6086  ",
      "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>SPY</th>\n      <th>IEF</th>\n      <th>TLT</th>\n      <th>GLD</th>\n      <th>VNQ</th>\n      <th>cash</th>\n      <th>total</th>\n    </tr>\n    <tr>\n      <th>datetime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2019-12-01</th>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>{'pos': 1, 'val': 0}</td>\n      <td>10000.0</td>\n      <td>10000.0000</td>\n    </tr>\n    <tr>\n      <th>2019-12-02</th>\n      <td>{'pos': 1, 'val': 308.3024}</td>\n      <td>{'pos': 1, 'val': 110.2145}</td>\n      <td>{'pos': 1, 'val': 137.1039}</td>\n      <td>{'pos': 1, 'val': 137.79}</td>\n      <td>{'pos': 1, 'val': 89.9846}</td>\n      <td>10000.0</td>\n      <td>10783.3954</td>\n    </tr>\n    <tr>\n      <th>2019-12-03</th>\n      <td>{'pos': 1, 'val': 306.2348}</td>\n      <td>{'pos': 1, 'val': 111.1974}</td>\n      <td>{'pos': 1, 'val': 139.9786}</td>\n      <td>{'pos': 1, 'val': 139.11}</td>\n      <td>{'pos': 1, 'val': 90.5638}</td>\n      <td>10000.0</td>\n      <td>10787.0846</td>\n    </tr>\n    <tr>\n      <th>2019-12-04</th>\n      <td>{'pos': 1, 'val': 308.1243}</td>\n      <td>{'pos': 1, 'val': 110.7705}</td>\n      <td>{'pos': 1, 'val': 138.5809}</td>\n      <td>{'pos': 1, 'val': 138.92}</td>\n      <td>{'pos': 1, 'val': 90.9369}</td>\n      <td>10000.0</td>\n      <td>10787.3326</td>\n    </tr>\n    <tr>\n      <th>2019-12-05</th>\n      <td>{'pos': 1, 'val': 308.6783}</td>\n      <td>{'pos': 1, 'val': 110.5124}</td>\n      <td>{'pos': 1, 'val': 137.9068}</td>\n      <td>{'pos': 1, 'val': 139.0}</td>\n      <td>{'pos': 1, 'val': 90.986}</td>\n      <td>10000.0</td>\n      <td>10787.0835</td>\n    </tr>\n    <tr>\n      <th>2019-12-06</th>\n      <td>{'pos': 1, 'val': 311.4978}</td>\n      <td>{'pos': 1, 'val': 110.1748}</td>\n      <td>{'pos': 1, 'val': 137.1435}</td>\n      <td>{'pos': 1, 'val': 137.62}</td>\n      <td>{'pos': 1, 'val': 91.1725}</td>\n      <td>10000.0</td>\n      <td>10787.6086</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "p.dataHandler.updateBars()\n",
    "p.updateTimeindex()\n",
    "p.positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update db data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[*********************100%***********************]  1 of 1 completed\nAXP is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nBA is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nCAT is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nCVX is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nDIS is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nDOW is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nGLD is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nGS is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nHD is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nIBM is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nJNJ is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nJPM is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nKO is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nMCD is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nMMM is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nMRK is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nNKE is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nPFE is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nPG is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nRTX is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nSPY is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nTRV is already up to date (latest date: 2020-06-19)\n[*********************100%***********************]  1 of 1 completed\nUNH has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nV has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nVNQ has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nVZ has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nWMT has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nXOM has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nAAPL has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nCSCO has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nIEF has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nINTC has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nMSFT has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nTLT has been updated from 2020-06-16\n[*********************100%***********************]  1 of 1 completed\nWBA has been updated from 2020-06-16\n"
    }
   ],
   "source": [
    "ds.updateEodData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data from the db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "              open    high     low   close  adj_close     volume\ndate                                                            \n2019-12-02  314.59  314.66  311.17  311.64   308.3024   75767800\n2019-12-03  308.65  309.64  307.13  309.55   306.2348   73941700\n2019-12-04  310.70  312.12  310.32  311.46   308.1243   49080000\n2019-12-05  312.23  312.25  310.58  312.02   308.6783   40709000\n2019-12-06  314.12  315.31  314.11  314.87   311.4978   48927000\n2019-12-09  314.44  315.18  313.80  313.88   310.5184   34838500\n2019-12-10  313.82  314.55  312.81  313.53   310.1722   52649800\n2019-12-11  314.03  314.70  313.44  314.42   311.0526   53429100\n2019-12-12  314.43  317.99  314.17  317.13   313.7336   96389600\n2019-12-13  316.87  318.67  316.02  317.32   313.9216   81503900\n2019-12-16  319.22  320.15  317.25  319.50   316.0782   82749700\n2019-12-17  319.92  320.25  319.48  319.57   316.1475   61097700\n2019-12-18  320.00  320.25  319.53  319.59   316.1673   48133000\n2019-12-19  319.80  320.98  319.52  320.90   317.4632   85310500\n2019-12-20  320.46  321.97  319.39  320.73   318.8550  147142100\n2019-12-23  321.59  321.65  321.06  321.22   319.3422   52990000\n2019-12-24  321.47  321.52  320.90  321.23   319.3521   20270000\n2019-12-26  321.65  322.95  321.64  322.94   321.0521   30911200\n2019-12-27  323.74  323.80  322.28  322.86   320.9726   42528800\n2019-12-30  322.95  323.10  320.55  321.08   319.2030   49729100\n2019-12-31  320.53  322.13  320.15  321.86   319.9784   57077300",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>open</th>\n      <th>high</th>\n      <th>low</th>\n      <th>close</th>\n      <th>adj_close</th>\n      <th>volume</th>\n    </tr>\n    <tr>\n      <th>date</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>2019-12-02</th>\n      <td>314.59</td>\n      <td>314.66</td>\n      <td>311.17</td>\n      <td>311.64</td>\n      <td>308.3024</td>\n      <td>75767800</td>\n    </tr>\n    <tr>\n      <th>2019-12-03</th>\n      <td>308.65</td>\n      <td>309.64</td>\n      <td>307.13</td>\n      <td>309.55</td>\n      <td>306.2348</td>\n      <td>73941700</td>\n    </tr>\n    <tr>\n      <th>2019-12-04</th>\n      <td>310.70</td>\n      <td>312.12</td>\n      <td>310.32</td>\n      <td>311.46</td>\n      <td>308.1243</td>\n      <td>49080000</td>\n    </tr>\n    <tr>\n      <th>2019-12-05</th>\n      <td>312.23</td>\n      <td>312.25</td>\n      <td>310.58</td>\n      <td>312.02</td>\n      <td>308.6783</td>\n      <td>40709000</td>\n    </tr>\n    <tr>\n      <th>2019-12-06</th>\n      <td>314.12</td>\n      <td>315.31</td>\n      <td>314.11</td>\n      <td>314.87</td>\n      <td>311.4978</td>\n      <td>48927000</td>\n    </tr>\n    <tr>\n      <th>2019-12-09</th>\n      <td>314.44</td>\n      <td>315.18</td>\n      <td>313.80</td>\n      <td>313.88</td>\n      <td>310.5184</td>\n      <td>34838500</td>\n    </tr>\n    <tr>\n      <th>2019-12-10</th>\n      <td>313.82</td>\n      <td>314.55</td>\n      <td>312.81</td>\n      <td>313.53</td>\n      <td>310.1722</td>\n      <td>52649800</td>\n    </tr>\n    <tr>\n      <th>2019-12-11</th>\n      <td>314.03</td>\n      <td>314.70</td>\n      <td>313.44</td>\n      <td>314.42</td>\n      <td>311.0526</td>\n      <td>53429100</td>\n    </tr>\n    <tr>\n      <th>2019-12-12</th>\n      <td>314.43</td>\n      <td>317.99</td>\n      <td>314.17</td>\n      <td>317.13</td>\n      <td>313.7336</td>\n      <td>96389600</td>\n    </tr>\n    <tr>\n      <th>2019-12-13</th>\n      <td>316.87</td>\n      <td>318.67</td>\n      <td>316.02</td>\n      <td>317.32</td>\n      <td>313.9216</td>\n      <td>81503900</td>\n    </tr>\n    <tr>\n      <th>2019-12-16</th>\n      <td>319.22</td>\n      <td>320.15</td>\n      <td>317.25</td>\n      <td>319.50</td>\n      <td>316.0782</td>\n      <td>82749700</td>\n    </tr>\n    <tr>\n      <th>2019-12-17</th>\n      <td>319.92</td>\n      <td>320.25</td>\n      <td>319.48</td>\n      <td>319.57</td>\n      <td>316.1475</td>\n      <td>61097700</td>\n    </tr>\n    <tr>\n      <th>2019-12-18</th>\n      <td>320.00</td>\n      <td>320.25</td>\n      <td>319.53</td>\n      <td>319.59</td>\n      <td>316.1673</td>\n      <td>48133000</td>\n    </tr>\n    <tr>\n      <th>2019-12-19</th>\n      <td>319.80</td>\n      <td>320.98</td>\n      <td>319.52</td>\n      <td>320.90</td>\n      <td>317.4632</td>\n      <td>85310500</td>\n    </tr>\n    <tr>\n      <th>2019-12-20</th>\n      <td>320.46</td>\n      <td>321.97</td>\n      <td>319.39</td>\n      <td>320.73</td>\n      <td>318.8550</td>\n      <td>147142100</td>\n    </tr>\n    <tr>\n      <th>2019-12-23</th>\n      <td>321.59</td>\n      <td>321.65</td>\n      <td>321.06</td>\n      <td>321.22</td>\n      <td>319.3422</td>\n      <td>52990000</td>\n    </tr>\n    <tr>\n      <th>2019-12-24</th>\n      <td>321.47</td>\n      <td>321.52</td>\n      <td>320.90</td>\n      <td>321.23</td>\n      <td>319.3521</td>\n      <td>20270000</td>\n    </tr>\n    <tr>\n      <th>2019-12-26</th>\n      <td>321.65</td>\n      <td>322.95</td>\n      <td>321.64</td>\n      <td>322.94</td>\n      <td>321.0521</td>\n      <td>30911200</td>\n    </tr>\n    <tr>\n      <th>2019-12-27</th>\n      <td>323.74</td>\n      <td>323.80</td>\n      <td>322.28</td>\n      <td>322.86</td>\n      <td>320.9726</td>\n      <td>42528800</td>\n    </tr>\n    <tr>\n      <th>2019-12-30</th>\n      <td>322.95</td>\n      <td>323.10</td>\n      <td>320.55</td>\n      <td>321.08</td>\n      <td>319.2030</td>\n      <td>49729100</td>\n    </tr>\n    <tr>\n      <th>2019-12-31</th>\n      <td>320.53</td>\n      <td>322.13</td>\n      <td>320.15</td>\n      <td>321.86</td>\n      <td>319.9784</td>\n      <td>57077300</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "spy = ds.getEodData(\"SPY\", start=\"2019-12-01\", end=\"2020-01-01\")\n",
    "spy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     Company Exchange    Symbol                      Industry  \\\n0                         3M     NYSE  NYSE:MMM                  Conglomerate   \n1           American Express     NYSE  NYSE:AXP            Financial services   \n2                 Apple Inc.   NASDAQ      AAPL        Information technology   \n3                     Boeing     NYSE   NYSE:BA           Aerospaceanddefense   \n4           Caterpillar Inc.     NYSE  NYSE:CAT         ConstructionandMining   \n5        Chevron Corporation     NYSE  NYSE:CVX            Petroleum industry   \n6              Cisco Systems   NASDAQ      CSCO        Information technology   \n7      The Coca-Cola Company     NYSE   NYSE:KO                 Food industry   \n8                   Dow Inc.     NYSE  NYSE:DOW             Chemical industry   \n9                 ExxonMobil     NYSE  NYSE:XOM            Petroleum industry   \n10             Goldman Sachs     NYSE   NYSE:GS            Financial services   \n11            The Home Depot     NYSE   NYSE:HD                     Retailing   \n12                       IBM     NYSE  NYSE:IBM        Information technology   \n13                     Intel   NASDAQ      INTC        Information technology   \n14         Johnson & Johnson     NYSE  NYSE:JNJ       Pharmaceutical industry   \n15            JPMorgan Chase     NYSE  NYSE:JPM            Financial services   \n16                McDonald's     NYSE  NYSE:MCD                 Food industry   \n17               Merck & Co.     NYSE  NYSE:MRK       Pharmaceutical industry   \n18                 Microsoft   NASDAQ      MSFT        Information technology   \n19                      Nike     NYSE  NYSE:NKE                       Apparel   \n20                    Pfizer     NYSE  NYSE:PFE       Pharmaceutical industry   \n21          Procter & Gamble     NYSE   NYSE:PG    Fast-moving consumer goods   \n22     Raytheon Technologies     NYSE  NYSE:RTX           Aerospaceanddefense   \n23   The Travelers Companies     NYSE  NYSE:TRV            Financial services   \n24        UnitedHealth Group     NYSE  NYSE:UNH           Managed health care   \n25                   Verizon     NYSE   NYSE:VZ             Telecommunication   \n26                 Visa Inc.     NYSE    NYSE:V            Financial services   \n27                   Walmart     NYSE  NYSE:WMT                     Retailing   \n28  Walgreens Boots Alliance   NASDAQ       WBA                     Retailing   \n29   The Walt Disney Company     NYSE  NYSE:DIS  Broadcastingandentertainment   \n\n    Date Added                                              Notes  \\\n0   1976-08-09              as Minnesota Mining and Manufacturing   \n1   1982-08-30                                                      \n2   2015-03-19                                                      \n3   1987-03-12                                                      \n4   1991-05-06                                                      \n5   2008-02-19                      also 1930-07-18 to 1999-11-01   \n6   2009-06-08                                                      \n7   1987-03-12                      also 1932-05-26 to 1935-11-20   \n8   2019-04-02                                                      \n9   1928-10-01                       asStandard Oil of New Jersey   \n10  2013-09-20                                                      \n11  1999-11-01                                                      \n12  1979-06-29                      also 1932-05-26 to 1939-03-04   \n13  1999-11-01                                                      \n14  1997-03-17                                                      \n15  1991-05-06                                                      \n16  1985-10-30                                                      \n17  1979-06-29                                                      \n18  1999-11-01                                                      \n19  2013-09-20                                                      \n20  2004-04-08                                                      \n21  1932-05-26                                                      \n22  1939-03-14  as United Aircraft, later transformed intoUnit...   \n23  2009-06-08                                                      \n24  2012-09-24                                                      \n25  2004-04-08                                                      \n26  2013-09-20                                                      \n27  1997-03-17                                                      \n28  2018-06-26                                                      \n29  1991-05-06                                                      \n\n   Index Weighting (Apr 30, 2020)  \n0                           4.35%  \n1                           2.68%  \n2                           8.01%  \n3                           3.87%  \n4                           3.34%  \n5                           2.63%  \n6                           1.21%  \n7                           1.31%  \n8                           1.04%  \n9                           1.32%  \n10                          5.29%  \n11                          6.17%  \n12                          3.58%  \n13                          1.72%  \n14                          4.18%  \n15                          2.72%  \n16                          5.23%  \n17                          2.25%  \n18                          4.94%  \n19                          2.45%  \n20                          1.06%  \n21                          3.26%  \n22                          1.89%  \n23                          2.95%  \n24                          8.01%  \n25                          1.62%  \n26                          5.06%  \n27                          3.44%  \n28                          1.26%  \n29                          3.12%  ",
      "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>Company</th>\n      <th>Exchange</th>\n      <th>Symbol</th>\n      <th>Industry</th>\n      <th>Date Added</th>\n      <th>Notes</th>\n      <th>Index Weighting (Apr 30, 2020)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3M</td>\n      <td>NYSE</td>\n      <td>NYSE:MMM</td>\n      <td>Conglomerate</td>\n      <td>1976-08-09</td>\n      <td>as Minnesota Mining and Manufacturing</td>\n      <td>4.35%</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>American Express</td>\n      <td>NYSE</td>\n      <td>NYSE:AXP</td>\n      <td>Financial services</td>\n      <td>1982-08-30</td>\n      <td></td>\n      <td>2.68%</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Apple Inc.</td>\n      <td>NASDAQ</td>\n      <td>AAPL</td>\n      <td>Information technology</td>\n      <td>2015-03-19</td>\n      <td></td>\n      <td>8.01%</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Boeing</td>\n      <td>NYSE</td>\n      <td>NYSE:BA</td>\n      <td>Aerospaceanddefense</td>\n      <td>1987-03-12</td>\n      <td></td>\n      <td>3.87%</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Caterpillar Inc.</td>\n      <td>NYSE</td>\n      <td>NYSE:CAT</td>\n      <td>ConstructionandMining</td>\n      <td>1991-05-06</td>\n      <td></td>\n      <td>3.34%</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Chevron Corporation</td>\n      <td>NYSE</td>\n      <td>NYSE:CVX</td>\n      <td>Petroleum industry</td>\n      <td>2008-02-19</td>\n      <td>also 1930-07-18 to 1999-11-01</td>\n      <td>2.63%</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Cisco Systems</td>\n      <td>NASDAQ</td>\n      <td>CSCO</td>\n      <td>Information technology</td>\n      <td>2009-06-08</td>\n      <td></td>\n      <td>1.21%</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>The Coca-Cola Company</td>\n      <td>NYSE</td>\n      <td>NYSE:KO</td>\n      <td>Food industry</td>\n      <td>1987-03-12</td>\n      <td>also 1932-05-26 to 1935-11-20</td>\n      <td>1.31%</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Dow Inc.</td>\n      <td>NYSE</td>\n      <td>NYSE:DOW</td>\n      <td>Chemical industry</td>\n      <td>2019-04-02</td>\n      <td></td>\n      <td>1.04%</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>ExxonMobil</td>\n      <td>NYSE</td>\n      <td>NYSE:XOM</td>\n      <td>Petroleum industry</td>\n      <td>1928-10-01</td>\n      <td>asStandard Oil of New Jersey</td>\n      <td>1.32%</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Goldman Sachs</td>\n      <td>NYSE</td>\n      <td>NYSE:GS</td>\n      <td>Financial services</td>\n      <td>2013-09-20</td>\n      <td></td>\n      <td>5.29%</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>The Home Depot</td>\n      <td>NYSE</td>\n      <td>NYSE:HD</td>\n      <td>Retailing</td>\n      <td>1999-11-01</td>\n      <td></td>\n      <td>6.17%</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>IBM</td>\n      <td>NYSE</td>\n      <td>NYSE:IBM</td>\n      <td>Information technology</td>\n      <td>1979-06-29</td>\n      <td>also 1932-05-26 to 1939-03-04</td>\n      <td>3.58%</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Intel</td>\n      <td>NASDAQ</td>\n      <td>INTC</td>\n      <td>Information technology</td>\n      <td>1999-11-01</td>\n      <td></td>\n      <td>1.72%</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Johnson &amp; Johnson</td>\n      <td>NYSE</td>\n      <td>NYSE:JNJ</td>\n      <td>Pharmaceutical industry</td>\n      <td>1997-03-17</td>\n      <td></td>\n      <td>4.18%</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>JPMorgan Chase</td>\n      <td>NYSE</td>\n      <td>NYSE:JPM</td>\n      <td>Financial services</td>\n      <td>1991-05-06</td>\n      <td></td>\n      <td>2.72%</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>McDonald's</td>\n      <td>NYSE</td>\n      <td>NYSE:MCD</td>\n      <td>Food industry</td>\n      <td>1985-10-30</td>\n      <td></td>\n      <td>5.23%</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Merck &amp; Co.</td>\n      <td>NYSE</td>\n      <td>NYSE:MRK</td>\n      <td>Pharmaceutical industry</td>\n      <td>1979-06-29</td>\n      <td></td>\n      <td>2.25%</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Microsoft</td>\n      <td>NASDAQ</td>\n      <td>MSFT</td>\n      <td>Information technology</td>\n      <td>1999-11-01</td>\n      <td></td>\n      <td>4.94%</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Nike</td>\n      <td>NYSE</td>\n      <td>NYSE:NKE</td>\n      <td>Apparel</td>\n      <td>2013-09-20</td>\n      <td></td>\n      <td>2.45%</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Pfizer</td>\n      <td>NYSE</td>\n      <td>NYSE:PFE</td>\n      <td>Pharmaceutical industry</td>\n      <td>2004-04-08</td>\n      <td></td>\n      <td>1.06%</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Procter &amp; Gamble</td>\n      <td>NYSE</td>\n      <td>NYSE:PG</td>\n      <td>Fast-moving consumer goods</td>\n      <td>1932-05-26</td>\n      <td></td>\n      <td>3.26%</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Raytheon Technologies</td>\n      <td>NYSE</td>\n      <td>NYSE:RTX</td>\n      <td>Aerospaceanddefense</td>\n      <td>1939-03-14</td>\n      <td>as United Aircraft, later transformed intoUnit...</td>\n      <td>1.89%</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>The Travelers Companies</td>\n      <td>NYSE</td>\n      <td>NYSE:TRV</td>\n      <td>Financial services</td>\n      <td>2009-06-08</td>\n      <td></td>\n      <td>2.95%</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>UnitedHealth Group</td>\n      <td>NYSE</td>\n      <td>NYSE:UNH</td>\n      <td>Managed health care</td>\n      <td>2012-09-24</td>\n      <td></td>\n      <td>8.01%</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Verizon</td>\n      <td>NYSE</td>\n      <td>NYSE:VZ</td>\n      <td>Telecommunication</td>\n      <td>2004-04-08</td>\n      <td></td>\n      <td>1.62%</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Visa Inc.</td>\n      <td>NYSE</td>\n      <td>NYSE:V</td>\n      <td>Financial services</td>\n      <td>2013-09-20</td>\n      <td></td>\n      <td>5.06%</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Walmart</td>\n      <td>NYSE</td>\n      <td>NYSE:WMT</td>\n      <td>Retailing</td>\n      <td>1997-03-17</td>\n      <td></td>\n      <td>3.44%</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Walgreens Boots Alliance</td>\n      <td>NASDAQ</td>\n      <td>WBA</td>\n      <td>Retailing</td>\n      <td>2018-06-26</td>\n      <td></td>\n      <td>1.26%</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Walt Disney Company</td>\n      <td>NYSE</td>\n      <td>NYSE:DIS</td>\n      <td>Broadcastingandentertainment</td>\n      <td>1991-05-06</td>\n      <td></td>\n      <td>3.12%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "url = \"https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average\"\n",
    "response = requests.get(url)\n",
    "response.raise_for_status()\n",
    "\n",
    "soup = BeautifulSoup(response.text, 'html.parser')\n",
    "table = soup.find(id=\"constituents\")\n",
    "headers = list(map(lambda x: x.get_text(strip=True), table.find_all(\"th\")))\n",
    "table_rows = table.find(\"tbody\").find_all(\"tr\")\n",
    "\n",
    "temp = []\n",
    "for tr in table_rows:\n",
    "    td = tr.find_all('td')\n",
    "    row = [tr.get_text(strip=True) for tr in td]\n",
    "    temp.append(row)\n",
    "\n",
    "df = pd.DataFrame(temp[1:], columns=headers)\n",
    "df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formatting data to put into database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                        name  exchange_id symbol currency instrument\n0                         3M            2    MMM      USD      Stock\n1           American Express            2    AXP      USD      Stock\n2                 Apple Inc.            3   AAPL      USD      Stock\n3                     Boeing            2     BA      USD      Stock\n4           Caterpillar Inc.            2    CAT      USD      Stock\n5        Chevron Corporation            2    CVX      USD      Stock\n6              Cisco Systems            3   CSCO      USD      Stock\n7      The Coca-Cola Company            2     KO      USD      Stock\n8                   Dow Inc.            2    DOW      USD      Stock\n9                 ExxonMobil            2    XOM      USD      Stock\n10             Goldman Sachs            2     GS      USD      Stock\n11            The Home Depot            2     HD      USD      Stock\n12                       IBM            2    IBM      USD      Stock\n13                     Intel            3   INTC      USD      Stock\n14         Johnson & Johnson            2    JNJ      USD      Stock\n15            JPMorgan Chase            2    JPM      USD      Stock\n16                McDonald's            2    MCD      USD      Stock\n17               Merck & Co.            2    MRK      USD      Stock\n18                 Microsoft            3   MSFT      USD      Stock\n19                      Nike            2    NKE      USD      Stock\n20                    Pfizer            2    PFE      USD      Stock\n21          Procter & Gamble            2     PG      USD      Stock\n22     Raytheon Technologies            2    RTX      USD      Stock\n23   The Travelers Companies            2    TRV      USD      Stock\n24        UnitedHealth Group            2    UNH      USD      Stock\n25                   Verizon            2     VZ      USD      Stock\n26                 Visa Inc.            2      V      USD      Stock\n27                   Walmart            2    WMT      USD      Stock\n28  Walgreens Boots Alliance            3    WBA      USD      Stock\n29   The Walt Disney Company            2    DIS      USD      Stock",
      "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>name</th>\n      <th>exchange_id</th>\n      <th>symbol</th>\n      <th>currency</th>\n      <th>instrument</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3M</td>\n      <td>2</td>\n      <td>MMM</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>American Express</td>\n      <td>2</td>\n      <td>AXP</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Apple Inc.</td>\n      <td>3</td>\n      <td>AAPL</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Boeing</td>\n      <td>2</td>\n      <td>BA</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Caterpillar Inc.</td>\n      <td>2</td>\n      <td>CAT</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Chevron Corporation</td>\n      <td>2</td>\n      <td>CVX</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Cisco Systems</td>\n      <td>3</td>\n      <td>CSCO</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>The Coca-Cola Company</td>\n      <td>2</td>\n      <td>KO</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Dow Inc.</td>\n      <td>2</td>\n      <td>DOW</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>ExxonMobil</td>\n      <td>2</td>\n      <td>XOM</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Goldman Sachs</td>\n      <td>2</td>\n      <td>GS</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>The Home Depot</td>\n      <td>2</td>\n      <td>HD</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>IBM</td>\n      <td>2</td>\n      <td>IBM</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Intel</td>\n      <td>3</td>\n      <td>INTC</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Johnson &amp; Johnson</td>\n      <td>2</td>\n      <td>JNJ</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>JPMorgan Chase</td>\n      <td>2</td>\n      <td>JPM</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>McDonald's</td>\n      <td>2</td>\n      <td>MCD</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Merck &amp; Co.</td>\n      <td>2</td>\n      <td>MRK</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Microsoft</td>\n      <td>3</td>\n      <td>MSFT</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Nike</td>\n      <td>2</td>\n      <td>NKE</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Pfizer</td>\n      <td>2</td>\n      <td>PFE</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Procter &amp; Gamble</td>\n      <td>2</td>\n      <td>PG</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Raytheon Technologies</td>\n      <td>2</td>\n      <td>RTX</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>The Travelers Companies</td>\n      <td>2</td>\n      <td>TRV</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>UnitedHealth Group</td>\n      <td>2</td>\n      <td>UNH</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Verizon</td>\n      <td>2</td>\n      <td>VZ</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Visa Inc.</td>\n      <td>2</td>\n      <td>V</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Walmart</td>\n      <td>2</td>\n      <td>WMT</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Walgreens Boots Alliance</td>\n      <td>3</td>\n      <td>WBA</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>The Walt Disney Company</td>\n      <td>2</td>\n      <td>DIS</td>\n      <td>USD</td>\n      <td>Stock</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "extraColumns = list(filter(lambda x: x not in [\"Company\", \"Exchange\", \"Symbol\"], headers))\n",
    "\n",
    "df.drop(labels=extraColumns, axis=1, inplace=True)\n",
    "\n",
    "df['Symbol'] = df['Symbol'].apply(lambda x: x[x.find(\":\") + 1:])\n",
    "df['Exchange'] = df['Exchange'].apply(lambda x: data.getExchangeId(x))\n",
    "\n",
    "df.rename(\n",
    "    columns={\n",
    "        \"Exchange\": \"exchange_id\",\n",
    "        \"Symbol\": \"symbol\",\n",
    "        \"Company\": \"name\"\n",
    "    },\n",
    "    inplace=True)\n",
    "\n",
    "df.insert(loc=len(df.columns), column=\"currency\", value=\"USD\")\n",
    "df.insert(loc=len(df.columns), column=\"instrument\", value=\"Stock\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.to_sql('assets', ds.con, index=False, if_exists=\"append\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from performance import CAGR, annualVolatility, sharpeRatio, maxDrawdown\n",
    "\n",
    "spy = ds.getEodData('SPY')\n",
    "returns = spy['adj_close'].pct_change().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.09472859969858205"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "CAGR(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.1893144250464249"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "annualVolatility(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.5003770825987089"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "sharpeRatio(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(0.5518946996706064, 0.9057561258518196, 1656)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "maxDrawdown(returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f9313fc4c10>"
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
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174.167851 \nL 95.621736 173.882884 \nL 95.652173 173.419811 \nL 95.682609 173.597916 \nL 95.713045 174.351864 \nL 95.804354 174.345956 \nL 95.895664 173.289201 \nL 95.9261 173.312948 \nL 96.017409 173.336696 \nL 96.078282 172.790508 \nL 96.230464 172.873566 \nL 96.2609 172.849819 \nL 96.291336 172.552977 \nL 96.321773 172.826071 \nL 96.473954 173.87101 \nL 96.504391 173.977873 \nL 96.534827 173.847263 \nL 96.565264 174.927765 \nL 96.717445 173.229775 \nL 96.778318 173.87101 \nL 96.869627 174.00162 \nL 96.960936 174.838684 \nL 96.991373 174.702165 \nL 97.173991 173.550421 \nL 97.204427 173.651319 \nL 97.326173 173.479179 \nL 97.387045 174.39345 \nL 97.417482 173.835389 \nL 97.508791 173.894757 \nL 97.630536 172.800489 \nL 97.721845 172.597891 \nL 97.752282 172.764753 \nL 97.813154 173.312948 \nL 97.843591 173.015017 \nL 97.9349 172.66942 \nL 97.965336 173.050753 \nL 97.995773 172.574086 \nL 98.178391 171.596891 \nL 98.208827 171.859029 \nL 98.2697 172.097362 \nL 98.361009 172.061626 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169.988155 \nL 102.226427 169.796685 \nL 102.317736 169.354033 \nL 102.409045 169.198471 \nL 102.439482 169.509537 \nL 102.469918 169.018989 \nL 102.530791 168.791669 \nL 102.6221 168.875416 \nL 102.652536 168.648096 \nL 102.682973 168.911324 \nL 102.713409 169.282217 \nL 102.743845 168.480603 \nL 102.835154 168.624176 \nL 102.926464 167.882331 \nL 102.9569 168.037893 \nL 103.2917 166.588218 \nL 103.322136 166.744182 \nL 103.352573 166.768159 \nL 103.474318 166.972133 \nL 103.565627 166.024191 \nL 103.596064 165.808229 \nL 103.748245 166.684183 \nL 103.778682 166.348164 \nL 103.900427 166.468162 \nL 103.991736 166.492197 \nL 104.022173 165.928227 \nL 104.113482 165.940215 \nL 104.174354 165.616243 \nL 104.204791 166.036237 \nL 104.356973 167.356105 \nL 104.448282 165.808229 \nL 104.570027 166.216177 \nL 104.6309 167.056166 \nL 104.661336 166.372198 \nL 104.752645 166.516173 \nL 104.813518 165.952204 \nL 104.843954 166.168166 \nL 104.9657 166.576173 \nL 105.026573 165.880216 \nL 105.087445 166.324187 \nL 105.3005 167.176165 \nL 105.422245 166.948156 \nL 105.452682 167.620136 \nL 105.483118 166.852135 \nL 105.513554 166.036237 \nL 105.604864 166.08419 \nL 105.6353 165.952204 \nL 105.665736 166.228166 \nL 105.817918 167.752065 \nL 105.878791 166.240211 \nL 105.939664 166.645694 \nL 106.030973 166.441031 \nL 106.152718 165.249364 \nL 106.244027 165.092827 \nL 106.274464 165.393798 \nL 106.3049 164.888221 \nL 106.883191 163.371492 \nL 107.004936 165.092827 \nL 107.096245 164.779867 \nL 107.217991 165.98358 \nL 107.3093 166.164209 \nL 107.339736 167.825315 \nL 107.431045 167.006836 \nL 107.522354 167.247521 \nL 107.583227 167.175304 \nL 107.6441 168.162366 \nL 107.765845 166.525294 \nL 107.826718 166.886379 \nL 107.857154 167.223487 \nL 107.9789 166.862345 \nL 108.009336 167.103087 \nL 108.161518 172.062487 \nL 108.191954 170.497632 \nL 108.283264 171.388385 \nL 108.405009 169.366136 \nL 108.435445 170.329106 \nL 108.465882 171.099517 \nL 108.496318 169.871655 \nL 108.6485 168.595783 \nL 108.678936 169.751313 \nL 108.709373 169.576305 \nL 108.800682 169.600454 \nL 108.831118 169.274302 \nL 108.861554 167.679734 \nL 108.922427 168.742798 \nL 109.013736 168.380394 \nL 109.044173 168.477046 \nL 109.105045 170.844721 \nL 109.135482 170.107809 \nL 109.3181 171.702319 \nL 109.439845 170.397709 \nL 109.470282 170.530614 \nL 109.500718 170.16821 \nL 109.561591 168.066344 \nL 109.683336 167.679734 \nL 109.713773 167.824741 \nL 109.744209 167.208631 \nL 109.774645 167.740134 \nL 109.865954 167.438188 \nL 109.896391 167.679734 \nL 109.926827 167.752237 \nL 110.079009 165.795322 \nL 110.109445 166.109372 \nL 110.139882 165.650315 \nL 110.200754 164.925563 \nL 110.383373 165.69867 \nL 110.413809 165.275865 \nL 110.596427 164.297407 \nL 110.718173 162.896144 \nL 110.779045 163.186045 \nL 110.839918 162.847789 \nL 111.022536 164.45446 \nL 111.144282 163.065243 \nL 111.357336 165.070514 \nL 111.479082 163.082222 \nL 111.813882 160.827892 \nL 111.844318 161.215708 \nL 111.9965 161.324807 \nL 112.118245 159.494659 \nL 112.209554 159.967311 \nL 112.300864 162.027762 \nL 112.3313 160.803629 \nL 112.453045 160.48849 \nL 112.483482 160.100674 \nL 112.544354 161.506641 \nL 112.635664 161.021771 \nL 112.757409 159.531026 \nL 112.879154 160.124938 \nL 112.909591 159.628023 \nL 112.940027 160.367344 \nL 113.092209 161.906559 \nL 113.153082 160.512753 \nL 113.183518 161.094505 \nL 113.3357 161.43385 \nL 113.487882 159.567393 \nL 113.518318 159.591656 \nL 113.609627 161.118768 \nL 113.700936 160.985405 \nL 113.731373 161.409644 \nL 113.761809 161.155135 \nL 114.0053 158.379626 \nL 114.035736 158.864438 \nL 114.127045 158.149381 \nL 114.157482 158.34326 \nL 114.187918 158.561459 \nL 114.218354 157.749405 \nL 114.248791 158.621229 \nL 114.3401 158.52406 \nL 114.370536 159.981938 \nL 114.461845 159.058604 \nL 114.553154 158.050204 \nL 114.583591 158.317505 \nL 114.614027 159.131509 \nL 114.644464 158.754879 \nL 114.979264 156.045565 \nL 115.192318 158.694134 \nL 115.222754 158.062365 \nL 115.283627 156.10631 \nL 115.314064 156.23996 \nL 115.435809 155.681096 \nL 115.527118 157.2362 \nL 115.618427 156.288545 \nL 115.6793 156.62875 \nL 115.709736 156.950658 \nL 115.740173 156.555845 \nL 115.831482 156.817065 \nL 115.922791 155.644614 \nL 115.953227 157.029701 \nL 116.044536 156.87173 \nL 116.074973 156.968899 \nL 116.105409 156.592269 \nL 116.166282 157.199776 \nL 116.3489 159.058604 \nL 116.379336 158.420754 \nL 116.501082 158.596908 \nL 116.531518 158.548324 \nL 116.6837 157.11471 \nL 116.714136 157.62499 \nL 116.744573 157.673574 \nL 116.805445 158.572645 \nL 116.896754 158.52406 \nL 117.109809 156.543627 \nL 117.140245 156.909187 \nL 117.231554 157.683039 \nL 117.3533 156.568006 \nL 117.444609 154.679178 \nL 117.596791 154.618261 \nL 117.627227 154.575584 \nL 117.657664 154.252644 \nL 117.779409 154.691338 \nL 117.809845 154.386696 \nL 117.870718 153.740815 \nL 117.962027 154.106432 \nL 118.083773 156.104933 \nL 118.175082 156.494871 \nL 118.235954 155.885586 \nL 118.266391 156.628923 \nL 118.296827 157.274803 \nL 118.388136 157.152912 \nL 118.631627 158.871207 \nL 118.844682 156.543627 \nL 118.905554 157.347938 \nL 118.935991 156.823891 \nL 119.088173 155.081333 \nL 119.118609 155.727156 \nL 119.270791 157.542907 \nL 119.3621 156.019638 \nL 119.544718 156.494871 \nL 119.575154 156.056177 \nL 119.6969 156.762974 \nL 119.727336 157.591663 \nL 119.788209 156.737965 \nL 119.879518 156.811272 \nL 120.001264 159.084244 \nL 120.092573 158.754306 \nL 120.123009 158.852048 \nL 120.153445 159.450893 \nL 120.183882 158.693217 \nL 120.214318 158.803177 \nL 120.427373 156.689037 \nL 120.518682 156.774619 \nL 120.640427 160.208512 \nL 120.731736 159.854138 \nL 120.853482 158.167736 \nL 120.944791 158.424367 \nL 120.975227 159.059808 \nL 121.005664 158.155518 \nL 121.066536 155.467026 \nL 121.188282 156.408027 \nL 121.218718 156.053596 \nL 121.3709 155.075941 \nL 121.401336 155.583124 \nL 121.462209 154.880456 \nL 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124.353664 154.355492 \nL 124.3841 153.736857 \nL 124.414536 154.551493 \nL 124.688464 156.774905 \nL 124.901518 153.718445 \nL 124.992827 154.25752 \nL 125.0537 155.384541 \nL 125.084136 153.816474 \nL 125.236318 155.482513 \nL 125.449373 150.409253 \nL 125.540682 148.689811 \nL 125.631991 149.328464 \nL 125.7233 150.60577 \nL 125.753736 149.942567 \nL 125.845045 149.598632 \nL 125.905918 150.409253 \nL 125.936354 149.899546 \nL 125.966791 149.524981 \nL 126.0581 149.758324 \nL 126.088536 149.930234 \nL 126.179845 155.592072 \nL 126.301591 152.263722 \nL 126.362464 152.521672 \nL 126.484209 153.135718 \nL 126.514645 150.814506 \nL 126.605954 152.018104 \nL 126.697264 151.244366 \nL 126.758136 153.72527 \nL 126.819009 152.632206 \nL 126.940754 153.504145 \nL 126.971191 154.756901 \nL 127.032064 152.914707 \nL 127.153809 151.391783 \nL 127.184245 151.993553 \nL 127.366864 154.806002 \nL 127.3973 153.921731 \nL 127.427736 154.867435 \nL 127.458173 154.806002 \nL 127.671227 150.937372 \nL 127.823409 151.078594 \nL 127.975591 152.104087 \nL 128.0669 150.814506 \nL 128.097336 151.292951 \nL 128.188645 150.554318 \nL 128.249518 150.788234 \nL 128.310391 152.167012 \nL 128.4017 151.434517 \nL 128.462573 151.699236 \nL 128.493009 152.240835 \nL 128.523445 151.809999 \nL 128.614754 151.022094 \nL 128.675627 152.068524 \nL 128.706064 151.625355 \nL 128.7365 150.702079 \nL 128.827809 150.80051 \nL 128.919118 150.037269 \nL 128.949554 149.458672 \nL 129.040864 149.557161 \nL 129.253918 151.194462 \nL 129.284354 151.009818 \nL 129.375664 153.065623 \nL 129.466973 152.708668 \nL 129.710464 150.468162 \nL 129.771336 151.243678 \nL 129.801773 150.972878 \nL 129.984391 149.877233 \nL 130.014827 150.074209 \nL 130.349627 149.243283 \nL 130.380064 149.815685 \nL 130.440936 148.966289 \nL 130.562682 149.446397 \nL 130.593118 150.123425 \nL 130.653991 150.024994 \nL 130.7453 150.11115 \nL 130.836609 150.092679 \nL 130.958354 151.913019 \nL 130.988791 151.394708 \nL 131.019227 151.820496 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162.104682 \nL 147.363554 162.004359 \nL 147.393991 162.032465 \nL 147.454864 162.55003 \nL 147.4853 162.107034 \nL 147.576609 162.099004 \nL 147.607045 161.785184 \nL 147.698354 162.754808 \nL 147.789664 163.330192 \nL 147.8201 163.06467 \nL 147.850536 162.943926 \nL 147.880973 162.964059 \nL 148.002718 162.943926 \nL 148.094027 163.708371 \nL 148.124464 163.70034 \nL 148.215773 163.603802 \nL 148.307082 164.54532 \nL 148.276645 163.410669 \nL 148.337518 164.211366 \nL 148.428827 164.55335 \nL 148.459264 163.495161 \nL 148.550573 163.623879 \nL 148.854936 166.046046 \nL 148.885373 165.643661 \nL 148.915809 165.112559 \nL 148.976682 165.756375 \nL 149.098427 166.754163 \nL 149.128864 165.18099 \nL 149.189736 166.504701 \nL 149.402791 164.42056 \nL 149.524536 165.305749 \nL 149.554973 165.209154 \nL 149.585409 164.742468 \nL 149.615845 165.309765 \nL 149.737591 165.547123 \nL 149.920209 167.047906 \nL 149.950645 166.943281 \nL 149.981082 166.548984 \nL 150.011518 167.414039 \nL 150.041954 167.462337 \nL 150.133264 167.301383 \nL 150.255009 168.2429 \nL 150.376754 166.806475 \nL 150.468064 168.956065 \nL 150.559373 168.746125 \nL 150.589809 169.65053 \nL 150.681118 169.085241 \nL 150.802864 170.696271 \nL 150.8333 170.478244 \nL 150.894173 168.943962 \nL 150.985482 169.444605 \nL 151.107227 171.955968 \nL 151.198536 171.758131 \nL 151.442027 176.760723 \nL 151.533336 174.479491 \nL 151.685518 172.234569 \nL 151.8377 175.218354 \nL 151.959445 172.182084 \nL 152.050754 172.452596 \nL 152.081191 173.118783 \nL 152.111627 171.806601 \nL 152.263809 170.522641 \nL 152.294245 170.930418 \nL 152.324682 170.381304 \nL 152.355118 170.005822 \nL 152.385554 170.845639 \nL 152.476864 170.579141 \nL 152.720354 173.397383 \nL 152.811664 172.702918 \nL 152.933409 172.016541 \nL 152.963845 172.246672 \nL 152.994282 172.925018 \nL 153.024718 172.836167 \nL 153.116027 172.747373 \nL 153.207336 174.842871 \nL 153.237773 174.831514 \nL 153.329082 175.111319 \nL 153.359518 175.658883 \nL 153.389954 174.831514 \nL 153.420391 174.271789 \nL 153.450827 175.480434 \nL 153.542136 175.869741 \nL 153.572573 174.275861 \nL 153.633445 175.658883 \nL 153.755191 176.948579 \nL 153.998682 173.067273 \nL 153.816064 177.36628 \nL 154.089991 173.091594 \nL 154.242173 172.458906 \nL 154.272609 173.205168 \nL 154.303045 172.458906 \nL 154.394354 172.698215 \nL 154.485664 173.140293 \nL 154.668282 171.307105 \nL 154.820464 173.245722 \nL 154.942209 171.972201 \nL 155.063954 172.394031 \nL 155.124827 170.881259 \nL 155.155264 171.152977 \nL 155.246573 171.128656 \nL 155.277009 171.850597 \nL 155.307445 170.804167 \nL 155.490064 171.376052 \nL 155.672682 172.742784 \nL 155.733554 172.223728 \nL 155.763991 172.227743 \nL 155.794427 172.807716 \nL 155.885736 171.870845 \nL 155.916173 172.195334 \nL 155.977045 172.880679 \nL 156.007482 172.36472 \nL 156.098791 172.352502 \nL 156.220536 173.428473 \nL 156.342282 173.082072 \nL 156.5249 171.154296 \nL 156.555336 171.248023 \nL 156.585773 171.794154 \nL 156.646645 171.11357 \nL 156.737954 171.12573 \nL 156.768391 171.003495 \nL 157.011882 173.106508 \nL 157.042318 172.886415 \nL 157.164064 174.316989 \nL 157.224936 173.795294 \nL 157.255373 174.630809 \nL 157.285809 173.966458 \nL 157.377118 173.897166 \nL 157.498864 175.042429 \nL 157.590173 174.801973 \nL 157.651045 175.580414 \nL 157.681482 175.478541 \nL 157.833664 174.141695 \nL 158.016282 174.887555 \nL 158.077154 175.115793 \nL 158.138027 174.439224 \nL 158.229336 174.769335 \nL 158.320645 175.315522 \nL 158.351082 175.083155 \nL 158.472827 176.224344 \nL 158.503264 176.004252 \nL 158.655445 173.673001 \nL 158.777191 172.347799 \nL 158.8685 173.567228 \nL 158.959809 173.37897 \nL 159.081554 174.365172 \nL 159.111991 173.833267 \nL 159.2033 172.941137 \nL 159.294609 173.010715 \nL 159.325045 172.953412 \nL 159.355482 173.428071 \nL 159.416354 173.37897 \nL 159.5381 172.30277 \nL 159.598973 172.392827 \nL 159.720718 172.356001 \nL 159.781591 171.320699 \nL 159.812027 171.656259 \nL 159.842464 172.118643 \nL 160.055518 170.899213 \nL 160.146827 170.972922 \nL 160.177264 170.612812 \nL 160.238136 171.210223 \nL 160.268573 170.686405 \nL 160.451191 170.121747 \nL 160.481627 170.219949 \nL 160.603373 171.20615 \nL 160.633809 171.128369 \nL 160.816427 170.003068 \nL 161.029482 169.041417 \nL 161.090354 168.263952 \nL 161.120791 168.423529 \nL 161.2121 168.836869 \nL 161.425154 167.441458 \nL 161.486027 167.478283 \nL 161.638209 168.621767 \nL 161.668645 168.580697 \nL 161.699082 168.987326 \nL 161.729518 168.465746 \nL 161.759954 168.933924 \nL 161.851264 168.946199 \nL 162.064318 167.685412 \nL 162.094754 167.500596 \nL 162.125191 167.734742 \nL 162.155627 168.260395 \nL 162.186064 167.874358 \nL 162.277373 167.67308 \nL 162.307809 167.763423 \nL 162.490427 168.6793 \nL 162.612173 167.878431 \nL 162.764354 168.317871 \nL 162.794791 168.223398 \nL 162.946973 169.443171 \nL 162.977409 169.213155 \nL 163.160027 168.15772 \nL 163.190464 168.367143 \nL 163.251336 168.128982 \nL 163.373082 167.619734 \nL 163.433954 167.656675 \nL 163.464391 168.067377 \nL 163.5557 168.001641 \nL 163.647009 167.660747 \nL 163.860064 166.572444 \nL 163.8905 166.810663 \nL 163.981809 166.461566 \nL 164.012245 166.740855 \nL 164.042682 167.167962 \nL 164.103554 166.966741 \nL 164.194864 167.114559 \nL 164.286173 166.083789 \nL 164.316609 166.302276 \nL 164.651409 167.835869 \nL 164.742718 166.417685 \nL 165.138391 165.584924 \nL 165.168827 166.059009 \nL 165.260136 165.737446 \nL 165.290573 165.81167 \nL 165.351445 166.434205 \nL 165.381882 166.339388 \nL 165.473191 166.318738 \nL 165.5645 165.589054 \nL 165.594936 165.630296 \nL 165.777554 165.176803 \nL 165.929736 165.692131 \nL 165.990609 165.193265 \nL 166.021045 165.564331 \nL 166.112354 165.782818 \nL 166.203664 166.256903 \nL 166.2341 166.079602 \nL 166.538464 164.682067 \nL 166.660209 164.991299 \nL 166.751518 164.694457 \nL 166.812391 165.040743 \nL 166.842827 164.546065 \nL 166.873264 164.459451 \nL 166.964573 164.682067 \nL 167.208064 163.590208 \nL 167.299373 163.602655 \nL 167.451554 162.94817 \nL 167.512427 162.968878 \nL 167.695045 162.078354 \nL 167.725482 162.488425 \nL 167.938536 161.726274 \nL 168.060282 161.738722 \nL 168.090718 161.365935 \nL 168.151591 161.643446 \nL 168.2429 161.046953 \nL 168.273336 161.53991 \nL 168.303773 162.082484 \nL 168.364645 162.03694 \nL 168.455954 161.833998 \nL 168.486391 161.912696 \nL 168.516827 162.297873 \nL 168.547264 162.161183 \nL 168.669009 161.622739 \nL 168.9125 160.922709 \nL 169.125554 161.660023 \nL 169.308173 160.926839 \nL 169.338609 161.208537 \nL 169.399482 160.997278 \nL 169.429918 160.835751 \nL 169.5821 162.409726 \nL 169.612536 163.014479 \nL 169.642973 162.409726 \nL 169.734282 162.981325 \nL 169.764718 162.736911 \nL 169.825591 162.206784 \nL 169.856027 162.878076 \nL 169.977773 163.543115 \nL 170.008209 163.505658 \nL 170.190827 162.084147 \nL 170.221264 162.030114 \nL 170.373445 161.128118 \nL 170.403882 161.281902 \nL 170.464754 161.502224 \nL 170.5865 161.315171 \nL 170.677809 162.088277 \nL 170.708245 161.726676 \nL 170.799554 161.726676 \nL 170.829991 162.520604 \nL 170.890864 161.55207 \nL 170.9213 161.506354 \nL 171.043045 161.531306 \nL 171.134354 162.919605 \nL 171.286536 162.163133 \nL 171.438718 163.804966 \nL 171.469154 163.422543 \nL 171.499591 163.13161 \nL 171.560464 163.302028 \nL 171.651773 163.692711 \nL 171.712645 163.6221 \nL 172.138754 162.017666 \nL 172.169191 162.449936 \nL 172.199627 162.07996 \nL 172.321373 161.298536 \nL 172.382245 161.510541 \nL 172.503991 161.98021 \nL 172.625736 161.637365 \nL 172.717045 161.816789 \nL 172.777918 161.170163 \nL 172.838791 161.549776 \nL 172.9301 161.712508 \nL 172.990973 161.26194 \nL 173.051845 161.950268 \nL 173.234464 162.551005 \nL 173.2649 162.430032 \nL 173.386645 162.375769 \nL 173.569264 163.051649 \nL 173.5997 162.467603 \nL 173.630136 163.326923 \nL 173.660573 163.201819 \nL 173.782318 163.673209 \nL 173.904064 162.801327 \nL 173.995373 162.705363 \nL 174.117118 164.465818 \nL 174.208427 164.403295 \nL 174.238864 163.827566 \nL 174.299736 164.411612 \nL 174.421482 163.86095 \nL 174.543227 162.951497 \nL 174.634536 163.068341 \nL 174.664973 163.005703 \nL 174.756282 162.546818 \nL 174.847591 162.930618 \nL 174.878027 162.688671 \nL 174.908464 162.601081 \nL 174.9389 162.075429 \nL 174.969336 162.267357 \nL 175.091082 161.958642 \nL 175.121518 162.075429 \nL 175.151954 162.11713 \nL 175.304136 161.624861 \nL 175.334573 161.983652 \nL 175.395445 161.641151 \nL 175.699809 162.646511 \nL 175.730245 162.424525 \nL 175.973736 161.000204 \nL 176.034609 161.909255 \nL 176.125918 161.716523 \nL 176.217227 162.692629 \nL 176.247664 162.4329 \nL 176.338973 162.256975 \nL 176.460718 162.96492 \nL 176.552027 163.019412 \nL 176.643336 161.61184 \nL 176.673773 161.620215 \nL 176.795518 161.473601 \nL 176.886827 159.91104 \nL 176.978136 159.982282 \nL 177.008573 160.078591 \nL 177.039009 160.040905 \nL 177.191191 159.156232 \nL 177.312936 159.706607 \nL 177.465118 159.278066 \nL 177.525991 159.315866 \nL 177.6173 159.542728 \nL 177.647736 159.509114 \nL 177.708609 158.904132 \nL 177.739045 158.937745 \nL 177.830354 158.954552 \nL 177.860791 159.420894 \nL 177.921664 158.954552 \nL 177.9521 158.904132 \nL 178.104282 158.252917 \nL 178.134718 158.282285 \nL 178.165154 158.620885 \nL 178.256464 158.608151 \nL 178.347773 158.059382 \nL 178.469518 158.164925 \nL 178.530391 157.810322 \nL 178.591264 158.017222 \nL 178.682573 158.257792 \nL 178.743445 159.224491 \nL 178.804318 159.043002 \nL 178.895627 158.806561 \nL 178.986936 159.389173 \nL 179.017373 159.127437 \nL 179.139118 158.608151 \nL 179.230427 159.743719 \nL 179.321736 159.840831 \nL 179.656536 158.287333 \nL 179.747845 158.354904 \nL 179.778282 158.295822 \nL 179.808718 158.675722 \nL 179.869591 158.059382 \nL 179.9609 158.097412 \nL 180.021773 157.873648 \nL 180.204391 158.975431 \nL 180.2957 157.780781 \nL 180.387009 158.118521 \nL 180.600064 157.206659 \nL 180.934864 158.80421 \nL 181.026173 158.990689 \nL 181.056609 159.499248 \nL 181.117482 159.397548 \nL 181.239227 159.325503 \nL 181.269664 159.656072 \nL 181.3001 158.956789 \nL 181.360973 159.274624 \nL 181.452282 159.189902 \nL 181.543591 158.507598 \nL 181.574027 159.033078 \nL 181.665336 158.994934 \nL 181.695773 158.736409 \nL 181.726209 159.329748 \nL 181.787082 160.664701 \nL 181.878391 160.516367 \nL 181.9697 159.876452 \nL 181.939264 160.813036 \nL 182.000136 160.062874 \nL 182.091445 159.740794 \nL 182.182754 160.643478 \nL 182.213191 159.986584 \nL 182.517554 159.109368 \nL 182.6393 159.999318 \nL 182.791482 158.698264 \nL 182.943664 158.278729 \nL 183.004536 158.435495 \nL 183.065409 158.079515 \nL 183.187154 158.40584 \nL 183.248027 157.863381 \nL 183.278464 158.121905 \nL 183.430645 158.223605 \nL 183.461082 157.98206 \nL 183.491518 158.100739 \nL 183.674136 157.59218 \nL 183.795882 157.384534 \nL 183.856754 157.312145 \nL 183.917627 158.414215 \nL 184.008936 158.34194 \nL 184.039373 157.916382 \nL 184.130682 158.180241 \nL 184.252427 157.771718 \nL 184.282864 158.201464 \nL 184.3133 158.001505 \nL 184.465482 157.018573 \nL 184.526354 156.74198 \nL 184.556791 156.771808 \nL 184.6481 156.980256 \nL 184.769845 156.473934 \nL 184.861154 156.622842 \nL 184.891591 156.559 \nL 184.952464 156.035642 \nL 184.9829 156.388811 \nL 185.074209 156.427128 \nL 185.135082 155.971799 \nL 185.165518 156.3973 \nL 185.287264 156.852629 \nL 185.378573 156.354796 \nL 185.409009 156.678195 \nL 185.500318 156.354796 \nL 185.591627 157.048343 \nL 185.622064 156.929205 \nL 185.743809 157.027063 \nL 185.835118 157.656825 \nL 186.200354 156.316479 \nL 186.230791 156.490913 \nL 186.261227 156.02285 \nL 186.352536 156.129254 \nL 186.443845 156.639878 \nL 186.474282 156.267264 \nL 186.565591 156.442443 \nL 186.626464 157.374037 \nL 186.687336 157.14752 \nL 186.839518 157.049261 \nL 186.900391 156.463839 \nL 186.9917 156.651809 \nL 187.083009 158.104697 \nL 187.113445 157.929517 \nL 187.296064 158.861111 \nL 187.417809 158.143186 \nL 187.509118 158.75855 \nL 187.478682 157.856899 \nL 187.539554 158.561976 \nL 187.630864 157.779978 \nL 187.691736 158.032078 \nL 187.722173 158.574767 \nL 187.752609 157.848352 \nL 187.874354 157.553461 \nL 187.935227 156.792859 \nL 187.965664 156.861233 \nL 188.117845 156.741579 \nL 188.178718 156.156156 \nL 188.270027 156.186041 \nL 188.300464 156.378314 \nL 188.3309 156.271566 \nL 188.513518 155.070779 \nL 188.604827 154.716118 \nL 188.757009 155.45108 \nL 188.817882 154.835715 \nL 189.0005 155.19898 \nL 189.030936 155.05793 \nL 189.122245 155.006649 \nL 189.183118 154.425529 \nL 189.243991 154.758909 \nL 189.3353 155.038083 \nL 189.396173 154.900647 \nL 189.457045 154.587056 \nL 189.6701 155.55364 \nL 190.035336 153.491641 \nL 190.096209 153.762268 \nL 190.217954 153.91261 \nL 190.309264 154.926402 \nL 190.431009 154.677227 \nL 190.461445 154.63002 \nL 190.522318 153.822382 \nL 190.613627 153.865345 \nL 190.735373 154.79757 \nL 190.826682 154.655774 \nL 190.857118 155.136915 \nL 190.887554 154.64717 \nL 190.917991 154.737399 \nL 190.948427 154.638624 \nL 191.039736 154.737399 \nL 191.131045 153.556057 \nL 191.161482 153.706399 \nL 191.283227 153.843835 \nL 191.374536 153.448678 \nL 191.465845 153.427167 \nL 191.496282 153.955573 \nL 191.587591 153.727852 \nL 191.770209 154.320674 \nL 191.952827 152.868704 \nL 192.0137 152.705685 \nL 192.105009 152.796258 \nL 192.135445 153.149943 \nL 192.196318 152.925663 \nL 192.226754 152.882528 \nL 192.318064 152.964496 \nL 192.3485 153.309521 \nL 192.409373 153.059371 \nL 192.531118 153.089543 \nL 192.591991 152.537504 \nL 192.622427 152.597905 \nL 192.774609 153.55967 \nL 192.805045 153.456134 \nL 192.835482 153.529499 \nL 192.957227 153.551066 \nL 193.078973 152.477161 \nL 193.170282 152.580639 \nL 193.200718 152.813523 \nL 193.261591 152.5289 \nL 193.292027 152.339094 \nL 193.383336 152.800617 \nL 193.505082 151.886231 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199.774973 148.814054 \nL 199.805409 148.552948 \nL 199.866282 147.895997 \nL 199.896718 148.030852 \nL 200.018464 147.299848 \nL 200.109773 147.508698 \nL 200.261954 147.312926 \nL 200.322827 146.763354 \nL 200.505445 147.078379 \nL 200.535882 147.459024 \nL 200.688064 146.688957 \nL 200.748936 147.078379 \nL 200.901118 147.187765 \nL 200.931554 147.056524 \nL 200.961991 147.550916 \nL 201.0533 147.266522 \nL 201.083736 147.319006 \nL 201.175045 146.369573 \nL 201.296791 146.492096 \nL 201.327227 146.465825 \nL 201.479409 146.745859 \nL 201.540282 146.058966 \nL 201.570718 146.798344 \nL 201.692464 146.890236 \nL 201.905518 145.665128 \nL 201.966391 145.507615 \nL 201.996827 145.590788 \nL 202.118573 146.277682 \nL 202.209882 145.249493 \nL 202.240318 145.280123 \nL 202.3925 145.170794 \nL 202.422936 145.218862 \nL 202.544682 145.52511 \nL 202.666427 148.369107 \nL 202.757736 148.946614 \nL 202.879482 147.445888 \nL 202.970791 147.353996 \nL 203.001227 148.552834 \nL 203.092536 148.189913 \nL 203.305591 146.055123 \nL 203.427336 146.287949 \nL 203.457773 146.744769 \nL 203.518645 146.665669 \nL 203.609954 146.595402 \nL 203.701264 145.681705 \nL 203.853445 145.519202 \nL 203.883882 145.778357 \nL 203.914318 145.497233 \nL 204.157809 143.757772 \nL 204.249118 144.003792 \nL 204.279554 143.977406 \nL 204.340427 143.305312 \nL 204.370864 143.358083 \nL 204.462173 143.902722 \nL 204.492609 143.735803 \nL 204.675227 142.734287 \nL 204.766536 143.336057 \nL 204.7361 142.642051 \nL 204.796973 142.773808 \nL 204.918718 142.901206 \nL 205.010027 142.000702 \nL 205.162209 142.079802 \nL 205.192645 142.685932 \nL 205.223082 142.409167 \nL 205.344827 142.167621 \nL 205.436136 141.359352 \nL 205.527445 141.350576 \nL 205.588318 142.343317 \nL 205.618754 143.546915 \nL 205.649191 142.694765 \nL 205.7405 142.580503 \nL 205.770936 143.305312 \nL 205.801373 142.321347 \nL 205.862245 141.513194 \nL 205.953554 141.592638 \nL 205.983991 141.424973 \nL 206.0753 142.624957 \nL 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151.876021 \nL 219.193373 151.709503 \nL 219.254245 150.449348 \nL 219.284682 151.079454 \nL 219.436864 151.488952 \nL 219.4673 153.221644 \nL 219.497736 153.046121 \nL 219.589045 151.889558 \nL 219.619482 153.586228 \nL 219.680354 152.55563 \nL 219.710791 152.294582 \nL 219.862973 156.561065 \nL 219.893409 155.003896 \nL 219.923845 152.857863 \nL 220.015154 154.129778 \nL 220.045591 155.379092 \nL 220.106464 154.365186 \nL 220.1369 154.337997 \nL 220.228209 158.624671 \nL 220.319518 158.41192 \nL 220.502136 164.902963 \nL 220.563009 168.981361 \nL 220.654318 163.164764 \nL 220.684754 163.843742 \nL 220.715191 168.29332 \nL 220.776064 166.849381 \nL 220.867373 164.314501 \nL 220.897809 165.649856 \nL 220.989118 169.642213 \nL 221.080427 171.040951 \nL 221.202173 165.21076 \nL 221.293482 165.084051 \nL 221.323918 163.590265 \nL 221.354354 165.500431 \nL 221.384791 167.913077 \nL 221.415227 166.555121 \nL 221.506536 167.111863 \nL 221.567409 170.194481 \nL 221.628282 169.832363 \nL 221.719591 170.352911 \nL 221.750027 169.624144 \nL 221.8109 174.888473 \nL 221.841336 173.046221 \nL 222.054391 168.261657 \nL 222.1457 171.873828 \nL 222.176136 170.443426 \nL 222.358754 167.84975 \nL 222.450064 169.234837 \nL 222.4805 168.759547 \nL 222.571809 169.320878 \nL 222.602245 167.451381 \nL 222.663118 168.623775 \nL 222.8153 169.723952 \nL 222.845736 169.495828 \nL 222.906609 169.267648 \nL 222.997918 169.381738 \nL 223.119664 166.620914 \nL 223.210973 166.671162 \nL 223.241409 166.388202 \nL 223.332718 168.386933 \nL 223.667518 172.274836 \nL 223.697954 170.686807 \nL 223.758827 171.115807 \nL 223.880573 170.467747 \nL 223.911009 169.162678 \nL 223.971882 171.243549 \nL 224.063191 171.357639 \nL 224.276245 169.295009 \nL 224.397991 171.275499 \nL 224.702354 174.976292 \nL 224.732791 173.684875 \nL 224.793664 174.533697 \nL 224.945845 177.066283 \nL 224.976282 176.308779 \nL 225.006718 177.645855 \nL 225.037154 177.591076 \nL 225.128464 177.960708 \nL 225.250209 174.319226 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166.196158 \nL 228.567773 166.542789 \nL 228.598209 166.36715 \nL 228.628645 167.531858 \nL 228.750391 167.536504 \nL 228.872136 168.386933 \nL 229.389554 163.584759 \nL 229.450427 163.908273 \nL 229.633045 162.498635 \nL 229.693918 162.872971 \nL 229.724354 162.267529 \nL 229.815664 162.364583 \nL 229.8461 162.946909 \nL 229.906973 162.09648 \nL 230.028718 163.603229 \nL 230.059154 163.242775 \nL 230.302645 161.356987 \nL 230.333082 161.250697 \nL 230.363518 161.259933 \nL 230.454827 161.685147 \nL 230.5157 162.905379 \nL 230.546136 162.521752 \nL 230.698318 161.463333 \nL 230.941809 159.438905 \nL 230.972245 159.512843 \nL 231.002682 159.481294 \nL 231.093991 159.606685 \nL 231.124427 159.318734 \nL 231.154864 159.732075 \nL 231.215736 160.535468 \nL 231.307045 159.667028 \nL 231.367918 160.006087 \nL 231.428791 161.445666 \nL 231.946209 158.055596 \nL 232.220136 160.554053 \nL 232.250573 159.513818 \nL 232.281009 160.948809 \nL 232.433191 160.317211 \nL 232.615809 158.148463 \nL 232.676682 158.408536 \nL 232.798427 157.396178 \nL 232.828864 157.335778 \nL 232.8593 157.368301 \nL 232.920173 158.222802 \nL 233.072354 157.31725 \nL 233.133227 158.157755 \nL 233.224536 157.98596 \nL 233.254973 157.354362 \nL 233.346282 157.489045 \nL 233.437591 157.568031 \nL 233.468027 158.13917 \nL 233.5289 157.660898 \nL 233.650645 157.089643 \nL 233.681082 157.331189 \nL 233.711518 157.252203 \nL 233.741954 157.874509 \nL 233.772391 157.585009 \nL 234.076754 156.413132 \nL 234.137627 156.506515 \nL 234.168064 157.010772 \nL 234.411554 155.549396 \nL 234.502864 155.474655 \nL 234.594173 155.381329 \nL 234.624609 155.983558 \nL 234.746354 155.320584 \nL 235.050718 158.901665 \nL 235.172464 157.50568 \nL 235.2029 157.762426 \nL 235.233336 159.345235 \nL 235.263773 159.242502 \nL 235.355082 159.601981 \nL 235.446391 158.556125 \nL 235.476827 158.59817 \nL 235.781191 157.14144 \nL 235.811627 157.771776 \nL 235.8725 157.370251 \nL 235.902936 157.337556 \nL 236.329045 155.133818 \nL 236.420354 155.119822 \nL 236.481227 154.368111 \nL 236.5421 154.672753 \nL 236.663845 153.997676 \nL 236.724718 154.353943 \nL 236.755154 154.386753 \nL 237.089954 153.23346 \nL 237.120391 153.552271 \nL 237.150827 153.360055 \nL 237.333445 152.22552 \nL 237.363882 152.178656 \nL 237.394318 153.083462 \nL 237.485627 152.87249 \nL 237.607373 151.934873 \nL 237.698682 152.150492 \nL 237.729118 153.496 \nL 237.820427 153.341298 \nL 237.911736 152.619358 \nL 238.033482 156.88085 \nL 238.185664 153.978919 \nL 238.2161 154.663346 \nL 238.368282 156.346422 \nL 238.398718 156.646475 \nL 238.429154 158.624843 \nL 238.459591 157.888791 \nL 238.611773 158.798301 \nL 238.642209 157.115283 \nL 238.672645 157.7669 \nL 238.794391 158.629546 \nL 238.855264 157.138686 \nL 238.8857 158.96241 \nL 238.977009 159.58592 \nL 239.037882 159.323381 \nL 239.098754 157.621606 \nL 239.190064 157.701279 \nL 239.311809 156.411296 \nL 239.403118 156.56204 \nL 239.494427 158.441518 \nL 239.524864 158.229571 \nL 239.616173 158.389722 \nL 239.737918 160.900396 \nL 239.859664 160.584798 \nL 239.950973 158.187181 \nL 240.042282 158.154199 \nL 240.133591 157.376962 \nL 240.164027 158.799506 \nL 240.255336 158.502722 \nL 240.316209 158.606373 \nL 240.468391 156.491372 \nL 240.498827 156.496075 \nL 240.590136 157.09905 \nL 240.742318 155.827193 \nL 240.772754 155.883751 \nL 240.803191 156.100402 \nL 240.8945 155.817786 \nL 240.924936 156.105163 \nL 241.016245 158.022327 \nL 241.107554 158.045845 \nL 241.168427 157.325108 \nL 241.198864 158.22481 \nL 241.411918 159.473092 \nL 241.442354 158.70532 \nL 241.5641 159.435406 \nL 241.655409 156.80697 \nL 241.777154 157.395777 \nL 241.807591 157.033086 \nL 242.020645 155.775396 \nL 242.051082 155.789507 \nL 242.081518 155.76966 \nL 242.172827 154.955082 \nL 242.2337 155.329246 \nL 242.264136 155.764899 \nL 242.294573 154.666214 \nL 242.477191 154.992998 \nL 242.507627 154.765677 \nL 242.598936 155.172938 \nL 242.720682 153.851693 \nL 242.811991 153.79961 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144.761985 \nL 249.294936 144.455966 \nL 249.447118 145.369204 \nL 249.507991 144.647263 \nL 249.538427 145.158805 \nL 249.660173 145.952504 \nL 249.690609 145.742163 \nL 249.873227 144.542064 \nL 249.964536 146.684024 \nL 250.116718 147.640283 \nL 250.147154 147.171704 \nL 250.177591 148.032343 \nL 250.2689 147.984562 \nL 250.299336 147.209964 \nL 250.329773 148.309682 \nL 250.360209 148.175802 \nL 250.512391 146.840963 \nL 250.573264 147.393575 \nL 250.6037 148.109493 \nL 250.725445 146.7641 \nL 250.816754 144.693152 \nL 250.9385 144.746038 \nL 250.968936 144.669118 \nL 250.999373 144.001268 \nL 251.029809 144.462505 \nL 251.212427 146.129863 \nL 251.242864 145.764648 \nL 251.334173 146.28359 \nL 251.455918 144.376062 \nL 251.577664 144.976685 \nL 251.668973 146.898668 \nL 251.760282 147.162928 \nL 251.973336 155.100721 \nL 252.095082 152.285003 \nL 252.186391 151.083756 \nL 252.247264 151.540232 \nL 252.308136 154.918142 \nL 252.399445 154.874892 \nL 252.460318 152.304218 \nL 252.521191 152.357105 \nL 252.673373 150.314952 \nL 252.703809 150.93003 \nL 252.734245 152.414752 \nL 252.855991 152.827978 \nL 252.886427 151.242301 \nL 252.9473 153.342101 \nL 253.069045 152.467581 \nL 253.160354 150.349254 \nL 253.2821 151.001272 \nL 253.342973 154.531876 \nL 253.373409 154.203429 \nL 253.495154 152.271523 \nL 253.556027 152.991169 \nL 253.677773 155.946962 \nL 253.890827 151.286239 \nL 253.921264 151.228248 \nL 254.012573 149.842128 \nL 254.103882 150.972305 \nL 254.225627 149.165904 \nL 254.316936 148.431803 \nL 254.347373 149.610277 \nL 254.377809 149.006556 \nL 254.408245 146.915245 \nL 254.438682 146.9297 \nL 254.560427 150.117402 \nL 254.590864 149.156267 \nL 254.6213 148.064752 \nL 254.651736 148.436621 \nL 254.743045 148.059876 \nL 254.773482 147.277477 \nL 254.803918 149.557161 \nL 254.864791 147.866686 \nL 255.016973 149.112788 \nL 255.230027 152.744805 \nL 255.2909 152.851095 \nL 255.656136 147.8329 \nL 255.686573 149.175598 \nL 255.717009 148.161347 \nL 256.021373 150.574853 \nL 256.143118 147.60994 \nL 256.264864 147.561355 \nL 256.2953 148.36819 \nL 256.356173 148.042553 \nL 256.538791 146.807981 \nL 256.569227 146.968361 \nL 256.660536 146.817675 \nL 256.751845 146.093497 \nL 256.782282 146.419133 \nL 256.904027 146.176096 \nL 256.995336 144.907566 \nL 257.117082 145.145727 \nL 257.147518 144.611069 \nL 257.208391 144.97072 \nL 257.330136 145.213757 \nL 257.421445 143.64873 \nL 257.512754 143.692438 \nL 257.604064 143.250131 \nL 257.6345 143.736147 \nL 257.725809 143.250131 \nL 257.756245 143.332787 \nL 257.786682 143.638978 \nL 257.817118 142.914743 \nL 257.847554 142.739793 \nL 257.9693 142.710597 \nL 257.999736 142.924494 \nL 258.030173 142.632873 \nL 258.182354 142.180814 \nL 258.212791 142.443296 \nL 258.243227 142.098215 \nL 258.273664 142.302361 \nL 258.364973 142.574537 \nL 258.395409 143.546628 \nL 258.456282 142.433602 \nL 258.791082 140.281718 \nL 258.882391 141.087234 \nL 258.912827 140.867542 \nL 259.004136 139.91071 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139.309857 \nL 265.6697 139.309857 \nL 265.700136 138.579714 \nL 265.730573 139.206264 \nL 265.821882 139.063149 \nL 265.852318 138.515585 \nL 265.913191 140.942742 \nL 265.943627 140.883546 \nL 266.034936 140.829283 \nL 266.126245 142.097125 \nL 266.156682 141.766613 \nL 266.369736 139.309857 \nL 266.461045 139.457848 \nL 266.491482 139.813025 \nL 266.521918 139.255537 \nL 266.582791 138.915159 \nL 266.704536 139.359187 \nL 266.734973 139.235862 \nL 266.948027 138.244269 \nL 267.0089 138.939824 \nL 267.130645 137.326728 \nL 267.191518 137.449996 \nL 267.313264 138.318436 \nL 267.404573 138.710955 \nL 267.435009 139.471098 \nL 267.648064 136.321254 \nL 267.739373 136.519951 \nL 267.769809 136.728629 \nL 267.800245 136.544788 \nL 267.830682 135.968486 \nL 267.861118 135.973419 \nL 268.0133 135.9834 \nL 268.074173 135.347443 \nL 268.408973 134.189848 \nL 268.439409 134.482961 \nL 268.469845 134.666802 \nL 268.500282 133.901668 \nL 268.591591 134.746303 \nL 268.622027 133.996084 \nL 268.895954 133.380031 \nL 268.6829 134.040768 \nL 268.926391 133.469456 \nL 269.048136 132.903077 \nL 269.109009 134.309101 \nL 269.139445 133.578728 \nL 269.230754 135.014579 \nL 269.261191 134.507855 \nL 269.3525 133.469456 \nL 269.443809 133.067071 \nL 269.565554 131.815003 \nL 269.748173 131.174171 \nL 269.778609 131.276961 \nL 269.900354 131.885786 \nL 269.930791 131.346826 \nL 269.961227 132.010546 \nL 269.991664 131.391739 \nL 270.082973 131.716114 \nL 270.174282 130.857769 \nL 270.296027 131.167115 \nL 270.326464 130.782914 \nL 270.3569 131.576326 \nL 270.387336 131.261932 \nL 270.417773 131.611316 \nL 270.539518 130.817846 \nL 270.600391 129.600195 \nL 270.630827 129.79482 \nL 270.813445 132.120334 \nL 270.996064 130.253934 \nL 271.178682 129.355667 \nL 271.209118 130.054319 \nL 271.239554 129.320734 \nL 271.391736 127.898477 \nL 271.483045 127.494257 \nL 271.574354 127.429382 \nL 271.6961 125.732655 \nL 271.787409 125.737645 \nL 271.817845 125.617934 \nL 271.909154 126.546143 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124.092887 \nL 275.439773 123.219915 \nL 275.470209 122.226659 \nL 275.531082 122.978083 \nL 275.835445 124.344241 \nL 275.865882 123.673753 \nL 275.926754 124.540873 \nL 275.957191 123.900614 \nL 276.078936 125.619655 \nL 276.109373 125.559139 \nL 276.261554 122.867148 \nL 276.291991 123.492264 \nL 276.322427 122.297499 \nL 276.505045 120.613736 \nL 276.535482 121.037229 \nL 276.596354 120.341559 \nL 276.687664 120.200395 \nL 276.7181 119.726539 \nL 276.778973 120.422208 \nL 276.900718 119.897932 \nL 276.961591 119.726539 \nL 276.992027 120.85574 \nL 277.113773 119.65088 \nL 277.144209 119.832369 \nL 277.235518 118.274627 \nL 277.3877 119.071136 \nL 277.448573 117.891515 \nL 277.539882 117.98226 \nL 277.570318 117.957078 \nL 277.600754 117.735265 \nL 277.661627 117.79578 \nL 277.752936 118.03268 \nL 277.844245 118.834237 \nL 277.965991 117.5941 \nL 278.087736 119.252625 \nL 278.179045 118.693072 \nL 278.209482 118.980392 \nL 278.300791 117.016536 \nL 278.635591 115.430056 \nL 278.818209 116.611054 \nL 278.939954 115.708829 \nL 279.031264 116.950629 \nL 279.0617 115.947047 \nL 279.092136 115.445256 \nL 279.153009 115.962248 \nL 279.305191 115.627721 \nL 279.518245 119.150409 \nL 279.548682 118.197477 \nL 279.579118 118.729726 \nL 279.670427 120.762185 \nL 279.7313 120.255346 \nL 279.913918 116.803613 \nL 279.944354 116.758012 \nL 280.005227 115.769631 \nL 280.126973 115.658122 \nL 280.157409 116.276469 \nL 280.218282 115.835538 \nL 280.431336 114.619091 \nL 280.522645 115.283098 \nL 280.644391 113.620615 \nL 280.7357 113.671264 \nL 280.857445 115.445256 \nL 281.040064 113.879082 \nL 281.0705 114.24814 \nL 281.161809 114.6402 \nL 281.192245 114.192156 \nL 281.222682 114.874346 \nL 281.253118 115.072871 \nL 281.283554 114.609627 \nL 281.435736 112.883818 \nL 281.466173 113.011101 \nL 281.587918 115.195107 \nL 281.618354 114.808152 \nL 281.648791 113.795106 \nL 281.679227 115.795845 \nL 281.709664 116.635776 \nL 281.831409 115.266349 \nL 281.892282 114.15143 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107.860174 \nL 285.301154 111.323265 \nL 285.4229 109.906343 \nL 285.453336 110.04441 \nL 285.666391 107.5584 \nL 285.879445 106.566004 \nL 285.940318 106.663173 \nL 285.970754 106.371666 \nL 286.122936 106.479045 \nL 286.153373 106.62738 \nL 286.183809 106.167003 \nL 286.275118 106.432985 \nL 286.396864 107.17982 \nL 286.488173 107.256569 \nL 286.579482 105.803796 \nL 286.609918 105.896835 \nL 286.792536 108.13757 \nL 286.822973 107.335841 \nL 286.944718 107.788072 \nL 286.975154 109.160253 \nL 287.036027 108.045048 \nL 287.127336 108.163268 \nL 287.188209 107.983385 \nL 287.249082 111.118315 \nL 287.4317 113.312761 \nL 287.675191 108.09128 \nL 287.7665 108.230036 \nL 287.888245 105.403476 \nL 287.979554 105.346919 \nL 288.009991 105.706684 \nL 288.040427 105.054035 \nL 288.192609 104.211178 \nL 288.314354 104.077528 \nL 288.405664 104.01076 \nL 288.527409 102.823567 \nL 288.679591 102.330209 \nL 288.831773 103.296391 \nL 288.862209 102.612882 \nL 288.953518 102.145221 \nL 289.075264 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293.001554 101.101372 \nL 293.214609 99.180824 \nL 293.366791 99.741409 \nL 293.397227 100.836653 \nL 293.427664 99.663571 \nL 293.518973 99.35216 \nL 293.579845 101.05468 \nL 293.610282 100.623845 \nL 293.640718 99.196426 \nL 293.732027 99.720644 \nL 293.762464 100.047657 \nL 293.7929 100.026893 \nL 293.945082 98.428195 \nL 294.005954 98.5424 \nL 294.036391 98.220607 \nL 294.066827 98.485269 \nL 294.188573 99.673953 \nL 294.219009 98.63584 \nL 294.279882 99.445543 \nL 294.432064 99.040692 \nL 294.584245 100.82627 \nL 294.614682 100.841873 \nL 294.675554 99.191206 \nL 294.705991 100.032113 \nL 294.7973 100.499258 \nL 294.888609 98.594311 \nL 294.919045 99.084573 \nL 295.040791 98.442994 \nL 295.223409 101.895931 \nL 295.253845 101.671651 \nL 295.284282 100.81107 \nL 295.314718 100.910189 \nL 295.436464 101.217871 \nL 295.4669 100.539812 \nL 295.497336 102.360208 \nL 295.558209 100.82151 \nL 295.862573 98.156191 \nL 296.075627 101.18139 \nL 296.166936 99.079353 \nL 296.197373 99.246272 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299.362754 101.643028 \nL 299.393191 102.848921 \nL 299.606245 99.299388 \nL 299.697554 99.425237 \nL 299.8193 99.168319 \nL 299.910609 99.624508 \nL 299.941045 98.581118 \nL 299.971482 99.708369 \nL 300.001918 101.239324 \nL 300.032354 99.136885 \nL 300.1541 100.536771 \nL 300.245409 103.194977 \nL 300.336718 102.660204 \nL 300.397591 99.970507 \nL 300.428027 101.632531 \nL 300.458464 103.546024 \nL 300.549773 102.675806 \nL 300.610645 100.381496 \nL 300.641082 100.560806 \nL 300.762827 100.808718 \nL 300.793264 99.653647 \nL 300.854136 101.515458 \nL 301.036754 104.178942 \nL 301.097627 107.818186 \nL 301.188936 107.717977 \nL 301.219373 106.900416 \nL 301.310682 109.985844 \nL 301.432427 109.85403 \nL 301.462864 111.125084 \nL 301.4933 110.576544 \nL 301.523736 108.556532 \nL 301.615045 110.075556 \nL 301.736791 106.868811 \nL 301.8281 106.905694 \nL 301.949845 109.912021 \nL 302.132464 112.596613 \nL 302.315082 107.311864 \nL 302.375954 107.775969 \nL 302.467264 106.309717 \nL 302.4977 107.607157 \nL 302.558573 105.908881 \nL 302.589009 106.146239 \nL 302.680318 106.953188 \nL 302.802064 103.329775 \nL 302.893373 103.245397 \nL 302.923809 104.400468 \nL 302.984682 103.799214 \nL 303.015118 102.100881 \nL 303.106427 102.23803 \nL 303.136864 102.412062 \nL 303.319482 100.536771 \nL 303.349918 100.595107 \nL 303.410791 101.358462 \nL 303.532536 101.294849 \nL 303.654282 99.343957 \nL 303.745591 99.699134 \nL 303.8369 100.918449 \nL 303.806464 99.609021 \nL 303.867336 100.626885 \nL 303.958645 100.881337 \nL 304.049954 98.766049 \nL 304.080391 98.888055 \nL 304.232573 97.658071 \nL 304.263009 98.257146 \nL 304.293445 98.257146 \nL 304.384754 98.447984 \nL 304.415191 98.283646 \nL 304.445627 98.055695 \nL 304.5065 99.656687 \nL 304.597809 98.787272 \nL 304.689118 100.37771 \nL 304.719554 99.980087 \nL 304.810864 99.889973 \nL 304.8413 98.532821 \nL 304.902173 99.534796 \nL 304.932609 100.489047 \nL 305.023918 99.418182 \nL 305.115227 100.785946 \nL 305.145664 100.102035 \nL 305.236973 100.250485 \nL 305.358718 97.583846 \nL 305.480464 97.795909 \nL 305.723954 96.454645 \nL 305.754391 96.608372 \nL 305.906573 98.75016 \nL 305.967445 98.57521 \nL 305.997882 98.986601 \nL 306.1805 96.700493 \nL 306.302245 102.674315 \nL 306.423991 97.174751 \nL 306.545736 97.979463 \nL 306.819664 93.870779 \nL 306.8501 94.025308 \nL 307.063154 93.273941 \nL 307.215336 93.657627 \nL 307.245773 93.524378 \nL 307.276209 93.337898 \nL 307.367518 93.433805 \nL 307.397954 94.17456 \nL 307.458827 93.716249 \nL 307.489264 92.773011 \nL 307.580573 92.842303 \nL 307.611009 92.773011 \nL 307.641445 93.060789 \nL 307.671882 92.522517 \nL 307.702318 92.623816 \nL 307.793627 92.288084 \nL 307.824064 92.890256 \nL 307.915373 92.581197 \nL 308.006682 92.586474 \nL 308.037118 92.352041 \nL 308.067554 92.948821 \nL 308.128427 93.247269 \nL 308.219736 92.677104 \nL 308.250173 92.868918 \nL 308.280609 93.199315 \nL 308.341482 92.671769 \nL 308.463227 92.320034 \nL 308.493664 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68.914662 \nL 321.733482 67.723454 \nL 321.763918 68.138803 \nL 321.855227 67.898347 \nL 321.976973 66.772702 \nL 322.068282 66.843714 \nL 322.159591 64.231798 \nL 322.190027 64.532311 \nL 322.281336 64.707203 \nL 322.311773 65.226317 \nL 322.372645 64.745463 \nL 322.524827 63.264641 \nL 322.555264 63.280989 \nL 322.5857 63.876621 \nL 322.616136 62.667746 \nL 322.707445 61.739594 \nL 322.737882 62.305285 \nL 322.768318 62.382205 \nL 322.798754 62.080144 \nL 322.829191 62.118576 \nL 322.950936 62.294271 \nL 322.981373 62.222915 \nL 323.042245 62.475531 \nL 323.864027 53.446338 \nL 323.894464 51.64487 \nL 323.985773 52.688374 \nL 324.290136 67.544833 \nL 324.320573 65.419393 \nL 324.533627 59.042899 \nL 324.655373 59.982065 \nL 324.685809 60.723508 \nL 324.716245 60.531293 \nL 324.837991 56.412111 \nL 324.9293 62.014179 \nL 324.959736 61.256273 \nL 325.172791 55.879346 \nL 325.294536 57.060229 \nL 325.477154 59.886903 \nL 325.507591 59.633197 \nL 325.538027 59.919943 \nL 325.5989 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "value.plot()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bitenvvenvbe8be8c3152048828b50ae345b2ffc8e",
   "display_name": "Python 3.8.2 64-bit ('env': venv)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}