project_1_starter.ipynb

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    "# Project 1: Trading with Momentum\n",
    "## Instructions\n",
    "Each problem consists of a function to implement and instructions on how to implement the function.  The parts of the function that need to be implemented are marked with a `# TODO` comment. After implementing the function, run the cell to test it against the unit tests we've provided. For each problem, we provide one or more unit tests from our `project_tests` package. These unit tests won't tell you if your answer is correct, but will warn you of any major errors. Your code will be checked for the correct solution when you submit it to Udacity.\n",
    "\n",
    "## Packages\n",
    "When you implement the functions, you'll only need to you use the packages you've used in the classroom, like [Pandas](https://pandas.pydata.org/) and [Numpy](http://www.numpy.org/). These packages will be imported for you. We recommend you don't add any import statements, otherwise the grader might not be able to run your code.\n",
    "\n",
    "The other packages that we're importing are `helper`, `project_helper`, and `project_tests`. These are custom packages built to help you solve the problems.  The `helper` and `project_helper` module contains utility functions and graph functions. The `project_tests` contains the unit tests for all the problems.\n",
    "\n",
    "### Install Packages"
   ]
  },
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     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import helper\n",
    "import project_helper\n",
    "import project_tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Market Data\n",
    "### Load Data\n",
    "The data we use for most of the projects is end of day data. This contains data for many stocks, but we'll be looking at stocks in the S&P 500. We also made things a little easier to run by narrowing down our range of time period instead of using all of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Data\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/project_1/eod-quotemedia.csv', parse_dates=['date'], index_col=False)\n",
    "\n",
    "close = df.reset_index().pivot(index='date', columns='ticker', values='adj_close')\n",
    "\n",
    "print('Loaded Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Run the cell below to see what the data looks like for `close`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>28.32843198</td>\n",
       "      <td>41.28388974</td>\n",
       "      <td>79.23671352</td>\n",
       "      <td>41.69639686</td>\n",
       "      <td>23.20721320</td>\n",
       "      <td>23.21996075</td>\n",
       "      <td>47.44778390</td>\n",
       "      <td>80.02819050</td>\n",
       "      <td>28.13385091</td>\n",
       "      <td>28.74031861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-31</th>\n",
       "      <td>32.09288410</td>\n",
       "      <td>15.55986096</td>\n",
       "      <td>79.33492514</td>\n",
       "      <td>63.64994327</td>\n",
       "      <td>36.09056668</td>\n",
       "      <td>53.01732111</td>\n",
       "      <td>30.01866909</td>\n",
       "      <td>64.82868748</td>\n",
       "      <td>45.75000000</td>\n",
       "      <td>41.01373554</td>\n",
       "      <td>...</td>\n",
       "      <td>26.71095532</td>\n",
       "      <td>38.60058125</td>\n",
       "      <td>74.17847651</td>\n",
       "      <td>40.83096325</td>\n",
       "      <td>23.87711213</td>\n",
       "      <td>23.18866549</td>\n",
       "      <td>45.56080402</td>\n",
       "      <td>75.81970579</td>\n",
       "      <td>26.58466104</td>\n",
       "      <td>28.10400159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-30</th>\n",
       "      <td>35.34697923</td>\n",
       "      <td>18.25587647</td>\n",
       "      <td>81.98212977</td>\n",
       "      <td>62.28266407</td>\n",
       "      <td>37.88620154</td>\n",
       "      <td>56.91072241</td>\n",
       "      <td>29.89257807</td>\n",
       "      <td>66.07591068</td>\n",
       "      <td>51.94000000</td>\n",
       "      <td>41.69611619</td>\n",
       "      <td>...</td>\n",
       "      <td>27.97701372</td>\n",
       "      <td>41.65075616</td>\n",
       "      <td>73.22528819</td>\n",
       "      <td>42.27738327</td>\n",
       "      <td>24.75516170</td>\n",
       "      <td>26.13637721</td>\n",
       "      <td>46.45223934</td>\n",
       "      <td>78.93635707</td>\n",
       "      <td>26.06190225</td>\n",
       "      <td>30.00331147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-31</th>\n",
       "      <td>35.00902763</td>\n",
       "      <td>21.15409315</td>\n",
       "      <td>98.34285959</td>\n",
       "      <td>68.28583759</td>\n",
       "      <td>41.39637606</td>\n",
       "      <td>60.85069550</td>\n",
       "      <td>33.05576095</td>\n",
       "      <td>66.82299697</td>\n",
       "      <td>54.22000000</td>\n",
       "      <td>43.69008561</td>\n",
       "      <td>...</td>\n",
       "      <td>27.75007493</td>\n",
       "      <td>40.38659157</td>\n",
       "      <td>76.27208656</td>\n",
       "      <td>45.87069305</td>\n",
       "      <td>23.91314940</td>\n",
       "      <td>32.41710942</td>\n",
       "      <td>44.24404652</td>\n",
       "      <td>84.05847719</td>\n",
       "      <td>26.98861102</td>\n",
       "      <td>30.58582019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-30</th>\n",
       "      <td>36.94707663</td>\n",
       "      <td>22.60801580</td>\n",
       "      <td>100.15741326</td>\n",
       "      <td>73.07037475</td>\n",
       "      <td>41.39637606</td>\n",
       "      <td>65.91719111</td>\n",
       "      <td>34.53897430</td>\n",
       "      <td>70.43234797</td>\n",
       "      <td>56.78000000</td>\n",
       "      <td>42.73298029</td>\n",
       "      <td>...</td>\n",
       "      <td>29.03908724</td>\n",
       "      <td>39.71607441</td>\n",
       "      <td>80.10221273</td>\n",
       "      <td>46.31377055</td>\n",
       "      <td>27.37742858</td>\n",
       "      <td>32.47348700</td>\n",
       "      <td>50.82634626</td>\n",
       "      <td>87.84481134</td>\n",
       "      <td>27.91674534</td>\n",
       "      <td>30.09312378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31</th>\n",
       "      <td>39.53485221</td>\n",
       "      <td>24.31228275</td>\n",
       "      <td>109.80605374</td>\n",
       "      <td>73.72082947</td>\n",
       "      <td>45.12162270</td>\n",
       "      <td>65.71157957</td>\n",
       "      <td>34.66559008</td>\n",
       "      <td>74.75084097</td>\n",
       "      <td>59.87900000</td>\n",
       "      <td>45.44926154</td>\n",
       "      <td>...</td>\n",
       "      <td>29.03762468</td>\n",
       "      <td>41.04798868</td>\n",
       "      <td>86.71741472</td>\n",
       "      <td>47.27020113</td>\n",
       "      <td>29.41596107</td>\n",
       "      <td>32.51107205</td>\n",
       "      <td>49.47193667</td>\n",
       "      <td>89.74777084</td>\n",
       "      <td>28.51638903</td>\n",
       "      <td>31.58087372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>40.19849023</td>\n",
       "      <td>32.30404302</td>\n",
       "      <td>113.90344263</td>\n",
       "      <td>65.78133976</td>\n",
       "      <td>42.40046983</td>\n",
       "      <td>62.82367201</td>\n",
       "      <td>33.34174832</td>\n",
       "      <td>72.62341494</td>\n",
       "      <td>59.19000000</td>\n",
       "      <td>43.07551255</td>\n",
       "      <td>...</td>\n",
       "      <td>26.21046901</td>\n",
       "      <td>41.49493978</td>\n",
       "      <td>78.97111601</td>\n",
       "      <td>44.98859489</td>\n",
       "      <td>26.22540490</td>\n",
       "      <td>31.34593537</td>\n",
       "      <td>44.15869815</td>\n",
       "      <td>90.49895842</td>\n",
       "      <td>27.36469241</td>\n",
       "      <td>29.39709516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-28</th>\n",
       "      <td>39.35511692</td>\n",
       "      <td>35.55851889</td>\n",
       "      <td>126.35434591</td>\n",
       "      <td>69.56208595</td>\n",
       "      <td>43.84740848</td>\n",
       "      <td>63.63196980</td>\n",
       "      <td>36.17934393</td>\n",
       "      <td>75.77818772</td>\n",
       "      <td>68.63000000</td>\n",
       "      <td>45.68115222</td>\n",
       "      <td>...</td>\n",
       "      <td>27.72436527</td>\n",
       "      <td>46.91977904</td>\n",
       "      <td>83.07167653</td>\n",
       "      <td>44.24756038</td>\n",
       "      <td>26.56379722</td>\n",
       "      <td>37.09867860</td>\n",
       "      <td>48.71595471</td>\n",
       "      <td>90.37375551</td>\n",
       "      <td>29.73505750</td>\n",
       "      <td>30.03616245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>38.65691442</td>\n",
       "      <td>35.24077420</td>\n",
       "      <td>125.56160936</td>\n",
       "      <td>70.95004943</td>\n",
       "      <td>44.26943225</td>\n",
       "      <td>61.51246720</td>\n",
       "      <td>35.02429700</td>\n",
       "      <td>72.47794991</td>\n",
       "      <td>65.74000000</td>\n",
       "      <td>47.76655705</td>\n",
       "      <td>...</td>\n",
       "      <td>28.65004581</td>\n",
       "      <td>48.78039097</td>\n",
       "      <td>84.28836983</td>\n",
       "      <td>44.95649687</td>\n",
       "      <td>27.46956274</td>\n",
       "      <td>34.33631194</td>\n",
       "      <td>49.57742745</td>\n",
       "      <td>91.30285476</td>\n",
       "      <td>29.52538722</td>\n",
       "      <td>28.02213221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-30</th>\n",
       "      <td>37.44602782</td>\n",
       "      <td>33.76759430</td>\n",
       "      <td>120.39025770</td>\n",
       "      <td>78.00222579</td>\n",
       "      <td>45.26058952</td>\n",
       "      <td>61.12795566</td>\n",
       "      <td>35.44286191</td>\n",
       "      <td>73.79678974</td>\n",
       "      <td>61.69000000</td>\n",
       "      <td>46.10362647</td>\n",
       "      <td>...</td>\n",
       "      <td>28.74172595</td>\n",
       "      <td>42.41655887</td>\n",
       "      <td>88.36990125</td>\n",
       "      <td>43.57967974</td>\n",
       "      <td>29.39000119</td>\n",
       "      <td>35.43937303</td>\n",
       "      <td>50.87349483</td>\n",
       "      <td>93.44593389</td>\n",
       "      <td>27.56211099</td>\n",
       "      <td>29.37084621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-31</th>\n",
       "      <td>39.45552968</td>\n",
       "      <td>38.66856535</td>\n",
       "      <td>123.24889355</td>\n",
       "      <td>84.14255587</td>\n",
       "      <td>47.21597213</td>\n",
       "      <td>68.86955598</td>\n",
       "      <td>36.60477297</td>\n",
       "      <td>74.92830371</td>\n",
       "      <td>64.54000000</td>\n",
       "      <td>47.41554943</td>\n",
       "      <td>...</td>\n",
       "      <td>29.75937558</td>\n",
       "      <td>42.46894464</td>\n",
       "      <td>87.33475361</td>\n",
       "      <td>46.17707943</td>\n",
       "      <td>30.02204423</td>\n",
       "      <td>35.28473104</td>\n",
       "      <td>51.08494461</td>\n",
       "      <td>100.73432555</td>\n",
       "      <td>27.28625466</td>\n",
       "      <td>29.79791734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-30</th>\n",
       "      <td>39.89336706</td>\n",
       "      <td>41.36458087</td>\n",
       "      <td>133.98342494</td>\n",
       "      <td>86.47010113</td>\n",
       "      <td>49.04968649</td>\n",
       "      <td>68.38018499</td>\n",
       "      <td>37.41902561</td>\n",
       "      <td>74.36714638</td>\n",
       "      <td>72.36000000</td>\n",
       "      <td>48.94537529</td>\n",
       "      <td>...</td>\n",
       "      <td>30.15342116</td>\n",
       "      <td>42.78547212</td>\n",
       "      <td>87.46506509</td>\n",
       "      <td>46.29945567</td>\n",
       "      <td>30.39399301</td>\n",
       "      <td>36.96856003</td>\n",
       "      <td>53.65538096</td>\n",
       "      <td>100.47064033</td>\n",
       "      <td>28.12612539</td>\n",
       "      <td>31.32178478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31</th>\n",
       "      <td>38.95576181</td>\n",
       "      <td>37.50350151</td>\n",
       "      <td>120.26928991</td>\n",
       "      <td>88.95449982</td>\n",
       "      <td>45.83414944</td>\n",
       "      <td>72.37985174</td>\n",
       "      <td>38.74046435</td>\n",
       "      <td>72.93205548</td>\n",
       "      <td>69.24990000</td>\n",
       "      <td>44.92618783</td>\n",
       "      <td>...</td>\n",
       "      <td>29.70199506</td>\n",
       "      <td>37.19650113</td>\n",
       "      <td>85.95345193</td>\n",
       "      <td>45.39008938</td>\n",
       "      <td>32.39745557</td>\n",
       "      <td>33.38332865</td>\n",
       "      <td>46.06203349</td>\n",
       "      <td>96.80432199</td>\n",
       "      <td>27.50576633</td>\n",
       "      <td>31.94297914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31</th>\n",
       "      <td>39.69890079</td>\n",
       "      <td>37.53342708</td>\n",
       "      <td>135.47301238</td>\n",
       "      <td>95.84930753</td>\n",
       "      <td>48.40870808</td>\n",
       "      <td>73.05495940</td>\n",
       "      <td>38.85083604</td>\n",
       "      <td>74.56953099</td>\n",
       "      <td>71.90000000</td>\n",
       "      <td>46.27496921</td>\n",
       "      <td>...</td>\n",
       "      <td>31.48927392</td>\n",
       "      <td>38.47755759</td>\n",
       "      <td>87.00906335</td>\n",
       "      <td>46.64657936</td>\n",
       "      <td>33.74124143</td>\n",
       "      <td>35.24689219</td>\n",
       "      <td>48.07309922</td>\n",
       "      <td>96.06912184</td>\n",
       "      <td>27.85038596</td>\n",
       "      <td>34.47163259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-30</th>\n",
       "      <td>39.66634256</td>\n",
       "      <td>34.25030202</td>\n",
       "      <td>129.45327437</td>\n",
       "      <td>94.21285594</td>\n",
       "      <td>50.58044463</td>\n",
       "      <td>72.97000080</td>\n",
       "      <td>38.25298937</td>\n",
       "      <td>74.80871281</td>\n",
       "      <td>69.19000000</td>\n",
       "      <td>45.12932334</td>\n",
       "      <td>...</td>\n",
       "      <td>30.70386959</td>\n",
       "      <td>38.56862873</td>\n",
       "      <td>82.27631618</td>\n",
       "      <td>44.65219370</td>\n",
       "      <td>32.47709229</td>\n",
       "      <td>33.68659998</td>\n",
       "      <td>47.77442609</td>\n",
       "      <td>97.47515588</td>\n",
       "      <td>27.77392642</td>\n",
       "      <td>35.94037314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-31</th>\n",
       "      <td>38.48289605</td>\n",
       "      <td>40.01590351</td>\n",
       "      <td>146.00501306</td>\n",
       "      <td>100.99244110</td>\n",
       "      <td>55.99653498</td>\n",
       "      <td>80.62571498</td>\n",
       "      <td>40.30485158</td>\n",
       "      <td>75.62824982</td>\n",
       "      <td>70.12000000</td>\n",
       "      <td>45.24786874</td>\n",
       "      <td>...</td>\n",
       "      <td>31.36108235</td>\n",
       "      <td>40.24433775</td>\n",
       "      <td>84.60332311</td>\n",
       "      <td>49.71473409</td>\n",
       "      <td>32.59983262</td>\n",
       "      <td>34.63853619</td>\n",
       "      <td>47.96561204</td>\n",
       "      <td>107.83824785</td>\n",
       "      <td>27.68790944</td>\n",
       "      <td>36.07460241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-30</th>\n",
       "      <td>41.16610236</td>\n",
       "      <td>46.96425145</td>\n",
       "      <td>146.12423326</td>\n",
       "      <td>111.69411705</td>\n",
       "      <td>61.06145951</td>\n",
       "      <td>86.22959708</td>\n",
       "      <td>41.15551603</td>\n",
       "      <td>80.48553756</td>\n",
       "      <td>73.68000000</td>\n",
       "      <td>49.82554510</td>\n",
       "      <td>...</td>\n",
       "      <td>32.87915127</td>\n",
       "      <td>41.65411985</td>\n",
       "      <td>79.77153052</td>\n",
       "      <td>53.83722829</td>\n",
       "      <td>34.26910116</td>\n",
       "      <td>36.52479311</td>\n",
       "      <td>51.58490227</td>\n",
       "      <td>108.85614147</td>\n",
       "      <td>27.09273552</td>\n",
       "      <td>43.78574560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>39.43238724</td>\n",
       "      <td>51.89970750</td>\n",
       "      <td>158.30385803</td>\n",
       "      <td>103.66431211</td>\n",
       "      <td>57.74366923</td>\n",
       "      <td>85.38671579</td>\n",
       "      <td>41.62708002</td>\n",
       "      <td>83.26379427</td>\n",
       "      <td>72.70000000</td>\n",
       "      <td>50.95949386</td>\n",
       "      <td>...</td>\n",
       "      <td>31.95893586</td>\n",
       "      <td>39.68324930</td>\n",
       "      <td>81.45436267</td>\n",
       "      <td>52.22676701</td>\n",
       "      <td>34.17400779</td>\n",
       "      <td>36.26757625</td>\n",
       "      <td>48.64673308</td>\n",
       "      <td>110.16356556</td>\n",
       "      <td>27.52722344</td>\n",
       "      <td>41.93413384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>36.46881439</td>\n",
       "      <td>47.49650651</td>\n",
       "      <td>158.02557400</td>\n",
       "      <td>110.03180655</td>\n",
       "      <td>53.66393519</td>\n",
       "      <td>90.01782759</td>\n",
       "      <td>41.60933349</td>\n",
       "      <td>78.34124546</td>\n",
       "      <td>70.13000000</td>\n",
       "      <td>47.82500770</td>\n",
       "      <td>...</td>\n",
       "      <td>32.07051783</td>\n",
       "      <td>35.36108436</td>\n",
       "      <td>77.02261098</td>\n",
       "      <td>49.04531669</td>\n",
       "      <td>32.47270437</td>\n",
       "      <td>32.48553586</td>\n",
       "      <td>48.53977698</td>\n",
       "      <td>108.88146444</td>\n",
       "      <td>23.13406782</td>\n",
       "      <td>41.72111906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-28</th>\n",
       "      <td>40.75585532</td>\n",
       "      <td>46.44954689</td>\n",
       "      <td>153.98051686</td>\n",
       "      <td>121.11705716</td>\n",
       "      <td>53.79731697</td>\n",
       "      <td>97.60234947</td>\n",
       "      <td>44.03561500</td>\n",
       "      <td>83.93505092</td>\n",
       "      <td>79.10000000</td>\n",
       "      <td>54.09576859</td>\n",
       "      <td>...</td>\n",
       "      <td>33.66056090</td>\n",
       "      <td>39.12997398</td>\n",
       "      <td>78.59767023</td>\n",
       "      <td>51.97185882</td>\n",
       "      <td>33.65621979</td>\n",
       "      <td>34.14257860</td>\n",
       "      <td>54.46958094</td>\n",
       "      <td>116.93344897</td>\n",
       "      <td>25.85223444</td>\n",
       "      <td>45.00178745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>40.21618088</td>\n",
       "      <td>51.18177630</td>\n",
       "      <td>148.83252640</td>\n",
       "      <td>117.31741727</td>\n",
       "      <td>52.05446174</td>\n",
       "      <td>107.96476318</td>\n",
       "      <td>43.06882084</td>\n",
       "      <td>87.34727226</td>\n",
       "      <td>73.94000000</td>\n",
       "      <td>58.21717495</td>\n",
       "      <td>...</td>\n",
       "      <td>34.37230484</td>\n",
       "      <td>39.06532687</td>\n",
       "      <td>75.45518376</td>\n",
       "      <td>49.96473725</td>\n",
       "      <td>31.85985442</td>\n",
       "      <td>33.49224377</td>\n",
       "      <td>52.86457172</td>\n",
       "      <td>114.35730077</td>\n",
       "      <td>26.10848438</td>\n",
       "      <td>45.19706533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30</th>\n",
       "      <td>40.04195916</td>\n",
       "      <td>46.91986067</td>\n",
       "      <td>142.18084892</td>\n",
       "      <td>117.99626112</td>\n",
       "      <td>57.97557525</td>\n",
       "      <td>108.56314271</td>\n",
       "      <td>43.37506462</td>\n",
       "      <td>87.30393281</td>\n",
       "      <td>76.06000000</td>\n",
       "      <td>57.14523966</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63383325</td>\n",
       "      <td>40.04426887</td>\n",
       "      <td>77.55905182</td>\n",
       "      <td>50.07273728</td>\n",
       "      <td>28.51271018</td>\n",
       "      <td>35.40499327</td>\n",
       "      <td>58.02888019</td>\n",
       "      <td>106.88398383</td>\n",
       "      <td>27.39940388</td>\n",
       "      <td>43.44886622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-31</th>\n",
       "      <td>39.86773744</td>\n",
       "      <td>41.17209272</td>\n",
       "      <td>152.34230539</td>\n",
       "      <td>123.34294830</td>\n",
       "      <td>59.70605561</td>\n",
       "      <td>107.18087522</td>\n",
       "      <td>45.41206680</td>\n",
       "      <td>90.49832387</td>\n",
       "      <td>79.09000000</td>\n",
       "      <td>63.16846904</td>\n",
       "      <td>...</td>\n",
       "      <td>35.19425126</td>\n",
       "      <td>44.10319245</td>\n",
       "      <td>76.26895364</td>\n",
       "      <td>51.08891930</td>\n",
       "      <td>28.31436089</td>\n",
       "      <td>35.11013446</td>\n",
       "      <td>60.83041407</td>\n",
       "      <td>111.23799412</td>\n",
       "      <td>27.98334305</td>\n",
       "      <td>48.68190166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>37.43482962</td>\n",
       "      <td>38.80593634</td>\n",
       "      <td>158.43730330</td>\n",
       "      <td>118.74646370</td>\n",
       "      <td>60.24402878</td>\n",
       "      <td>101.25812252</td>\n",
       "      <td>45.86058104</td>\n",
       "      <td>91.19562458</td>\n",
       "      <td>81.01000000</td>\n",
       "      <td>59.65962603</td>\n",
       "      <td>...</td>\n",
       "      <td>34.89283405</td>\n",
       "      <td>41.07121423</td>\n",
       "      <td>74.47860261</td>\n",
       "      <td>50.68301939</td>\n",
       "      <td>26.54863674</td>\n",
       "      <td>35.59017458</td>\n",
       "      <td>60.81016202</td>\n",
       "      <td>106.49948745</td>\n",
       "      <td>30.74970193</td>\n",
       "      <td>47.16578859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-31</th>\n",
       "      <td>39.73448090</td>\n",
       "      <td>38.96627137</td>\n",
       "      <td>173.27743492</td>\n",
       "      <td>114.84110860</td>\n",
       "      <td>63.23267196</td>\n",
       "      <td>100.69631800</td>\n",
       "      <td>47.59360476</td>\n",
       "      <td>97.16037250</td>\n",
       "      <td>81.99000000</td>\n",
       "      <td>54.21743377</td>\n",
       "      <td>...</td>\n",
       "      <td>35.66197716</td>\n",
       "      <td>38.82978248</td>\n",
       "      <td>70.90685232</td>\n",
       "      <td>55.95287359</td>\n",
       "      <td>27.49680234</td>\n",
       "      <td>33.15157077</td>\n",
       "      <td>59.51729361</td>\n",
       "      <td>101.46847624</td>\n",
       "      <td>30.22162291</td>\n",
       "      <td>47.90917307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-31</th>\n",
       "      <td>35.23221004</td>\n",
       "      <td>37.96773731</td>\n",
       "      <td>174.31186769</td>\n",
       "      <td>107.23801710</td>\n",
       "      <td>56.36839104</td>\n",
       "      <td>95.52512638</td>\n",
       "      <td>42.52346340</td>\n",
       "      <td>88.83045597</td>\n",
       "      <td>78.57000000</td>\n",
       "      <td>52.30462528</td>\n",
       "      <td>...</td>\n",
       "      <td>34.97725219</td>\n",
       "      <td>39.24982927</td>\n",
       "      <td>67.98750927</td>\n",
       "      <td>51.52855570</td>\n",
       "      <td>25.37590561</td>\n",
       "      <td>31.28978316</td>\n",
       "      <td>54.09861567</td>\n",
       "      <td>100.97122513</td>\n",
       "      <td>28.15471462</td>\n",
       "      <td>43.96458895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-30</th>\n",
       "      <td>33.40767610</td>\n",
       "      <td>37.82163263</td>\n",
       "      <td>188.57875989</td>\n",
       "      <td>104.56562594</td>\n",
       "      <td>49.14283218</td>\n",
       "      <td>90.70303633</td>\n",
       "      <td>37.76316400</td>\n",
       "      <td>92.59022599</td>\n",
       "      <td>82.22000000</td>\n",
       "      <td>52.81961891</td>\n",
       "      <td>...</td>\n",
       "      <td>34.25161012</td>\n",
       "      <td>39.69957666</td>\n",
       "      <td>67.18329763</td>\n",
       "      <td>49.78755231</td>\n",
       "      <td>24.45432240</td>\n",
       "      <td>31.67548157</td>\n",
       "      <td>54.22068852</td>\n",
       "      <td>91.79998287</td>\n",
       "      <td>26.73727036</td>\n",
       "      <td>40.34904776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>36.74552431</td>\n",
       "      <td>45.01972341</td>\n",
       "      <td>197.43409130</td>\n",
       "      <td>113.64795179</td>\n",
       "      <td>54.29511396</td>\n",
       "      <td>92.15443769</td>\n",
       "      <td>42.31010114</td>\n",
       "      <td>102.09916985</td>\n",
       "      <td>88.66000000</td>\n",
       "      <td>56.29348500</td>\n",
       "      <td>...</td>\n",
       "      <td>35.91137978</td>\n",
       "      <td>44.61868870</td>\n",
       "      <td>74.76457359</td>\n",
       "      <td>59.90849433</td>\n",
       "      <td>23.59980342</td>\n",
       "      <td>35.10819738</td>\n",
       "      <td>48.41248187</td>\n",
       "      <td>102.19870338</td>\n",
       "      <td>27.93141860</td>\n",
       "      <td>42.14212103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>40.69644669</td>\n",
       "      <td>40.27495546</td>\n",
       "      <td>161.91326754</td>\n",
       "      <td>112.99053695</td>\n",
       "      <td>53.01865452</td>\n",
       "      <td>94.51876053</td>\n",
       "      <td>42.42343177</td>\n",
       "      <td>102.11821821</td>\n",
       "      <td>91.46000000</td>\n",
       "      <td>57.70737659</td>\n",
       "      <td>...</td>\n",
       "      <td>36.00568487</td>\n",
       "      <td>46.86342730</td>\n",
       "      <td>74.44597631</td>\n",
       "      <td>59.72143412</td>\n",
       "      <td>26.51522110</td>\n",
       "      <td>36.12390903</td>\n",
       "      <td>49.50485207</td>\n",
       "      <td>98.71943223</td>\n",
       "      <td>29.14561278</td>\n",
       "      <td>45.83910949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>40.79862571</td>\n",
       "      <td>41.33893271</td>\n",
       "      <td>149.81538015</td>\n",
       "      <td>100.53578968</td>\n",
       "      <td>54.01246937</td>\n",
       "      <td>99.37693283</td>\n",
       "      <td>42.41398755</td>\n",
       "      <td>99.52764225</td>\n",
       "      <td>93.94000000</td>\n",
       "      <td>52.14011467</td>\n",
       "      <td>...</td>\n",
       "      <td>37.14621703</td>\n",
       "      <td>44.29815215</td>\n",
       "      <td>71.06372586</td>\n",
       "      <td>59.98019070</td>\n",
       "      <td>26.89106382</td>\n",
       "      <td>35.33018970</td>\n",
       "      <td>49.87352701</td>\n",
       "      <td>100.47823768</td>\n",
       "      <td>26.55791819</td>\n",
       "      <td>47.03661941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>36.73925516</td>\n",
       "      <td>38.05914962</td>\n",
       "      <td>151.34827289</td>\n",
       "      <td>92.97124993</td>\n",
       "      <td>50.61292076</td>\n",
       "      <td>85.81812848</td>\n",
       "      <td>35.97710518</td>\n",
       "      <td>100.51815659</td>\n",
       "      <td>89.13000000</td>\n",
       "      <td>50.76403789</td>\n",
       "      <td>...</td>\n",
       "      <td>34.37779044</td>\n",
       "      <td>47.41043450</td>\n",
       "      <td>70.97256007</td>\n",
       "      <td>58.04820756</td>\n",
       "      <td>24.66489861</td>\n",
       "      <td>34.79781698</td>\n",
       "      <td>49.73852166</td>\n",
       "      <td>97.21678402</td>\n",
       "      <td>22.06350126</td>\n",
       "      <td>42.34934182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>36.44651209</td>\n",
       "      <td>40.13373983</td>\n",
       "      <td>147.75493343</td>\n",
       "      <td>92.84754723</td>\n",
       "      <td>50.34556653</td>\n",
       "      <td>83.32649499</td>\n",
       "      <td>36.82306617</td>\n",
       "      <td>95.48939151</td>\n",
       "      <td>85.15000000</td>\n",
       "      <td>50.34892051</td>\n",
       "      <td>...</td>\n",
       "      <td>32.59537880</td>\n",
       "      <td>44.82629704</td>\n",
       "      <td>73.73546496</td>\n",
       "      <td>60.08861832</td>\n",
       "      <td>24.31073596</td>\n",
       "      <td>36.36262153</td>\n",
       "      <td>49.80724975</td>\n",
       "      <td>94.81721601</td>\n",
       "      <td>20.79786927</td>\n",
       "      <td>40.39172997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>38.88603766</td>\n",
       "      <td>40.14352854</td>\n",
       "      <td>159.66025522</td>\n",
       "      <td>104.65874623</td>\n",
       "      <td>52.65956346</td>\n",
       "      <td>83.25915657</td>\n",
       "      <td>39.76016670</td>\n",
       "      <td>109.90899441</td>\n",
       "      <td>93.80000000</td>\n",
       "      <td>56.23990573</td>\n",
       "      <td>...</td>\n",
       "      <td>35.08467460</td>\n",
       "      <td>45.02565160</td>\n",
       "      <td>76.90015616</td>\n",
       "      <td>60.82633633</td>\n",
       "      <td>28.43385069</td>\n",
       "      <td>39.75491100</td>\n",
       "      <td>56.25394497</td>\n",
       "      <td>104.67189060</td>\n",
       "      <td>23.61709263</td>\n",
       "      <td>43.60850924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>40.04407095</td>\n",
       "      <td>33.95705938</td>\n",
       "      <td>155.43823026</td>\n",
       "      <td>90.01478000</td>\n",
       "      <td>56.77932921</td>\n",
       "      <td>81.86428912</td>\n",
       "      <td>37.19691465</td>\n",
       "      <td>108.57606705</td>\n",
       "      <td>94.22000000</td>\n",
       "      <td>53.51294967</td>\n",
       "      <td>...</td>\n",
       "      <td>31.20438586</td>\n",
       "      <td>40.89616426</td>\n",
       "      <td>81.32520403</td>\n",
       "      <td>58.82280781</td>\n",
       "      <td>24.45922640</td>\n",
       "      <td>40.61027339</td>\n",
       "      <td>54.98625339</td>\n",
       "      <td>113.64404740</td>\n",
       "      <td>26.84603012</td>\n",
       "      <td>46.36042449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>44.90768367</td>\n",
       "      <td>31.32651866</td>\n",
       "      <td>153.18781130</td>\n",
       "      <td>96.47778143</td>\n",
       "      <td>58.57578996</td>\n",
       "      <td>72.45584367</td>\n",
       "      <td>37.89495443</td>\n",
       "      <td>114.39332888</td>\n",
       "      <td>99.47000000</td>\n",
       "      <td>55.98493800</td>\n",
       "      <td>...</td>\n",
       "      <td>32.74887425</td>\n",
       "      <td>45.32888670</td>\n",
       "      <td>82.58719221</td>\n",
       "      <td>61.34942506</td>\n",
       "      <td>25.40192575</td>\n",
       "      <td>43.56421944</td>\n",
       "      <td>56.73481072</td>\n",
       "      <td>119.86762225</td>\n",
       "      <td>27.39638148</td>\n",
       "      <td>46.74487199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-30</th>\n",
       "      <td>43.52297301</td>\n",
       "      <td>27.79234545</td>\n",
       "      <td>161.00763909</td>\n",
       "      <td>92.36206594</td>\n",
       "      <td>57.62636511</td>\n",
       "      <td>76.64974019</td>\n",
       "      <td>37.58896439</td>\n",
       "      <td>108.93183348</td>\n",
       "      <td>95.79000000</td>\n",
       "      <td>54.20490408</td>\n",
       "      <td>...</td>\n",
       "      <td>31.95111137</td>\n",
       "      <td>44.12368735</td>\n",
       "      <td>86.96611321</td>\n",
       "      <td>61.30575593</td>\n",
       "      <td>24.38556528</td>\n",
       "      <td>43.55446481</td>\n",
       "      <td>57.30844811</td>\n",
       "      <td>118.40845940</td>\n",
       "      <td>24.57070188</td>\n",
       "      <td>46.88004805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-31</th>\n",
       "      <td>47.20221441</td>\n",
       "      <td>34.85087472</td>\n",
       "      <td>169.20594924</td>\n",
       "      <td>100.68044865</td>\n",
       "      <td>62.18997907</td>\n",
       "      <td>82.32213019</td>\n",
       "      <td>43.05587987</td>\n",
       "      <td>108.47029866</td>\n",
       "      <td>97.86000000</td>\n",
       "      <td>61.08578791</td>\n",
       "      <td>...</td>\n",
       "      <td>33.19807759</td>\n",
       "      <td>48.85839909</td>\n",
       "      <td>82.52225059</td>\n",
       "      <td>63.28208591</td>\n",
       "      <td>26.46694651</td>\n",
       "      <td>46.63693085</td>\n",
       "      <td>62.13235384</td>\n",
       "      <td>128.99223597</td>\n",
       "      <td>27.25949735</td>\n",
       "      <td>49.85326644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-31</th>\n",
       "      <td>46.09353634</td>\n",
       "      <td>35.74268695</td>\n",
       "      <td>156.77400383</td>\n",
       "      <td>103.05832580</td>\n",
       "      <td>60.18990877</td>\n",
       "      <td>84.36337595</td>\n",
       "      <td>40.42923066</td>\n",
       "      <td>110.57605128</td>\n",
       "      <td>102.31000000</td>\n",
       "      <td>60.27143407</td>\n",
       "      <td>...</td>\n",
       "      <td>32.83357977</td>\n",
       "      <td>52.18139308</td>\n",
       "      <td>81.54472705</td>\n",
       "      <td>60.73262024</td>\n",
       "      <td>25.31062361</td>\n",
       "      <td>49.76283955</td>\n",
       "      <td>63.02869399</td>\n",
       "      <td>127.48729377</td>\n",
       "      <td>29.99272827</td>\n",
       "      <td>50.47556796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>46.31429082</td>\n",
       "      <td>36.04792753</td>\n",
       "      <td>148.60671775</td>\n",
       "      <td>109.80908324</td>\n",
       "      <td>59.22273863</td>\n",
       "      <td>78.35889972</td>\n",
       "      <td>40.68900916</td>\n",
       "      <td>117.47022770</td>\n",
       "      <td>108.54000000</td>\n",
       "      <td>62.09229421</td>\n",
       "      <td>...</td>\n",
       "      <td>32.44904599</td>\n",
       "      <td>52.30652831</td>\n",
       "      <td>81.67573763</td>\n",
       "      <td>58.80295844</td>\n",
       "      <td>26.23024573</td>\n",
       "      <td>51.31853981</td>\n",
       "      <td>63.09817772</td>\n",
       "      <td>128.13190088</td>\n",
       "      <td>30.41433249</td>\n",
       "      <td>51.37444794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-31</th>\n",
       "      <td>42.85227545</td>\n",
       "      <td>39.97666915</td>\n",
       "      <td>139.59783411</td>\n",
       "      <td>110.28503592</td>\n",
       "      <td>52.86517692</td>\n",
       "      <td>68.21240194</td>\n",
       "      <td>37.99400979</td>\n",
       "      <td>112.92602551</td>\n",
       "      <td>107.51000000</td>\n",
       "      <td>61.75509788</td>\n",
       "      <td>...</td>\n",
       "      <td>33.48147177</td>\n",
       "      <td>48.96638011</td>\n",
       "      <td>77.97000984</td>\n",
       "      <td>56.96258316</td>\n",
       "      <td>25.29807511</td>\n",
       "      <td>47.28741714</td>\n",
       "      <td>60.30309303</td>\n",
       "      <td>103.86942280</td>\n",
       "      <td>31.58109767</td>\n",
       "      <td>47.21589332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>43.25552156</td>\n",
       "      <td>45.84312511</td>\n",
       "      <td>169.13581100</td>\n",
       "      <td>107.90875084</td>\n",
       "      <td>57.62285329</td>\n",
       "      <td>75.99699597</td>\n",
       "      <td>36.86116088</td>\n",
       "      <td>116.02507938</td>\n",
       "      <td>102.81000000</td>\n",
       "      <td>71.92879264</td>\n",
       "      <td>...</td>\n",
       "      <td>34.86125577</td>\n",
       "      <td>52.30410593</td>\n",
       "      <td>82.41266747</td>\n",
       "      <td>57.56614710</td>\n",
       "      <td>24.21054271</td>\n",
       "      <td>50.62829165</td>\n",
       "      <td>61.76028216</td>\n",
       "      <td>100.38082929</td>\n",
       "      <td>39.09431152</td>\n",
       "      <td>49.86442156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>44.93909238</td>\n",
       "      <td>46.08991196</td>\n",
       "      <td>168.59615688</td>\n",
       "      <td>113.08352807</td>\n",
       "      <td>59.34774792</td>\n",
       "      <td>76.19188505</td>\n",
       "      <td>37.19036483</td>\n",
       "      <td>113.79065183</td>\n",
       "      <td>102.95000000</td>\n",
       "      <td>70.35922577</td>\n",
       "      <td>...</td>\n",
       "      <td>36.14475876</td>\n",
       "      <td>58.49571832</td>\n",
       "      <td>85.20695722</td>\n",
       "      <td>57.19702346</td>\n",
       "      <td>22.79351388</td>\n",
       "      <td>48.60630094</td>\n",
       "      <td>61.70182472</td>\n",
       "      <td>101.93820644</td>\n",
       "      <td>42.28748851</td>\n",
       "      <td>52.98218512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>48.30261971</td>\n",
       "      <td>43.68127231</td>\n",
       "      <td>163.73127251</td>\n",
       "      <td>118.48287110</td>\n",
       "      <td>58.52291275</td>\n",
       "      <td>85.04959364</td>\n",
       "      <td>40.70456972</td>\n",
       "      <td>110.62359364</td>\n",
       "      <td>113.38000000</td>\n",
       "      <td>72.60700054</td>\n",
       "      <td>...</td>\n",
       "      <td>36.44548005</td>\n",
       "      <td>56.39308939</td>\n",
       "      <td>79.19357015</td>\n",
       "      <td>56.17653266</td>\n",
       "      <td>26.32055115</td>\n",
       "      <td>48.40017568</td>\n",
       "      <td>64.14226484</td>\n",
       "      <td>116.88321674</td>\n",
       "      <td>41.45234991</td>\n",
       "      <td>54.48487863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>50.60086565</td>\n",
       "      <td>45.86299598</td>\n",
       "      <td>156.12490616</td>\n",
       "      <td>134.32908516</td>\n",
       "      <td>59.22200826</td>\n",
       "      <td>89.53185633</td>\n",
       "      <td>43.93014132</td>\n",
       "      <td>119.00755442</td>\n",
       "      <td>118.34000000</td>\n",
       "      <td>79.81469100</td>\n",
       "      <td>...</td>\n",
       "      <td>39.27808058</td>\n",
       "      <td>57.31683497</td>\n",
       "      <td>77.47406925</td>\n",
       "      <td>62.93356886</td>\n",
       "      <td>28.25756141</td>\n",
       "      <td>47.41055399</td>\n",
       "      <td>63.93671203</td>\n",
       "      <td>115.64850010</td>\n",
       "      <td>44.19325569</td>\n",
       "      <td>52.86838150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>52.27967020</td>\n",
       "      <td>41.84652135</td>\n",
       "      <td>147.85939394</td>\n",
       "      <td>140.86952606</td>\n",
       "      <td>62.40145631</td>\n",
       "      <td>86.58692258</td>\n",
       "      <td>43.27723105</td>\n",
       "      <td>116.46224999</td>\n",
       "      <td>130.13000000</td>\n",
       "      <td>79.83417463</td>\n",
       "      <td>...</td>\n",
       "      <td>38.88040598</td>\n",
       "      <td>56.41060144</td>\n",
       "      <td>78.13143654</td>\n",
       "      <td>61.94954316</td>\n",
       "      <td>28.11741981</td>\n",
       "      <td>49.47959312</td>\n",
       "      <td>62.54678351</td>\n",
       "      <td>120.85551089</td>\n",
       "      <td>41.33890287</td>\n",
       "      <td>52.92788446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>54.43532901</td>\n",
       "      <td>42.16309078</td>\n",
       "      <td>141.75593049</td>\n",
       "      <td>140.85972030</td>\n",
       "      <td>63.77814835</td>\n",
       "      <td>80.27635025</td>\n",
       "      <td>42.78546766</td>\n",
       "      <td>119.07013051</td>\n",
       "      <td>133.74000000</td>\n",
       "      <td>74.23263096</td>\n",
       "      <td>...</td>\n",
       "      <td>40.82150000</td>\n",
       "      <td>61.49720257</td>\n",
       "      <td>77.78846230</td>\n",
       "      <td>62.74325928</td>\n",
       "      <td>27.54281314</td>\n",
       "      <td>50.65204864</td>\n",
       "      <td>64.65857676</td>\n",
       "      <td>118.42078354</td>\n",
       "      <td>39.39991148</td>\n",
       "      <td>55.75615969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>59.66626253</td>\n",
       "      <td>47.99551598</td>\n",
       "      <td>133.26892494</td>\n",
       "      <td>150.40575387</td>\n",
       "      <td>63.85552554</td>\n",
       "      <td>90.16427240</td>\n",
       "      <td>44.76591323</td>\n",
       "      <td>122.18185609</td>\n",
       "      <td>141.86000000</td>\n",
       "      <td>83.54580618</td>\n",
       "      <td>...</td>\n",
       "      <td>42.61628041</td>\n",
       "      <td>65.35904193</td>\n",
       "      <td>77.41380602</td>\n",
       "      <td>63.02105992</td>\n",
       "      <td>27.08312780</td>\n",
       "      <td>51.54641544</td>\n",
       "      <td>71.43420556</td>\n",
       "      <td>117.98530385</td>\n",
       "      <td>39.51688006</td>\n",
       "      <td>61.88725050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>58.77942143</td>\n",
       "      <td>49.88916265</td>\n",
       "      <td>116.33305213</td>\n",
       "      <td>141.80044954</td>\n",
       "      <td>70.13275003</td>\n",
       "      <td>92.87597984</td>\n",
       "      <td>47.65814810</td>\n",
       "      <td>121.40637874</td>\n",
       "      <td>141.44000000</td>\n",
       "      <td>76.21326984</td>\n",
       "      <td>...</td>\n",
       "      <td>42.94541296</td>\n",
       "      <td>63.01744232</td>\n",
       "      <td>77.63498832</td>\n",
       "      <td>64.41695873</td>\n",
       "      <td>27.74892476</td>\n",
       "      <td>54.79896064</td>\n",
       "      <td>72.53561401</td>\n",
       "      <td>127.31820357</td>\n",
       "      <td>43.30387330</td>\n",
       "      <td>62.09166499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 495 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         AAL          AAP         AAPL        ABBV  \\\n",
       "date                                                                       \n",
       "2013-07-31 30.77861719 18.63139292  81.73270857  58.73000866 38.52144972   \n",
       "2013-08-31 32.09288410 15.55986096  79.33492514  63.64994327 36.09056668   \n",
       "2013-09-30 35.34697923 18.25587647  81.98212977  62.28266407 37.88620154   \n",
       "2013-10-31 35.00902763 21.15409315  98.34285959  68.28583759 41.39637606   \n",
       "2013-11-30 36.94707663 22.60801580 100.15741326  73.07037475 41.39637606   \n",
       "2013-12-31 39.53485221 24.31228275 109.80605374  73.72082947 45.12162270   \n",
       "2014-01-31 40.19849023 32.30404302 113.90344263  65.78133976 42.40046983   \n",
       "2014-02-28 39.35511692 35.55851889 126.35434591  69.56208595 43.84740848   \n",
       "2014-03-31 38.65691442 35.24077420 125.56160936  70.95004943 44.26943225   \n",
       "2014-04-30 37.44602782 33.76759430 120.39025770  78.00222579 45.26058952   \n",
       "2014-05-31 39.45552968 38.66856535 123.24889355  84.14255587 47.21597213   \n",
       "2014-06-30 39.89336706 41.36458087 133.98342494  86.47010113 49.04968649   \n",
       "2014-07-31 38.95576181 37.50350151 120.26928991  88.95449982 45.83414944   \n",
       "2014-08-31 39.69890079 37.53342708 135.47301238  95.84930753 48.40870808   \n",
       "2014-09-30 39.66634256 34.25030202 129.45327437  94.21285594 50.58044463   \n",
       "2014-10-31 38.48289605 40.01590351 146.00501306 100.99244110 55.99653498   \n",
       "2014-11-30 41.16610236 46.96425145 146.12423326 111.69411705 61.06145951   \n",
       "2014-12-31 39.43238724 51.89970750 158.30385803 103.66431211 57.74366923   \n",
       "2015-01-31 36.46881439 47.49650651 158.02557400 110.03180655 53.66393519   \n",
       "2015-02-28 40.75585532 46.44954689 153.98051686 121.11705716 53.79731697   \n",
       "2015-03-31 40.21618088 51.18177630 148.83252640 117.31741727 52.05446174   \n",
       "2015-04-30 40.04195916 46.91986067 142.18084892 117.99626112 57.97557525   \n",
       "2015-05-31 39.86773744 41.17209272 152.34230539 123.34294830 59.70605561   \n",
       "2015-06-30 37.43482962 38.80593634 158.43730330 118.74646370 60.24402878   \n",
       "2015-07-31 39.73448090 38.96627137 173.27743492 114.84110860 63.23267196   \n",
       "2015-08-31 35.23221004 37.96773731 174.31186769 107.23801710 56.36839104   \n",
       "2015-09-30 33.40767610 37.82163263 188.57875989 104.56562594 49.14283218   \n",
       "2015-10-31 36.74552431 45.01972341 197.43409130 113.64795179 54.29511396   \n",
       "2015-11-30 40.69644669 40.27495546 161.91326754 112.99053695 53.01865452   \n",
       "2015-12-31 40.79862571 41.33893271 149.81538015 100.53578968 54.01246937   \n",
       "2016-01-31 36.73925516 38.05914962 151.34827289  92.97124993 50.61292076   \n",
       "2016-02-29 36.44651209 40.13373983 147.75493343  92.84754723 50.34556653   \n",
       "2016-03-31 38.88603766 40.14352854 159.66025522 104.65874623 52.65956346   \n",
       "2016-04-30 40.04407095 33.95705938 155.43823026  90.01478000 56.77932921   \n",
       "2016-05-31 44.90768367 31.32651866 153.18781130  96.47778143 58.57578996   \n",
       "2016-06-30 43.52297301 27.79234545 161.00763909  92.36206594 57.62636511   \n",
       "2016-07-31 47.20221441 34.85087472 169.20594924 100.68044865 62.18997907   \n",
       "2016-08-31 46.09353634 35.74268695 156.77400383 103.05832580 60.18990877   \n",
       "2016-09-30 46.31429082 36.04792753 148.60671775 109.80908324 59.22273863   \n",
       "2016-10-31 42.85227545 39.97666915 139.59783411 110.28503592 52.86517692   \n",
       "2016-11-30 43.25552156 45.84312511 169.13581100 107.90875084 57.62285329   \n",
       "2016-12-31 44.93909238 46.08991196 168.59615688 113.08352807 59.34774792   \n",
       "2017-01-31 48.30261971 43.68127231 163.73127251 118.48287110 58.52291275   \n",
       "2017-02-28 50.60086565 45.86299598 156.12490616 134.32908516 59.22200826   \n",
       "2017-03-31 52.27967020 41.84652135 147.85939394 140.86952606 62.40145631   \n",
       "2017-04-30 54.43532901 42.16309078 141.75593049 140.85972030 63.77814835   \n",
       "2017-05-31 59.66626253 47.99551598 133.26892494 150.40575387 63.85552554   \n",
       "2017-06-30 58.77942143 49.88916265 116.33305213 141.80044954 70.13275003   \n",
       "\n",
       "                    ABC         ABT          ACN         ADBE         ADI  \\\n",
       "date                                                                        \n",
       "2013-07-31  53.87744989 32.99081455  66.22844876  47.28000000 43.44107832   \n",
       "2013-08-31  53.01732111 30.01866909  64.82868748  45.75000000 41.01373554   \n",
       "2013-09-30  56.91072241 29.89257807  66.07591068  51.94000000 41.69611619   \n",
       "2013-10-31  60.85069550 33.05576095  66.82299697  54.22000000 43.69008561   \n",
       "2013-11-30  65.91719111 34.53897430  70.43234797  56.78000000 42.73298029   \n",
       "2013-12-31  65.71157957 34.66559008  74.75084097  59.87900000 45.44926154   \n",
       "2014-01-31  62.82367201 33.34174832  72.62341494  59.19000000 43.07551255   \n",
       "2014-02-28  63.63196980 36.17934393  75.77818772  68.63000000 45.68115222   \n",
       "2014-03-31  61.51246720 35.02429700  72.47794991  65.74000000 47.76655705   \n",
       "2014-04-30  61.12795566 35.44286191  73.79678974  61.69000000 46.10362647   \n",
       "2014-05-31  68.86955598 36.60477297  74.92830371  64.54000000 47.41554943   \n",
       "2014-06-30  68.38018499 37.41902561  74.36714638  72.36000000 48.94537529   \n",
       "2014-07-31  72.37985174 38.74046435  72.93205548  69.24990000 44.92618783   \n",
       "2014-08-31  73.05495940 38.85083604  74.56953099  71.90000000 46.27496921   \n",
       "2014-09-30  72.97000080 38.25298937  74.80871281  69.19000000 45.12932334   \n",
       "2014-10-31  80.62571498 40.30485158  75.62824982  70.12000000 45.24786874   \n",
       "2014-11-30  86.22959708 41.15551603  80.48553756  73.68000000 49.82554510   \n",
       "2014-12-31  85.38671579 41.62708002  83.26379427  72.70000000 50.95949386   \n",
       "2015-01-31  90.01782759 41.60933349  78.34124546  70.13000000 47.82500770   \n",
       "2015-02-28  97.60234947 44.03561500  83.93505092  79.10000000 54.09576859   \n",
       "2015-03-31 107.96476318 43.06882084  87.34727226  73.94000000 58.21717495   \n",
       "2015-04-30 108.56314271 43.37506462  87.30393281  76.06000000 57.14523966   \n",
       "2015-05-31 107.18087522 45.41206680  90.49832387  79.09000000 63.16846904   \n",
       "2015-06-30 101.25812252 45.86058104  91.19562458  81.01000000 59.65962603   \n",
       "2015-07-31 100.69631800 47.59360476  97.16037250  81.99000000 54.21743377   \n",
       "2015-08-31  95.52512638 42.52346340  88.83045597  78.57000000 52.30462528   \n",
       "2015-09-30  90.70303633 37.76316400  92.59022599  82.22000000 52.81961891   \n",
       "2015-10-31  92.15443769 42.31010114 102.09916985  88.66000000 56.29348500   \n",
       "2015-11-30  94.51876053 42.42343177 102.11821821  91.46000000 57.70737659   \n",
       "2015-12-31  99.37693283 42.41398755  99.52764225  93.94000000 52.14011467   \n",
       "2016-01-31  85.81812848 35.97710518 100.51815659  89.13000000 50.76403789   \n",
       "2016-02-29  83.32649499 36.82306617  95.48939151  85.15000000 50.34892051   \n",
       "2016-03-31  83.25915657 39.76016670 109.90899441  93.80000000 56.23990573   \n",
       "2016-04-30  81.86428912 37.19691465 108.57606705  94.22000000 53.51294967   \n",
       "2016-05-31  72.45584367 37.89495443 114.39332888  99.47000000 55.98493800   \n",
       "2016-06-30  76.64974019 37.58896439 108.93183348  95.79000000 54.20490408   \n",
       "2016-07-31  82.32213019 43.05587987 108.47029866  97.86000000 61.08578791   \n",
       "2016-08-31  84.36337595 40.42923066 110.57605128 102.31000000 60.27143407   \n",
       "2016-09-30  78.35889972 40.68900916 117.47022770 108.54000000 62.09229421   \n",
       "2016-10-31  68.21240194 37.99400979 112.92602551 107.51000000 61.75509788   \n",
       "2016-11-30  75.99699597 36.86116088 116.02507938 102.81000000 71.92879264   \n",
       "2016-12-31  76.19188505 37.19036483 113.79065183 102.95000000 70.35922577   \n",
       "2017-01-31  85.04959364 40.70456972 110.62359364 113.38000000 72.60700054   \n",
       "2017-02-28  89.53185633 43.93014132 119.00755442 118.34000000 79.81469100   \n",
       "2017-03-31  86.58692258 43.27723105 116.46224999 130.13000000 79.83417463   \n",
       "2017-04-30  80.27635025 42.78546766 119.07013051 133.74000000 74.23263096   \n",
       "2017-05-31  90.16427240 44.76591323 122.18185609 141.86000000 83.54580618   \n",
       "2017-06-30  92.87597984 47.65814810 121.40637874 141.44000000 76.21326984   \n",
       "\n",
       "               ...              XL        XLNX         XOM        XRAY  \\\n",
       "date           ...                                                       \n",
       "2013-07-31     ...     28.32843198 41.28388974 79.23671352 41.69639686   \n",
       "2013-08-31     ...     26.71095532 38.60058125 74.17847651 40.83096325   \n",
       "2013-09-30     ...     27.97701372 41.65075616 73.22528819 42.27738327   \n",
       "2013-10-31     ...     27.75007493 40.38659157 76.27208656 45.87069305   \n",
       "2013-11-30     ...     29.03908724 39.71607441 80.10221273 46.31377055   \n",
       "2013-12-31     ...     29.03762468 41.04798868 86.71741472 47.27020113   \n",
       "2014-01-31     ...     26.21046901 41.49493978 78.97111601 44.98859489   \n",
       "2014-02-28     ...     27.72436527 46.91977904 83.07167653 44.24756038   \n",
       "2014-03-31     ...     28.65004581 48.78039097 84.28836983 44.95649687   \n",
       "2014-04-30     ...     28.74172595 42.41655887 88.36990125 43.57967974   \n",
       "2014-05-31     ...     29.75937558 42.46894464 87.33475361 46.17707943   \n",
       "2014-06-30     ...     30.15342116 42.78547212 87.46506509 46.29945567   \n",
       "2014-07-31     ...     29.70199506 37.19650113 85.95345193 45.39008938   \n",
       "2014-08-31     ...     31.48927392 38.47755759 87.00906335 46.64657936   \n",
       "2014-09-30     ...     30.70386959 38.56862873 82.27631618 44.65219370   \n",
       "2014-10-31     ...     31.36108235 40.24433775 84.60332311 49.71473409   \n",
       "2014-11-30     ...     32.87915127 41.65411985 79.77153052 53.83722829   \n",
       "2014-12-31     ...     31.95893586 39.68324930 81.45436267 52.22676701   \n",
       "2015-01-31     ...     32.07051783 35.36108436 77.02261098 49.04531669   \n",
       "2015-02-28     ...     33.66056090 39.12997398 78.59767023 51.97185882   \n",
       "2015-03-31     ...     34.37230484 39.06532687 75.45518376 49.96473725   \n",
       "2015-04-30     ...     34.63383325 40.04426887 77.55905182 50.07273728   \n",
       "2015-05-31     ...     35.19425126 44.10319245 76.26895364 51.08891930   \n",
       "2015-06-30     ...     34.89283405 41.07121423 74.47860261 50.68301939   \n",
       "2015-07-31     ...     35.66197716 38.82978248 70.90685232 55.95287359   \n",
       "2015-08-31     ...     34.97725219 39.24982927 67.98750927 51.52855570   \n",
       "2015-09-30     ...     34.25161012 39.69957666 67.18329763 49.78755231   \n",
       "2015-10-31     ...     35.91137978 44.61868870 74.76457359 59.90849433   \n",
       "2015-11-30     ...     36.00568487 46.86342730 74.44597631 59.72143412   \n",
       "2015-12-31     ...     37.14621703 44.29815215 71.06372586 59.98019070   \n",
       "2016-01-31     ...     34.37779044 47.41043450 70.97256007 58.04820756   \n",
       "2016-02-29     ...     32.59537880 44.82629704 73.73546496 60.08861832   \n",
       "2016-03-31     ...     35.08467460 45.02565160 76.90015616 60.82633633   \n",
       "2016-04-30     ...     31.20438586 40.89616426 81.32520403 58.82280781   \n",
       "2016-05-31     ...     32.74887425 45.32888670 82.58719221 61.34942506   \n",
       "2016-06-30     ...     31.95111137 44.12368735 86.96611321 61.30575593   \n",
       "2016-07-31     ...     33.19807759 48.85839909 82.52225059 63.28208591   \n",
       "2016-08-31     ...     32.83357977 52.18139308 81.54472705 60.73262024   \n",
       "2016-09-30     ...     32.44904599 52.30652831 81.67573763 58.80295844   \n",
       "2016-10-31     ...     33.48147177 48.96638011 77.97000984 56.96258316   \n",
       "2016-11-30     ...     34.86125577 52.30410593 82.41266747 57.56614710   \n",
       "2016-12-31     ...     36.14475876 58.49571832 85.20695722 57.19702346   \n",
       "2017-01-31     ...     36.44548005 56.39308939 79.19357015 56.17653266   \n",
       "2017-02-28     ...     39.27808058 57.31683497 77.47406925 62.93356886   \n",
       "2017-03-31     ...     38.88040598 56.41060144 78.13143654 61.94954316   \n",
       "2017-04-30     ...     40.82150000 61.49720257 77.78846230 62.74325928   \n",
       "2017-05-31     ...     42.61628041 65.35904193 77.41380602 63.02105992   \n",
       "2017-06-30     ...     42.94541296 63.01744232 77.63498832 64.41695873   \n",
       "\n",
       "                   XRX         XYL         YUM          ZBH        ZION  \\\n",
       "date                                                                      \n",
       "2013-07-31 23.20721320 23.21996075 47.44778390  80.02819050 28.13385091   \n",
       "2013-08-31 23.87711213 23.18866549 45.56080402  75.81970579 26.58466104   \n",
       "2013-09-30 24.75516170 26.13637721 46.45223934  78.93635707 26.06190225   \n",
       "2013-10-31 23.91314940 32.41710942 44.24404652  84.05847719 26.98861102   \n",
       "2013-11-30 27.37742858 32.47348700 50.82634626  87.84481134 27.91674534   \n",
       "2013-12-31 29.41596107 32.51107205 49.47193667  89.74777084 28.51638903   \n",
       "2014-01-31 26.22540490 31.34593537 44.15869815  90.49895842 27.36469241   \n",
       "2014-02-28 26.56379722 37.09867860 48.71595471  90.37375551 29.73505750   \n",
       "2014-03-31 27.46956274 34.33631194 49.57742745  91.30285476 29.52538722   \n",
       "2014-04-30 29.39000119 35.43937303 50.87349483  93.44593389 27.56211099   \n",
       "2014-05-31 30.02204423 35.28473104 51.08494461 100.73432555 27.28625466   \n",
       "2014-06-30 30.39399301 36.96856003 53.65538096 100.47064033 28.12612539   \n",
       "2014-07-31 32.39745557 33.38332865 46.06203349  96.80432199 27.50576633   \n",
       "2014-08-31 33.74124143 35.24689219 48.07309922  96.06912184 27.85038596   \n",
       "2014-09-30 32.47709229 33.68659998 47.77442609  97.47515588 27.77392642   \n",
       "2014-10-31 32.59983262 34.63853619 47.96561204 107.83824785 27.68790944   \n",
       "2014-11-30 34.26910116 36.52479311 51.58490227 108.85614147 27.09273552   \n",
       "2014-12-31 34.17400779 36.26757625 48.64673308 110.16356556 27.52722344   \n",
       "2015-01-31 32.47270437 32.48553586 48.53977698 108.88146444 23.13406782   \n",
       "2015-02-28 33.65621979 34.14257860 54.46958094 116.93344897 25.85223444   \n",
       "2015-03-31 31.85985442 33.49224377 52.86457172 114.35730077 26.10848438   \n",
       "2015-04-30 28.51271018 35.40499327 58.02888019 106.88398383 27.39940388   \n",
       "2015-05-31 28.31436089 35.11013446 60.83041407 111.23799412 27.98334305   \n",
       "2015-06-30 26.54863674 35.59017458 60.81016202 106.49948745 30.74970193   \n",
       "2015-07-31 27.49680234 33.15157077 59.51729361 101.46847624 30.22162291   \n",
       "2015-08-31 25.37590561 31.28978316 54.09861567 100.97122513 28.15471462   \n",
       "2015-09-30 24.45432240 31.67548157 54.22068852  91.79998287 26.73727036   \n",
       "2015-10-31 23.59980342 35.10819738 48.41248187 102.19870338 27.93141860   \n",
       "2015-11-30 26.51522110 36.12390903 49.50485207  98.71943223 29.14561278   \n",
       "2015-12-31 26.89106382 35.33018970 49.87352701 100.47823768 26.55791819   \n",
       "2016-01-31 24.66489861 34.79781698 49.73852166  97.21678402 22.06350126   \n",
       "2016-02-29 24.31073596 36.36262153 49.80724975  94.81721601 20.79786927   \n",
       "2016-03-31 28.43385069 39.75491100 56.25394497 104.67189060 23.61709263   \n",
       "2016-04-30 24.45922640 40.61027339 54.98625339 113.64404740 26.84603012   \n",
       "2016-05-31 25.40192575 43.56421944 56.73481072 119.86762225 27.39638148   \n",
       "2016-06-30 24.38556528 43.55446481 57.30844811 118.40845940 24.57070188   \n",
       "2016-07-31 26.46694651 46.63693085 62.13235384 128.99223597 27.25949735   \n",
       "2016-08-31 25.31062361 49.76283955 63.02869399 127.48729377 29.99272827   \n",
       "2016-09-30 26.23024573 51.31853981 63.09817772 128.13190088 30.41433249   \n",
       "2016-10-31 25.29807511 47.28741714 60.30309303 103.86942280 31.58109767   \n",
       "2016-11-30 24.21054271 50.62829165 61.76028216 100.38082929 39.09431152   \n",
       "2016-12-31 22.79351388 48.60630094 61.70182472 101.93820644 42.28748851   \n",
       "2017-01-31 26.32055115 48.40017568 64.14226484 116.88321674 41.45234991   \n",
       "2017-02-28 28.25756141 47.41055399 63.93671203 115.64850010 44.19325569   \n",
       "2017-03-31 28.11741981 49.47959312 62.54678351 120.85551089 41.33890287   \n",
       "2017-04-30 27.54281314 50.65204864 64.65857676 118.42078354 39.39991148   \n",
       "2017-05-31 27.08312780 51.54641544 71.43420556 117.98530385 39.51688006   \n",
       "2017-06-30 27.74892476 54.79896064 72.53561401 127.31820357 43.30387330   \n",
       "\n",
       "                   ZTS  \n",
       "date                    \n",
       "2013-07-31 28.74031861  \n",
       "2013-08-31 28.10400159  \n",
       "2013-09-30 30.00331147  \n",
       "2013-10-31 30.58582019  \n",
       "2013-11-30 30.09312378  \n",
       "2013-12-31 31.58087372  \n",
       "2014-01-31 29.39709516  \n",
       "2014-02-28 30.03616245  \n",
       "2014-03-31 28.02213221  \n",
       "2014-04-30 29.37084621  \n",
       "2014-05-31 29.79791734  \n",
       "2014-06-30 31.32178478  \n",
       "2014-07-31 31.94297914  \n",
       "2014-08-31 34.47163259  \n",
       "2014-09-30 35.94037314  \n",
       "2014-10-31 36.07460241  \n",
       "2014-11-30 43.78574560  \n",
       "2014-12-31 41.93413384  \n",
       "2015-01-31 41.72111906  \n",
       "2015-02-28 45.00178745  \n",
       "2015-03-31 45.19706533  \n",
       "2015-04-30 43.44886622  \n",
       "2015-05-31 48.68190166  \n",
       "2015-06-30 47.16578859  \n",
       "2015-07-31 47.90917307  \n",
       "2015-08-31 43.96458895  \n",
       "2015-09-30 40.34904776  \n",
       "2015-10-31 42.14212103  \n",
       "2015-11-30 45.83910949  \n",
       "2015-12-31 47.03661941  \n",
       "2016-01-31 42.34934182  \n",
       "2016-02-29 40.39172997  \n",
       "2016-03-31 43.60850924  \n",
       "2016-04-30 46.36042449  \n",
       "2016-05-31 46.74487199  \n",
       "2016-06-30 46.88004805  \n",
       "2016-07-31 49.85326644  \n",
       "2016-08-31 50.47556796  \n",
       "2016-09-30 51.37444794  \n",
       "2016-10-31 47.21589332  \n",
       "2016-11-30 49.86442156  \n",
       "2016-12-31 52.98218512  \n",
       "2017-01-31 54.48487863  \n",
       "2017-02-28 52.86838150  \n",
       "2017-03-31 52.92788446  \n",
       "2017-04-30 55.75615969  \n",
       "2017-05-31 61.88725050  \n",
       "2017-06-30 62.09166499  \n",
       "\n",
       "[48 rows x 495 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# project_helper.print_dataframe(close)\n",
    "close.head()\n",
    "# close.describe()\n",
    "# close.columns\n",
    "\n",
    "\n",
    "cols = list(close.columns)\n",
    "cols\n",
    "df = pd.DataFrame(columns = cols)\n",
    "for i in cols:\n",
    "    df[i]=close.resample('M').ohlc()[i]['close']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Example\n",
    "Let's see what a single stock looks like from the closing prices. For this example and future display examples in this project, we'll use Apple's stock (AAPL). If we tried to graph all the stocks, it would be too much information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "line": {
          "color": "#2D3ECF"
         },
         "name": "Index",
         "type": "scatter",
         "x": [
          "2013-07-01",
          "2013-07-02",
          "2013-07-03",
          "2013-07-05",
          "2013-07-08",
          "2013-07-09",
          "2013-07-10",
          "2013-07-11",
          "2013-07-12",
          "2013-07-15",
          "2013-07-16",
          "2013-07-17",
          "2013-07-18",
          "2013-07-19",
          "2013-07-22",
          "2013-07-23",
          "2013-07-24",
          "2013-07-25",
          "2013-07-26",
          "2013-07-29",
          "2013-07-30",
          "2013-07-31",
          "2013-08-01",
          "2013-08-02",
          "2013-08-05",
          "2013-08-06",
          "2013-08-07",
          "2013-08-08",
          "2013-08-09",
          "2013-08-12",
          "2013-08-13",
          "2013-08-14",
          "2013-08-15",
          "2013-08-16",
          "2013-08-19",
          "2013-08-20",
          "2013-08-21",
          "2013-08-22",
          "2013-08-23",
          "2013-08-26",
          "2013-08-27",
          "2013-08-28",
          "2013-08-29",
          "2013-08-30",
          "2013-09-03",
          "2013-09-04",
          "2013-09-05",
          "2013-09-06",
          "2013-09-09",
          "2013-09-10",
          "2013-09-11",
          "2013-09-12",
          "2013-09-13",
          "2013-09-16",
          "2013-09-17",
          "2013-09-18",
          "2013-09-19",
          "2013-09-20",
          "2013-09-23",
          "2013-09-24",
          "2013-09-25",
          "2013-09-26",
          "2013-09-27",
          "2013-09-30",
          "2013-10-01",
          "2013-10-02",
          "2013-10-03",
          "2013-10-04",
          "2013-10-07",
          "2013-10-08",
          "2013-10-09",
          "2013-10-10",
          "2013-10-11",
          "2013-10-14",
          "2013-10-15",
          "2013-10-16",
          "2013-10-17",
          "2013-10-18",
          "2013-10-21",
          "2013-10-22",
          "2013-10-23",
          "2013-10-24",
          "2013-10-25",
          "2013-10-28",
          "2013-10-29",
          "2013-10-30",
          "2013-10-31",
          "2013-11-01",
          "2013-11-04",
          "2013-11-05",
          "2013-11-06",
          "2013-11-07",
          "2013-11-08",
          "2013-11-11",
          "2013-11-12",
          "2013-11-13",
          "2013-11-14",
          "2013-11-15",
          "2013-11-18",
          "2013-11-19",
          "2013-11-20",
          "2013-11-21",
          "2013-11-22",
          "2013-11-25",
          "2013-11-26",
          "2013-11-27",
          "2013-11-29",
          "2013-12-02",
          "2013-12-03",
          "2013-12-04",
          "2013-12-05",
          "2013-12-06",
          "2013-12-09",
          "2013-12-10",
          "2013-12-11",
          "2013-12-12",
          "2013-12-13",
          "2013-12-16",
          "2013-12-17",
          "2013-12-18",
          "2013-12-19",
          "2013-12-20",
          "2013-12-23",
          "2013-12-24",
          "2013-12-26",
          "2013-12-27",
          "2013-12-30",
          "2013-12-31",
          "2014-01-02",
          "2014-01-03",
          "2014-01-06",
          "2014-01-07",
          "2014-01-08",
          "2014-01-09",
          "2014-01-10",
          "2014-01-13",
          "2014-01-14",
          "2014-01-15",
          "2014-01-16",
          "2014-01-17",
          "2014-01-21",
          "2014-01-22",
          "2014-01-23",
          "2014-01-24",
          "2014-01-27",
          "2014-01-28",
          "2014-01-29",
          "2014-01-30",
          "2014-01-31",
          "2014-02-03",
          "2014-02-04",
          "2014-02-05",
          "2014-02-06",
          "2014-02-07",
          "2014-02-10",
          "2014-02-11",
          "2014-02-12",
          "2014-02-13",
          "2014-02-14",
          "2014-02-18",
          "2014-02-19",
          "2014-02-20",
          "2014-02-21",
          "2014-02-24",
          "2014-02-25",
          "2014-02-26",
          "2014-02-27",
          "2014-02-28",
          "2014-03-03",
          "2014-03-04",
          "2014-03-05",
          "2014-03-06",
          "2014-03-07",
          "2014-03-10",
          "2014-03-11",
          "2014-03-12",
          "2014-03-13",
          "2014-03-14",
          "2014-03-17",
          "2014-03-18",
          "2014-03-19",
          "2014-03-20",
          "2014-03-21",
          "2014-03-24",
          "2014-03-25",
          "2014-03-26",
          "2014-03-27",
          "2014-03-28",
          "2014-03-31",
          "2014-04-01",
          "2014-04-02",
          "2014-04-03",
          "2014-04-04",
          "2014-04-07",
          "2014-04-08",
          "2014-04-09",
          "2014-04-10",
          "2014-04-11",
          "2014-04-14",
          "2014-04-15",
          "2014-04-16",
          "2014-04-17",
          "2014-04-21",
          "2014-04-22",
          "2014-04-23",
          "2014-04-24",
          "2014-04-25",
          "2014-04-28",
          "2014-04-29",
          "2014-04-30",
          "2014-05-01",
          "2014-05-02",
          "2014-05-05",
          "2014-05-06",
          "2014-05-07",
          "2014-05-08",
          "2014-05-09",
          "2014-05-12",
          "2014-05-13",
          "2014-05-14",
          "2014-05-15",
          "2014-05-16",
          "2014-05-19",
          "2014-05-20",
          "2014-05-21",
          "2014-05-22",
          "2014-05-23",
          "2014-05-27",
          "2014-05-28",
          "2014-05-29",
          "2014-05-30",
          "2014-06-02",
          "2014-06-03",
          "2014-06-04",
          "2014-06-05",
          "2014-06-06",
          "2014-06-09",
          "2014-06-10",
          "2014-06-11",
          "2014-06-12",
          "2014-06-13",
          "2014-06-16",
          "2014-06-17",
          "2014-06-18",
          "2014-06-19",
          "2014-06-20",
          "2014-06-23",
          "2014-06-24",
          "2014-06-25",
          "2014-06-26",
          "2014-06-27",
          "2014-06-30",
          "2014-07-01",
          "2014-07-02",
          "2014-07-03",
          "2014-07-07",
          "2014-07-08",
          "2014-07-09",
          "2014-07-10",
          "2014-07-11",
          "2014-07-14",
          "2014-07-15",
          "2014-07-16",
          "2014-07-17",
          "2014-07-18",
          "2014-07-21",
          "2014-07-22",
          "2014-07-23",
          "2014-07-24",
          "2014-07-25",
          "2014-07-28",
          "2014-07-29",
          "2014-07-30",
          "2014-07-31",
          "2014-08-01",
          "2014-08-04",
          "2014-08-05",
          "2014-08-06",
          "2014-08-07",
          "2014-08-08",
          "2014-08-11",
          "2014-08-12",
          "2014-08-13",
          "2014-08-14",
          "2014-08-15",
          "2014-08-18",
          "2014-08-19",
          "2014-08-20",
          "2014-08-21",
          "2014-08-22",
          "2014-08-25",
          "2014-08-26",
          "2014-08-27",
          "2014-08-28",
          "2014-08-29",
          "2014-09-02",
          "2014-09-03",
          "2014-09-04",
          "2014-09-05",
          "2014-09-08",
          "2014-09-09",
          "2014-09-10",
          "2014-09-11",
          "2014-09-12",
          "2014-09-15",
          "2014-09-16",
          "2014-09-17",
          "2014-09-18",
          "2014-09-19",
          "2014-09-22",
          "2014-09-23",
          "2014-09-24",
          "2014-09-25",
          "2014-09-26",
          "2014-09-29",
          "2014-09-30",
          "2014-10-01",
          "2014-10-02",
          "2014-10-03",
          "2014-10-06",
          "2014-10-07",
          "2014-10-08",
          "2014-10-09",
          "2014-10-10",
          "2014-10-13",
          "2014-10-14",
          "2014-10-15",
          "2014-10-16",
          "2014-10-17",
          "2014-10-20",
          "2014-10-21",
          "2014-10-22",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "apple_ticker = 'AAPL'\n",
    "project_helper.plot_stock(close[apple_ticker], '{} Stock'.format(apple_ticker))\n",
    "\n",
    "# df = close['AAPL']\n",
    "# df1= df.resample('6M').median()\n",
    "# import matplotlib.pyplot as plt\n",
    "# plt.plot(df.index,df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resample Adjusted Prices\n",
    "\n",
    "The trading signal you'll develop in this project does not need to be based on daily prices, for instance, you can use month-end prices to perform trading once a month. To do this, you must first resample the daily adjusted closing prices into monthly buckets, and select the last observation of each month.\n",
    "\n",
    "Implement the `resample_prices` to resample `close_prices` at the sampling frequency of `freq`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def resample_prices(close_prices, freq='M'):\n",
    "    \"\"\"\n",
    "    Resample close prices for each ticker at specified frequency.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    close_prices : DataFrame\n",
    "        Close prices for each ticker and date\n",
    "    freq : str\n",
    "        What frequency to sample at\n",
    "        For valid freq choices, see http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    prices_resampled : DataFrame\n",
    "        Resampled prices for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "  \n",
    "    cols = list(close_prices.columns)\n",
    "    cols\n",
    "    df = pd.DataFrame(columns = cols)\n",
    "    for i in cols:\n",
    "        df[i]=close_prices.resample('M').ohlc()[i]['close']\n",
    "    \n",
    "    return df \n",
    "\n",
    "project_tests.test_resample_prices(resample_prices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Let's apply this function to `close` and view the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_close = resample_prices(close)\n",
    "# project_helper.plot_resampled_prices(\n",
    "#     monthly_close.loc[:, apple_ticker],\n",
    "#     close.loc[:, apple_ticker],\n",
    "#     '{} Stock - Close Vs Monthly Close'.format(apple_ticker))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Log Returns\n",
    "\n",
    "Compute log returns ($R_t$) from prices ($P_t$) as your primary momentum indicator:\n",
    "\n",
    "$$R_t = log_e(P_t) - log_e(P_{t-1})$$\n",
    "\n",
    "Implement the `compute_log_returns` function below, such that it accepts a dataframe (like one returned by `resample_prices`), and produces a similar dataframe of log returns. Use Numpy's [log function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html) to help you calculate the log returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def compute_log_returns(prices):\n",
    "    \"\"\"\n",
    "    Compute log returns for each ticker.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    prices : DataFrame\n",
    "        Prices for each ticker and date\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    log_returns : DataFrame\n",
    "        Log returns for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    df = np.log((prices/(prices.shift())))\n",
    "                \n",
    "    return df\n",
    "\n",
    "project_tests.test_compute_log_returns(compute_log_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Using the same data returned from `resample_prices`, we'll generate the log returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_close_returns = compute_log_returns(monthly_close)\n",
    "# project_helper.plot_returns(\n",
    "#     monthly_close_returns.loc[:, apple_ticker],\n",
    "#     'Log Returns of {} Stock (Monthly)'.format(apple_ticker))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shift Returns\n",
    "Implement the `shift_returns` function to shift the log returns to the previous or future returns in the time series. For example, the parameter `shift_n` is 2 and `returns` is the following:\n",
    "\n",
    "```\n",
    "                           Returns\n",
    "               A         B         C         D\n",
    "2013-07-08     0.015     0.082     0.096     0.020     ...\n",
    "2013-07-09     0.037     0.095     0.027     0.063     ...\n",
    "2013-07-10     0.094     0.001     0.093     0.019     ...\n",
    "2013-07-11     0.092     0.057     0.069     0.087     ...\n",
    "...            ...       ...       ...       ...\n",
    "```\n",
    "\n",
    "the output of the `shift_returns` function would be:\n",
    "```\n",
    "                        Shift Returns\n",
    "               A         B         C         D\n",
    "2013-07-08     NaN       NaN       NaN       NaN       ...\n",
    "2013-07-09     NaN       NaN       NaN       NaN       ...\n",
    "2013-07-10     0.015     0.082     0.096     0.020     ...\n",
    "2013-07-11     0.037     0.095     0.027     0.063     ...\n",
    "...            ...       ...       ...       ...\n",
    "```\n",
    "Using the same `returns` data as above, the `shift_returns` function should generate the following with `shift_n` as -2:\n",
    "```\n",
    "                        Shift Returns\n",
    "               A         B         C         D\n",
    "2013-07-08     0.094     0.001     0.093     0.019     ...\n",
    "2013-07-09     0.092     0.057     0.069     0.087     ...\n",
    "...            ...       ...       ...       ...       ...\n",
    "...            ...       ...       ...       ...       ...\n",
    "...            NaN       NaN       NaN       NaN       ...\n",
    "...            NaN       NaN       NaN       NaN       ...\n",
    "```\n",
    "_Note: The \"...\" represents data points we're not showing._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def shift_returns(returns, shift_n):\n",
    "    \"\"\"\n",
    "    Generate shifted returns\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    returns : DataFrame\n",
    "        Returns for each ticker and date\n",
    "    shift_n : int\n",
    "        Number of periods to move, can be positive or negative\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    shifted_returns : DataFrame\n",
    "        Shifted returns for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    df = returns.shift(shift_n)\n",
    "                \n",
    "    return df\n",
    "\n",
    "    \n",
    "\n",
    "project_tests.test_shift_returns(shift_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Let's get the previous month's and next month's returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'apple_ticker' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-96d8b7d1767b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m project_helper.plot_shifted_returns(\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mprev_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapple_ticker\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mmonthly_close_returns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapple_ticker\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     'Previous Returns of {} Stock'.format(apple_ticker))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'apple_ticker' is not defined"
     ]
    }
   ],
   "source": [
    "prev_returns = shift_returns(monthly_close_returns, 1)\n",
    "lookahead_returns = shift_returns(monthly_close_returns, -1)\n",
    "\n",
    "project_helper.plot_shifted_returns(\n",
    "    prev_returns.loc[:, apple_ticker],\n",
    "    monthly_close_returns.loc[:, apple_ticker],\n",
    "    'Previous Returns of {} Stock'.format(apple_ticker))\n",
    "project_helper.plot_shifted_returns(\n",
    "    lookahead_returns.loc[:, apple_ticker],\n",
    "    monthly_close_returns.loc[:, apple_ticker],\n",
    "    'Lookahead Returns of {} Stock'.format(apple_ticker))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Trading Signal\n",
    "\n",
    "A trading signal is a sequence of trading actions, or results that can be used to take trading actions. A common form is to produce a \"long\" and \"short\" portfolio of stocks on each date (e.g. end of each month, or whatever frequency you desire to trade at). This signal can be interpreted as rebalancing your portfolio on each of those dates, entering long (\"buy\") and short (\"sell\") positions as indicated.\n",
    "\n",
    "Here's a strategy that we will try:\n",
    "> For each month-end observation period, rank the stocks by _previous_ returns, from the highest to the lowest. Select the top performing stocks for the long portfolio, and the bottom performing stocks for the short portfolio.\n",
    "\n",
    "Implement the `get_top_n` function to get the top performing stock for each month. Get the top performing stocks from `prev_returns` by assigning them a value of 1. For all other stocks, give them a value of 0. For example, using the following `prev_returns`:\n",
    "\n",
    "```\n",
    "                                     Previous Returns\n",
    "               A         B         C         D         E         F         G\n",
    "2013-07-08     0.015     0.082     0.096     0.020     0.075     0.043     0.074\n",
    "2013-07-09     0.037     0.095     0.027     0.063     0.024     0.086     0.025\n",
    "...            ...       ...       ...       ...       ...       ...       ...\n",
    "```\n",
    "\n",
    "The function `get_top_n` with `top_n` set to 3 should return the following:\n",
    "```\n",
    "                                     Previous Returns\n",
    "               A         B         C         D         E         F         G\n",
    "2013-07-08     0         1         1         0         1         0         0\n",
    "2013-07-09     0         1         0         1         0         1         0\n",
    "...            ...       ...       ...       ...       ...       ...       ...\n",
    "```\n",
    "*Note: You may have to use Panda's [`DataFrame.iterrows`](https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFrame.iterrows.html) with [`Series.nlargest`](https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.Series.nlargest.html) in order to implement the function. This is one of those cases where creating a vecorization solution is too difficult.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def get_top_n(prev_returns, top_n):\n",
    "    \"\"\"\n",
    "    Select the top performing stocks\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    prev_returns : DataFrame\n",
    "        Previous shifted returns for each ticker and date\n",
    "    top_n : int\n",
    "        The number of top performing stocks to get\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    top_stocks : DataFrame\n",
    "        Top stocks for each ticker and date marked with a 1\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    \n",
    "    top_stocks = pd.DataFrame(index = prev_returns.index, columns= prev_returns.columns)\n",
    "    \n",
    "    for index,row  in prev_returns.iterrows():\n",
    "        \n",
    "        x = [0]*len(row)\n",
    "        top_nn = row.nlargest(top_n)\n",
    "        \n",
    "        for i in range(len(top_nn)):\n",
    "        \n",
    "            a = (row == top_nn[i])*1\n",
    "            x=x+a\n",
    "            x = [int(i) for i in x]\n",
    "            \n",
    "        top_stocks.loc[index] = x\n",
    "            \n",
    "            \n",
    "        \n",
    "        \n",
    "        \n",
    "    \n",
    "    return top_stocks\n",
    "\n",
    "project_tests.test_get_top_n(get_top_n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "We want to get the best performing and worst performing stocks. To get the best performing stocks, we'll use the `get_top_n` function. To get the worst performing stocks, we'll also use the `get_top_n` function. However, we pass in `-1*prev_returns` instead of just `prev_returns`. Multiplying by negative one will flip all the positive returns to negative and negative returns to positive. Thus, it will return the worst performing stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 Most Longed Stocks:\n",
      "INCY, AMD, AVGO, NFX, SWKS, NFLX, ILMN, UAL, NVDA, MU\n",
      "10 Most Shorted Stocks:\n",
      "RRC, FCX, CHK, MRO, FTI, WYNN, DVN, GPS, MAT, TRIP\n"
     ]
    }
   ],
   "source": [
    "top_bottom_n = 50\n",
    "df_long = get_top_n(prev_returns, top_bottom_n)\n",
    "df_short = get_top_n(-1*prev_returns, top_bottom_n)\n",
    "project_helper.print_top(df_long, 'Longed Stocks')\n",
    "project_helper.print_top(df_short, 'Shorted Stocks')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Projected Returns\n",
    "It's now time to check if your trading signal has the potential to become profitable!\n",
    "\n",
    "We'll start by computing the net returns this portfolio would return. For simplicity, we'll assume every stock gets an equal dollar amount of investment. This makes it easier to compute a portfolio's returns as the simple arithmetic average of the individual stock returns.\n",
    "\n",
    "Implement the `portfolio_returns` function to compute the expected portfolio returns. Using `df_long` to indicate which stocks to long and `df_short` to indicate which stocks to short, calculate the returns using `lookahead_returns`. To help with calculation, we've provided you with `n_stocks` as the number of stocks we're investing in a single period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def portfolio_returns(df_long, df_short, lookahead_returns, n_stocks):\n",
    "    \"\"\"\n",
    "    Compute expected returns for the portfolio, assuming equal investment in each long/short stock.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    df_long : DataFrame\n",
    "        Top stocks for each ticker and date marked with a 1\n",
    "    df_short : DataFrame\n",
    "        Bottom stocks for each ticker and date marked with a 1\n",
    "    lookahead_returns : DataFrame\n",
    "        Lookahead returns for each ticker and date\n",
    "    n_stocks: int\n",
    "        The number number of stocks chosen for each month\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    portfolio_returns : DataFrame\n",
    "        Expected portfolio returns for each ticker and date\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    a  = [ list(i) for i in list(np.zeros([len(df_long.index),len(df_long.columns)]))]\n",
    "    #portfolio_returns = pd.DataFrame(a,dates, tickers)\n",
    "\n",
    "    portfolio_returns = 1/(n_stocks)*df_long*lookahead_returns - 1/(n_stocks)*df_short*lookahead_returns\n",
    "    \n",
    "    return portfolio_returns\n",
    "\n",
    "project_tests.test_portfolio_returns(portfolio_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Time to see how the portfolio did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "<div id=\"9f33a0bd-d102-4d22-9655-32c3779b54fa\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"9f33a0bd-d102-4d22-9655-32c3779b54fa\", [{\"type\": \"scatter\", \"name\": \"Returns\", \"x\": [\"2013-07-31\", \"2013-08-31\", \"2013-09-30\", \"2013-10-31\", \"2013-11-30\", \"2013-12-31\", \"2014-01-31\", \"2014-02-28\", \"2014-03-31\", \"2014-04-30\", \"2014-05-31\", \"2014-06-30\", \"2014-07-31\", \"2014-08-31\", \"2014-09-30\", \"2014-10-31\", \"2014-11-30\", \"2014-12-31\", \"2015-01-31\", \"2015-02-28\", \"2015-03-31\", \"2015-04-30\", \"2015-05-31\", \"2015-06-30\", \"2015-07-31\", \"2015-08-31\", \"2015-09-30\", \"2015-10-31\", \"2015-11-30\", \"2015-12-31\", \"2016-01-31\", \"2016-02-29\", \"2016-03-31\", \"2016-04-30\", \"2016-05-31\", \"2016-06-30\", \"2016-07-31\", \"2016-08-31\", \"2016-09-30\", \"2016-10-31\", \"2016-11-30\", \"2016-12-31\", \"2017-01-31\", \"2017-02-28\", \"2017-03-31\", \"2017-04-30\", \"2017-05-31\", \"2017-06-30\"], \"y\": [0.0, 0.0, -0.0023645619500001405, -0.0022274399233187065, -0.0009682848595743458, 0.002644290703890718, -0.004869723041469767, -0.007614062918999878, -0.008930449416135371, -0.007255007281348488, -0.009384574025405889, -0.0024722818682461345, 0.010265657898703862, 0.01706371812849735, 0.009955862815269861, 0.03362185053666665, 0.012017855737879505, 0.011523131190895063, -0.017597091851168267, 0.022544552969585085, 0.007921122775377175, 0.02539667126261483, -0.012030123217369577, 0.03572642550283469, -0.003048934418441999, 0.039522773746594284, -0.011829474798826163, 0.012645697917860752, -0.0022217155584036124, -0.01901050600070181, 0.009874804062808343, -0.010678938060237293, -0.02589223522724368, -0.0024255819500312867, -0.0005645897832662056, -0.002552477774413712, -0.032999620666274106, 0.0013687279596141051, 0.009895776024915958, 0.007437763223292402, 0.013181132158052392, -0.010546089504143378, 0.013530612712879185, 0.022901610967604593, 0.010740369231499505, 0.01645727690692477, 0.0014763342897174633, 0.0], \"mode\": \"line\", \"line\": {\"color\": \"#2D3ECF\"}}], {\"title\": \"Portfolio Returns\"}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"displayModeBar\": false})});</script>"
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   "source": [
    "expected_portfolio_returns = portfolio_returns(df_long, df_short, lookahead_returns, 2*top_bottom_n)\n",
    "project_helper.plot_returns(expected_portfolio_returns.T.sum(), 'Portfolio Returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Tests\n",
    "### Annualized Rate of Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean:                       0.003130\n",
      "Standard Error:             0.002154\n",
      "Annualized Rate of Return:  3.83%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "expected_portfolio_returns_by_date = expected_portfolio_returns.T.sum().dropna()\n",
    "portfolio_ret_mean = expected_portfolio_returns_by_date.mean()\n",
    "portfolio_ret_ste = expected_portfolio_returns_by_date.sem()\n",
    "portfolio_ret_annual_rate = (np.exp(portfolio_ret_mean * 12) - 1) * 100\n",
    "\n",
    "print(\"\"\"\n",
    "Mean:                       {:.6f}\n",
    "Standard Error:             {:.6f}\n",
    "Annualized Rate of Return:  {:.2f}%\n",
    "\"\"\".format(portfolio_ret_mean, portfolio_ret_ste, portfolio_ret_annual_rate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The annualized rate of return allows you to compare the rate of return from this strategy to other quoted rates of return, which are usually quoted on an annual basis. \n",
    "\n",
    "### T-Test\n",
    "Our null hypothesis ($H_0$) is that the actual mean return from the signal is zero. We'll perform a one-sample, one-sided t-test on the observed mean return, to see if we can reject $H_0$.\n",
    "\n",
    "We'll need to first compute the t-statistic, and then find its corresponding p-value. The p-value will indicate the probability of observing a t-statistic equally or more extreme than the one we observed if the null hypothesis were true. A small p-value means that the chance of observing the t-statistic we observed under the null hypothesis is small, and thus casts doubt on the null hypothesis. It's good practice to set a desired level of significance or alpha ($\\alpha$) _before_ computing the p-value, and then reject the null hypothesis if $p < \\alpha$.\n",
    "\n",
    "For this project, we'll use $\\alpha = 0.05$, since it's a common value to use.\n",
    "\n",
    "Implement the `analyze_alpha` function to perform a t-test on the sample of portfolio returns. We've imported the `scipy.stats` module for you to perform the t-test.\n",
    "\n",
    "Note: [`scipy.stats.ttest_1samp`](https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_1samp.html) performs a two-sided test, so divide the p-value by 2 to get 1-sided p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "expected_portfolio_returns_by_date\n",
    "def analyze_alpha(expected_portfolio_returns_by_date):\n",
    "    \"\"\"\n",
    "    Perform a t-test with the null hypothesis being that the expected mean return is zero.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    expected_portfolio_returns_by_date : Pandas Series\n",
    "        Expected portfolio returns for each date\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    t_value\n",
    "        T-statistic from t-test\n",
    "    p_value\n",
    "        Corresponding p-value\n",
    "    \"\"\"\n",
    "    # TODO: Implement Function\n",
    "    ttest,pvalue = stats.ttest_1samp(expected_portfolio_returns_by_date,0.0)\n",
    "    \n",
    "    return  ttest,pvalue/2\n",
    "\n",
    "project_tests.test_analyze_alpha(analyze_alpha)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View Data\n",
    "Let's see what values we get with our portfolio. After you run this, make sure to answer the question below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Alpha analysis:\n",
      " t-value:        0.727\n",
      " p-value:        0.152799\n",
      "\n"
     ]
    }
   ],
   "source": [
    "t_value, p_value = analyze_alpha(expected_portfolio_returns_by_date)\n",
    "print(\"\"\"\n",
    "Alpha analysis:\n",
    " t-value:        {:.3f}\n",
    " p-value:        {:.6f}\n",
    "\"\"\".format(t_value, p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: What p-value did you observe? And what does that indicate about your signal?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*#TODO: Put Answer In this Cell* p-value returns 0.152799 and  sincep>alpha, the null hypothesis is rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submission\n",
    "Now that you're done with the project, it's time to submit it. Click the submit button in the bottom right. One of our reviewers will give you feedback on your project with a pass or not passed grade. You can continue to the next section while you wait for feedback."
   ]
  }
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