ml-finance-python

python scripts for finance machine learning

git clone https://9o.is/git/ml-finance-python.git

notebook.ipynb

(372914B)


      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {
      6     "deletable": true,
      7     "editable": true
      8    },
      9    "source": [
     10     "# Exercises: Introduction to pandas - Answer Key\n",
     11     "By Christopher van Hoecke, Maxwell Margenot\n",
     12     "\n",
     13     "## Lecture Link : \n",
     14     "https://www.quantopian.com/lectures/introduction-to-pandas\n",
     15     "\n",
     16     "### IMPORTANT NOTE: \n",
     17     "This lecture corresponds to the Introduction to Pandas lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.\n",
     18     "\n",
     19     "Part of the Quantopian Lecture Series:\n",
     20     "\n",
     21     "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
     22     "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
     23     "\n",
     24     "\n",
     25     "----"
     26    ]
     27   },
     28   {
     29    "cell_type": "code",
     30    "execution_count": 1,
     31    "metadata": {
     32     "collapsed": true,
     33     "deletable": true,
     34     "editable": true
     35    },
     36    "outputs": [],
     37    "source": [
     38     "# Useful Functions\n",
     39     "import numpy as np\n",
     40     "import pandas as pd\n",
     41     "import matplotlib.pyplot as plt"
     42    ]
     43   },
     44   {
     45    "cell_type": "markdown",
     46    "metadata": {
     47     "deletable": true,
     48     "editable": true
     49    },
     50    "source": [
     51     "----"
     52    ]
     53   },
     54   {
     55    "cell_type": "markdown",
     56    "metadata": {
     57     "deletable": true,
     58     "editable": true
     59    },
     60    "source": [
     61     "# Exercise 1 \n",
     62     "## a. Series \n",
     63     "Given an array of data, please create a pandas Series `s` with a datetime index starting `2016-01-01`. The index should be daily frequency and should be the same length as the data."
     64    ]
     65   },
     66   {
     67    "cell_type": "code",
     68    "execution_count": 2,
     69    "metadata": {
     70     "collapsed": false,
     71     "deletable": true,
     72     "editable": true
     73    },
     74    "outputs": [
     75     {
     76      "name": "stdout",
     77      "output_type": "stream",
     78      "text": [
     79       "2016-01-01    80\n",
     80       "2016-01-02    96\n",
     81       "2016-01-03    32\n",
     82       "2016-01-04    88\n",
     83       "2016-01-05     9\n",
     84       "2016-01-06     8\n",
     85       "2016-01-07    84\n",
     86       "2016-01-08    46\n",
     87       "2016-01-09    94\n",
     88       "2016-01-10    43\n",
     89       "2016-01-11     7\n",
     90       "2016-01-12    82\n",
     91       "2016-01-13    95\n",
     92       "2016-01-14    33\n",
     93       "2016-01-15    73\n",
     94       "2016-01-16    43\n",
     95       "2016-01-17    39\n",
     96       "2016-01-18    52\n",
     97       "2016-01-19    72\n",
     98       "2016-01-20    27\n",
     99       "2016-01-21    48\n",
    100       "2016-01-22    32\n",
    101       "2016-01-23    86\n",
    102       "2016-01-24    13\n",
    103       "2016-01-25    30\n",
    104       "2016-01-26    60\n",
    105       "2016-01-27    65\n",
    106       "2016-01-28    58\n",
    107       "2016-01-29    76\n",
    108       "2016-01-30    14\n",
    109       "              ..\n",
    110       "2018-08-28    60\n",
    111       "2018-08-29    77\n",
    112       "2018-08-30    91\n",
    113       "2018-08-31    75\n",
    114       "2018-09-01    68\n",
    115       "2018-09-02    95\n",
    116       "2018-09-03    30\n",
    117       "2018-09-04    59\n",
    118       "2018-09-05    31\n",
    119       "2018-09-06    76\n",
    120       "2018-09-07     4\n",
    121       "2018-09-08    71\n",
    122       "2018-09-09    73\n",
    123       "2018-09-10    89\n",
    124       "2018-09-11    11\n",
    125       "2018-09-12    36\n",
    126       "2018-09-13     7\n",
    127       "2018-09-14    59\n",
    128       "2018-09-15    77\n",
    129       "2018-09-16    64\n",
    130       "2018-09-17    82\n",
    131       "2018-09-18    19\n",
    132       "2018-09-19    81\n",
    133       "2018-09-20    91\n",
    134       "2018-09-21     9\n",
    135       "2018-09-22    16\n",
    136       "2018-09-23    19\n",
    137       "2018-09-24    65\n",
    138       "2018-09-25    83\n",
    139       "2018-09-26    69\n",
    140       "Freq: D, dtype: int64\n"
    141      ]
    142     }
    143    ],
    144    "source": [
    145     "l = np.random.randint(1,100, size=1000)\n",
    146     "s = pd.Series(l)\n",
    147     "\n",
    148     "new_index = pd.date_range(\"2016-01-01\", periods=len(s), freq=\"D\")\n",
    149     "s.index = new_index\n",
    150     "print s"
    151    ]
    152   },
    153   {
    154    "cell_type": "markdown",
    155    "metadata": {
    156     "deletable": true,
    157     "editable": true
    158    },
    159    "source": [
    160     "## b. Accessing Series Elements.\n",
    161     "- Print every other element of the first 50 elements of series `s`.\n",
    162     "- Find the value associated with the index `2017-02-20`."
    163    ]
    164   },
    165   {
    166    "cell_type": "code",
    167    "execution_count": 3,
    168    "metadata": {
    169     "collapsed": false,
    170     "deletable": true,
    171     "editable": true
    172    },
    173    "outputs": [
    174     {
    175      "data": {
    176       "text/plain": [
    177        "81"
    178       ]
    179      },
    180      "execution_count": 3,
    181      "metadata": {},
    182      "output_type": "execute_result"
    183     }
    184    ],
    185    "source": [
    186     "# Print every other element of the first 50 elements\n",
    187     "s.iloc[:50:2];\n",
    188     "# Values associated with the index 2017-02-20\n",
    189     "s.loc['2017-02-20']"
    190    ]
    191   },
    192   {
    193    "cell_type": "markdown",
    194    "metadata": {
    195     "deletable": true,
    196     "editable": true
    197    },
    198    "source": [
    199     "## c. Boolean Indexing.\n",
    200     "In the series `s`, print all the values between 1 and 3."
    201    ]
    202   },
    203   {
    204    "cell_type": "code",
    205    "execution_count": 4,
    206    "metadata": {
    207     "collapsed": false,
    208     "deletable": true,
    209     "editable": true,
    210     "scrolled": true
    211    },
    212    "outputs": [
    213     {
    214      "data": {
    215       "text/plain": [
    216        "2016-06-13    2\n",
    217        "2016-08-15    2\n",
    218        "2017-02-05    2\n",
    219        "2017-03-20    2\n",
    220        "2017-03-22    2\n",
    221        "2017-04-11    2\n",
    222        "2017-07-14    2\n",
    223        "2017-07-30    2\n",
    224        "2017-09-22    2\n",
    225        "2017-12-22    2\n",
    226        "2018-03-14    2\n",
    227        "2018-05-30    2\n",
    228        "2018-07-02    2\n",
    229        "2018-07-12    2\n",
    230        "dtype: int64"
    231       ]
    232      },
    233      "execution_count": 4,
    234      "metadata": {},
    235      "output_type": "execute_result"
    236     }
    237    ],
    238    "source": [
    239     "# Print s between 1 and 3\n",
    240     "s.loc[(s>1) & (s<3)]"
    241    ]
    242   },
    243   {
    244    "cell_type": "markdown",
    245    "metadata": {
    246     "deletable": true,
    247     "editable": true
    248    },
    249    "source": [
    250     "----"
    251    ]
    252   },
    253   {
    254    "cell_type": "markdown",
    255    "metadata": {
    256     "deletable": true,
    257     "editable": true
    258    },
    259    "source": [
    260     "#Exercise 2 : Indexing and time series. \n",
    261     "###a. Display\n",
    262     "Print the first and last 5 elements of the series `s`."
    263    ]
    264   },
    265   {
    266    "cell_type": "code",
    267    "execution_count": 5,
    268    "metadata": {
    269     "collapsed": false,
    270     "deletable": true,
    271     "editable": true
    272    },
    273    "outputs": [
    274     {
    275      "data": {
    276       "text/plain": [
    277        "2018-09-22    16\n",
    278        "2018-09-23    19\n",
    279        "2018-09-24    65\n",
    280        "2018-09-25    83\n",
    281        "2018-09-26    69\n",
    282        "Freq: D, dtype: int64"
    283       ]
    284      },
    285      "execution_count": 5,
    286      "metadata": {},
    287      "output_type": "execute_result"
    288     }
    289    ],
    290    "source": [
    291     "# First 5 elements\n",
    292     "s.head(5)\n",
    293     "# Last 5 elements\n",
    294     "s.tail(5)"
    295    ]
    296   },
    297   {
    298    "cell_type": "markdown",
    299    "metadata": {
    300     "deletable": true,
    301     "editable": true
    302    },
    303    "source": [
    304     "### b. Resampling\n",
    305     "- Using the resample method, upsample the daily data to monthly frequency. Use the median method so that each monthly value is the median price of all the days in that month.\n",
    306     "- Take the daily data and fill in every day, including weekends and holidays, using forward-fills. "
    307    ]
    308   },
    309   {
    310    "cell_type": "code",
    311    "execution_count": 6,
    312    "metadata": {
    313     "collapsed": false,
    314     "deletable": true,
    315     "editable": true
    316    },
    317    "outputs": [
    318     {
    319      "data": {
    320       "text/plain": [
    321        "2012-01-31 00:00:00+00:00    355.380\n",
    322        "2012-02-29 00:00:00+00:00    378.295\n",
    323        "2012-03-31 00:00:00+00:00    408.850\n",
    324        "2012-04-30 00:00:00+00:00    420.900\n",
    325        "2012-05-31 00:00:00+00:00    405.390\n",
    326        "2012-06-30 00:00:00+00:00    402.790\n",
    327        "2012-07-31 00:00:00+00:00    380.370\n",
    328        "2012-08-31 00:00:00+00:00    295.380\n",
    329        "2012-09-30 00:00:00+00:00    332.990\n",
    330        "2012-10-31 00:00:00+00:00    286.440\n",
    331        "2012-11-30 00:00:00+00:00    263.559\n",
    332        "2012-12-31 00:00:00+00:00    282.040\n",
    333        "2013-01-31 00:00:00+00:00    299.570\n",
    334        "2013-02-28 00:00:00+00:00    315.520\n",
    335        "2013-03-31 00:00:00+00:00    321.510\n",
    336        "2013-04-30 00:00:00+00:00    340.875\n",
    337        "2013-05-31 00:00:00+00:00    369.900\n",
    338        "2013-06-30 00:00:00+00:00    364.080\n",
    339        "2013-07-31 00:00:00+00:00    386.145\n",
    340        "2013-08-31 00:00:00+00:00    405.190\n",
    341        "2013-09-30 00:00:00+00:00    418.919\n",
    342        "2013-10-31 00:00:00+00:00    442.150\n",
    343        "2013-11-30 00:00:00+00:00    535.985\n",
    344        "2013-12-31 00:00:00+00:00    522.864\n",
    345        "Freq: M, Name: Equity(28016 [CMG]), dtype: float64"
    346       ]
    347      },
    348      "execution_count": 6,
    349      "metadata": {},
    350      "output_type": "execute_result"
    351     }
    352    ],
    353    "source": [
    354     "symbol = \"CMG\"\n",
    355     "start = \"2012-01-01\"\n",
    356     "end = \"2016-01-01\"\n",
    357     "prices = get_pricing(symbol, start_date=start, end_date=end, fields=\"price\")\n",
    358     "\n",
    359     "# Resample daily prices to get monthly prices using median. \n",
    360     "monthly_prices = prices.resample('M').median()\n",
    361     "monthly_prices.head(24)"
    362    ]
    363   },
    364   {
    365    "cell_type": "code",
    366    "execution_count": 7,
    367    "metadata": {
    368     "collapsed": false,
    369     "deletable": true,
    370     "editable": true,
    371     "scrolled": true
    372    },
    373    "outputs": [
    374     {
    375      "data": {
    376       "text/plain": [
    377        "2012-01-01 00:00:00+00:00        NaN\n",
    378        "2012-01-02 00:00:00+00:00        NaN\n",
    379        "2012-01-03 00:00:00+00:00    340.980\n",
    380        "2012-01-04 00:00:00+00:00    348.740\n",
    381        "2012-01-05 00:00:00+00:00    349.990\n",
    382        "2012-01-06 00:00:00+00:00    348.950\n",
    383        "2012-01-07 00:00:00+00:00    348.950\n",
    384        "2012-01-08 00:00:00+00:00    348.950\n",
    385        "2012-01-09 00:00:00+00:00    339.522\n",
    386        "2012-01-10 00:00:00+00:00    340.700\n",
    387        "2012-01-11 00:00:00+00:00    347.330\n",
    388        "2012-01-12 00:00:00+00:00    347.830\n",
    389        "2012-01-13 00:00:00+00:00    354.390\n",
    390        "2012-01-14 00:00:00+00:00    354.390\n",
    391        "2012-01-15 00:00:00+00:00    354.390\n",
    392        "Freq: D, Name: Equity(28016 [CMG]), dtype: float64"
    393       ]
    394      },
    395      "execution_count": 7,
    396      "metadata": {},
    397      "output_type": "execute_result"
    398     }
    399    ],
    400    "source": [
    401     "# Data for every day, (including weekends and holidays)\n",
    402     "calendar_dates = pd.date_range(start=start, end=end, freq='D', tz='UTC')\n",
    403     "calendar_prices = prices.reindex(calendar_dates, method='ffill')\n",
    404     "calendar_prices.head(15)"
    405    ]
    406   },
    407   {
    408    "cell_type": "markdown",
    409    "metadata": {
    410     "deletable": true,
    411     "editable": true
    412    },
    413    "source": [
    414     "---"
    415    ]
    416   },
    417   {
    418    "cell_type": "markdown",
    419    "metadata": {
    420     "deletable": true,
    421     "editable": true
    422    },
    423    "source": [
    424     "# Exercise 3 : Missing Data\n",
    425     "- Replace all instances of `NaN` using the forward fill method. \n",
    426     "- Instead of filling, remove all instances of `NaN` from the data."
    427    ]
    428   },
    429   {
    430    "cell_type": "code",
    431    "execution_count": 8,
    432    "metadata": {
    433     "collapsed": false,
    434     "deletable": true,
    435     "editable": true
    436    },
    437    "outputs": [
    438     {
    439      "data": {
    440       "text/plain": [
    441        "2012-01-01 00:00:00+00:00    340.980\n",
    442        "2012-01-02 00:00:00+00:00    340.980\n",
    443        "2012-01-03 00:00:00+00:00    340.980\n",
    444        "2012-01-04 00:00:00+00:00    348.740\n",
    445        "2012-01-05 00:00:00+00:00    349.990\n",
    446        "2012-01-06 00:00:00+00:00    348.950\n",
    447        "2012-01-07 00:00:00+00:00    348.950\n",
    448        "2012-01-08 00:00:00+00:00    348.950\n",
    449        "2012-01-09 00:00:00+00:00    339.522\n",
    450        "2012-01-10 00:00:00+00:00    340.700\n",
    451        "Freq: D, Name: Equity(28016 [CMG]), dtype: float64"
    452       ]
    453      },
    454      "execution_count": 8,
    455      "metadata": {},
    456      "output_type": "execute_result"
    457     }
    458    ],
    459    "source": [
    460     "# Fill missing data using Backwards fill method\n",
    461     "bfilled_prices = calendar_prices.fillna(method='bfill')\n",
    462     "bfilled_prices.head(10)"
    463    ]
    464   },
    465   {
    466    "cell_type": "code",
    467    "execution_count": 9,
    468    "metadata": {
    469     "collapsed": false,
    470     "deletable": true,
    471     "editable": true,
    472     "scrolled": true
    473    },
    474    "outputs": [
    475     {
    476      "data": {
    477       "text/plain": [
    478        "2012-01-03 00:00:00+00:00    340.980\n",
    479        "2012-01-04 00:00:00+00:00    348.740\n",
    480        "2012-01-05 00:00:00+00:00    349.990\n",
    481        "2012-01-06 00:00:00+00:00    348.950\n",
    482        "2012-01-07 00:00:00+00:00    348.950\n",
    483        "2012-01-08 00:00:00+00:00    348.950\n",
    484        "2012-01-09 00:00:00+00:00    339.522\n",
    485        "2012-01-10 00:00:00+00:00    340.700\n",
    486        "2012-01-11 00:00:00+00:00    347.330\n",
    487        "2012-01-12 00:00:00+00:00    347.830\n",
    488        "Freq: D, Name: Equity(28016 [CMG]), dtype: float64"
    489       ]
    490      },
    491      "execution_count": 9,
    492      "metadata": {},
    493      "output_type": "execute_result"
    494     }
    495    ],
    496    "source": [
    497     "# Drop instances of nan in the data\n",
    498     "dropped_prices = calendar_prices.dropna()\n",
    499     "dropped_prices.head(10)"
    500    ]
    501   },
    502   {
    503    "cell_type": "markdown",
    504    "metadata": {
    505     "deletable": true,
    506     "editable": true
    507    },
    508    "source": [
    509     "----"
    510    ]
    511   },
    512   {
    513    "cell_type": "markdown",
    514    "metadata": {
    515     "deletable": true,
    516     "editable": true
    517    },
    518    "source": [
    519     "#Exercise 4 : Time Series Analysis with pandas\n",
    520     "## a. General Information\n",
    521     "Print the count, mean, standard deviation, minimum, 25th, 50th, and 75th percentiles, and the max of our series s. "
    522    ]
    523   },
    524   {
    525    "cell_type": "code",
    526    "execution_count": 10,
    527    "metadata": {
    528     "collapsed": false,
    529     "deletable": true,
    530     "editable": true
    531    },
    532    "outputs": [
    533     {
    534      "name": "stdout",
    535      "output_type": "stream",
    536      "text": [
    537       "Summary Statistics\n",
    538       "count    1006.000000\n",
    539       "mean      501.637439\n",
    540       "std       146.697204\n",
    541       "min       236.240000\n",
    542       "25%       371.605000\n",
    543       "50%       521.280000\n",
    544       "75%       646.753750\n",
    545       "max       757.770000\n",
    546       "Name: Equity(28016 [CMG]), dtype: float64\n"
    547      ]
    548     }
    549    ],
    550    "source": [
    551     "print \"Summary Statistics\"\n",
    552     "print prices.describe()"
    553    ]
    554   },
    555   {
    556    "cell_type": "markdown",
    557    "metadata": {
    558     "deletable": true,
    559     "editable": true
    560    },
    561    "source": [
    562     "## b. Series Operations\n",
    563     "- Get the additive and multiplicative returns of this series. \n",
    564     "- Calculate the rolling mean with a 60 day window.\n",
    565     "- Calculate the standard deviation with a 60 day window."
    566    ]
    567   },
    568   {
    569    "cell_type": "code",
    570    "execution_count": 11,
    571    "metadata": {
    572     "collapsed": false,
    573     "deletable": true,
    574     "editable": true
    575    },
    576    "outputs": [],
    577    "source": [
    578     "data = get_pricing('GE', fields='open_price', start_date='2016-01-01', end_date='2017-01-01')\n",
    579     "\n",
    580     "mult_returns = data.pct_change()[1:] #Multiplicative returns \n",
    581     "add_returns = data.diff()[1:] #Additive returns "
    582    ]
    583   },
    584   {
    585    "cell_type": "code",
    586    "execution_count": 12,
    587    "metadata": {
    588     "collapsed": false,
    589     "deletable": true,
    590     "editable": true
    591    },
    592    "outputs": [],
    593    "source": [
    594     "# Rolling mean\n",
    595     "rolling_mean = data.rolling(window=60).mean()\n",
    596     "rolling_mean.name = \"60-day rolling mean\""
    597    ]
    598   },
    599   {
    600    "cell_type": "code",
    601    "execution_count": 13,
    602    "metadata": {
    603     "collapsed": true,
    604     "deletable": true,
    605     "editable": true
    606    },
    607    "outputs": [],
    608    "source": [
    609     "# Rolling Standard Deviation\n",
    610     "rolling_std = data.rolling(window=60).std()\n",
    611     "rolling_std.name = \"60-day rolling volatility\""
    612    ]
    613   },
    614   {
    615    "cell_type": "markdown",
    616    "metadata": {
    617     "deletable": true,
    618     "editable": true
    619    },
    620    "source": [
    621     "----"
    622    ]
    623   },
    624   {
    625    "cell_type": "markdown",
    626    "metadata": {
    627     "deletable": true,
    628     "editable": true
    629    },
    630    "source": [
    631     "# Exercise 5 : DataFrames\n",
    632     "## a. Indexing\n",
    633     "Form a DataFrame out of `dict_data` with `l` as its index."
    634    ]
    635   },
    636   {
    637    "cell_type": "code",
    638    "execution_count": 14,
    639    "metadata": {
    640     "collapsed": false,
    641     "deletable": true,
    642     "editable": true
    643    },
    644    "outputs": [
    645     {
    646      "name": "stdout",
    647      "output_type": "stream",
    648      "text": [
    649       "        a  b         c\n",
    650       "First   1  L -0.382419\n",
    651       "Second  2  K -0.007889\n",
    652       "Third   3  J  1.121356\n",
    653       "Fourth  4  M -1.099923\n",
    654       "Fifth   5  Z  0.052579\n"
    655      ]
    656     }
    657    ],
    658    "source": [
    659     "l = ['First','Second', 'Third', 'Fourth', 'Fifth']\n",
    660     "dict_data = {'a' : [1, 2, 3, 4, 5], \n",
    661     "             'b' : ['L', 'K', 'J', 'M', 'Z'],\n",
    662     "             'c' : np.random.normal(0, 1, 5)\n",
    663     "            }\n",
    664     "\n",
    665     "# Adding l as an index to dict_data\n",
    666     "frame_data = pd.DataFrame(dict_data, index=l)\n",
    667     "print frame_data"
    668    ]
    669   },
    670   {
    671    "cell_type": "markdown",
    672    "metadata": {
    673     "deletable": true,
    674     "editable": true
    675    },
    676    "source": [
    677     "## b. DataFrames Manipulation\n",
    678     "- Concatenate the following two series to form a dataframe. \n",
    679     "- Rename the columns to `Good Numbers` and `Bad Numbers`. \n",
    680     "- Change the index to be a datetime index starting on `2016-01-01`."
    681    ]
    682   },
    683   {
    684    "cell_type": "code",
    685    "execution_count": 15,
    686    "metadata": {
    687     "collapsed": false,
    688     "deletable": true,
    689     "editable": true
    690    },
    691    "outputs": [
    692     {
    693      "name": "stdout",
    694      "output_type": "stream",
    695      "text": [
    696       "            Useful Numbers  Not Useful Numbers\n",
    697       "2016-01-01               2                   1\n",
    698       "2016-01-02               3                   4\n",
    699       "2016-01-03               5                   6\n",
    700       "2016-01-04               7                   8\n",
    701       "2016-01-05              11                   9\n",
    702       "2016-01-06              13                  10\n"
    703      ]
    704     }
    705    ],
    706    "source": [
    707     "s1 = pd.Series([2, 3, 5, 7, 11, 13], name='prime')\n",
    708     "s2 = pd.Series([1, 4, 6, 8, 9, 10], name='other')\n",
    709     "\n",
    710     "numbers = pd.concat([s1, s2], axis=1) # Concatenate the two series\n",
    711     "numbers.columns = ['Useful Numbers', 'Not Useful Numbers'] # Rename the two columns\n",
    712     "numbers.index = pd.date_range(\"2016-01-01\", periods=len(numbers)) # Index change\n",
    713     "print numbers"
    714    ]
    715   },
    716   {
    717    "cell_type": "markdown",
    718    "metadata": {
    719     "deletable": true,
    720     "editable": true
    721    },
    722    "source": [
    723     "----"
    724    ]
    725   },
    726   {
    727    "cell_type": "markdown",
    728    "metadata": {
    729     "deletable": true,
    730     "editable": true
    731    },
    732    "source": [
    733     "# Exercise 6 : Accessing DataFrame elements.\n",
    734     "## a. Columns\n",
    735     "- Check the data type of one of the DataFrame's columns.\n",
    736     "- Print the values associated with time range `2013-01-01` to `2013-01-10`."
    737    ]
    738   },
    739   {
    740    "cell_type": "code",
    741    "execution_count": 16,
    742    "metadata": {
    743     "collapsed": false,
    744     "deletable": true,
    745     "editable": true
    746    },
    747    "outputs": [
    748     {
    749      "data": {
    750       "text/plain": [
    751        "2012-01-03 00:00:00+00:00    76.706\n",
    752        "2012-01-04 00:00:00+00:00    76.804\n",
    753        "2012-01-05 00:00:00+00:00    76.518\n",
    754        "2012-01-06 00:00:00+00:00    76.054\n",
    755        "2012-01-09 00:00:00+00:00    76.286\n",
    756        "Freq: C, Name: XOM, dtype: float64"
    757       ]
    758      },
    759      "execution_count": 16,
    760      "metadata": {},
    761      "output_type": "execute_result"
    762     }
    763    ],
    764    "source": [
    765     "symbol = [\"XOM\", \"BP\", \"COP\", \"TOT\"]\n",
    766     "start = \"2012-01-01\"\n",
    767     "end = \"2016-01-01\"\n",
    768     "prices = get_pricing(symbol, start_date=start, end_date=end, fields=\"price\")\n",
    769     "if isinstance(symbol, list):\n",
    770     "    prices.columns = map(lambda x: x.symbol, prices.columns)\n",
    771     "else:\n",
    772     "    prices.name = symbol\n",
    773     "\n",
    774     "# Check Type of Data for these two.    \n",
    775     "prices.XOM.head()\n",
    776     "prices.loc[:, 'XOM'].head()"
    777    ]
    778   },
    779   {
    780    "cell_type": "code",
    781    "execution_count": 17,
    782    "metadata": {
    783     "collapsed": false,
    784     "deletable": true,
    785     "editable": true
    786    },
    787    "outputs": [
    788     {
    789      "name": "stdout",
    790      "output_type": "stream",
    791      "text": [
    792       "<class 'pandas.core.series.Series'>\n",
    793       "<class 'pandas.core.series.Series'>\n"
    794      ]
    795     }
    796    ],
    797    "source": [
    798     "# Print data type\n",
    799     "print type(prices.XOM)\n",
    800     "print type(prices.loc[:, 'XOM'])"
    801    ]
    802   },
    803   {
    804    "cell_type": "code",
    805    "execution_count": 18,
    806    "metadata": {
    807     "collapsed": false,
    808     "deletable": true,
    809     "editable": true
    810    },
    811    "outputs": [
    812     {
    813      "data": {
    814       "text/html": [
    815        "<div>\n",
    816        "<table border=\"1\" class=\"dataframe\">\n",
    817        "  <thead>\n",
    818        "    <tr style=\"text-align: right;\">\n",
    819        "      <th></th>\n",
    820        "      <th>XOM</th>\n",
    821        "      <th>BP</th>\n",
    822        "      <th>COP</th>\n",
    823        "      <th>TOT</th>\n",
    824        "    </tr>\n",
    825        "  </thead>\n",
    826        "  <tbody>\n",
    827        "    <tr>\n",
    828        "      <th>2013-01-02 00:00:00+00:00</th>\n",
    829        "      <td>81.186</td>\n",
    830        "      <td>36.119</td>\n",
    831        "      <td>52.038</td>\n",
    832        "      <td>45.732</td>\n",
    833        "    </tr>\n",
    834        "    <tr>\n",
    835        "      <th>2013-01-03 00:00:00+00:00</th>\n",
    836        "      <td>81.040</td>\n",
    837        "      <td>36.843</td>\n",
    838        "      <td>52.021</td>\n",
    839        "      <td>45.366</td>\n",
    840        "    </tr>\n",
    841        "    <tr>\n",
    842        "      <th>2013-01-04 00:00:00+00:00</th>\n",
    843        "      <td>81.360</td>\n",
    844        "      <td>37.183</td>\n",
    845        "      <td>52.592</td>\n",
    846        "      <td>45.523</td>\n",
    847        "    </tr>\n",
    848        "    <tr>\n",
    849        "      <th>2013-01-07 00:00:00+00:00</th>\n",
    850        "      <td>80.472</td>\n",
    851        "      <td>36.953</td>\n",
    852        "      <td>52.003</td>\n",
    853        "      <td>44.966</td>\n",
    854        "    </tr>\n",
    855        "    <tr>\n",
    856        "      <th>2013-01-08 00:00:00+00:00</th>\n",
    857        "      <td>80.912</td>\n",
    858        "      <td>36.953</td>\n",
    859        "      <td>51.309</td>\n",
    860        "      <td>44.853</td>\n",
    861        "    </tr>\n",
    862        "    <tr>\n",
    863        "      <th>2013-01-09 00:00:00+00:00</th>\n",
    864        "      <td>80.674</td>\n",
    865        "      <td>37.685</td>\n",
    866        "      <td>51.203</td>\n",
    867        "      <td>44.844</td>\n",
    868        "    </tr>\n",
    869        "    <tr>\n",
    870        "      <th>2013-01-10 00:00:00+00:00</th>\n",
    871        "      <td>81.561</td>\n",
    872        "      <td>38.315</td>\n",
    873        "      <td>51.441</td>\n",
    874        "      <td>45.584</td>\n",
    875        "    </tr>\n",
    876        "  </tbody>\n",
    877        "</table>\n",
    878        "</div>"
    879       ],
    880       "text/plain": [
    881        "                              XOM      BP     COP     TOT\n",
    882        "2013-01-02 00:00:00+00:00  81.186  36.119  52.038  45.732\n",
    883        "2013-01-03 00:00:00+00:00  81.040  36.843  52.021  45.366\n",
    884        "2013-01-04 00:00:00+00:00  81.360  37.183  52.592  45.523\n",
    885        "2013-01-07 00:00:00+00:00  80.472  36.953  52.003  44.966\n",
    886        "2013-01-08 00:00:00+00:00  80.912  36.953  51.309  44.853\n",
    887        "2013-01-09 00:00:00+00:00  80.674  37.685  51.203  44.844\n",
    888        "2013-01-10 00:00:00+00:00  81.561  38.315  51.441  45.584"
    889       ]
    890      },
    891      "execution_count": 18,
    892      "metadata": {},
    893      "output_type": "execute_result"
    894     }
    895    ],
    896    "source": [
    897     "# Print values associated with time range\n",
    898     "prices.loc['2013-01-01':'2013-01-10']"
    899    ]
    900   },
    901   {
    902    "cell_type": "markdown",
    903    "metadata": {
    904     "deletable": true,
    905     "editable": true
    906    },
    907    "source": [
    908     "----"
    909    ]
    910   },
    911   {
    912    "cell_type": "markdown",
    913    "metadata": {
    914     "deletable": true,
    915     "editable": true
    916    },
    917    "source": [
    918     "# Exercise 7 : Boolean Indexing\n",
    919     "## a. Filtering.\n",
    920     "- Filter pricing data from the last question (stored in `prices`) to only print values where:\n",
    921     "    - BP > 30\n",
    922     "    - XOM < 100\n",
    923     "    - The intersection of both above conditions (BP > 30 **and** XOM < 100)\n",
    924     "    - The union of the previous composite condition along with TOT having no `nan` values ((BP > 30 **and** XOM < 100) **or** TOT is non-`NaN`).\n",
    925     "- Add a column for TSLA and drop the column for XOM."
    926    ]
    927   },
    928   {
    929    "cell_type": "code",
    930    "execution_count": 19,
    931    "metadata": {
    932     "collapsed": false,
    933     "deletable": true,
    934     "editable": true
    935    },
    936    "outputs": [
    937     {
    938      "name": "stdout",
    939      "output_type": "stream",
    940      "text": [
    941       "                              XOM      BP     COP     TOT\n",
    942       "2012-01-03 00:00:00+00:00  76.706  35.867  47.522  43.390\n",
    943       "2012-01-04 00:00:00+00:00  76.804  36.371  47.304  43.200\n",
    944       "2012-01-05 00:00:00+00:00  76.518  35.949  46.913  42.356\n",
    945       "2012-01-06 00:00:00+00:00  76.054  35.819  46.561  41.951\n",
    946       "2012-01-09 00:00:00+00:00  76.286  35.811  46.721  42.381\n",
    947       "                              XOM      BP     COP     TOT\n",
    948       "2012-01-03 00:00:00+00:00  76.706  35.867  47.522  43.390\n",
    949       "2012-01-04 00:00:00+00:00  76.804  36.371  47.304  43.200\n",
    950       "2012-01-05 00:00:00+00:00  76.518  35.949  46.913  42.356\n",
    951       "2012-01-06 00:00:00+00:00  76.054  35.819  46.561  41.951\n",
    952       "2012-01-09 00:00:00+00:00  76.286  35.811  46.721  42.381\n",
    953       "                              XOM      BP     COP     TOT\n",
    954       "2012-01-03 00:00:00+00:00  76.706  35.867  47.522  43.390\n",
    955       "2012-01-04 00:00:00+00:00  76.804  36.371  47.304  43.200\n",
    956       "2012-01-05 00:00:00+00:00  76.518  35.949  46.913  42.356\n",
    957       "2012-01-06 00:00:00+00:00  76.054  35.819  46.561  41.951\n",
    958       "2012-01-09 00:00:00+00:00  76.286  35.811  46.721  42.381\n",
    959       "                              XOM      BP     COP     TOT\n",
    960       "2012-01-03 00:00:00+00:00  76.706  35.867  47.522  43.390\n",
    961       "2012-01-04 00:00:00+00:00  76.804  36.371  47.304  43.200\n",
    962       "2012-01-05 00:00:00+00:00  76.518  35.949  46.913  42.356\n",
    963       "2012-01-06 00:00:00+00:00  76.054  35.819  46.561  41.951\n",
    964       "2012-01-09 00:00:00+00:00  76.286  35.811  46.721  42.381\n"
    965      ]
    966     }
    967    ],
    968    "source": [
    969     "# Filter data \n",
    970     "# BP > 30\n",
    971     "print prices.loc[prices.BP > 30].head()\n",
    972     "# XOM < 100\n",
    973     "print prices.loc[prices.XOM < 100].head()\n",
    974     "# BP > 30 AND XOM < 100\n",
    975     "print prices.loc[(prices.BP > 30) & (prices.XOM < 100)].head()\n",
    976     "# The union of (BP > 30 AND XOM < 100) with TOT being non-nan\n",
    977     "print prices.loc[((prices.BP > 30) & (prices.XOM < 100)) | (~ prices.TOT.isnull())].head()"
    978    ]
    979   },
    980   {
    981    "cell_type": "code",
    982    "execution_count": 20,
    983    "metadata": {
    984     "collapsed": false,
    985     "deletable": true,
    986     "editable": true
    987    },
    988    "outputs": [
    989     {
    990      "data": {
    991       "text/html": [
    992        "<div>\n",
    993        "<table border=\"1\" class=\"dataframe\">\n",
    994        "  <thead>\n",
    995        "    <tr style=\"text-align: right;\">\n",
    996        "      <th></th>\n",
    997        "      <th>BP</th>\n",
    998        "      <th>COP</th>\n",
    999        "      <th>TOT</th>\n",
   1000        "      <th>TSLA</th>\n",
   1001        "    </tr>\n",
   1002        "  </thead>\n",
   1003        "  <tbody>\n",
   1004        "    <tr>\n",
   1005        "      <th>2012-01-03 00:00:00+00:00</th>\n",
   1006        "      <td>35.867</td>\n",
   1007        "      <td>47.522</td>\n",
   1008        "      <td>43.390</td>\n",
   1009        "      <td>28.06</td>\n",
   1010        "    </tr>\n",
   1011        "    <tr>\n",
   1012        "      <th>2012-01-04 00:00:00+00:00</th>\n",
   1013        "      <td>36.371</td>\n",
   1014        "      <td>47.304</td>\n",
   1015        "      <td>43.200</td>\n",
   1016        "      <td>27.71</td>\n",
   1017        "    </tr>\n",
   1018        "    <tr>\n",
   1019        "      <th>2012-01-05 00:00:00+00:00</th>\n",
   1020        "      <td>35.949</td>\n",
   1021        "      <td>46.913</td>\n",
   1022        "      <td>42.356</td>\n",
   1023        "      <td>27.12</td>\n",
   1024        "    </tr>\n",
   1025        "    <tr>\n",
   1026        "      <th>2012-01-06 00:00:00+00:00</th>\n",
   1027        "      <td>35.819</td>\n",
   1028        "      <td>46.561</td>\n",
   1029        "      <td>41.951</td>\n",
   1030        "      <td>26.94</td>\n",
   1031        "    </tr>\n",
   1032        "    <tr>\n",
   1033        "      <th>2012-01-09 00:00:00+00:00</th>\n",
   1034        "      <td>35.811</td>\n",
   1035        "      <td>46.721</td>\n",
   1036        "      <td>42.381</td>\n",
   1037        "      <td>27.21</td>\n",
   1038        "    </tr>\n",
   1039        "  </tbody>\n",
   1040        "</table>\n",
   1041        "</div>"
   1042       ],
   1043       "text/plain": [
   1044        "                               BP     COP     TOT   TSLA\n",
   1045        "2012-01-03 00:00:00+00:00  35.867  47.522  43.390  28.06\n",
   1046        "2012-01-04 00:00:00+00:00  36.371  47.304  43.200  27.71\n",
   1047        "2012-01-05 00:00:00+00:00  35.949  46.913  42.356  27.12\n",
   1048        "2012-01-06 00:00:00+00:00  35.819  46.561  41.951  26.94\n",
   1049        "2012-01-09 00:00:00+00:00  35.811  46.721  42.381  27.21"
   1050       ]
   1051      },
   1052      "execution_count": 20,
   1053      "metadata": {},
   1054      "output_type": "execute_result"
   1055     }
   1056    ],
   1057    "source": [
   1058     "# Adding TSLA \n",
   1059     "s_1 = get_pricing('TSLA', start_date=start, end_date=end, fields='price')\n",
   1060     "prices.loc[:, 'TSLA'] = s_1\n",
   1061     "\n",
   1062     "# Dropping XOM\n",
   1063     "prices = prices.drop('XOM', axis=1)\n",
   1064     "prices.head(5)"
   1065    ]
   1066   },
   1067   {
   1068    "cell_type": "markdown",
   1069    "metadata": {
   1070     "deletable": true,
   1071     "editable": true
   1072    },
   1073    "source": [
   1074     "## b. DataFrame Manipulation (again)\n",
   1075     "- Concatenate these DataFrames.\n",
   1076     "- Fill the missing data with 0s"
   1077    ]
   1078   },
   1079   {
   1080    "cell_type": "code",
   1081    "execution_count": 21,
   1082    "metadata": {
   1083     "collapsed": false,
   1084     "deletable": true,
   1085     "editable": true
   1086    },
   1087    "outputs": [
   1088     {
   1089      "data": {
   1090       "text/html": [
   1091        "<div>\n",
   1092        "<table border=\"1\" class=\"dataframe\">\n",
   1093        "  <thead>\n",
   1094        "    <tr style=\"text-align: right;\">\n",
   1095        "      <th></th>\n",
   1096        "      <th>Equity(8554 [SPY])</th>\n",
   1097        "      <th>Equity(38054 [VXX])</th>\n",
   1098        "      <th>Equity(5061 [MSFT])</th>\n",
   1099        "      <th>Equity(24 [AAPL])</th>\n",
   1100        "      <th>Equity(46631 [GOOG])</th>\n",
   1101        "    </tr>\n",
   1102        "  </thead>\n",
   1103        "  <tbody>\n",
   1104        "    <tr>\n",
   1105        "      <th>2012-01-03 00:00:00+00:00</th>\n",
   1106        "      <td>118.414</td>\n",
   1107        "      <td>538.72</td>\n",
   1108        "      <td>23.997</td>\n",
   1109        "      <td>54.684</td>\n",
   1110        "      <td>NaN</td>\n",
   1111        "    </tr>\n",
   1112        "    <tr>\n",
   1113        "      <th>2012-01-04 00:00:00+00:00</th>\n",
   1114        "      <td>118.498</td>\n",
   1115        "      <td>527.84</td>\n",
   1116        "      <td>24.498</td>\n",
   1117        "      <td>54.995</td>\n",
   1118        "      <td>NaN</td>\n",
   1119        "    </tr>\n",
   1120        "    <tr>\n",
   1121        "      <th>2012-01-05 00:00:00+00:00</th>\n",
   1122        "      <td>118.850</td>\n",
   1123        "      <td>516.32</td>\n",
   1124        "      <td>24.749</td>\n",
   1125        "      <td>55.597</td>\n",
   1126        "      <td>NaN</td>\n",
   1127        "    </tr>\n",
   1128        "    <tr>\n",
   1129        "      <th>2012-01-06 00:00:00+00:00</th>\n",
   1130        "      <td>118.600</td>\n",
   1131        "      <td>508.00</td>\n",
   1132        "      <td>25.151</td>\n",
   1133        "      <td>56.194</td>\n",
   1134        "      <td>NaN</td>\n",
   1135        "    </tr>\n",
   1136        "    <tr>\n",
   1137        "      <th>2012-01-09 00:00:00+00:00</th>\n",
   1138        "      <td>118.795</td>\n",
   1139        "      <td>500.64</td>\n",
   1140        "      <td>24.811</td>\n",
   1141        "      <td>56.098</td>\n",
   1142        "      <td>NaN</td>\n",
   1143        "    </tr>\n",
   1144        "  </tbody>\n",
   1145        "</table>\n",
   1146        "</div>"
   1147       ],
   1148       "text/plain": [
   1149        "                           Equity(8554 [SPY])  Equity(38054 [VXX])  \\\n",
   1150        "2012-01-03 00:00:00+00:00             118.414               538.72   \n",
   1151        "2012-01-04 00:00:00+00:00             118.498               527.84   \n",
   1152        "2012-01-05 00:00:00+00:00             118.850               516.32   \n",
   1153        "2012-01-06 00:00:00+00:00             118.600               508.00   \n",
   1154        "2012-01-09 00:00:00+00:00             118.795               500.64   \n",
   1155        "\n",
   1156        "                           Equity(5061 [MSFT])  Equity(24 [AAPL])  \\\n",
   1157        "2012-01-03 00:00:00+00:00               23.997             54.684   \n",
   1158        "2012-01-04 00:00:00+00:00               24.498             54.995   \n",
   1159        "2012-01-05 00:00:00+00:00               24.749             55.597   \n",
   1160        "2012-01-06 00:00:00+00:00               25.151             56.194   \n",
   1161        "2012-01-09 00:00:00+00:00               24.811             56.098   \n",
   1162        "\n",
   1163        "                           Equity(46631 [GOOG])  \n",
   1164        "2012-01-03 00:00:00+00:00                   NaN  \n",
   1165        "2012-01-04 00:00:00+00:00                   NaN  \n",
   1166        "2012-01-05 00:00:00+00:00                   NaN  \n",
   1167        "2012-01-06 00:00:00+00:00                   NaN  \n",
   1168        "2012-01-09 00:00:00+00:00                   NaN  "
   1169       ]
   1170      },
   1171      "execution_count": 21,
   1172      "metadata": {},
   1173      "output_type": "execute_result"
   1174     }
   1175    ],
   1176    "source": [
   1177     "df_1 = get_pricing(['SPY', 'VXX'], start_date=start, end_date=end, fields='price')\n",
   1178     "df_2 = get_pricing(['MSFT', 'AAPL', 'GOOG'], start_date=start, end_date=end, fields='price')\n",
   1179     "# Concatenate the dataframes\n",
   1180     "df_3 = pd.concat([df_1, df_2], axis=1)\n",
   1181     "df_3.head()"
   1182    ]
   1183   },
   1184   {
   1185    "cell_type": "code",
   1186    "execution_count": 22,
   1187    "metadata": {
   1188     "collapsed": false,
   1189     "deletable": true,
   1190     "editable": true
   1191    },
   1192    "outputs": [
   1193     {
   1194      "data": {
   1195       "text/html": [
   1196        "<div>\n",
   1197        "<table border=\"1\" class=\"dataframe\">\n",
   1198        "  <thead>\n",
   1199        "    <tr style=\"text-align: right;\">\n",
   1200        "      <th></th>\n",
   1201        "      <th>Equity(8554 [SPY])</th>\n",
   1202        "      <th>Equity(38054 [VXX])</th>\n",
   1203        "      <th>Equity(5061 [MSFT])</th>\n",
   1204        "      <th>Equity(24 [AAPL])</th>\n",
   1205        "      <th>Equity(46631 [GOOG])</th>\n",
   1206        "    </tr>\n",
   1207        "  </thead>\n",
   1208        "  <tbody>\n",
   1209        "    <tr>\n",
   1210        "      <th>2012-01-03 00:00:00+00:00</th>\n",
   1211        "      <td>118.414</td>\n",
   1212        "      <td>538.72</td>\n",
   1213        "      <td>23.997</td>\n",
   1214        "      <td>54.684</td>\n",
   1215        "      <td>0.0</td>\n",
   1216        "    </tr>\n",
   1217        "    <tr>\n",
   1218        "      <th>2012-01-04 00:00:00+00:00</th>\n",
   1219        "      <td>118.498</td>\n",
   1220        "      <td>527.84</td>\n",
   1221        "      <td>24.498</td>\n",
   1222        "      <td>54.995</td>\n",
   1223        "      <td>0.0</td>\n",
   1224        "    </tr>\n",
   1225        "    <tr>\n",
   1226        "      <th>2012-01-05 00:00:00+00:00</th>\n",
   1227        "      <td>118.850</td>\n",
   1228        "      <td>516.32</td>\n",
   1229        "      <td>24.749</td>\n",
   1230        "      <td>55.597</td>\n",
   1231        "      <td>0.0</td>\n",
   1232        "    </tr>\n",
   1233        "    <tr>\n",
   1234        "      <th>2012-01-06 00:00:00+00:00</th>\n",
   1235        "      <td>118.600</td>\n",
   1236        "      <td>508.00</td>\n",
   1237        "      <td>25.151</td>\n",
   1238        "      <td>56.194</td>\n",
   1239        "      <td>0.0</td>\n",
   1240        "    </tr>\n",
   1241        "    <tr>\n",
   1242        "      <th>2012-01-09 00:00:00+00:00</th>\n",
   1243        "      <td>118.795</td>\n",
   1244        "      <td>500.64</td>\n",
   1245        "      <td>24.811</td>\n",
   1246        "      <td>56.098</td>\n",
   1247        "      <td>0.0</td>\n",
   1248        "    </tr>\n",
   1249        "  </tbody>\n",
   1250        "</table>\n",
   1251        "</div>"
   1252       ],
   1253       "text/plain": [
   1254        "                           Equity(8554 [SPY])  Equity(38054 [VXX])  \\\n",
   1255        "2012-01-03 00:00:00+00:00             118.414               538.72   \n",
   1256        "2012-01-04 00:00:00+00:00             118.498               527.84   \n",
   1257        "2012-01-05 00:00:00+00:00             118.850               516.32   \n",
   1258        "2012-01-06 00:00:00+00:00             118.600               508.00   \n",
   1259        "2012-01-09 00:00:00+00:00             118.795               500.64   \n",
   1260        "\n",
   1261        "                           Equity(5061 [MSFT])  Equity(24 [AAPL])  \\\n",
   1262        "2012-01-03 00:00:00+00:00               23.997             54.684   \n",
   1263        "2012-01-04 00:00:00+00:00               24.498             54.995   \n",
   1264        "2012-01-05 00:00:00+00:00               24.749             55.597   \n",
   1265        "2012-01-06 00:00:00+00:00               25.151             56.194   \n",
   1266        "2012-01-09 00:00:00+00:00               24.811             56.098   \n",
   1267        "\n",
   1268        "                           Equity(46631 [GOOG])  \n",
   1269        "2012-01-03 00:00:00+00:00                   0.0  \n",
   1270        "2012-01-04 00:00:00+00:00                   0.0  \n",
   1271        "2012-01-05 00:00:00+00:00                   0.0  \n",
   1272        "2012-01-06 00:00:00+00:00                   0.0  \n",
   1273        "2012-01-09 00:00:00+00:00                   0.0  "
   1274       ]
   1275      },
   1276      "execution_count": 22,
   1277      "metadata": {},
   1278      "output_type": "execute_result"
   1279     }
   1280    ],
   1281    "source": [
   1282     "# Fill GOOG missing data with nan\n",
   1283     "filled0_df_3 = df_3.fillna(0)\n",
   1284     "filled0_df_3.head(5)"
   1285    ]
   1286   },
   1287   {
   1288    "cell_type": "markdown",
   1289    "metadata": {
   1290     "deletable": true,
   1291     "editable": true
   1292    },
   1293    "source": [
   1294     "---"
   1295    ]
   1296   },
   1297   {
   1298    "cell_type": "markdown",
   1299    "metadata": {
   1300     "deletable": true,
   1301     "editable": true
   1302    },
   1303    "source": [
   1304     "# Exercise 8 : Time Series Analysis\n",
   1305     "## a. Summary\n",
   1306     "- Print out a summary of the `prices` DataFrame from above.\n",
   1307     "- Take the log returns and print the first 10 values.\n",
   1308     "- Print the multiplicative returns of each company.\n",
   1309     "- Normalize and plot the returns from 2014 to 2015.\n",
   1310     "- Plot a 60 day window rolling mean of the prices.\n",
   1311     "- Plot a 60 day window rolling standfard deviation of the prices."
   1312    ]
   1313   },
   1314   {
   1315    "cell_type": "code",
   1316    "execution_count": 23,
   1317    "metadata": {
   1318     "collapsed": false,
   1319     "deletable": true,
   1320     "editable": true
   1321    },
   1322    "outputs": [
   1323     {
   1324      "data": {
   1325       "text/html": [
   1326        "<div>\n",
   1327        "<table border=\"1\" class=\"dataframe\">\n",
   1328        "  <thead>\n",
   1329        "    <tr style=\"text-align: right;\">\n",
   1330        "      <th></th>\n",
   1331        "      <th>BP</th>\n",
   1332        "      <th>COP</th>\n",
   1333        "      <th>TOT</th>\n",
   1334        "      <th>TSLA</th>\n",
   1335        "    </tr>\n",
   1336        "  </thead>\n",
   1337        "  <tbody>\n",
   1338        "    <tr>\n",
   1339        "      <th>count</th>\n",
   1340        "      <td>1006.000000</td>\n",
   1341        "      <td>1006.000000</td>\n",
   1342        "      <td>1006.000000</td>\n",
   1343        "      <td>1006.000000</td>\n",
   1344        "    </tr>\n",
   1345        "    <tr>\n",
   1346        "      <th>mean</th>\n",
   1347        "      <td>38.079349</td>\n",
   1348        "      <td>58.185060</td>\n",
   1349        "      <td>49.209090</td>\n",
   1350        "      <td>147.455109</td>\n",
   1351        "    </tr>\n",
   1352        "    <tr>\n",
   1353        "      <th>std</th>\n",
   1354        "      <td>4.122068</td>\n",
   1355        "      <td>9.379062</td>\n",
   1356        "      <td>7.354246</td>\n",
   1357        "      <td>89.673401</td>\n",
   1358        "    </tr>\n",
   1359        "    <tr>\n",
   1360        "      <th>min</th>\n",
   1361        "      <td>28.911000</td>\n",
   1362        "      <td>41.647000</td>\n",
   1363        "      <td>35.264000</td>\n",
   1364        "      <td>22.750000</td>\n",
   1365        "    </tr>\n",
   1366        "    <tr>\n",
   1367        "      <th>25%</th>\n",
   1368        "      <td>35.382250</td>\n",
   1369        "      <td>49.921250</td>\n",
   1370        "      <td>44.005500</td>\n",
   1371        "      <td>35.085000</td>\n",
   1372        "    </tr>\n",
   1373        "    <tr>\n",
   1374        "      <th>50%</th>\n",
   1375        "      <td>37.366500</td>\n",
   1376        "      <td>57.437000</td>\n",
   1377        "      <td>47.947500</td>\n",
   1378        "      <td>178.125000</td>\n",
   1379        "    </tr>\n",
   1380        "    <tr>\n",
   1381        "      <th>75%</th>\n",
   1382        "      <td>40.820750</td>\n",
   1383        "      <td>64.554000</td>\n",
   1384        "      <td>53.452000</td>\n",
   1385        "      <td>227.107500</td>\n",
   1386        "    </tr>\n",
   1387        "    <tr>\n",
   1388        "      <th>max</th>\n",
   1389        "      <td>48.904000</td>\n",
   1390        "      <td>81.824000</td>\n",
   1391        "      <td>68.841000</td>\n",
   1392        "      <td>286.040000</td>\n",
   1393        "    </tr>\n",
   1394        "  </tbody>\n",
   1395        "</table>\n",
   1396        "</div>"
   1397       ],
   1398       "text/plain": [
   1399        "                BP          COP          TOT         TSLA\n",
   1400        "count  1006.000000  1006.000000  1006.000000  1006.000000\n",
   1401        "mean     38.079349    58.185060    49.209090   147.455109\n",
   1402        "std       4.122068     9.379062     7.354246    89.673401\n",
   1403        "min      28.911000    41.647000    35.264000    22.750000\n",
   1404        "25%      35.382250    49.921250    44.005500    35.085000\n",
   1405        "50%      37.366500    57.437000    47.947500   178.125000\n",
   1406        "75%      40.820750    64.554000    53.452000   227.107500\n",
   1407        "max      48.904000    81.824000    68.841000   286.040000"
   1408       ]
   1409      },
   1410      "execution_count": 23,
   1411      "metadata": {},
   1412      "output_type": "execute_result"
   1413     }
   1414    ],
   1415    "source": [
   1416     "# Summary\n",
   1417     "prices.describe()"
   1418    ]
   1419   },
   1420   {
   1421    "cell_type": "code",
   1422    "execution_count": 24,
   1423    "metadata": {
   1424     "collapsed": false,
   1425     "deletable": true,
   1426     "editable": true
   1427    },
   1428    "outputs": [
   1429     {
   1430      "data": {
   1431       "text/html": [
   1432        "<div>\n",
   1433        "<table border=\"1\" class=\"dataframe\">\n",
   1434        "  <thead>\n",
   1435        "    <tr style=\"text-align: right;\">\n",
   1436        "      <th></th>\n",
   1437        "      <th>BP</th>\n",
   1438        "      <th>COP</th>\n",
   1439        "      <th>TOT</th>\n",
   1440        "      <th>TSLA</th>\n",
   1441        "    </tr>\n",
   1442        "  </thead>\n",
   1443        "  <tbody>\n",
   1444        "    <tr>\n",
   1445        "      <th>2012-01-03 00:00:00+00:00</th>\n",
   1446        "      <td>3.579818</td>\n",
   1447        "      <td>3.861193</td>\n",
   1448        "      <td>3.770229</td>\n",
   1449        "      <td>3.334345</td>\n",
   1450        "    </tr>\n",
   1451        "    <tr>\n",
   1452        "      <th>2012-01-04 00:00:00+00:00</th>\n",
   1453        "      <td>3.593772</td>\n",
   1454        "      <td>3.856595</td>\n",
   1455        "      <td>3.765840</td>\n",
   1456        "      <td>3.321793</td>\n",
   1457        "    </tr>\n",
   1458        "    <tr>\n",
   1459        "      <th>2012-01-05 00:00:00+00:00</th>\n",
   1460        "      <td>3.582101</td>\n",
   1461        "      <td>3.848295</td>\n",
   1462        "      <td>3.746110</td>\n",
   1463        "      <td>3.300271</td>\n",
   1464        "    </tr>\n",
   1465        "    <tr>\n",
   1466        "      <th>2012-01-06 00:00:00+00:00</th>\n",
   1467        "      <td>3.578478</td>\n",
   1468        "      <td>3.840763</td>\n",
   1469        "      <td>3.736502</td>\n",
   1470        "      <td>3.293612</td>\n",
   1471        "    </tr>\n",
   1472        "    <tr>\n",
   1473        "      <th>2012-01-09 00:00:00+00:00</th>\n",
   1474        "      <td>3.578255</td>\n",
   1475        "      <td>3.844194</td>\n",
   1476        "      <td>3.746700</td>\n",
   1477        "      <td>3.303585</td>\n",
   1478        "    </tr>\n",
   1479        "    <tr>\n",
   1480        "      <th>2012-01-10 00:00:00+00:00</th>\n",
   1481        "      <td>3.586154</td>\n",
   1482        "      <td>3.849254</td>\n",
   1483        "      <td>3.751948</td>\n",
   1484        "      <td>3.318540</td>\n",
   1485        "    </tr>\n",
   1486        "    <tr>\n",
   1487        "      <th>2012-01-11 00:00:00+00:00</th>\n",
   1488        "      <td>3.578004</td>\n",
   1489        "      <td>3.831507</td>\n",
   1490        "      <td>3.738288</td>\n",
   1491        "      <td>3.340385</td>\n",
   1492        "    </tr>\n",
   1493        "    <tr>\n",
   1494        "      <th>2012-01-12 00:00:00+00:00</th>\n",
   1495        "      <td>3.581183</td>\n",
   1496        "      <td>3.814256</td>\n",
   1497        "      <td>3.724801</td>\n",
   1498        "      <td>3.341093</td>\n",
   1499        "    </tr>\n",
   1500        "    <tr>\n",
   1501        "      <th>2012-01-13 00:00:00+00:00</th>\n",
   1502        "      <td>3.571418</td>\n",
   1503        "      <td>3.808594</td>\n",
   1504        "      <td>3.714182</td>\n",
   1505        "      <td>3.124565</td>\n",
   1506        "    </tr>\n",
   1507        "    <tr>\n",
   1508        "      <th>2012-01-17 00:00:00+00:00</th>\n",
   1509        "      <td>3.582101</td>\n",
   1510        "      <td>3.814256</td>\n",
   1511        "      <td>3.742017</td>\n",
   1512        "      <td>3.279030</td>\n",
   1513        "    </tr>\n",
   1514        "  </tbody>\n",
   1515        "</table>\n",
   1516        "</div>"
   1517       ],
   1518       "text/plain": [
   1519        "                                 BP       COP       TOT      TSLA\n",
   1520        "2012-01-03 00:00:00+00:00  3.579818  3.861193  3.770229  3.334345\n",
   1521        "2012-01-04 00:00:00+00:00  3.593772  3.856595  3.765840  3.321793\n",
   1522        "2012-01-05 00:00:00+00:00  3.582101  3.848295  3.746110  3.300271\n",
   1523        "2012-01-06 00:00:00+00:00  3.578478  3.840763  3.736502  3.293612\n",
   1524        "2012-01-09 00:00:00+00:00  3.578255  3.844194  3.746700  3.303585\n",
   1525        "2012-01-10 00:00:00+00:00  3.586154  3.849254  3.751948  3.318540\n",
   1526        "2012-01-11 00:00:00+00:00  3.578004  3.831507  3.738288  3.340385\n",
   1527        "2012-01-12 00:00:00+00:00  3.581183  3.814256  3.724801  3.341093\n",
   1528        "2012-01-13 00:00:00+00:00  3.571418  3.808594  3.714182  3.124565\n",
   1529        "2012-01-17 00:00:00+00:00  3.582101  3.814256  3.742017  3.279030"
   1530       ]
   1531      },
   1532      "execution_count": 24,
   1533      "metadata": {},
   1534      "output_type": "execute_result"
   1535     }
   1536    ],
   1537    "source": [
   1538     "# Natural Log of the returns and print out the first 10 values\n",
   1539     "np.log(prices).head(10)"
   1540    ]
   1541   },
   1542   {
   1543    "cell_type": "code",
   1544    "execution_count": 25,
   1545    "metadata": {
   1546     "collapsed": false,
   1547     "deletable": true,
   1548     "editable": true
   1549    },
   1550    "outputs": [
   1551     {
   1552      "data": {
   1553       "text/html": [
   1554        "<div>\n",
   1555        "<table border=\"1\" class=\"dataframe\">\n",
   1556        "  <thead>\n",
   1557        "    <tr style=\"text-align: right;\">\n",
   1558        "      <th></th>\n",
   1559        "      <th>BP</th>\n",
   1560        "      <th>COP</th>\n",
   1561        "      <th>TOT</th>\n",
   1562        "      <th>TSLA</th>\n",
   1563        "    </tr>\n",
   1564        "  </thead>\n",
   1565        "  <tbody>\n",
   1566        "    <tr>\n",
   1567        "      <th>2012-01-04 00:00:00+00:00</th>\n",
   1568        "      <td>0.014052</td>\n",
   1569        "      <td>-0.004587</td>\n",
   1570        "      <td>-0.004379</td>\n",
   1571        "      <td>-0.012473</td>\n",
   1572        "    </tr>\n",
   1573        "    <tr>\n",
   1574        "      <th>2012-01-05 00:00:00+00:00</th>\n",
   1575        "      <td>-0.011603</td>\n",
   1576        "      <td>-0.008266</td>\n",
   1577        "      <td>-0.019537</td>\n",
   1578        "      <td>-0.021292</td>\n",
   1579        "    </tr>\n",
   1580        "    <tr>\n",
   1581        "      <th>2012-01-06 00:00:00+00:00</th>\n",
   1582        "      <td>-0.003616</td>\n",
   1583        "      <td>-0.007503</td>\n",
   1584        "      <td>-0.009562</td>\n",
   1585        "      <td>-0.006637</td>\n",
   1586        "    </tr>\n",
   1587        "    <tr>\n",
   1588        "      <th>2012-01-09 00:00:00+00:00</th>\n",
   1589        "      <td>-0.000223</td>\n",
   1590        "      <td>0.003436</td>\n",
   1591        "      <td>0.010250</td>\n",
   1592        "      <td>0.010022</td>\n",
   1593        "    </tr>\n",
   1594        "    <tr>\n",
   1595        "      <th>2012-01-10 00:00:00+00:00</th>\n",
   1596        "      <td>0.007931</td>\n",
   1597        "      <td>0.005073</td>\n",
   1598        "      <td>0.005262</td>\n",
   1599        "      <td>0.015068</td>\n",
   1600        "    </tr>\n",
   1601        "  </tbody>\n",
   1602        "</table>\n",
   1603        "</div>"
   1604       ],
   1605       "text/plain": [
   1606        "                                 BP       COP       TOT      TSLA\n",
   1607        "2012-01-04 00:00:00+00:00  0.014052 -0.004587 -0.004379 -0.012473\n",
   1608        "2012-01-05 00:00:00+00:00 -0.011603 -0.008266 -0.019537 -0.021292\n",
   1609        "2012-01-06 00:00:00+00:00 -0.003616 -0.007503 -0.009562 -0.006637\n",
   1610        "2012-01-09 00:00:00+00:00 -0.000223  0.003436  0.010250  0.010022\n",
   1611        "2012-01-10 00:00:00+00:00  0.007931  0.005073  0.005262  0.015068"
   1612       ]
   1613      },
   1614      "execution_count": 25,
   1615      "metadata": {},
   1616      "output_type": "execute_result"
   1617     }
   1618    ],
   1619    "source": [
   1620     "# Multiplicative returns\n",
   1621     "mult_returns = prices.pct_change()[1:]\n",
   1622     "mult_returns.head()"
   1623    ]
   1624   },
   1625   {
   1626    "cell_type": "code",
   1627    "execution_count": 26,
   1628    "metadata": {
   1629     "collapsed": false,
   1630     "deletable": true,
   1631     "editable": true
   1632    },
   1633    "outputs": [
   1634     {
   1635      "data": {
   1636       "image/png": 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ktmPDcVjjQsh+glExQshewqjY/kCJEADgpFKQtgtH0nEZOLK5xoXCpWtkvKPtzmLEdAJx\nHCuKivE8E7JfKEddxQJfxPFaQggZFewqtj+wZF24CMdJCBc6LgNDma5iDovze6WjGhdRryWybYvC\nhZB9hHFclOIYSQgZDDIMUVpc7GgxpN5VjOPPWBPVtNgpF4qOy3DYvgEl1Xy3mIlMO8fF/MxxbTiO\nzagYIfsEKSQq5fpqGeNihJBBcPXhr+P5d/0fKJ06vetjzdyMjst4k3RcpOPCVQK2ZcMP6bgMDKPi\nXddp+Jp0jjlnYVvHJapxsW3YDh0XQvYLlUoAJC5XFuiTSeXlxQ2sbJT3+jCuGYJ8vuH/7TDpGM4d\nxhs7Ei5WKgXlpuAqgbTt0nEZJCzO75/OalzMeWZXMUL2EyYmZqDjQiYRIRXe/eFv48P/8OJeH8o1\ng4piRTIMd31sfZ7BucM4Y6koKua6UI4LC0AGKQqXQVLfx8UCQOHSC3FUrAPHxXEYFSNkP1EuUriQ\nyScIBfxQolwdjwnWtYARLqrN5tWAnqeZMhhGxcYbs+GkFQkXAJiCMzbF+W4/v/ze974Xx48fhxAC\nv/Irv4K3v/3tgzqurqh3FYuiYrwouqaT4nyZEC4szidk/2Acl3TGhe+FjIqRicREnQPOAUZGXbi0\nd1yS++vRcRlvbJUQLm4KAJCRLrb2u3B56qmncPbsWTz44IPI5XL4mZ/5mT0TLnFXsQmIim3kq5AS\nuO7g1Eift5Mdbc1gY8cbUPLmQMh+wLRCPnBoCleXi3RcyERiBEu7Wk3SH9L3IX0f7uys/jrsLCqW\nnJexgdJ446h6VzG4WiZklLP/o2Jvectb8Md//McAgPn5eVSr1T3bG8BcEJMQFft3f/4d/JuPfnvk\nz9tZO+SE4+LYUKpxFYUQMp4Yx+XAwWkAQEjhQiaQwDguFC5D49T7/gjP/tr/Xv9G1CrXbFq4E43C\nhfOGcUVKCSd6fyzXBSLHJS0t+CLAuSu7N2EYNj0LF8uykM1mAQCf+tSn8La3vQ2WZQ3swLqhXuOy\n/x2XXMnDZsHb/YEDppPifJHYgHISRCIh1wqVonFctHBhVIxMIsZpCZkGGBre2jr8jY34a+O47BYV\nkw1RMb4/40ogQzjR22OnUkDKCBcHUkn8zocf28Oj0/RV4wIADz/8MD796U/j4x//+K6PPXbsWL9P\n15Krl2sAgJXVZQBAPl8Y2nP1QyfHVKl6qPly5McfRoPP4uJ5CGet5WMuXigBAM6dO4diUbebfObY\nMbjuzvp3HN+HvWA/nYf8ZoBXn8vjjT94ENkpZ2B/dz+dg2Ez6nNx+bKeaBTL+v+nT51FNVwe6TEk\n4WehDs9FnX7PxdWcjrJUqrV9eV73wzHnNvLIKoVnnn4alm0juLoKALh0/gKW2xy/79XFypXLyzh2\nrH3L6v1wLobJXr3+SliDHYnMKysrqPh60Sss1YADQKFSw5PfeQYpZ2+MCqBP4fLoo4/iIx/5CD7+\n8Y9jNso7tuP+++/v5+l25GR2BU9/axO33norTr1wAjPTM0N7rl45duxYZ8f0D6sQUuJNb7ovdpBG\nwRcfXAagcPPNt+D+++9q+Zj1qy8AKOC1996DwsZ5rC2v4o3f+yZkp1ItH9/xa55w9tt5ePThU1hf\nWcORA7fh3u85OpC/ud/OwTDZi3Px7GOPwnECfNfr7sYrzz6Lm2++Fffff8dIj8HAz0Idnos6gzgX\nZ5ZywEOrsGx3353X/fJZ+JInkAXwfW96E+xUCqce/TbWANx09ChubXP85aKHr/79CgDguuuux/33\nv37Hx+6XczEs9vL1rxa34Dyq/33rnXfg6tZ5AMB8egbAFuAI3HPv63F4Ybh12O2EW88z41KphD/4\ngz/Ahz70IczNzfX6ZwaCqbOwbb054n6OL5lsrjfCDLpSKlHj0vrcFb0SHnr16wDq7ZABdnCbRGpV\nbfnTzp8cKiUPM7NppNN6rYo1LmQSYVRs+FhRTYsMtLvVaVcx1rjsD6q+D0fUa1ysKCpmRXNDyxIo\nlPe2u1jPjstDDz2EXC6HX//1X4dSCpZl4b3vfS+OHh3MCm03mJ4Alm3t+x3dzYDr+QLT2dZOxqBp\nbFPYekKzVt6AkI1dxQDE3yOTg1fTN6ReryMvEMikBhcxI/1TLvk4cv0s3Oh9YVcxMomYrmIszh8e\ndiRcAj+EO10vyu9GuITeeHSnItup+V4cFbNdF1Y6mofWJDAPwJb7V7g88MADeOCBBwZ5LD1jivMt\ny9rX+4sIWXc+Rum4NBTN7eC45L0iLKXFiu4qFhXnc2Vr4vBq+gbUy3v7/Ok1vPsjT+Df/uoP4Q13\nHxn0oZEeMPu2TM+mkUppp5TF+WQSCRKOi1lQJYPFOC6hbxwXfc67aYcsfAqXcaXqB3FxvuW6sFNp\nAIAyNUq2QLGyt8JldEUUQ0TGUTEtXPZri94gsfOsN8KJRSfdPvK1IiypPy6OY8Gx938HN9KaWuS4\n9LJJ2JX1MqRUWLpaGvRhkR4xrZBnZjNIpY3jwgUHMnmYqJhSTAMMC7M5YV24dOa4JLfLEEH7x17L\nLOWX8Y31pxDu0l56WNRCP26HbKdSsCPHRfrRppS2QHGPHZeJEC5GqOx3xyW5adZeOS47bdyVr7V2\nXLgD7uThRTUuvWwSZj4/NY83pl5QSkENeHM2s/nkzGwGqSgqxhoXMokEiYU3bkI5HBwTFYvEh3Fc\ndo2KJd4bwfFnR75x7tv4Tu5FPLdyYk+evxY0OS7pyHGpRu+ZLVGg49I/Rslrx2X/FucnB91OHJdS\nNRjIqlJyQNkpHqSjYvrjYjs2bJvF+d2gpEK5OPr9eXohrnHpQZQKSeHSD4sf+Riee9dvDHQzX/O5\na3BcGBUjE0iytmWvC/TzL5/Aqff/cVzEPikYx0WY4vxIsMiw/ZjSuEDK+8NOmN3pz2ye25Pn9wI/\nrnGxXBdOJFykZ4TL3hfnT4RwMQuUloWoOH9/TqaTg27Nb39h17wQv/T/fgV//vmX+37eThyXQq0u\nXHRXMW5A2Q3PPX0Jf/ier2B9tbjXh7IrtajGpRdRbD7DNU6Me6J8dhGV8xcg/cHdGOKo2Fyaxflk\nogkTk+dRF+grpRoWHNYffQxr33wE5fMXRnocw0QJARv6NYbRHMU4xLs6LolzIzn+7Egg9Xk8vXF+\nT56/FgRwjEZxXdgZLVxETX/TsiWjYoMgLs6Palx6WSkeB7qJiuXLPqpeiCvr/dcSdFTj4hUSUTEL\nls2oWDcsL+UBBWxtVvb6UHal7rj0EBWLPg/VXYQ3aY1ZtRSVwX1OWkXFKFzIfuLUxS088eKVXR8X\nJO5HwYgdl1f+3e/h1X//+/HXMopShcXxX6zqFJFwj0ydihEsu0fFdu9eSoAgdlzOY3Wz/Sadw3n+\nsO64pFJw0mkUMoehREY/wBYoVvbWRZwI4RIX5+/zGpfkCtFuUTE/mnhUBxDJaewq1q7GJem4MCrW\nDcV8FUBv8atRIqWCH1nCvVxHJp7BqFhvqFDfEMLyIIWLiYql68KFjhjZR3ziH0/gvX95bNcxKdng\nZtQ1LqUzZ1E6sxh/bfY3CQoTJFwS3cC8yBWO93HZpZi8oTif9Uc7YhyXalDDL//hZ/DcqaujfX4h\n4hoXO6pxefamn0CYuhcAYDl0XAbCNsdlvwoX0bnjMjTh0q7GRWqXxXasejvkfXquR02xUAMw/Jtp\n8fQZXPnHh3r+feO2AL25aebzw6hYb5hJwEAdF1PjMpeBZVtwXfuadVxeXTuL33vkA6gGtb0+FNIF\nfiAQColyrf1Kb3J8HbXjooSIr18gsQhRmpwOi36tPmENvEbhsms75MT9RFK47Eiym5g9m8Pi5cJo\nn18GcVcxK+VC2S5CJwMF3V1sKmuxxmUQNHYV27/F+WEXjotxZ6q1AQiXZLePFgOKUmpbjYspzufu\n6p1RzOvJ47DP16W/+Vuc++jH4edyPf2+l/g89eKmmcnCIAT1tYiJW4TlwUUE4qjYjLb63ZRzzXYV\ne+bK8zi+/BLO5y7t9aGQLhDR4uRu+0fsZVcxFYYNcSkVuT+TFBWrVqvxv31f39Nix2WXFsedLJAS\nIJB1cW7P5LFa2MIHnvoLnNsazZgViLDBcQlCIxP0/9MZsKvYIJBxV7HJKc7fzXHxhuS4tBrsy34F\nQslYuMBWLM7vAikVSkW9wjtsi7y6vKyfp9rbinItsaLZU1SM7ZD7Yhg1LpWSh+xUCo6rr99U2rlm\no2JmNVPIa/P171dMqmK3iEqwl45LGDa4DubfkyRcSuX6awm3RcV26SqmKFw6IYjGKEs5sGfzeLby\nML51/kk8cenYSJ4/lKKhxsUX+r6hLB0zTqcVytWg5XuolMK3Lz6DXG24LtFECJek42Lt4+L8hq5i\nu0z8zGMrA5ggCtF+QMl7erAyxflChXRcusCvSZgxe5irgEoIeFfXAADS6631stnDBejtvTXF+YyK\n9UbdcRlsjcvMbDr+OpVyrtmoWGh2/e5SuAyyPfUgqIXenm1QtxeYRZTdioKT4+uoHRe5zXHR/w4m\nSLgUEsIlCCPhEvYQFeO8YUcCEcCBjaw8BHumgJx9EcDoFlsCGTZ0FfN807ZXCxc3upWUqtuvxcuF\nFfzREx/HF05+bajHOBnCJboe9n2NS6KwsJ3jsnqlgGq0Mu75ou+9XBot3O1/K18zwsWGtCQCGcY1\nLmqfnutRUqvW38thCj1vfaPeU79H4dK34xLXuFw7k6pBYt6/QTkuUipUyj6mZzPx91Kpa9dxMYWv\noez883nl81/A07/4ywgr1d0fPAKklHjXQ+/Bx44/uNeHMnB2cqTrwqULx2WEwkUJAUip42LRhMQ4\nEGFxcmpcytWEcDGOSzSh3r2rWPI+yHnDTgQyhGM5cGoH9TeiUzUq4RJKUa9xcV3UalG7a8sBFOC6\n+met6lwqgR4jh11DOBHCRTbUuGjhMm4rZJ0QJi7mnWpcnnp0ER/+w2/hwonV+Hv9xnKS0TrRYhOp\nQuS4QFmAJeGLAA7bIXdMrZIYsHu4mZ64ehrv+NxvYym/3P55lus/F706Lg01Lv10Fbs2J8b9Igdc\n41It+1AKmJ2rCxc37SAM9Pu0eDmP4ydH27VmLwlj4dL557N05gyCrS3462vDOqyu8IWPjeoWFjcn\nZ38QACgXQrz3//oSnn3q4rafyU6jYskalxGu6jcU5YvGifwkRcUq1fq4JJocF7XLBpQiUQMzkI2z\npcQj55+KN2ycFAIZwrUdiNz1UNICVu8B0L1L3CtChg0bUNYSKQxL2XBcfV21Ei5CRZ30ulgY6oWJ\nEC4qrnGx4gjTqHRLpeThQ3/wTZx8eaXvv7Wb43LuzDq+8rkTAIBqqf6h6bfOZTfHxeQVHeVAWQq+\n8GGzHXLH1Kr9xRdOrp/FRnULF/Pt9zGordTFbK8bGHrVpOPSS1QsKs6n49ITseMyoKhYshWyIZVy\nIISEFBIf+cyL+L2/+M5Anms/UI+Kdf75lFELWFHrbTFg0JhJwbBz5KNmZamKwBe4urJ9om+Got2K\nghuiYiO8N8kWETE5gVGxSrXuHoXRdVHvKtZeQIjEPWEQqZhvX3oGf/rUJ/DI+Sf7/lu9IMMQx9/5\na7jwV38z0L8biACO5aCytoDaM29HbfUoACBUo3JcEsX5qRSq5USzAOHAin7Yyv0UPYyvvTBRwsU4\nLkBvk65eWFst4epKERcXN/v+W0EocUflCu4uL21zXHKbFfz9fzoWR7OSGfWBCpcWE2vjuLhWCipy\nXOrnmY7LbvQbFSsHehK7m1VcW6mL583cVfzNC5/tepWmVkvWuPRSnK9/ZxARxmsNpVQ9XjKgqJjp\nKNYQFUubTSglShUfVU+MdJK3lwQ9OC4y0OdQ1MajhbJ5DXmvuG8b0bRibTnqvNhi5d44LqVdalz2\nLCrWoptY3XGZnKhYzauPSyJsFC67RcVEsnHBAG4Ni1GXravljf7/WA+ExSKql5aw8cRghVMgQzhw\nojpRWyddsHdRsXKhvmBjSweWHQmXFo6LGVfpuHSAGbttO7G/yIgiTGaFZxDtRQMh8RNrT+Gfr3wL\nYaLtYOCH+OQnnkal7OOHfvw1256vb+GyW3F+VOPiwImFi+kqlnz82iOP4Yl/8T/BWxuPSMW44PUZ\nFSt52p7zpiwYAAAgAElEQVQXu6y4VJfrwuWlpZfxD698CSeunurqubxan45L4nc8ui7dIWVsFYvK\nYKJi8R4uDTUuetgPAxE7u9dKMwXRQ42LjPar6LVubNCYonylVD3Gu8coKeOOhr3geyE21/R5DoLt\n405c4zKmUTGZiEHJpp3kRaWya+H6fsFvEC7R6+y0q1hizjKIRMyl/GUAwGa1t9b//SKimrfq5Ss9\nR7NbEYgQVmJqrqJursN2MQxCibg4X1l2fA8BAEs6gK1/2Coq1mvzk26ZCOFS7yqGkTsBZuI+iA4m\nYSiRFT5SSuDA8lkA+ub0+U++gJXLBXzfW2/DW99217bn63cvl8Yal527irWMiiXOc+nsWchaDeUL\n2zPK1zLJqFhPwsXXN4vdBoOk4+JFE99uB/W+a1ySnfGaJsPve/yj+Ngzg7XVJ4nk5GZQXcVMVGx2\nrjEqBmjX1ji714rI7KXGRQbjFRVLTmDGJS62/ujjOP6r70ThlVd7+v3zZzegoqGj1SKgcVy6iYrt\nneNiomL11xGWBrcv017i+fUFVdnkuMjd9nEZsONyMaej01t7JVzM4rKUqFwc3B4rgQxhqaj1sGsD\nkXARI3JXw6jGRTkOykWvoV7cVSko6Pe7VVQsHl+HXHc0EcLFDGqmqxgwQuFiHJddCtM6IQglUkq/\n8UeX9Ur5k48s4qVnL+OW2w/ip37me5COYh4isSrVb0vk3fZxydeKutW0sqHspqhY0q2J9g6ZlEF6\nUPQbFSv5kePSZrKllGqocfFreuLbrXCpJWpcemuHnBQuyRuVxHcuP4dHLjwFqUY3odhPJCc/g+oq\nZoRLy6iYf+05LmaPhKCLVsKmXkx64xUVA4CtWn4Pj6ROZWkJAFA6u9jT7y+erLv0rYSLWZwsddFV\nbJTtkFXi82T+nbyeJ6VAP/Dr14AIuoyKJV2pKP7UK0WvFH/2N6vDvQZyL7yIF3/rd7bVKolEKqZ8\n7vzAni8QQbxn3m03zsdRsVHVuAgldI2L4yCfa+ykmEEGAvp9ZHF+n8TF+Va9OH/kwqWFvd0tQRAg\nFb3xRzfP48zLV/Dw509gdj6D//4Xvh+u68CNVkuTK/dVrz91q2T7qFihVsR8Zg5QiByXAI6zfRXA\nXMiiPDmZ3kFQq4h4stjLzbQcOS7tomJBLgdZq8GZmdbPU9PvxVaXg3q/jkuyLibZWawqPEglUQs9\nXC2td/13rwWGI1z0zSUZFXNbOC7XyoahvRXnj1dULCm6ctXxcFxMHUevMeGzJ6/C1h/L1lGx2HHZ\nZR+XxP0rGGHHy+S1a5yHXoRLrRrg6vJ4vKetCBNtbqUIG+rydouKJWuXFPoTLslGNcOOiuWOP4vC\niVdQXjzX8P1G4XKu+dd6QkoJqaTuJgbgzoRwGWWNiy0V4Lgo5PT77Qg99qVVGjISLq2L82X8N4bJ\nZAiXaOJ96g/fDxlNmkdVtGgmooPYET2523loZ/H3//k4bNvGA7/wZszNZwHoKJzr2pADjYq1L87P\ne0UsZOagBOrF+S1qiWSNjkszvhciDBQOHNKCopfPSSGqcan6O9+0jdsyc/vt+nmi96Jb4dLvPi7J\njHmy9qos6hPx87mlrv/utUBDtGRA7ZB36ioGaLFiGihcK45L2KLGpXjyVNuMuhEu4xMVq79X4+K4\nmIm52QC3G3KbFWyslXHkBi2uW6UXzO181xqXhqjY6D7TrbqKJV2YoMMC/a9+/gQ+8v5HGpzvcUL4\n9WtAirD+xqATx2UwwmU9V8Vnv/MsIC3M5a5HzfNRG+K+IfGCbLXa9P36cw7KcTFOhZJ6jL7jxvk4\nKja6GpcQrgDgOihEjosb6vv3lHARyACWtVONi4mK0XHZFTPB8q+uQpT0ADqq4nxhugANYJCU0YWw\nnlrA2cPfB89X+Kf/7ffgltsPNjwulXYahFnfUbGk+JCqwYEJRIBKUMVCdhZKol7j0qJ7m7mwBzXp\nmgSKBf2exsKlh/hVMRIuS2s7T1LMHi7Td2jhYgqKu83/erUQ6Yzb87HuFBUri/qgfz43uDzwJKGa\nalwGsRdVuejBdixkp1Lx94z7V02smO20b9Sk0dxVrLK0hBf+9W/hymc/v+PvhFFEbFy6ioWyPqlt\nrnEpFmp46fjlUR9SHKPpxXE5G8XErrsxC9e1W6YXjONS9cK2RfdJsRKO0nEJtgsX2YPjUip6kEKh\nVNwbkbxVrOGPPvF15Eutn9/s3QLo19dQt7KLcAkS9wNl9S5cvvb0RTxz7gzm8tfh9lPfj4WNG7E1\nxFovc91vFy6NUTE1gMXyIKoNMY7L7UdH77hIpR0Xy3Fj4WJLfQ6yQQqBCDA7ldqhxoXtkDumfoNX\nQKT0RhcV08+TjAAde+ICXnq2+5uH9PSHZGn6BhTTB2ArgTfef9O2x6XSDlRiUO6/HXLjBZeMf5nC\n/PnMnG56FDsuUVQsWeNCx2Ubxbw+JwcPRxGuLh0XXwQIlR7M/DY3BtNRzDguiNyZzS5XZL1qgOkZ\nPcnt5RpqKM5PRMXKYVK4jH5itR9IrtBCyoFEk8olHzOzGViJiYJxXCqJjcVq10xxfuONNcjr66Ny\naWcx7Uexy3yh/5b3BqUUvvB3L2BlqdrRY5cubMXXYzI/3hwVe/zrZ/DpvzqOtdXR1lTUHZfuNzNd\nPGWESwZuymldnJ8Yi1pNmOLjSNyPunFc+k1oNDgucc1HQkR1KFwCE93cI8fli5/+FN78Dx/AZz/1\n1y1/LhNF10qEUEkRKWXbuFgYJH7Xcno+514gYE2V4IbaRXbD9FDjYsZZSTos+mt97TozM5C1Gmqr\nq9t+t1vMtS2Fnl8dWshifiYDKGtkwkUoqWtcXDeucclEncTSwkU19DA7nUKxvP0zKtgOuXOMbrHU\n6IVL2KLG5esPvYJHvtJdG1oAkCaKkMmi5k4jHVYQbG1te1wq5TQMGP0Kl+b9OpJxpkLUCnk+PR/V\nuETtkOPi/KTjYoTL+NW4eGtrKJ8/P/C/e2Yp1/YGaRyXg3FUrLvBx9S3AEDQ5qZgomLGcXGj9zRX\nzXdVDF+rhchmU7Bsq6vNRbeOHccL//q3YHtVHARgofFzWUpExS5sMSrWiuaoxSA6i5VLXkNMDKjX\nuCRjgddeVKxxcumt7Vx3ZUWPqVUGJwaKhRqOPXEBp17Y/W+eO72OP/uTx/BytBjWUOPStDCR29Cf\nmcoukapBYybmQb7QVWtYKSQWT63hwKEpTM85SKWchj3K4scl3Md2cbFeHJdqUMO7P/rr+LsnP9nx\ncTeTnLDH3bbCULc6ReebUJrXXt2lCcGwqK7qWo3chWda/lwJP/Fv0bjYgvZ1LkHT4ohJBXSLHwjY\n00VM23MAAFu4Q+0sFkfFmuoOzffnX/ddAAYTFzOOixD6c7Mwm8Gh+SygrJEW59tSwXK14+K4Nt5w\nzwEAQFakEMoQczMOihV/WyqA+7h0QdwOuclxWV0uNPSgHgatomK+L+D3ICZktLKHTAbSTiMtqvA3\ntq/ypTNuQ5xrkBtQAo3CJXZcUrMAAGXv3A55nKNiJ//g/Xjpd/7NQP/my4sbeNf7v4UvPL5zYZ5x\nXOpRse4EdSvhIqTAex/7EL598Vj8s9ryCizXxdRN2qFzIydQKImi15mQlFLB90JkplKwbaurDSTX\nH3scxZOncLiYx2tg4yAaW+wax2UhO4+N6hYKHR7TtURz1KLfAn3fCxH4oqEwH6hHxZKbjV4z7ZBF\nY42LmWj5662Fi1IKdjQeegPaWweoL3QVcyHyW+1dF/Pzrc3tbdGbIzJmocTrs+6xW5I1HN3ExS5f\nzMGrhbj73uthWRbclN3SlW50XHZ2Ixq6inW48HJ26VX85Bcvw/r8Ix0fdzMN7ZCDetF6an5eH0uH\ni3nGbdorx8Xz9XHa5TwWNxu3NQhFCCshTLY5LmgfF2t20vxKb9HLUpiH5QhklZ6TWNIZruOyQ1Qs\nrBjh8joAAxIu0bgkQhu2bWF2KoVD81koaXfVCbEfhArhSMB2U8jnalg4MIXMlL6HpKSOkU/PKgip\nUGkaZ0wDIUbFOiAe1JSKLyyvGuDP/uQxfPmzLw/1uZsdFyUVRCjh97KCGa1UWZlpwLKRCavwN7bv\nCuumHCAxpxyUcLEiFyU54JvNJ+fSenVDOy5hXOPSKiomxky4iFoNxdOnERZLu3Y+6YbvvKzjWefb\ndIExE4m5hSxsx+o6KmZaIQP1Sdd6ZRPPXH4ef/vi5+IVj9rKCjLXXw9nSjdxSCXel07bRRqxncm6\ncJzuHBfj+KSj1UYXQDVxDZji/Dcd1YP8hTYF+koInP3QR3reE2K/oprcuH6Fi1l13yZc6LgkHBf9\ntbex2XJsUIl4i18dTKc3oDFSeebV9hETPxKV1ej9bLePSyFaKOll4axXZBDEjVmA7gr0z0Yxsbte\nex0A7BwVS9zv2kfFut/HZenKImwFOJXeFzmTe5goIeLPVfqQrk8NC11GxXbpnjYsRKAn47NViS+f\n+VbDz0pBBXbivtLScWknXMz4FiUAwuruMclW5IVeZEirGQB6N/dhtkSuF+e3jopdyujP7iA6ixlx\nEoYW5qfTsG0LB+d1VKxdVHyQSCXhSAXlplAuepg/kEU6Ei5uqO8dmSn9HjYX6LM4vwvM5E07LvqC\nz+eqCHwRTxyHhalxiTeijP7fyu7eFdOxI6svyLSowmshXNJpBxaA1KErsLKlgW1AmYpbLdcHJyNc\nZl19TKY437RDNpNbpVTdcRmzqFjp9Jm4+8kgdzB+9pTOc1/d3HkALhf1hT07l4Hr2l1HxRqES9Ou\ntMulqzi7eQFhqYywWMTUjUdhuS5gW3BDhcNT+qbZqY1uVvmy2RRs2+4qbml2zXYj4eKgscWuKc5/\n49HvBgCcbxMXq1y6hJUvfhmLH/nYQArU9wvxTT9q6d6vc2kKfKebomLGcfES78+kChffCxvch+Ya\nl3g8kBL+1vbrRCY6+Q2yOD85OT/9SmNdyFp5A08tPRt/bURINZrMBok6Ay/04o5KQsi4i9wohYtp\nhWw+t904LmdPrsGyLdx5zxEAQCplt46KyU6jYhLpVHet55c3dARP7bKBYjtUU5G6+Tq1sKCPq9uo\n2B45LjLUn6XZMvDYxacb3PqyX9G1D/GDxTax37z4ksR0FXPMXh/V3oRiUeoUiiumAAC2GK7jIndw\nXKoFfW6+crqM9KFDKC+e7/u5gqjxRhBYmI/GbR0Vs0cnXKR2XAJXn9/ZuSwyM1q4OCISLtnIMa40\nC5fGOcqwmAzhEl1MFhQQvbkmotOcq2yFVwvxhb97AVsb3a+oxVGxoPH/IpTd19lE3WvstP7ApMMq\n/PXtwsVMPDK3v4LUzYsDc1zizS3F9qjYnImKWU0bUEa/q4IgFgeD2vV7UBRePRn/O7mC2g9bxRrO\nXSnAmsljZWvnm5L5/KXSDhzH7joqVkpExcxgkCzSe+zi06itaOcne/QoLMuCSqeQCoHbDtwMoHPH\nxUzw6o5LZ8cqqlUE0aQvFX0GHFioJq69UljBXGYWdx+6A0D7zmJmZau8eA7Fk93Xiu1XzCQ6Naev\ntX6vIzOJnZ1rdFzclB72fV8gIzzMhpWJjYp98hPP4C8++Hj8dXNXseREy2sRFzOtkAFADHADyqRw\nOXd6veHrT738Bfzh4x9BLrpuvVpj3YM59qyr31cTFysVvNiJ90YoXMykfOpmHVPt1HGpVnxcubiF\nW247EHe9c1wHYSi3LVg01LjsEhWbjroiduq4rG3qRRdrQMJFhWE8obczGTgz0/uiOF8pBUT7dcyU\ntUD+xrlvxz8v+xW9v4ehhXAx9T2tMI6LZYRLpQYZBFj71qNdJSEy+Tx+5HgRKtI9jnS7bvsfH6+U\n+MBTf4HjV17a8TE7RcX8aHxeKwvM3HkH/I0NBIX+upvVHRcbC5FYODSfhVLWyNohw4yNrhZOqZSD\nzHS0HUdUe+NmWm9CKYIAP/Z0EQtrw50DToRwMYOapRQQXTh1y3z3C+LVF5dx7IkLePF490XD8QaU\n4XanpRPRlMSKHBcrpT8kOzkuRrjY0oGTCgfWDtn83VbF+Vlb12gou/U+LsnVSFGpDDSS1S/Fk3Xh\nIgckXJ4/tQZrJofs659ALvvKjq2DzWZqqbQDx7W72sfl9CurePYzm7CE6eO+PT/67YvPoBK5Hdkb\nbwAASNeBKxRuj4RLt46LrnGxO26HbGJiAOBGNyXtuCSjYlUcyM7jhtkjyLiZtnu5JLtpLX/hix0d\nwyRgJj9ulIsXfdZUVFpsPgkAqZSe2PleiH929XH8q0tfmMgNKJVSuHR+E5vr+iZqNncDkjUu9dfd\nqs5FBoli5B6LiVth7heWrSer58/Wx3kjWGpho3sSOy7RsV83fUg/PirQT6YL/BHWuJhJ+ezddwEA\nah0Kl3On16EUcNe918ffS6Vs3Ry0aexRSsWLZbtFxaaybvzv3RBSYDOvj9fqY0uD5OdIBvU2wZbr\nwJ2d7brGpdpFVOxrT1/ERz/7YhdH25qCV4QTLTzNeD7SdgpfPvNInMgoNTkuOirW7Ljs/LkzaQML\n0X2s5mHtm9/Cqff9ETae/E7Hx3nrhcu4/9Uq0tHnLo1Mz8X5V8vr+Nb5J/HF09/Y+birjcKl4lfx\nf3/9fSgWNiFhYbUYYvrOOwD0X+diHBdIG/MzKVSWlnBwNg0ou21znkFiPsvK0YsJbspGZkYvpiOa\ni9ip1ptQOssbeOPpKu49Wx5qWmIihEu9UF1BRcKl2EXWd+Vy1BKzh04szTUuye5iQZfxCyu6SRql\nm5Ee/PXtxfkm0mVJB7YrB+a4pNo4LkE+2isnU2mIipnWydsL18bDdVFKofjq4IXLs6fWYM/qwdKa\nyWFjh0iiEbKOY+s9Crq4OZ56eRXFJYlsVdcXiabVYkBn3C+c1jet7I036selbLhhUrh0thplah6y\nWRe2Y3XsGBrHBwCcaLBKRsX80IcnfRzIzsO2bNyxcDMuF1bgi9bvRTJLvPHtJ+Dnhrsz8rhgJgEm\nXjIox2WnqJjvCywEJcyJKtTWzl219iuloofAF/E1l+zKEzsuIum4bF8kSnY+stpsANst5r5hNl08\nk4iLFaN4qDneuMal0ljjcmTmMABgK2qJXMzXx+BROi4mKjZ9++2AbXccFTP7t9x973Xx90zHu+R9\nVCkFpYCFGf053km4SKkQCoWpLhyX5eJV2NH5tXvYHDh+7oZ2yGEcO7PdFFJzc/U4XRuUUvHnotZF\nV7EvP3kBn3tkse9OquuVrbgbpQOFN8+8DmvlDRxf1m6EjorVn8OSYls0rG1UrIVwqa3qz7233nm8\nMBaYfrQoiDS2qvm2E+XjT17A+97zlW3d2sw96OIOLfplEMRizNyXTm+ew4m10/AqBfi2i1og4d5y\nGwD03bk0LsBXNl57+tt49n/9NcxcPqPbIY+oq5htxkbHuKA2MrNGuERt9d3IIGiaM6vIoU6Faqjt\nmydCuMTF5UoB0eS/EA3inRTJL0fCpdqDcDEr6EJIKKkaHJduC/TtQE80JPTAm55ydnBc9M9t6cB2\nxABqXIxwiVaqkl3FagVk3AzyG/rYvKkSgmRUzDguTYVr47KXS/XylYabhhzA5EMphedOXUV2Xk8s\nrekirm62nmSGgYDjWLAsq+uomBG+N68AP/vwFjKlxq5Cr7/+tQCA5fM6TpU9qh2X0LGQEgq3H7gF\nQOd7udSjYik4jt1xVMzsIQPUBxQHiKNiuUj8HszqCfntB2+BVBJL+Sst/56J5MzceSdUGGL1Kw93\ndBz7nTgXHzsufQqXqMZlu+Oi36UgEEhFk2Bnvf89CMaNzXU9BkmhokltstYlWlVMRsVaTLiTCx1W\nILpqLd4Os7J+5GgG6YyL06+sxhOvUrThrLnx71Tj0uy4mJRB8ndGgYmKpQ8cQObwoY6iYkopLJ5a\nQ3YqhZtuPRB/33WNcEm0F47uTwvR57jVjt1A3WExwqUTx+V8bgmpqKbTbhFR65TmDSjNqrXlOHDn\n5iB9f9c20cl7Qzc1LredP45/tvpYw/5rvbBW3oCTmLLcq+4AAHz5zDcB6HrLhhoXIaGaJqftakjr\nC6JGnFXiurIg13nUy44WG4yAcVUKgQwb6kGbubi4iVLBi91XgxEuW7V8nC5pOOZkkiTq+rpZiRYs\nfR++pSf3tUP63ttvnYtxXG7bzOP6Fx/Tz1PIA8oe2NizG5YZGx19HbmujcysTtzISLgoS1+DzfVm\nZrxMhWqoLZEnQrg0FOcHTY7LLnEtJRVWLusVq14cl6Q7EQrZMOB267jYkeiSSr8t6ak0/M3t3W6S\nUTHLEah6YV+23Lbi/CbH5UBmDssn9YrEgdJWwwaU5nebC1d3ssZlEAzM9egEExOz03q1TrXJ4HbK\nxZUiNgsesgf0QGlnqrjUwhkD9OTQcfXF3m1UzIjg73slxC1XAxxa059Ts/LyuuvuwZHpQ/BWVgHL\nQvYGPXgGri7Ov2HmCDJOGlvVHEpeGb/3yAfw4urOnbq8asJxsa2Ob4S1KKrmzM7CRvRaUd+N3URf\nDkzpCfkdB24FgB3jYmY/o6M/9RMAgPyLO+ePJwnTmSg1rx22fovzy6V6Y4gkqcSqdkpFYnWr+40D\nx53Ntfr5E6FoiFjWHZdES+gWjktYq7sYqVB13Fp8N8zikJuycPe912Fro4KNq/pvmwmYOUazoFCt\n+FBSxd8/MmOEi3FcEp299iAq5s7PIXPddfC3tnZtgrJxtYT8VhV3vfZIvAgGJEV1fewxUfD5yHEp\n7RCj6kW4XEgIF0eoHV3g3ZBNNS5xVCzlwp2LruddOoslm+J0U+Ny8+ppfE9xcde5zm6sVzbhJMST\nsyHw3de9Bs+vvIIrhRWUm7qKWUq2cFzaCZfomrP0/yulYrxPXdCFq24ZsRRdQ07Uorddgb5xWprP\nURzNAnAxv911SSZJzEKSeR7XD+HbWrjk0/Ows9m+O4sFIsR0VeK/Of2qns8CsEUAKAsKI4rfm4ZL\ncVTMQWZeN2eKT72l79GFys7CZZg1ORMnXFQ0+Td5XylU28ni5kY5saLVe1QM0O5LQ41Ll53F7FA/\nv9l8yJmZ0t1umlYjzMTDFg6ULSCkarDFt6p5vNRmgtrMtuL8sN4pLLW8iR9/+AouPXcKlpL4nosb\n8EMfjtPYDtlc4FZKf9h3aon88rvfg1d+9/c7PrZ+MTGxue+6F0Bj28peefXCJgAJz6oPlGfWW0/C\nw0DE9UDdRsUqa1oMCSuK+phGENGAkHJS+JHb34y5QgAcmIMdnXvfUXAl4MLGwakFrBY28ZUzj+L4\n8kv46plHd3w+s69HZiqlo2IdOi61yHGZfu1rIexolQYWMutXIDwvnlgdyBrhop2gnTqLmTaZ664P\ny3EaCqQnGTOJdgfluOwSFQsDgZQp9M53HtXYL2xuJDryhbIhYlnvKpZwyFvUuHi1+nvghqrnIuBm\nzAKX7Vi457t1jcfpV69CSIFy1JK22XFRSkfA4hoXI1xMVKywN45LLFxmZ5G5/jp9z2qRFEhSj4ld\n3/D9elQs4bhEw5Dr2pjJujtGxcw9MJNyYNtWR1GxpHBxhUI17K0BQ2NXsXo7ZNt16802dqlzSTou\n3bRDNivk3dxbWrFW3oyjYgBQubqOn3zNjwEAvnzmkW01LpaU22tc2tRhxO31o13YK5VKHAMO8t04\nLlF9mPlgRHUX7a5N41Y2XxfJvVEutIiLJdt8m1RJLFwCEQuXzYKHmdtvR3Xpcl/3q1CGeMPpCmYC\nH+pOnaiwQi1cJEbruMiE45KenYalBISMNlRV+lxs6/AXCRd3yI6LO7S/PEIa9nExJz1xAfp+iCk3\n3epXsbJU/7BXyt2vtiRFURiIhmxutzcPNxIuFc9DCAU1qycw/sYGMocPxY9zolUpWzqAFYkuL4zb\nQP7dE5/EiZefwv/5C7+Lo3ONN4ZWtCrOr16+gsW/+iv8y8c3oACcfM1hTPlFzHqiZVcxc4FnDh9G\nbWVFrxZnM9ueq3JpSd+BB0hlaQmn/sMfxVYugHjHYn99A3Y2i9nX3I38Cy/21FXsSmEFq+V13Hfj\n9wDQUQUrW4GAQMbJwBMeLhdbx56CQMaOi91lVKy2VQCQweUjM7ilgHiPIjOhcSwHP3T0jbhc/Uvk\nr3Pi3/PsyAXzfNhiCuVwDZ878XUAwMmNs1BKwbIsNONFNS66q5gdu2m7UV1eQfrwYaSuux5yVX82\nM5D4L595ECsPpZF7g27LbITLbQs3wbKsHTuLbeT16v/zm6dxVyo1UoduL2luoTqIGpdM1o3jNwY3\nVb/OjeMyU2xf4yKFxJ/9x8dx973X4cf/6Xf1dVyjYmu9UbgEdv1z1LwBJQB4a9vPQS2xd4srgY3S\nJu44eGvfx2buE45j4TXfFQmXE1fx+rfekDhGU+NSP8ZqJYgjb9dNRzUupjg/6biMNCqmJ+SpOe24\nAEBt9WrsALeief8Wg+l4l5yEm3uMbVmYm0nvGBUzQsV1bLiOjaAjx+Uy3oQsgDJcoeKGCN2S/Byp\nMIydCMtx4EzrmM1uLZFl2FtUzDgQodffOLla3MBtCd3hb27gLbe8CQezC/jm+SfwvTd8N67fratY\nm7HaCBdpSUABXrWC9KZ2XJoXZ9thXm8sXEJ9L2vXPdMsSgdNzZp8Uf8sXWjpuCSFSxVKKWxUc7Cl\nXhz0o4W6jUINd911B4onT6JyaSluVNEtvgiQ9fXryrzxPvjnTsEWIZS0oSB3vHcPEhPFU2YR0nXg\nZLNwZF24eFKfl201LiY1EKqhtkSeDMdF1h0Xq8WkuF1nseUoJgarv+J88+9+omJOJFyqNQ8BFIJp\nbTE3r14Z0WBJBxJ14WI4+NXj+NmHt/DKiad3fK58yUMQDa5xVCwSLkv/+EUcf+evIff4k1g95OLM\nf/ejCOBiVpYwVZONUTHRWJyfuU73428XFQtLpYGuohdePoHyuXMICkVIz4f0fIhqDaJagzMzjaM/\n9VKRT+AAACAASURBVBOwM1pE9fK8f/n8p/H7j/5/qEQroeVqAHta34R+4Jb7AADrXusagcAXcKJ5\no+vaUFJ1XEQZRo+rpHWXufgGFf3ftR1cX4ves7SHkq87edSs6Abhe7DDLCwLqEh9vFvVPDYqWy2f\nz6xKZbMpWLbVILLyJa9l9EL6PvyNDWRvPAp74UDsuNjRdeitrccZfCNc0m4aN83dgAu5yy1zu7Wy\n/ux4joKdTl8zjotZ/XdnZwHLGoDj4m+rbwF0owjbtiBCATuKI8yXt9qull5ZyuPKpRwuLLZfSR8n\nNtbqY1AYNDsuph1yfe+cIJ/fNvHyvcb3IJcfTBMDMzG3HQuz81nceMsCLi5uYLNQn3wZcZVcAKtW\n/Pj7s+lpTLnZhqjY9Gwa6YyzJ13F3Lk57big/V4uYShw4ewGjtwwi4WDUw0/M6K6ePFy3Fq2QbhM\np1HawXEx41PKtZFy7V33cSnUitiq5XHI0cLCFUDV721TxOTnpiEq5iaiYrsIl+R4G/ii466OdnT/\n7raLaTNXyxsNUTGZy8G1Hbz9NT+KalDDM5efh504JDvhuASWft/aRcXMPEM6psalGjst3UTFzOs1\n9xgj+NpHxfT70yzo/YTj0qpAPxkVU0JABQG2KjmkgqhZUXRzX89VMXPnHQD624gylGH8HmQX9P0S\ngQ9E5QOjqHMx5zcWLikbluPAhhYuU6ksykEFUxlnu/uZFC49xi47Osah/eUeOHMph1/9vYdxZa27\nHLHRKpZSsFq8se2ynyuX9Yf9xpsX4HthVzUIQHvHpVvh4ooANdeGG6YRZsvwpvSA15y9tuy64yIt\nAUA1CJep1QIsAGvPHmv5PKcvbeF//ndfxX/85HMAEo5LdNPIvXgCUzffhJl3/Bwe/MmDkDfcCQCY\nd/1YuDh2c1RMK/D0kUi47LBabAa2QXaKMgX3r3nnO/DmP/8o3vznH8VbPvGx6L+P485f/Fc4lb+o\nH9vDJk5b1TykkvEEoVwLYUXC5UdufzOgLJTRekKXjIo5btSJrUNL38wjQ0TCRTQKF8d2UF3Wgmlr\n1sKTl55FNazBj4SS9DyoIBv/vXvmXg9Auy6tWF7KwXFtHDwyDce2YlGaL3n4pX/7Vfz1l7fHD2ur\nVwGlMHXjjbAWFiAsY+LqKbGo1eIoy4GoOB/QcbFqWMNaeft586p6pdx3ADuVGtjeO53w8Hcu4NTF\n1sJu2MTxknQKztRUX8JFSoVKycPMbGunOZV2IBOLLI4SDW2tmzl3Wk9Eux3T9gqlVFycDwCBHzTV\nuJji/CgeeUS7F81jrd/ULTFfbF3Lthu5oof/9NCJeNJtJtUmcnvPd98AKRXOnKzXGplatmS9SrXi\nxxEM10nhQHYeuaijUiFfw/x8FumMO3DHZfmLX9pxT6WwWAQsC+7MNDKH9Xn0N3Y+TxcXNxH4oqGb\nmMHcg0598KNY/OifAajXuNg2MDedhh9K1Frc043jknJtpBx716jYpYKuzVuwtLi3AFRrvdWVbduA\nUtSFSyoSLkEXNS5A53Ex0wUq7HOc3KxuwQltqGhF3y7rcfu/uutH4NgOhJINwiYZFQuNcGkXFTPp\njGhhzd/Kx3u/BflCx3W6sXAxf1cAUBbWK60/c0qqHWtcko7LpcLytk5YzbW7olrFZjWHdPReyax+\n3Rv5Gmbu1POkfgr0fRHEcb3sgo4YmqgYMPyNHZVSsKM5tLT1azPzFhcCobIwm55ByS9jbjq9LSoW\npwaupeL8YydXcXmtjOdPd5e3lokaF1PQlGQnx0UpXZh/8PA0Dh7Wqy6VLutcGorz+6xxSQkflYw+\nDjFdxEYqusCb88KRaFgoAllPArZApVaPPszmtN0tTp3f9hyrmxX8Px9/CjVf4IrpuhMNKLklPZBn\nbrsN9/3x+1B7/e2AZcEu6cnvgSmJjCcQBB6sOCrWWJxvJgCtHBelVPzBDlrsUt0pp06s4suffSke\n6MxqlynAb2a5eBVPr+oCb9HD6r0pyDUT8FLFjx2Xuw7dhlS4gDBV2NZnXQi9CamZnMQtpDuMi5n7\nrkR0YxWNe1C4thu3Is7POng82uk4jKJp0vMQVHUGV5YWUDivN4g7ub647bl8L8TqlQJuumUBruvA\ndmwopQf99VwVfiDwwpkWUZp4D5mjwFzdcYFlQVguRLVar3GJivOB9gX6QRTP8RwJu4uo2MqVfF85\n75oX4k8++VxLgTYKkrl4Z3q666jYxhNP4uyHPgolpS7kVsDM3HbHBdATRNW0olu5uPOmoOdO6/e+\n2zFtrygXPQR+/fVVPa8hz97cDtnEmprrXAJPCxcZXcOFYm+i9qmXV/Cpr53GM69ocVivcdE/v+d1\nOi528VQObzhdxb/48iZCz4NSqslxCerCxXZwYGoBBa+ESkW3fp5byCKTcQda4xJWKlj80Edx6VN/\n1/LnQbEId2ZGx6JmdBFvO9G9uENMDNCuNKDHyMIZvcASdw2NHBcAKLaIdcdRMdeG61i7FuebRZMp\nUY9S1gYgXFSYbIfswu2hxgXoLC6mlIo3dAz7qN+shR4qYQVuCMhUBr6bQbpahFIKB6YW8NYoWeDI\nekzJkqruuETjfjvHRcUTYv21zNXPhwrDHetimzHCxUL9WGzh4GqptRvqeWG8uN08FzRjQsZJIxAB\nVkqNc8/mbR5qpSLyXhHpyHGxp1OYyjjYyFcxffttgG331RI5lCHc6BAz89FCXxDEjsswWwwDgFCy\nvsloIioGAI6lIGBjNj2Nkl/B/EwahWZxbRwXcQ1FxbYKesK9nu+uQC65j0srx2UnC7WQq6FS9nHD\nTXNY9vQE7D/8xdN47PnWPb1bIdpExbrt8pESASppbZ2HKQ+vuKcBYHtL5Ohde8OpAP/DlzYxK4qx\n41JZWYkV++HLxYYWf6WKj/d87Ankih4sq96dxdwYCs9rB+bQD/0QLMdBPvpdUdQf4INzUVOAigfL\nsmDbVqIdchQVM45Li3bIyUHN3+x9VfvYExfw1CPn4mifiRLtJFwefPFzCKNzZiYi3WD2VYgdl2oA\na6qIg9kFzGdmMe8cgeUInFxunISH8R4u9eJ8ALtGGOLfj1ZZVCRczIAtElExUxh/6LY7ceLqaZzP\nLSGIhIuoeajl9E3zTvc+nF+04FgOTrUQLksXtqAUcOudupYqWcNkNji9sFzYFnMzrZCzR48Ccwt1\n4QIgtNMII+HiwMZMajr+2R0Hdy7QN52cPFu7D520sL5yKYeP/OEjOPbEhV0fuxPV6AZX3oNdq4H6\nJNpyXbgz0113FXvp03+LlS9+CfmTJ+OOYq2iYoB2XMy4WUrrz0jl4sWWjw0CgUvno+4/+8RxSbot\nAFDz/YYbqR91FzQObCYSLs11LkHUwlZGO0eXehQuJpZbjc5fs+Ny0y0HMDObxtVzZdz3SgVHN0KE\nm7l48cOQrHFJ2S4OZOehoLASdTWcW8gik+3NcXnypWX88u9+FSsbjefOTCjlDhtwhsVSPDl3I+HS\nbh+vsyfX4Lg27rj78LafxfVXloPaygqUEAnHxcLcjF6IKVW3H4s5xynHRsp1dnVcTGQ2k/hIV3vc\n9HXHrmI9RMXMAlcnncWCUMKN5jyiD+GyXtafH0cAcF34U3OYDSrxguhP3fNj8c9NLCzZVSyInPZ2\nzW/M1ExGr88qNt6Lv/H8V+N7bDvsuPayLlwOpg9itdxauCSbLjULeuO43HVI78PSXKBvFmRN3aGJ\nimajuhqZtXFofgob+RqcTAZTN92I8rnzPXd59UU9KjZ1UC/0qSDhuAx5L5dQhnEDBhW9z2be4loK\nwnIwm56BF3qYmXbgB6LR/TTXYKgaFooGzVgJl82oK8p6rrvJZXIfl6TjYgaAnfZTMRtPejMlnCxo\nkXD6/Caeemml5eNb0VjjIhraOHZ7k0/JENVochcAuJi5BFgW/Kb4grFypeVioSzxLy99E9XoxrV5\nXq9SKQAzNYlXX9I70gahwO9+4mlcWi3hn7/tbtw1m0UYTW7M+ZsL9QqIc0AXU+c9PYh4WwqwgEOH\n9M3bLeuVQL1JYaPjko4cl1arJ8lBzc/1LlzM4BN32zGOS9RVK8ni5gU8cemYaTwCr9bdZysQAY5e\nKOCtz5eQizK0Ba8EO1OLJ9/XZfWk58TK+cbfNQW4bo9RMWVqmfTrsmPHJSFcIsflja//YSgofOn0\nNxFGC4iiVkNudRY3XP4Z/NwP/heAspEVh3E+t7StCHXpgn4/br1DC5e6OyTjfYJqvsDKZtOE0AiX\nG49Czs4lomJAaKcQVLRwmXGnG4oKzR4zrQr0RdQOuWoLWKl0R47L6hX9Wd1a3z5hUkq1jTDEryW6\nXiu1vREuDZOdmZm4GLRTigU9Biw/8XjcUWwn4ZJOu7FwyS8cBQCUdxAul85txgs0+024BCl9vdcC\nHRU7UAjxg8+X4liN+VxM3ajPgdfkuITRQocV7RxdLu0+sWpFGE1GTItwEymO6xVtXaTvVxScQO9r\nInwvrlURUWOBZI2LES4AsLKur9+5hSmkMy7CQNa7OHXI15+5hJWNCr7yVKP4DyuR69TCrVZKISyV\n4sm5KUQXO7iFpUINq1cKuO3OQ/G+YUlMVEzaLmwpUbt6Nb4/OQnHpVWBvjnHKdeG6+7uuBjh4iYi\nWl6vjktifNFRMbMI4dS7iu0mXKLjmFvQ12wnXU79QMCJFEHYR43LWkXPMVwBWG4KanYeWenj6pq+\n57328F34J7f/ANwwBWE7EBZgSxXv41KPirVzXPTrU9F90Incj9RBPd/4wvF/xFfOPLLrsZpNMJVV\nn75elzmM9cpmw15NhmTTpW1dxWSIua3rcUfmDgDAxXzjQlocgT+kjzEXbdR7g6uvUTll4ciBLApl\nH34gMHPnHRCVCryrvbWXDxNRsakD9RoXFc0Fhu24JGtsZHR+TcMM19FiZsbW1/j0jH5c0v20ojme\nK4AgHF5t6lgKl418d5PLuMalyXE5cEjfbHayzc3Gk3l7BXcsRwWGQFc70TfWuMiGqEo3N/kwCJFW\nIbxUdMxBGtK2YC/MbXNclLFabRfVjIXDfhHy7/9Sv5bzujBs+TY9WK48+zSUUviTTz6HF8+u44e+\n90b87I/chUNFHwvVAEqpWHw40Q3RDKDGcSlvBjhwcApTB/Wqw5QnEcoQtm1v24Cy7ri0iIol9lDp\nx3ExDpqxfds5Ln/9wmcBALMzehAIvO7cvKJfxn2vVvADL1dQvqQHtaLU78dtCzc3/H9xo3HiZ97/\nXqNiIro8LRUJF7Oypuo1LrWVFaQW5vHWe34Aju3g5aunYselVKjA8wWutzJ4/V2HcNdNC8ivTkMq\nibObjZOTS+f1pPeW2/UAbepypFSo1AIsBEW4MsS5K40TNyOcskePIrBSQOJmIpw0wkpFCxensQj3\nQHYeB7MLOJ9bwtKFLfzVR5+Mb9QqEi41S0aOS/sBUCmFrWiV2EzYk5x63x/juXf9xq7ipVr18L9c\n+Ae84cxj+PJnX8LXH3ql7eMHjXEk405EUm7b2LUtNX2e8seeTWw+uXONi1nj8Q5cB89yUb7QOip2\nLhER3C9RMSNcatN6HKp5esL/usUa3vJyBYfX9D3GrBjHjkvTIlFoNg2c0Tfrark34WIixV50Y0+2\nQzbc8zp9DOvTWtQLz4cXjXFBRn8OGmpcbBcHp/SYvL6pj2tuXm9oCXTXWUwphRPn9Gv/5vGlBpfH\nxL5aLSCIag0qDOM6Djc6T60cl8KrJ/Hiw8cBoGV9C1Bf3ZXRRLh6+Up8f7ftRFSsxaTeOC6uayPl\ndOC4VPU9yPYFTh75AWxM3wS/2ltdWcMGlKKxHbIRdbt1FTP3hdl5vUDYSY2LH8r4vhD2UL9pWC/r\nc+EICSuVgh0tXm5c1NFGy7Lwzrf+ApwgBWnZkHYkXKJz7ncUFYsWmKMFRjMxNkXt0zUZ7/nVDuO4\nmM8IABxOH4ZSCmst6lwaHJcmcVfK+bj99PcjfEVfR9sclyhJYoRLoaCvkSMq6vyYUTi8oO9tm4X+\n61wCGcKVgISFqTl9LSnfhxVN+oa5N4r++yJ2XMz5jWtcorFqTuj3LzOlH9hwLSbmv2GXi8TdMFbC\nZSt2XLbfrINCYUf7ub4q2ei4HDyibeudBIRphSzPPos7lvUNzgVaFv7tRJCYVDXv47KT09MKL9oV\n3Y8meL6INtGan4a/sQmVaE1rXqGwXLz4milcmToA5/xphOUyypf05MP60fv18b26iL/68qv45rEl\n3Hv7Qfxv/+P9uHROX9w29CqzER8y+stmo6i89/+T9+bRll13fefnzHd686t5VpVUsgZL2JYt2xhj\nsE0gCQ0JoTudNMlKh9VJp7Oyupte6ZWEJISEhMxhpSEQDDTgWOBRYCzbkixZ86wqlapKNU+v6s3v\nvjufYQ/9x97n3HvfUJOI40Xvf2p49917z7T3/v6+w6+FJwJ6HcH01hHemrV9IWJFJgWe5yDVsFQs\nGBvFCYINZS6DjMu78bjk5zUHpGoTxuXs8kXemj/JfVsPM+bey8Xx+8nSWwQuSZtyYis81jPUw0zy\nOWA5vGUfALOdYaZu7ebkVqRiWmsUOSVvJxA17HHxtEOysEhp+w5GohoPbr/H/Nx+XmOlxQSgZ5qc\nOHKNH//+g8i2mYBPLfUN+lppZi7WmZyuFp6IQiomFb2VOj996VE+Un+LC9eGF5Z4do5gfBy/UiZZ\nE8cp3ADR6yGUoOqVeefSytDv75/YzXKnzle/cIRz7ywWciSdpGQerMY9c0036BcAJhXol178Df7H\nL/xtZubM5nowGfDEhWUuzTbpnL9A99JlGsdPXPecx80OE1mL0U6Dl5+5wKvP3z7lfztjcLPT883n\nZtfRxX/h+B/xwmUTwJGKFN/OW3JmjsasWWA39biEHuCgcHCjkKVwnPjatQ3DKy6cWcJ1HbbvGiVL\n5Xf0nNzuyDtkJ2WzWUysOT+vZuZdt/MKcWl77nEZ1rjnwMUdMWtJ0uvcVtUzr/4nm0jFwHo+tGK5\naoCLytJio5WGZn7tdfshA57rFYzLat3Mt7nHBW4tjn9moU3DMvCL9R7HB9Lj8rl9owLCYKIYgBtF\nOJ63IeN+7pf/E8e+ae7Xtf1b8jEoFQMDXPoeFxip5sBlc49L4N0c47LUrVMOSiQpzIy/h5nRu0lu\nE7gMScUyUax1jvWr4bqI1g08LrZgODpmgMvNeFwM42LvqZuQ1G42cmO7r0ySY95+YfXqcGCHqyXK\n9VGOY6RihTn/xlKxfONSNIO2wMXdbZ69Sqxo3kSDV68w+feBy0Ro1rX59np/9CAAXOtx6a7aAIxV\nmCiNrQMueZuHnBVqNy2zKQ2oSEPFlL1exqC/H7j9ZLHMSsWE65rz5DioNMXhO+NxEVIUHpeCcfFz\nyZiZq6rCrClhyXyXQYO+M1i47/3/ALhorVmxHpflxnqJxNGf+buc/rf/YcPfVUqbHi4wxLhMTNqb\na5MJfO5qg+poyOjlqwTKfLYBLjd/cwwmgZhUsUHG5eYXjqQzDFyy1DzcvVqAFmIokURZbad0PdLA\n4eLYNI5WNN4+QXZtDunC6APvpVcNGL1S5/ceP8mOqSo/+9c+RBR4RaSpg/G55NXA/Jvnx9SMW0Sx\n1S5XAp56xzy0JlksxfXcfja7fcC9Ugm/Wt0QuAwxLvU/BqmYPb+59toNh4HLH5x6HIAfe88PsTy3\nhXNT7yONbx24lBJzjOGFWbTWJI6ZXLfVDLt0YNsWdBqxkg3TwzmIvR2pmBSq6EXj4qFwi67FuVTM\nrbfRUlLaYSb+j+37oPm5/bxmo100amo1E773wV2Mu0YSc2L+bPFZi/MtkliwZ/9E8X8FO6Q06fIK\nPoqptMnFAcZFCUG8sFBs+pJ4+Lia/gjKTl4lL+Kf/eYr/MJvvVL8fN/4bkZWt7Jwrd0/ZoBUkPkO\nnTgpwOhQ3KjWPHfpFf73x36O5y6/ilSShUXzvXLgorXmn3zmZf7hr71QyBgXv715802A2D6D7ZKV\nDcWC3m1EpN/uyOOQncDnojLPxztn39jwtYlI+f23v8qXTjwGmI1HHtEJsHzWsH/X87gASDfAjUos\nRhMg5TqDftzLmL2yys694wUIyjfdjxx7lP/rG//sv/piejtjZamNdhVpZAFMmplqov2qeUJfXjEO\nRkfwKpV1jItMzbrg1ew8KHTBRN/KKKRiWQ5c1jMubtxmPF6gGU2TeiVk0peKZdEAcJGCwPVxHKdI\n6subT46OlYlKOeNy89clByofe9AUY556vX8f5MBlo3S/HLi41SrPHb2K1mwaLJE2myxH26hWfLbu\nGNnwe+SyFGU3wr2r14Y9LhUzH6xrfMfaOGQPIdV1QfZyt850eaLof5L6JdJbZOTzsWmqmGeuUzBS\nu6FULC8gjtiN8M14XJIkw8sLju+CDc17AXnasNxVG2vdWRgGAq5SfcZF6+I4c3N+dh3wlF8KzwJr\nbbefFyvmWlZiRSO58bPlFYxLf/s65hsAP7+BQb97HY9L3DT/btdT9o7vYqm7Qift37uFBN4Cl27L\nnKcwtvdnKArGZWm1R/UOy7hcuHjD49hoZMpIxYRj7hs3DE0/NgvSvjMel7yQbQuu9pkM7P6llFnT\nfmi+S7O7MXBZy7i89sJFHn3kyB9L4eu7Bri0ulkx8cSppDMQASnjmGR+YdObQas+0zLYx2XSMi4b\nmeQ7rYRmI6Y65bFnPiWU5gb1cYhvRSq2JlVsKA75FiaSHLgIN5eKmT8bNs12MFlM2mOUTkDmO1wc\nM9WRxtGjMLdEfcRjvDpOc89WKolib3mOf/TTDzNmNzGXzpn3cjEmx9R+Z+H0fQ1gpGLjmdmct4Sk\n65kvU05U0YRSrjHnu1GEX6uuM+e//uIlZi73WZb0XTAuWcG4WKlYZh4cJ+jLYuZaC7w88yYHxvfw\nnqk7SWMfHIe4d2sNxlpph7JNJxq9XCdJBYTmWtX0CL/8i0/RWuigejUS2rTTgQjWNeb8W5GKrb13\nMi/C02YhzjeKzpI5h6XtZqP9/p3vpeRHZPbzWqtt8ppUr5vhey4//tF7UHGZdxbPF5nwOdOxe3+/\nyWkhFZOaxGrcyzIZYkySxUVQitKOHebf9jlzldXj+xW0rVhHTshqK2Fuucv8ijl/+8Z2s/XqXcX7\nCWGq+V4mEb6DRBRgNDfoL3VX+MVnf5lfeuk3SWXGpw9+HwBx0/pT7IYmTiWdXsZKMynYzOUXX1oH\ngFaWOoXXI+n00EC9uqd4zcryu+ulciujLxXzmTP7ZC4df3PD1+a9cWZb8yilmG8vEQpNs2rusdWr\nZsOxmVQstP4C6fi4UcTVktmotE4Oy+MunVtGazhw53ThP8js/Hhq6TyXVmfWJfH8tx7munbJSj2U\n7dKdZcLEuOfVe2UksmrgnEfTU+s8LnlRpDCfC33dfhGbDanWMC5FA8r+a1ZefZ3pjvE1Lld2obK0\nkHuJMAZHF1Kxw5cSWqfPFIxLt2Xu65GxUiEVuxXG5biVif33n7yL6fEyz791rQBZhVRsg01pLgk+\nu5zxi7/9GkdOL+JXKxumiq2mEZlfZtf45k30/MLjYv6Mr11b18cFNpOKDaeKaU2hCFg7ulmPbtZj\nqjJBZq9x6pXJ4tuUiq1NFRvwq4FhpLKbZFxGRs3a39tEKrbc6BVy9nTAj/duUsUacRO0xtcKLwoZ\n22kYsXgNkHeVRLuekbErXSQT5ub8ZIPQhGLYSxHkwMVxwHV5RRoJ9mji0rwBcJFKF4yAHmBcRjwD\nhDdiXHrdzT0uqf24NJbsLhvQfnmgEWVfKmbWxsszhoHSXXPcHTdleoBxCcfHCSbGbztZLGdc8vvf\n9DFLcL9TjMtAcUfqNYyLLXZFmZWwB+a8NocYl4H98BrG5YWnznH01Ss0b9HDvtH4rgEuuUwsH8sD\nB1c0KarXhyRT+dADpnyHAcYlBy4bVJ5yf4vWS5RSTSBzxkUXyS83MwbZaCHWMi43/z6Z3VwJpwQO\neE4JR4UsBuamGKwE5sBFuR6p7zA7UkP6AYvPPo+TZNRHfbptl7eEMZB9YmuTXVvMwttqxiwvms21\nA7R7GamtOAmLsPOKaiNpUU3Me8w04wK4VGJFIlK8NeZ8t1TCcV38ag3RbhfIutNK+KMvvMXjT/Q9\nINm7YlwscMkZlzSPQ+4zLl899SRaa370PZ+yhm1zbLcCSgFarXo/njAWrJy9gBP2cLRHZ0mxtNDm\n3Ml5QmEqMpdXrxW/u1Yq5hVSsRvfF9laE6Eb4imNGIgZdKwht2SNxZEf8gMHPkJQNgtft9nFzY/b\nLiif/tA+3N4kqY65XDdJerm/JU8UA3BtryClFFnHPItVMhbqPdq2Epgb83Njc14djoR5fepFOELg\nKI1L/9q8fc5uDucqlLujOL4FzpkiEQmBVGS+g9SiAKMijfnm2W/zfz7287wx+zb3bzvMv/5T/4D/\n4b0/iis9VGyOs9tO0VrTGPC6KFtFlZ0O9Tf6QOCpx97hP/7zb3Hy2Kz93Tb18naSoFZs0uvLt2fW\nvZ0xKBW7ULWdiS+sT4CDfsJdpgSL3WUW6/O4GlZGfZZGK3Sa9ppdVypmfHJeKeJKyWxUmieGo6Bz\nf0ttp8O5hpE/5KA6tebLq82bDzP5ToxOOyVNBL2ohXZzb0lGL0kLqZinNEIN9tvwCKenkZ3ukCQ5\nB7q5hyMQ+qaSj9aOTRkXt7+BX3nlFaY6ZhO3VNmNSkWx0ZKuwAk1vU6KTFM+8dwyp//Nv2M8MvN6\n2lZ4vku5EvQ9LrfQhPLE+WVGKiF7t4/wiffvphsLXs+jm3NzfrYBWLBKgEstl/04LK10DOOyBrgo\nIVgKzD02LTZv4lmY8welYgOMy2j1xsAl8Fx87/qy3NyYP1WZRNp1JPNKfU/TLY5BU7qWomDy3KAP\nXES7veH+JR+yYFzMM7sR4xIngr/1r57i1x818f7pwPe9nR5l+VjtNQmE/a5RyJSVb8k1/dZccCEU\ngQAAIABJREFUrQxwcXLgkjMu5nol8ebnT9u1qJh7HBenVuGcMuvPWObeUCompRqQivW3r1XX7PXm\nNkgWi4c8LmsKggMfN6XM/TkoFysYF+txSbttwEFbMNRyEiZGzfXKvdnV/ftJFhZvGH+90chsHLLM\ngUsUodIU18k9Lt9BxkUPS9zzZzOwwAXPnoOB8+sO7G0Ge+C0mzGrtmA5d/X2fIKD47sGuCxb4BLZ\nm3pxELisWpAh5YZNnLQ2UjHp9hkX13MKrehGACJPFGPFaP19KxUreTe/uVVKM8h6mT4u/Ylps/4x\nG400XxycEmHZpRQFeOkIs745L4OMS6Y0oJCOTxY4KB+a03upxz4r5e2sjHn85pfPcA7jvQgv943Y\nOdsCuVQsJcupcrtYKKnIZEY36xH1zITwzmyD0a0mMawcK948Nzdszo9jvLJNHasZYzF20V+tm2Nb\nXIqJfQsmV1dvKulp7ZBSFYxQn3EZ7uPSiJs8dfFFtlSneHj3+1ic708gawHBjUanbibVPE555c2j\nOFGPkjNSAJOlhTbjnqlYX6z3JRaFOd9RXGvN88bcMXMM4saMS9odBvLCi/CUJhOyMOfrHLhYxgXg\npx78Cf63j/00AN12t2Bc8kWwUgq4d9shAP7wDaM3n7lYp1QO2LK1VrxPzhJJqQutag0zQV20rMtg\nohj0gWQkzQSVueZ6BELj6j5wOXZuCa00x55ZQKPp7Zu3n6VYaK2aKEXPQSMLMPrI61/i119/BNdx\n+JsP/U/8g4//HbbVtlALq4zIvsRNSkWayKIK5KMMU1U1x7b0zHMAvPr8RZ570sjlciDfbC8xO2LO\nzXvuM/d6/SYYl8XO8oZpNrc68k1AV6VcLQuUA6WF5oaN1QY3z1eb8yzXzbVIAoezW8skboTrQKm0\nPmkPIMylYo6PXy6zGoxAbZTmiZNDVP750ws4nua3Xvh3jJ4y93bBeNrOyDO2kd93y8iN+WnUKZrd\nZamg2YsL46mnzCZAC0nPr6Edl2iLYZcHe7lo6+uIxkwBJxCabtJh/vEnig39RqN16jRz3/hm8e9i\nzsr6jIvrOUU/LBnHNI4eI6v10G6HlcouIxXL5zhPQqjo9TLcXoKrIZ6bR5+9guu4yJ5Z7xzHGfC4\n3JznYaHeZaHe4947JnEch7v3mQJGHovc97hsLhWb7Y2xBYf5y6v41So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Mp/nkp95nIpFt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bFzS7zwer8x9FrG5YGHdvOhjx3gJ//qB4pUpxy4aNtjq9VMyLL+PXgj43aekKmSZAiE5cfl2YQ9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syUwinVVrR+y3AU8dPrfaCag2BUeVfG954NETOFevY0kf/mdWGXqBi0M/+fG+1yyEBGUEtLCn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3rTm/Ctb30LqVQKx48fx7ve9S688Y1vBAAIIeb9Xo899hj6+/vx7W9/m4ELEREREVHMWCpW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\ny6M2XNSoUaNGjRo1atSoUfOX5/8B6D7hfUvxYr0AAAAASUVORK5CYII=\n",
   1637       "text/plain": [
   1638        "<matplotlib.figure.Figure at 0x7fc7e04f5ad0>"
   1639       ]
   1640      },
   1641      "metadata": {},
   1642      "output_type": "display_data"
   1643     }
   1644    ],
   1645    "source": [
   1646     "# Normalizing the returns and plotting one year of data\n",
   1647     "norm_returns = (mult_returns - mult_returns.mean(axis=0))/mult_returns.std(axis=0)\n",
   1648     "norm_returns.loc['2014-01-01':'2015-01-01'].plot();"
   1649    ]
   1650   },
   1651   {
   1652    "cell_type": "code",
   1653    "execution_count": 27,
   1654    "metadata": {
   1655     "collapsed": false,
   1656     "deletable": true,
   1657     "editable": true,
   1658     "scrolled": true
   1659    },
   1660    "outputs": [
   1661     {
   1662      "data": {
   1663       "image/png": 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WjtQgKkIiIiLSIu3dnkFhfhndo0JsZnrs2zFgWEcAdm9Js3ISaaiUA5nUVBuM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\nGUZiIVL59AXv6zI72Fa9AZ/Vy7pgM9uqN+A0O27yMxA3SjYcJna6m0x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oIQDq6+sJBAIc\nP36cbDaLyWQiFApRUVFxRce6ngGxS0WxWCT1rvVMkrnU7ClNJp/BoBrmuiGZDSYcs7/e+6yeBZ8R\n5HoHJS+Eol4knk0ynZxhOlU6jcXHGYtPkMgm3znN/nvqXP53AKtmwWm24zN6sRmtOE12VFXFoKio\niopBMaAqCqpa2pbOq3MtPIqioM7etnT70tZo0DAbTJg1E2aDGbNmwmQwYZndmmcnCTBrJsyaGYvB\nhEkzndf1azG+DotNNpPnlf96GavNyCMfvgOj8cYOEl6Kr0Exl2P6wEFmOo6SHhklevLUO93cFAVr\nXS2+W3bgaFmLdXYmtKUwUcBSfC2WG3kNFoeb9ToU83ky4+Nz3e6yk5Pk43Hy8QT5eJxcLE4+HiMf\nT1AIjV/Bp+7lqSYTJo8bLRgsTZxiOjeRiqm0r81+/5lt4QG9FLB05vZLG332PLNT7evoRR29WCiF\nMUUtTa3vdKC5nBhdLiyVlRjdblBVFFUphRRVRVFmt7OXo6oc6ehgy/r15JNJ8tEYqZERstNh8okE\nhUSCfCIx+++UIDMxQe5MD5zpmf9kFaU0oYzfh9HjwWCzodlsaC4nJq8HzenC5HFjra3BFAhc14+b\nt3ALH9EfZSw+Qc/0WV7p3cfx8S76UyPXdDy70UqFw4/P5mWtv4lNla00eupvWGvPpei6Pjde9dCR\nIxe93U0PQj/96U+ZmJjgySefZGJigqmpKT784Q/zwgsv8Oijj/Liiy+yZ8+eyx9oCeoLDzKemHxX\noHkn2KQusp++woFi72VQVIJ2P16rh0pHgLW+JiocflxmJ16rG49l8QxOvpx0PsNEYorh6BjTqRmm\nkmHG4hOEU5G57mLpQpZMPkP2IrOFnWPVLNhMVoI2HzaTDafJTqUjgNfqmTdVstVowWq04DDZcZud\nK6bVZDmankzw7LfeJpXMsee+tTc8BC0Vuq4T6zpN+ODbpMfHiXQcJReJzl1vb2oicNedpZmlqquk\n778Q4oqomoa1pgZrTc1lb6sXCuQTyVIwisVLQWA2KOVicYqZDIqqlrqKGY0YzGYMVisGqwXN4cBc\nEcTodqOazUvic1kxmTC63aXgVF09N9PfxeTjcZJDw6UlBoaGSI+FyEWj5GZmSI2Okeg7e8n7qxYL\ntvo6bPV1WOtmt/X1WCqCVzxLoKIoVDsrqHZWcOeqXYTiE4zGxi/4vTWTz2DRzNhMNuxGa+mHeJsH\nv9WDz+rBco2Bp5BKkZmYJDM5SXZ6mnRonNTwCIVkkmIu987yB7P7pWUQZrfvuhxdRzEYMP/Pz130\nsW56ELr33nt56qmn+OUvf0k+n+ev//qvaWtr43Of+xw/+MEPqKmp4bHHHrvZZS24kegYn3vp7y57\nO4Oizn4ht1LtKPWRPHf+vfsWzTw7TWCObCFPOp8hkU0Sy8YZjYYYT04TmjjDyYluXu17c97jeC1u\nKh0BAjYfNa4qqp1B3GYXLrMDn9WD3WS7qX9kdF0nFJ9gJBZiPDHFwMwwZ6bPMp6YIplLXfA+mqph\n0cxYNDMuswOLzY9ZM2Ez2fBZ3fhm34iVjgDVzkqcJvs1T48slqZCoci3v/4mkXCKbbc2sOe+teUu\n6aYo5vNkp6ZI9J0ldqqLdGicRG/vvDE+mtNJzaOPELz7Lqx1tTKZgVgw8WSW0HSSSCJLNJ4hkc6T\nTOdIZfJksgXS2QLpbB50sFuN804Oy7l9DbvViN9txWqW9U+WKsVgwOgqTbYgzqc5HLjaWnG1tZ53\nna7rFLNZCslkKUxGo+QiEXKR6Nz6bMnBQRJ9Z4l3n5l3X9VkKoVIlwvN5cJaW4O1ugqT34/J58NS\nXYXBfOGxNJWOIJWO4BU/h3NLEOiFIvlsojReqlAsjaEqFGbHTxUppJJzY9Sy02Eix0+Q6OklF4uV\nWuMuRVVLC14bjaWtppXWeLPZUI0aimac3WoUMxkudbSb/tfEbrfz9a9//bzLn3766Ztdyk1V5ajg\nj3f8LtlCthRmTNZS68Tsvm1232gw3tAAki8WGImOcWa6n3BqhmgmzkRiir6ZQbqmejk12XPB+xkU\nFavROtes6bV6cJkcOM0OHCY7qqLQHx0g0pumNL8H8+pWZgcinhvkX1ofJku2kJ+bUjmVTxPPJIhl\n4kymwiSy8xdTNBmMVNoDrPU34bd5qXVWEbB7Z8NNUFpqxGWdPDpKJJxix+5VfOCjm8tdzg2lFwqk\nRkZJDgyQHh0jOxMhNxMm0XeW1MjoeVNYqxYLwb13EdhzJ/ZVqzD5vLKOiLghkukcg6EYU5E0k5EU\nB49O85NDbxJPZpmOpJmMXHj9qGtl1FQsJg2rRcNqMmA1a1jMGlazhstuwuu04HWZ8TjMc/s+lwXT\nCm0NFsuDoiilFjKzGZPXC1x4EgO9UCA9FiI5NERqcIjk4CDJgUEyk1OkhkdmuwC+h6piqazAVl+P\ntb4Oa001mt2BwW5Ds9tRNK3U0pLNkpuJkIvFKCQS5KJR0qOjZMMzpVA2E6GQuvCP11fCHAxgb1xV\n6goY8GPy+zEHApgDfqy1tWguZyn4XOVn16XmFZCfVW4SVVW5v/nmd/nTVAMNntoLzvpRKBaYSoYZ\njo0Rik8SzcSJpKNMp2aIZRKkcini2STHQl0Xf4Dx1667RqvRgsfsYkvVela5a6lw+Kl2VNLgqUWT\nFhxxjfSizusvd6MosHvvmnKXc9V0XScfi5OdniI5MER2eprczAyZqWmSAwOkhobnTV19jsFqxdXW\nirkiiLWmBue6Nmz1dRjd7psy4FYsT4WiTjSRYSqSpuvsNCfPhglNJ5iMpJmcudAXnySaQcXjMLG9\nrYKagB2Pw4zLYcZhMZZCjFnDYjJgmd0CJFI5Eqk8iXSOeCo3e750iiWzTEXTJJI5kpk86WyeqUia\nVCZPoXjpkSeqqtBQ6aQ26CDotVJf6aR1lZdqv10CklhWFIOh1OJTWwO37pp3nV4okIvFSsFodjmE\nzORkqTVpYJDpAwfhwMGrf1BVxeh2Ya4szRh4bj22uTFTBsPc9txlBqtlroXK6HbhaF6D5QrnCLiR\nJAjdRPGe3lJSnh23B8wlc0XTZgfAWTFYbRislgX/tdagGqhwBKhwBC55u0w+SyQTI5aJE83EiGf+\n//buPEquskz8+Pfeqrq1dq29L+nO0iEhSUNIiEDABYKA408QERRHxOM2o4Bn9MwZRY/DMCLDOLii\nAyoOjLIp6vxQRnbBn6whiVkh6c7aa/Va+3ar6v7+uNWVbkgga1cvz+fQ1Ha7897qfure577v+7wp\nDAz27t1LW1vbpIIDhjH+yPy/VbXisjnQLJq5poxFw6Za0awaDouGR3Of9KIO4sgYhkFudJR0bx96\n1Oxyzw4Ookeik8fhvnFsrp6fMEZXn7SNYRiopS5rxWZD1WzmJNdAAC0QMP/m3S4sbjc2n7e8VoXN\n58Pm9x3X5PzXt/UzOBCnY1UzwerpVd0sn0ySCYfNK2ulYQG54WHzfY/FyA4Okh0eOWSiA6Da7bjn\nt+GaNw9X6zxzkmwggM3rlZ4ecVjFokFWN4ehlYeklRKKTK5AIpVjLJ4lksgSjeeIJDJE4lmiiRyx\nZJY35hoWVSHgddCxqJq2Ri81fheBKjupsR7ec95q7DbLUffah3zOY9o3PV8glckTS+YYi2cYi5n7\nMRbLMBbP0j+cZE9flH39sTd9b6DKzqnzQ6w8pYaagIsFjT78VW9dcleImUixWND8fjS//5Cv69Eo\nqW5zblI+mSwNw0ti5PPl4WbmMdprlgCvqsJRXzejL7TJGegUSff2sflL/3hU32ML+HHU1qIFA2jB\nEI76OrRS1RBXc5M5+W4K2K0atdYQte7QpOfdI1ZWzZfKQNNdUdfNCj7jE1MTCbOSTyxG6oDZZa6P\njaFHYxRzubf/gQCKgmqzmeNyS+NwFasVi91des58DUUxk6Pxr5xuTvrs7jmif8bidmMPBXEvWICr\npRktFEQLmV3kWsB/2A/eVCLLs4/vQlHg3BM4L2h8fLNZfai0boRhYGQy6PG4+XyxSCGZIjc6SnZo\nmFwkQmYgTLqnh1zpfS4kk2/579h8PtwL5pcSRj/O5iZzbQ6fDy0QwF5TPWMPOuLEyukFRmMZc1ha\nJM1INMNILM1IxBymFolny4lONld4+x/4Bm6HFZ/HTmONG3+VOdxsYbOfZQtCNITcqOqbE50NG8I4\ntKk9vbBZLfg8FnweOy2HWSy5WDSIJLIMjqXY0xul80CEwbEUfUMJnt/Sx/NbzMpYqgJL54dYsbCa\nar+TjkXVNEyziylCnAw2nw+fz4dv+bJKN2XKSCI0RRz1dSz8+8+RHRkpP1e+UqYoFHWdQjpNIZU2\nJ5AlU2SHhkh07cYoHPrg5aivx9FQj6OulqqlS/GtWIYWCMgJ0klQ1HWywyNkBgbIjY5iFMxJfxTN\nCYHG+CTA8YmAhQKKxYJa6gVRrLbySfIbJwyOP0ZVsbpcWFwuLC6nOXl9/G9kfEyvYVDM5ylmsxSz\nWQrZHPldu9izaQuF5MFypRNLl75dcqNYrebJdkszjtpanC3NpZ4ZL/aaGrOHwaaVJiZaj2l87qHe\nTz0SJZ9KmSVEE4mDkz4jB8cam71SQ6QOdL/5h6iqWdK0yovNf/Bq1HDBw8vZBWSx0WYZYuCH/0F/\nPk8hk6GQMd83MynLme9NsQil71XecGuWRFXKbT5cDw3AK2+306qKzevFHgpiX7IYe11duRfHVkpu\nbD4fNm/VjChTLSYrFA0SqRzRRLZUFCBHNFnqTUlkSWR0igWDfLFIoWBQKBoUCkWKxsQedfPnFEtf\nhVLCXSwa5Avm9vnS9xUKBnq+QDJz+L9Ji6rgr7LjcWpU+y04tNJQNM2KfXxujWbBXnr/iiNTAAAg\nAElEQVTe47Thr7Lj89jLSc9sGjamqgpBr4Og18GS1iCcYz5vGAbd4Tg7948xFEnz111D7Ng7wvY9\nB4/Xi5p9nNIapKHazdnLG6gNuiq0F0KIE0kSoSmiWCzUX/zeo/4+o1hEj8XJjY6YE6JHx9AjERJ7\n9pLo2k1kk1kbfeCxJ8x/x2rFe+pS6tZdQGD1GVhlwcOjVshm0SNRUgcOMPz8i0T+uhl9bKzSzXpL\n/RMfKIrZZe1x42xpwepxY/V4Sl8H79uqPDibGnE0Nh5cc2GKqDYb9ppqjmTwiVEsku7rM9enGBkx\nJ3x295CLRMyqOZEI6R6zhymn2nlp3mXkLSqLRl5lXmQ7UQyzB6s0yVS127E5neZ6EzZbOemZ1NNj\nmGtJgAGlW3NoX2l9CkUx14lQVFAUorEYgWCg9BgsThea34e9rtbswamuxtnUKCWpp5msXiCWyKHn\nC+TyRXJ6AX3ibb5ATi+i5wtk9QLJlF6ueJbM6OUhY9FEjkQq96ahY8dKVZXS2mQKFrU0SVpVsVoU\nLBa1VCxAxWrRWOixU+13EvI5CHkdhEr3q31OfB77IXtsxGSKojCv3su8enNZiasvWsJYLMOBgTh9\nI0le2tbP5l1DdPVEAfjZ/93GioXVLGkL0FJXxZLWIPWhqa20KoQ4MSQRmuYUVUXz+9D8PjwLFrzp\n9UI6Tbq3j8hfN5Po2k1mIEx0y1aiW7YCYPN5CZ19FvWXXIS7rW2KWz+95VNp0r29xHa8Rqavj+zI\nCOnefjL9/ZOqqtgCAXwrlpsn7rW12GtqzKFg4xMALRMmA06YCGgUixRzOXNoWE43T55LEwZR1cnf\np6oYhQKFdIZ8KlnqGSxNQC4dXMcPsuMn5JbS4nF7evs49YyV5STHMkWrUE8VRVVxNTfjam4+7DbF\nfJ7BgTiP/mYb+oEIF75/CWe98/8c3GBCz87JsGHDBpbIApLTjmEYDEcyDI6lGBpLsa8/Rs9ggmjC\nnD8yOJo67uSlymXD67bTXOvB57HjdWv4PHZ8bg1v6dbnseNx2rBaVSyqYn5ZzPuqalbdHKeqipxQ\nTwMBr4OA18Fp1HDJ2W2kMjrh0RS7DkT404Zutu4eZuvu4YPbV9mpD7mpDbhobaiiscZDyOegLuAi\n4JXS9EJMV5IIzXAWpxPPooV4Fh2sipXu7WPwT8+S3LOHxJ59DDz2BAOPPYHqcKAFg3hPXUr9xe/F\nPb9tynsCKqGYz5PY1WnOh+nuJtG1m1R3zyHnaVjcbrzLTi2XawyuORNP+6JpnVjs37Bh0u9/LjEM\ngy0benju8V1ERs3y68tXNnHWuxaZPTZizkhldPqHk/x1T5Lt4R2ER1Ls2Dd6yIpmFlXB57FzSmuQ\nuqALzWZBs6rYyrcqNosFzaZis5q3mtWCx2XD7bDhclpx2W1UuWxYLNP3s0GcOC6HjfmNPuY3+rjo\nrFaiiSwHBuLs7Y+yfc8IXd0Rdh4Y47V9o7Bp8vf6PXbqQ2ZC1FrvpWNRNc11HvyembEoqBCz2ew/\nC56DnE2NtP7t1YA5uXv01Y0M/elZMgNhskNDDD71NINPPY1iseBesIC6C8+n+rzzsLqOrVrPdGMY\nBunubtK9fcRe38nQs39Gj0QObqCqOBsbcSxZjL22jqoli3G3tWGvrsbiluENM4Gey9OzP8ILz3ax\n+/UhbJqFU09rYNnpTSxZUS+/w1kok8sTS+ToHozTO5Qw16eJZBgYSdI/kiSWnDgXzhzKWuXSOHtF\nA43Vbmr8TuY1eJlXV4XXrcnfiDguPo+dFYvsrFhUzQfOMy9EFQpFhiJp9vXHGBhJMRrL0DeUYF9/\njM7uCIWiwYtb+3nwSXNJCodmYX6jj4XNPqp9TkJ+J821Hmr8TvkbncYSaZ2xWMYsQpItkM7lyWTz\nJNM6I1GzSmGx1NU8PDLKXzrNzFhRwOPSyr3IXrd5W+WyUeXW8LllKGslSCI0yykWC6F3nEnoHWcC\nZmI0tumvjLzwEumeHuKdXSQ6O9n9nz9BCwbxn7aCuvdeSNWSU2bMh3AhmyW24zWSu/eQ7u8ntm07\nmYFw+XVrlYf6Sy6mavEinM3NuFrnHXYFZTG96XqBF5/dzQt/6iKXNYuILFhcw/s/3IFfJi/PaMm0\nzuBYivBoisFR83b8q38kediKZ1aLQm3AxaIWPw0hN8XMGOetWUZdyEW1zyknFmLKWCwq9SE39aE3\nz80tFg2iiSy7DoyxY+8o/SNJ+oeTB3uR3sBmVan2OakLuWipq6Iu6GJxS4BTWgPyN30UDMMsGZ9M\n68SSOdLZ8QqK5m0mVyCnm1/5fBG9UCzPE8zpxXK5+VgiRyKdI5bMEU/pR9eIPQeOaDO7ZqG1voq2\nBh+tDVXMb/Axr14u3JxskgjNMYrFQnD1KoKrzfkM2ZERBp96hsiWraR7exl85lkGn3kWm8+Hs6UZ\n9/w2/Kd1lEv5VmqImGEYZMNh0v0DZPrMnp7U/gPkRkfJxxOTtlUdDqrPW4unvR1nUyP+0zqkCtcM\nltcLbNnQw76uEfbtHiYRy+LyaJxxVisLFtew8JQaOUjMEIZhMBrLsHP/GHv6ogyOpugbStIzlCCZ\nPvTJhdNuoanag8+j4XXbaapx01xbRcjvKF9Ft0w4MdywYQMrFr312mhCTDW1tObSO5Y38I7lDeXn\nM9k8PYMJRuMZwiNm0j8cSTMUSTNcqmD3111D5e2DXgcLmny0NXhZc2o97fP8WOfo8EzDMEhm8kTi\nGQZH0xwIxxgaSzMay5gXVMZSJFL62y62eySsFhWPy1YeUhv0OnDaraUvc1Fgl91KyOck4LWXfydb\nt21jxfLlwMHKkvGUTiyZJZY0b8cf9w8n2dMbZdeByKR/W7NZqAu6OHV+kPaWAPVBFw01Zi+3HPuO\nnyRCc5w9FKLlqg/TctWHMYpFotu2E37yaRK7dhHbvoPYtu30//5RAFRNwz1/PlVLT0ELBMgPDTGc\nyR1cELPKYxYSOI7SyoZhkE8kSHf3lIo/DJAJDxLf1Uk+NnkhPIvLhRYM4m5rwz2/De/yZeaQt/o6\nSXxmuHg0w77dwwyHE2x6+QCJeBYAu8PKOe9ZxHnrFmF3yO94usrqBfb1RRkcM9e16eqOsKt7jKGx\nNPlCcdK2FlWhscbN0rYgtQEndUEXdUE3tUEndUE3VS6bHOzFrOWwW1nUcujFLcEchtU/nGBgOMXG\nnYO8+lq4/PXwM50oCnjdGoEqByGfg0CVgyq3htetERtNovmHqfE7qfE7Z+x8NsMwiKd0unoi7OuL\nEh5N0TecZHdP5LC9M+M9xQ0hNy6nObfP59ZwOqzYbQdLxpvl4y3YrBZsVnMuoM1qVma0axbsNgua\nzYLLYT2mz6Fej/WQPYSHky8U6R1MsLc/xv7+GN3hOMPRNH1DSR5/aT+Pv7S/vG2gyk5rvZf6ajcL\nm3yccUotNQFJjo6WJEJTJJnW+c79G4kms+a6EIYZ3EYRioaBqirYbQeDcnydh/L9Ca/ZNSshr4PG\nGjdBr+OE/dErqoq/YwX+jhWAOeQs0dlFZPMW0r19ZPr6iXd2Et+5s/w9O//wv5N/hsWCze/H6nFj\ncbmwut3YvFXm+iiBgNmrVEqUjGKRfCyKHk+YayZ1dpHpHzjkujdaKET1O8/F2dSEo76OqvZ2HI0N\nEvCzRDqVY2/nMMODCfoOROh8fbBUvho0u5Vz3rOQle+YRzDkrkgRBLN8cpFisbSui2FgjMewAZFk\nnvBoqvy4UCyawy5KwzDSWXMMeTqXJ5szh1wUSuvJKIp5MlRe42V8bZfSAVizqWi2g4/H14E50cNj\nJn0ujd8vHtzXYmk9m6xuLsyZzRWIJLKMRDOMRtOMxDKMxjIMjaXpDsffdBW2ymVjQZOXkM/JomY/\ni+f5qQu6qfY7sVln5gmaECebx2mjvSVAe0uA81Y2AZBI5dixb5T1O8J0h+NE4lnCo2ZVxDf63YvP\nA2Zi0FDtJlDlwO+x01DjpqW2CncpSXA5zN4Nh92Ky2E9rl4mwzDXvcqWhpxlc4XS54b5eRhL5iY8\nd/C1yY8LDI2lGY6mSaT18pybiRpCbpa0BfGXSsjPq6+iPugm6HPg89gn9RTPFFaLSmuDl9YG76Tn\nC0WDPb0R9vfHGRxLsX8gxs79Y/y1cwg6D/YYOu0WWuu9rD2tibOW11Ptd87ZHsMjJYnQFMnk8nR2\njxFP6ZjLjyjmrWKWSi2WTpyMo+zBdWgWGqrdNFZ7aKxx01jtZl69l4XN/uP+ELDY7fiWL5u0wnA+\nlSa1bx96PMHuHdtprqk1F8RMpdAjUdJ9feiRKLmRUfLdPeZilUdItdtxtjRjD4Vw1NfhaV+Es7kJ\ne00NVo9Hkp4pUCwapEqTPhOpHIm0fnBuhgIKE8p4j68HXHpBKT03/pgJjw0MjEKRQt4gl80THUkS\nHUuTz+SJj6UZ7ImWEx+AukYvp61uJlTroaUtiMN5Ynp/ikWDsXiGWDJHIqUTT5ljvsfLKccS5uNE\nOkcynSeR1klndfKFIwjM/ztwQtp4pJx2MyEaL8E8ce2Z8n1FwazWbt7PFw3zhKM0Nj6rF9D1wglb\n/wbMce7tLX7a5wVoCJkXa1rqPLTUVUkMC3ECeFwaa06tZ82p9ZOeT2V0IoksiZR5u2nLTly+WgbH\nUvQNJegdStIdThzmp07m0CxmkjQhUTKAYmkx4KJxcIHf3BsSmKxeOGTicrQ0m4Uav5OmGg8el422\nBi/tLX7qQ27qgi5cc2hUgEVVygnxROlsnoGRJNv3jLCla5j+4SS7uiO8vn+Mux/ZBkBbg5fzTm/i\n9MU1BKock4buCUmEpkzI5+S/b7r4LbcxDAM9X5qcly2Q1ccn9RVKJy750mt5hiJmV2nfcIK+4SR7\n+yZfCfJ5NE6ZFyTkd9CxqJozTqk9IR8aVpcT76lLAdhnVWl8i7VTDMOgkE6Tj8XIRaLoY2PkxiJg\nHEyOrF4fNp8Xze/D2dR0XMPqTrZi0SgvsJjTCwevdumF8sRKRQGrqmKxmAsgWiwK1tJ6IeP3VfXg\nc4YBBgal/8qJhlJKkg8mywdv1VLyPP6aMSF7Hk9kEqmcmcyUvszERi+d4L/5eXMCqdlr8XbHLwVQ\nS1+WCfftgB0FG2ADHJgfMOOvT14tZbIkBmMYJIEMkOuL8ORjcaqcNjwuDY/LVj7pn/h+vvE9VlWF\nbKkHZuJXcsL+Hsl4cc1mweO04a/SaKx2l4dTjCcUlH8P5u3o6CjV1aHyY1VVsGsWnJp5hdU53tNT\n6u3RrJZS+82/gUwuTzprXhVNl3qSxv+2xj8T9NLE3Yn7lcnmyRfNnppC3uyxGu+5Gb9fKFK+b7Wo\nZu+y3YLPo2G3mUNCxvdLmbBfyqGeU5RJvdQ+j0bI5yDoLX35nLiPcQiJEOL4uBy2Scd5S7qXVauW\nTtpGzxeJxLN0D8bpH06SyuikMnmSGb382ZlMmwsGJ0vV0XrC8UMeF6wWBVVVS0PNLLgdNoJVjvKQ\nsjfeaqX7Xrc2adTLoba12yy4HDYpDPE2nHZruaz7+88115qMJrK8sKWPrbtHynMyf/HH1/jFH18r\nfY+F805v5vT2GppqPcxv9M7pz2xJhKYRRVFKQ2EsVB1FAazxCcjjiVFnd4RXtg/wyg7zCvUfX9iH\n1aKWS3IubPZxTkcj7S3+k3pFRVEUrC4XVpcLR33923/DSVIsGoxEM0STWeLJ8aovOfP+hF6PgwlN\nqVrMG5/LH3nv1pR7oBdVMZOpo+lVtFoU3E4bLrsNn8c8OHlcNtx2Kw5FQUnpFJI6ekZHz+TRUzrF\nwpG9D6pVxaKZC86qFsVcQNaioFpU7B4NZ5UdbBZUzQJWFT1fNA/CGZ1UOk8ibU4qNRfCzB/b+wJo\nVhWX04bXbSY1Ib+TgMdeTrCqXBr+KnPhS3+VuSCmzXp0CfmGDRtYteqMY26jEEJMBZtVpSbgpCbg\nhFOO7HsMwyCbK6CUFgMev9gjpiefx84l58znknPmA+Y8s5e29rO3P0oklmXHvlGeeHk/T7xszjdq\nrHZz/pkttLcEaGvwEpxjCwBLIjSDGMbBK7/5fLF8WywWsdutnDo/yIpF1Vx0FhhXGCTTOn3DSdbv\nCLPh9TAj0TS7e1PsPDDG/76wD4Bqv5PlC0N0LKw2h9jVeGZEEIx/MI8P40pn86QyOsl0nqFIisGx\nNIOjKYbG0vSPJElnj/xEWrOq5YTUbjOvXo3P0xh/buL8jfHHNpulPDZ6fP5HvmBepc8XihSKhvlc\n6bVCsYjChN4ezESmWJqjMXEOyhvnpEzcJhaL4/Z4MAwDRVFwO2x4XDY8pWENHuf4Y23SY7fDhl2z\nmD8jkmZkKMHObWG6Xg8TGUuTfENCpdmtVNe68VQ50Ozm/to089aqWajyOqiu9eDxOnC7NVyeE1fy\ns1AokssXKZTex0nv5xveY7tmwVmq4OO0W2fsBGEhhJgOFEXBYZfTxZnK47Sxbs288uNC0WDHnhH2\n9kfZtT/Ci1v7+OUfXwfM4ewrF9fy7lXNLGj00VTrmfXD6OQve5rR9QKxSJrhwQTde8eIjKZIp3Ik\nE1lGh5Lk36ZXwu6wUuV10NIWpK7Ri8dr57zFNbz/HfNwV5lr52zbM8Krrw1yYCDG3r4Yz27o4dkN\nPeWfEfI5mFdXha/Kbq7P0eynsVSqcSrH5Ob0ApG4OXejOxxnX3+M4YhZhapnMH5EtfzHy062NXgJ\neO14XRpVbo0ql4bXpeH1mMmBQ7OWV4+faVe6zN6Iww9RfCPDMIjHMuzvGmbrhl46XwujT1ijxemy\n0bogRDDkpqnVT3NbEJ/fid1RuY8Li0XFOcs/jIUQQoiTzaIqrFhUbS4zcB4k0x1seD1Mz2CCTTsH\n2Vj6AnNR6rWnNbK0LWhWHwxMTQXCQtEgk82TyuRJZXXSmTypbN68zejl4eGFCcPBDaM0hUE3L5Jn\nSsU57DYLF604/CgPSYQqxDAMhgcTjI2kSCdzjI2meH1rP4P98UNur9ktVNd5cLo0LFYVq1XFYjFv\nVYtKNqOTSuZIJ3UiYyk2vXLoBbxUVcHhshGq8bCmzsP7ltRRtFuJFgoMRTMcGIiz68AYmyasWzCR\nz6PRVOPB5bCRTsb4f50bJ1W1czltOO1mxRmbRcVqVbBZLVgtChaLiqoo5bkcxaI5JyqZ0UlldMIj\nKYYiacbiWfpLc58ONcxLVRXqgy7aWwLmsK5StRtziJeVkN9JbcBJbcAlC5FhlqIO98dIJ3OE+2Ns\n3dhLPJopvx6sdtPY4idQ7aKlLciC9mpUSTqEEEKIWc/ttPHOlc0AXH3REg4MxNi4c5ADA3FefS3M\nYy/u47EX95W3t1oUPE4N7XDzu95wa1HVcnGNosEh57Lq+aK5gPZIkmRGJ5099ALax+qiFc2HfU0S\noSmUjGfZsbmP4cEE+/eMvCnpsVhVWheGCIRc+IMumlsD1NRX4XTZsB7FnIVi0SDcF2V0OEUiniER\ny5Z7lnLZAqlkjp59o3TvnbCatQKeKjtNITdnrmzBX+2iCMRzeUbSOsOlMrm9Qwle2zdaTlC2H+g+\nAe/Mm7mdNk6dH6Im4MTnttNQqpNfE3Dir3LMyLKYJ5NRNEglc0QjacJ9Mfp7IkRG04wOJxkdTk7a\n1u6wsrSjgepaD4uX1dPY4pvzyaIQQgghYF69l3n1ZvnuQtHg9X2jHAjHGYmkCY+l6B9OkkzrZPUC\n0US2XC3weKkKhPxO6kPuUuEPK26HeXHd5bDidFhx2Q9e/B6/6K4oTKqaarOq5dftmoWcXqTz9a2H\n/XclEZoi0bEUP/73Z8tDkFSLwpIV9TTNC+AqzadoXRA6IWWCVVWhodlPQ/PhF2nL5wuMDCUZGojT\ne2CMgd4Y0bEUPfvHJidIJTbNQo3PwZJ6H7UrW9DsFrr7+pjX2gKKQlGBogJZvWhWvMrlzflL4+uP\nKIBmMbctdWEqClitaqk0p40av5O6kItAlR2nXSpPTWQYBtlMnuhYmmgkzXA4TnQsTTKRo79vmCce\n/uOk4W3jnC4bi5bW0twawO3RqPI5WdBejdU2favzCSGEEKLyLKrCsgUhli0IveV2hmGQyxfLVY6z\ner5ccKpQMNfKLC/1oJhVXycu92BRFYJeB9pJODdxaG/9uiRCU8Th1Fi+sonahipaF4QIhNwVnXNh\ntVqoa/BS1+BleWmRNoBcNk9/T5RoJF2am5RjsD9GbCxNLJph1/Ywu7aHy9t3btp+VP+ux2snEHQR\nCLkJhFxm71fIjcutYbWqGAak41lS0Uy5y9QoUg4WRVUOrl+jKFitKlabBZtNxWq1VGSxzRPFKBpE\nxlIMDsQZDieIjKbKiU90LE3uLQo+1NR5CNV68PqcVNd5aJrnJ1jtxj6H1lkQQgghxNRTFKVcOAp3\npVtzdCQRmiJ2h5X/c+VplW7G29LsVloXHj7zj8cyDPbHyWV1du3cTWNjC7lSD1AuWyCvF8zERLOg\nKgqFYpFiwSCb0RkbSTE2kqLnQITufWMnpf0Wi4rTZcMfdOFw2czKZjYLmt2CZrdid1hxe+y4PXY0\nhxW73YrDacPrc0xJL4lRNEilcsRjGcK9MYbCCaJjKUaGEgwPJsjrby6G4XDa8Aed+PxOfAEnXr+T\nUI2bQLUbt8fO6zu3ceaZq09624UQQgghZhNJhMRRqfI6qCqV107p/axa1XbUP6NQKBIdS5cSoyRj\nIymyGZ28Xiwv4qhaSouGlnqAzOF0pcogRUAxu2LzepF8vkBeL6LrBfL5IqlElt7uCMZRrmxt0yw0\nzQvQ0OzDU2U3kyfNima3YtPM3iY9V6CQL1DIl0pklxaxLBSKFPIGxWLRTAyzBbIZnWw2Tzqlk0mb\nxSyS8ewhV9y2WlWqaz1U11VRU19FTZ2HYLUbX8D5tr06M63KnRBCCCHEdCCJkJhyFotKsNpNsNoN\n1JyUf6NYNMjrBXS9gJ4rkMsVyGXzZkKSyJJM5shl8mSzeVLJHIlYlngsw76uYfZ1DZ/w9tgdVpwu\njcZ5fjxVdjxV5po7dU1eAkEXHq9DEhohhBBCiCkkiZCYlVRVQbObvTlHI53KMTQQJ53W0bOF0pC/\nPLlcgWLRMBcPtZmly80vBXXSrYpNs2AvDcPTSkPvZFFPIYQQQojpRRIhISZwujTmvU11FCGEEEII\nMfPJZWohhBBCCCHEnCOJkBBCCCGEEGLOkURICCGEEEIIMedIIiSEEEIIIYSYcyQREkIIIYQQQsw5\nkggJIYQQQggh5hxJhIQQQgghhBBzjiRCQgghhBBCiDlHEiEhhBBCCCHEnCOJkBBCCCGEEGLOkURI\nCCGEEEIIMedIIiSEEEIIIYSYcyQREkIIIYQQQsw5kggJIYQQQggh5hxJhIQQQgghhBBzjiRCQggh\nhBBCiDlHEiEhhBBCCCHEnCOJkBBCCCGEEGLOkURICCGEEEIIMedIIiSEEEIIIYSYcyQREkIIIYQQ\nQsw5kggJIYQQQggh5hxJhIQQQgghhBBzjiRCQgghhBBCiDlHEiEhhBBCCCHEnCOJkBBCCCGEEGLO\nkURICCGEEEIIMedIIiSEEEIIIYSYcyQREkIIIYQQQsw5kggJIYQQQggh5hxJhIQQQgghhBBzjiRC\nQgghhBBCiDnHWukGTHTrrbeyefNmFEXhxhtvZMWKFZVukhBCCCGEEGIWmjaJ0Pr169m/fz8PPvgg\nu3fv5mtf+xoPPvhgpZslhBBCCCGEmIWmzdC4F198kXXr1gGwcOFCYrEYyWSywq0SQgghhBBCzEbT\nJhEaHh4mGAyWHwcCAYaHhyvYIiGEEEIIIcRsNW0SoTcyDKPSTRBCCCGEEELMUooxTTKOO+64g9ra\nWq688koA1q1bxyOPPILL5Trk9hs2bJjK5gkhhBBCCCFmoFWrVh3y+WlTLGHt2rXccccdXHnllWzf\nvp26urrDJkFw+B0SQgghhBBCiLczbRKhlStXsmzZMj7ykY9gsVj4xje+UekmCSGEEEIIIWapaTM0\nTgghhBBCCCGmyrQtliCEEEIIIYQQJ4skQkIIIYQQQog5RxIhIYQQQgghxJwjiZB4S6lUqtJNEGJa\nSKfTlW6CENPGyMgIAMViscItEfI7qKyenp5KN0Ech2lTNU5ML/v37+fnP/85qVSKj33sYyxduhS7\n3V7pZs1JjzzyCLW1tSxZsgS/31/p5sw5+/fv57/+67/QdZ2rr76aU089FUVRKt2sOeuBBx7A7/dz\nySWXUCwWUVW5njeVisUiDz74IL///e+599570TSt0k2a0x566CESiQRXXXUVHo+n0s2ZU3p7e7nj\njjtIJpPceuutuN3uSjdJHAM5gog3SSaT3HzzzXR0dHDGGWfw29/+ltdee63SzZpz9u7dy7XXXstT\nTz3Fk08+yS9/+UtyuVylmzWn7N27l2984xt0dHTQ3NzMf/7nf1a6SXOaYRg89thj/OhHPwKQJKgC\nVFWlt7eXcDjM/fffD5i/FzG1Xn31VT796U+zceNGzj//fEmCpthPf/pTrr/+elatWsUPfvADSYJm\nMDmKiDfZvHkzuVyOD33oQ1x55ZWEw2EZFlQB27Zt48ILL+QHP/gB55xzDoVCAU3T5KRjCu3evZv6\n+nouv/xyPvvZz6JpmgwXrSBFUZg3bx66rvOTn/wEgEKhUOFWzR35fB6AQCDA17/+df70pz+xd+9e\nFEWRz6UpFIvF+OlPf8rixYu57bbbmD9/vnwuTbFcLkdVVRVXXHEFAFu2bCGRSFS4VeJYWG666aab\nKt0IUVmdnZ3cfvvtnHnmmdjtdlpaWmhra6OhoQFVVVm/fj3t7e20tLRUuqmzWj6fZ+PGjQSDQaxW\nKy+++CLLli2jsbGRn/70pwwODlJdXY3D4cDlclW6ubPSG2Nh3rx5tLe3A3DDDTeQSCTYunUrfr+f\nhoaGCrd2dhuPh1AohNVqpVAoEIlE6O3t5XOf+xzf/va3+cQnPiG9QifReDysWV93dsUAABEZSURB\nVLMGu91efq8ffPBBli9fTk1NDc899xzLly+Xz6STbDweAoEAHo+HdDpNKpXC7/fz8MMP8/DDD5NM\nJvH7/VRVVVW6ubPOeCysXr0ah8PBmjVruPfee0kmkzz88MM888wz/OUvf0FVVRYuXFjp5oqjIEcQ\nwcaNG3nqqad4+eWXy1dXzzjjjPLrr732Gs3NzZVq3pxx88038/3vf59XXnkFgGuvvZZVq1axa9cu\nWlpauPDCCycNCxIn3ngsvPTSSxSLRaxWKwsWLMBms3HDDTdw7733Mm/ePJ566il2795d6ebOauPx\n8OqrrwJgsVgIBoNs3LiRpUuX8r73vY8Pf/jDfPvb365wS2evifEw3uOj6zqLFi3i7LPPZvXq1Tzx\nxBP84z/+I+l0Wibtn0Tj8bB+/XoALr30UsLhMN/+9rdJp9N88IMfZOfOnXz/+9+vcEtnp/FYeOWV\nV9B1HYDrrruOBx98kAsuuICf/exnrF27lg0bNrBr164Kt1YcDUmE5qiBgQGy2SxgVsP66Ec/yq9/\n/WsGBwfL2xiGwcsvv0x9fX25N2jTpk3l4RHi+I3P+YnH4+zbt4+Ojg527tzJ0NBQeZvFixfz+c9/\nnve///187GMfIxqN0t3dXakmzzqHioWHH36YcDhc3sbj8bB8+XIALrvsMg4cOIDT6axIe2ezQ8XD\njh07yvEwOjpKW1sbr7/+Ort27WL//v3lHjsZIndiHO7YMDAwAIDNZmPnzp1cf/313HzzzeXeU6fT\nKb1zJ9jhjg8DAwPY7XauuOIK1q1bx9/93d9x7rnncu2115JKpThw4ECFWz47HC4Wxj+P1q1bx9e+\n9jXWrl0LwAUXXEB/f78cG2YYGRo3xzz//PN84QtfoKuriyeeeIKLL76Y1tZW3vOe9/D8888zPDzM\n6aefjqIoKIrCwMAALpeL4eFhvvGNb+BwOOjo6MBisVR6V2a0cDjMD3/4Q15++WUaGhqor69n+fLl\ntLS0sGXLFgzDYNGiRQB0d3czMjJCIBBg79697NixozwuWRy7I40FVVUZHh7m5z//Oaeddhrbt2/n\npZde4t3vfjc+n6/SuzErvF08FItF2tvbcTqd3HrrrTz77LNcf/31nHbaadx777185CMfkZPw43Qk\n8TD+2d/b24uqqnzlK1/h8ssv5/HHH8fn88nw6RPkSI4P7e3tNDY2smLFClRVRVVVurq66Ozs5EMf\n+lCld2FGO5pYaGtrY8uWLTQ0NPDXv/6VV155hfe85z14vd5K74Y4QpIIzSGxWIyf/OQn3HDDDVxz\nzTX8z//8D6OjoyxatAiXy0VTUxO//OUvWbJkCbW1tQD8/ve/57bbbsNut/OZz3yGSy65RJKg45RM\nJvnqV7/K0qVLcbvdPPnkkxQKBdasWUN9fT27d++mp6eHYDBIKBRiz549fP3rX6erq4tHHnmEtWvX\nctppp2EYhpRxPkZHGwsul4snn3yS3/zmNzz//PN8+ctfZvHixZXejVnhSOKht7cXv99PdXU1q1ev\n5gtf+ALNzc0sXboUi8XCsmXLJB6Ow5HGw9KlS6mtrWXZsmW8+93vLlfKOvfcc8sXbsTxOdJ4CIVC\nhEIhuru7+Zd/+Rc2b97M7373O9auXUtHR4fEwzE62lgA+NWvfsXPf/5zXnzxRb70pS+Ve6nFzCCJ\n0CwXi8X43e9+R319PYFAgEcffZR58+axYMECFi1axGOPPUYoFKKxsZG6ujoOHDjA7t27aWtr44UX\nXuB973sfCxYs4LrrrqO+vr7SuzOjDQ0N4Xa76e/v5/HHH+fmm29m5cqVpFIptmzZgs/no66uDpfL\nxbZt27DZbLS3t9PQ0MC5556Lqqp88pOf5JxzzgGQg9xROtZYmD9/Ps899xyf/vSnOffcc/noRz9K\nXV1dpXdnxjvaeNA0jfb2dhwOB5qmkcvlykkQSDwcrWONhwULFvDss8+yZMmS8sm2rDF3/I42HqxW\nK+3t7Xi9XhYvXoyu63zmM5/h7LPPBiQejsbxHBv+/Oc/8/GPf5w1a9bw8Y9/XI4NM5AkQrPYo48+\nyi233EI8Hmfr1q0MDg7S1NRENBpl6dKl1NXVsX//frq6ujjjjDPQNI0VK1bwT//0TzzzzDPU1tZy\nzjnnsHTp0krvyoy2a9cubrrpJp5++mk6Ozs5//zzefzxx6mqqmL+/Pm43W56enro6enhjDPOoLq6\nmlwux+OPP85dd93F0NAQ69ato729XdYqOEbHGwsNDQ2sWrUKh8NR6V2Z8Y43HqLRKGvWrJGe6eNw\nPPHw9NNP09zczKpVq+Rk+wQ4nni48847GR4e5qKLLmLp0qWyltAxON5jQ319PatWrZL3fgaTQdWz\n2JYtW/jqV7/K7bffzuLFi0kkEvh8Pvr6+ti0aRNgTvz+85//zOjoKPF4nO985zucffbZ/PjHP+Zz\nn/tchfdgdvje977Hu971Lm677TZGR0e55557uOqqq/jjH/8IQHNzMwsXLiQejxONRgF46qmn2LVr\nF5/4xCe47rrrKtn8WeF4Y+Gzn/1shfdg9pB4qLzjjYfPfOYzFd6D2eN44uHaa6+VeDhOcmwQkgjN\nMhMXtRsZGSl303o8Hnbs2FGe4L1hwwbC4TA1NTV0dHQwMDCAqqpcc801fOc735Fy2SeAYRgcOHCA\n2tpazjvvPLxeL0uWLEHTNBYvXoyqqjz00EMAdHR08PLLL2OxWOjr62PlypX89re/5dJLL63wXsxc\nEgvTi8RDZUk8TC8SD5UjsSAmkqFxs8Qdd9xBXV0dfr8fXdexWCxceOGF5YXVXnjhBQKBAO94xzvw\n+Xzs2LGDhx9+mK6uLnbs2MFHP/pR/H4/fr+/wnsyeyiKgsvlYvny5eUP2ieffBKPx8O73vUu/H4/\n3/ve9zjrrLMIh8Ps27ePc845h/r6epYtWyZDf46RxML0JPFQGRIP05PEw9STWBCHIj1CM9z4mj7h\ncJjbb78dMNd5APODdnzhrz179pTn+rS3t/PFL36Ryy+/nFAoxF133UV1dXUFWj+7vHEdE8MwsNls\nkyZPhsPh8no0q1ev5pprruG+++7j9ttv5+qrr6ampmZK2zybSCxMLxIPlSXxML1IPFSOxIJ4S4aY\nFYrFovGBD3zAeO655wzDMIxCoVB+LZ/PG5/73OeMeDxubNq0ybjxxhuNLVu2VKqps04+ny/fT6VS\nxoYNGw65XXd3t3HDDTcYhmEYkUjE+PWvf20YxuTflTh+EguVJfEwvUg8VJbEw/QhsSAOxVrpREwc\nnUKh8KYu8R/+8IcEAgG+8pWv8B//8R+8853vLC8uaBgGg4ODOJ1O/vmf/5lIJMInPvEJVqxYUYnm\nz0rjv48tW7bwzW9+k3Q6zbXXXsu6devw+XwUi0VUVaVQKKDrOn/4wx/43e9+x6mnnko+n5chDsdI\nYmF6knioDImH6UniYepJLIijIUPjZohCocB3v/tdHnroIXK5HACvv/46AOeffz733HMPq1atorq6\nmnvvvReAYrGIoijlsa6rVq3i7rvv5p3vfGfF9mM2MAyDYrE46bkvfvGLPPDAA/zwhz/kpptuYtu2\nbWzZsgWg/GE7MjJCZ2cnf/nLX7jxxhv58pe/jNVqlRK0R0liYXqReKgsiYfpReKhciQWxLGQYgkz\nxG9+8xsee+wxMpkM9fX1rF+/nj/84Q8sW7aMBQsW0NXVxfr167nuuuv41re+xWWXXYbdbkfXdRwO\nB1dddRWnn356pXdjRisUCqiqiqIoKIpCT08PmzdvprW1FZvNxm9+8xs++clP0tLSwo4dOxgaGqKp\nqak8EdPhcLBq1SquueYagsFghfdm5pJYmB4kHqYHiYfpQeKh8iQWxLGQRGiGWLZsGVdeeSXbtm0j\nnU7T0NBAKpWiv7+fjo4OzjzzTP7t3/6ND33oQ4TDYZ544gne+973lruHpXv92BUKBb7//e+zb98+\n5s+fj6Zp/PjHP+ZnP/sZ+Xyehx56iL//+7/nueeeIx6Pc/rpp+PxeFi/fj26rrNkyRIURcHpdNLY\n2Fjp3ZnxJBYqS+JhepF4qCyJh+lDYkEcC0mEZoh8Po+qqrhcLp555hkWL16Mw+Ggs7OTuro6Ghoa\n2LhxI3/4wx/493//dxwOB/Pnz690s2eF8atM2WyWtra28u/gX//1XzEMg0ceeQRN07j66qu59dZb\nueyyy2hqamLv3r0EAgEWLlwowxtOIImFypJ4mF4kHipL4mH6kFgQx0IxjAkrS4kZ4c4778QwDM48\n80zWr1/P8PAwra2t5UmX11xzTaWbOCt997vfxePxcPHFFzM2NsYDDzxAIpHgb/7mb/jlL3/J3Xff\nzY033ojD4eCWW24hn89jtUo9kpNJYqFyJB6mH4mHypF4mF4kFsSRkmIJM8j4BMzLLruMzZs343Q6\nufzyy8lkMmzZsoVLL71UgvskGF+D4IILLqCrq4uenh5aW1vRNI2bb76Z9773vQD87d/+LWeffTbn\nn38+gBzkTiKJhcqReJh+JB4qR+JhepFYEEdLeoRmmMHBQWpra7n11ltpb2/niiuukCtLU+iuu+4i\nl8uxfPlyHn30US6++GJ6e3tpbW1leHiYK664otJNnDMkFipP4mH6kHioPImH6UFiQRwN+auYQcLh\nMN/61rfI5XIkk0k++MEPAnJlaSqMd6dfeuml3HTTTVx44YWcf/75/Pa3v8VisXD55ZeXq/+Ik09i\nobIkHqYXiYfKkniYPiQWxNGSHqEZZnR0lFdeeYXzzz8fTdMq3Zw5Zfwq0y233MLy5cu59NJLSSQS\neDyeSjdtTpJYqCyJh+lF4qGyJB6mD4kFcTQkERLiCLzxKtONN97IkiVLKt0sISpC4kGIgyQehJi5\nJBES4gjJVSYhDpJ4EOIgiQchZiZJhIQQQgghhBBzjpTPFkIIIYQQQsw5kggJIYQQQggh5hxJhIQQ\nQgghhBBzjiRCQgghhBBCiDlHEiEhhBBCCCHEnCOJkBBCCCGEEGLOsVa6AUIIIcRb6e3t5eKLL2bl\nypUYhkGhUGD16tV8/vOfx+FwHPb7HnnkET7wgQ9MYUuFEELMJNIjJIQQYtoLhUL893//N7/4xS+4\n5557SCQSfPnLXz7s9oVCgR/96EdT2EIhhBAzjfQICSGEmFE0TePGG2/koosuoqurix/84AdEo1GS\nySQXX3wxn/70p/na175GX18fn/rUp7j77rv53//9X+677z4AgsEg3/zmN/H5fBXeEyGEEJUkPUJC\nCCFmHKvVyrJly3j22WdZt24d9957L/fffz933nknyWSS66+/nlAoxN13383AwAB33XUX99xzD/fd\ndx9nnnkmd955Z6V3QQghRIVJj5AQQogZKZFIUF1dzauvvsr999+PzWYjl8sRjUYnbbdp0yaGhob4\n1Kc+hWEY6LpOc3NzhVothBBiupBESAghxIyTTqd57bXXWLNmDbqu8+CDDwJw1llnvWlbTdPo6OiQ\nXiAhhBCTyNA4IYQQ055hGOX7uq5zyy23sHbtWkZGRli4cCEATz/9NNlsllwuh6qq6LoOwIoVK9i6\ndSvDw8MAPPbYYzzzzDNTvxNCCCGmFcWYeHQRQgghppne3l4uueQSTj/9dAqFArFYjHPPPZd/+Id/\nYM+ePXzpS1+itraWCy64gM7OTnbs2MGvfvUrPvjBD2K1Wrnvvvt45plnuPvuu3G5XDgcDm677TaC\nwWCld00IIUQFSSIkhBBCCCGEmHNkaJwQQgghhBBizpFESAghhBBCCDHnSCIkhBBCCCGEmHMkERJC\nCCGEEELMOZIICSGEEEIIIeYcSYSEEEIIIYQQc44kQkIIIYQQQog55/8DMEewAnC8cqEAAAAASUVO\nRK5CYII=\n",
   1664       "text/plain": [
   1665        "<matplotlib.figure.Figure at 0x7fc7f00efad0>"
   1666       ]
   1667      },
   1668      "metadata": {},
   1669      "output_type": "display_data"
   1670     },
   1671     {
   1672      "data": {
   1673       "image/png": 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ISBO53T5iYq1YrZZmvT4x0a4ubSIiUaKER0REpIk8bl+zOrQFJSTZqXN68Pn8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\ngrYSANQKFbdOu4bRUSNZu6uBl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mCtCZ0qCI/fg1vw4vF7aLN10mBupqxxH5JWhlapwaDW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\nIYTotyThEUIIIYQQQvRb/x+6ufbva8bmOQAAAABJRU5ErkJggg==\n",
   1674       "text/plain": [
   1675        "<matplotlib.figure.Figure at 0x7fc7df9608d0>"
   1676       ]
   1677      },
   1678      "metadata": {},
   1679      "output_type": "display_data"
   1680     }
   1681    ],
   1682    "source": [
   1683     "# Rolling mean\n",
   1684     "rolling_mean = prices.rolling(window=60).mean()\n",
   1685     "rolling_mean.columns = prices.columns\n",
   1686     "\n",
   1687     "# Rolling standard deviation\n",
   1688     "rolling_std = prices.rolling(window=60).std()\n",
   1689     "rolling_mean.columns = prices.columns\n",
   1690     "\n",
   1691     "# Plotting \n",
   1692     "mean = rolling_mean.plot();\n",
   1693     "plt.title(\"Rolling Mean of Prices\")\n",
   1694     "plt.xlabel(\"Date\")\n",
   1695     "plt.ylabel(\"Price\")\n",
   1696     "plt.legend();\n",
   1697     "\n",
   1698     "std = rolling_std.plot();\n",
   1699     "plt.title(\"Rolling standard deviation of Prices\")\n",
   1700     "plt.xlabel(\"Date\")\n",
   1701     "plt.ylabel(\"Price\")\n",
   1702     "plt.legend();"
   1703    ]
   1704   },
   1705   {
   1706    "cell_type": "markdown",
   1707    "metadata": {},
   1708    "source": [
   1709     "---"
   1710    ]
   1711   },
   1712   {
   1713    "cell_type": "markdown",
   1714    "metadata": {
   1715     "deletable": true,
   1716     "editable": true
   1717    },
   1718    "source": [
   1719     "Congratulations on completing the Introduction to pandas exercises!\n",
   1720     "\n",
   1721     "As you learn more about writing trading algorithms and the Quantopian platform, be sure to check out the daily [Quantopian Contest](https://www.quantopian.com/contest), in which you can compete for a cash prize every day.\n",
   1722     "\n",
   1723     "Start by going through the [Writing a Contest Algorithm](https://www.quantopian.com/tutorials/contest) Tutorial."
   1724    ]
   1725   },
   1726   {
   1727    "cell_type": "markdown",
   1728    "metadata": {
   1729     "collapsed": true,
   1730     "deletable": true,
   1731     "editable": true
   1732    },
   1733    "source": [
   1734     "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
   1735    ]
   1736   }
   1737  ],
   1738  "metadata": {
   1739   "kernelspec": {
   1740    "display_name": "Python 2",
   1741    "language": "python",
   1742    "name": "python2"
   1743   },
   1744   "language_info": {
   1745    "codemirror_mode": {
   1746     "name": "ipython",
   1747     "version": 2
   1748    },
   1749    "file_extension": ".py",
   1750    "mimetype": "text/x-python",
   1751    "name": "python",
   1752    "nbconvert_exporter": "python",
   1753    "pygments_lexer": "ipython2",
   1754    "version": "2.7.12"
   1755   }
   1756  },
   1757  "nbformat": 4,
   1758  "nbformat_minor": 2
   1759 }