CCanalysis010714

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "input": "import pandas as pd\n\n_cc = pd.read_csv(\"All_covered_call172014.csv\", index_col=0)\nto_keep = [u'ExDivDate', u'Expiry', u'OptionPrice', u'StockPrice', u'Strike', u'Vol', u'Volatility', \n           u'StaticRet', u'CapitalRet', u'AnnStaticRet', u'AnnCapitalRet']\ncc = _cc[to_keep]",
     "cell_type": "code",
     "prompt_number": 16,
     "outputs": [],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "input": "# Get a glimpse of what returns look like by stock\ncc.groupby(axis=0, level=0).mean()",
     "cell_type": "code",
     "prompt_number": 21,
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OptionPrice</th>\n      <th>StockPrice</th>\n      <th>Strike</th>\n      <th>Vol</th>\n      <th>Volatility</th>\n      <th>StaticRet</th>\n      <th>CapitalRet</th>\n      <th>AnnStaticRet</th>\n      <th>AnnCapitalRet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AA</th>\n      <td>  0.585600</td>\n      <td>  10.5300</td>\n      <td>  10.960000</td>\n      <td> 543.680000</td>\n      <td> 13.847709</td>\n      <td> 0.058462</td>\n      <td> 0.099297</td>\n      <td> 0.407711</td>\n      <td> 0.764060</td>\n    </tr>\n    <tr>\n      <th>AAN</th>\n      <td>  3.650000</td>\n      <td>  29.3600</td>\n      <td>  25.625000</td>\n      <td>  14.250000</td>\n      <td>  7.175687</td>\n      <td> 0.125000</td>\n      <td>-0.002214</td>\n      <td> 0.952276</td>\n      <td>-0.108353</td>\n    </tr>\n    <tr>\n      <th>AAON</th>\n      <td>  3.275000</td>\n      <td>  31.5200</td>\n      <td>  28.750000</td>\n      <td>   4.750000</td>\n      <td>  8.840970</td>\n      <td> 0.105489</td>\n      <td> 0.017608</td>\n      <td> 1.541939</td>\n      <td>-0.872767</td>\n    </tr>\n    <tr>\n      <th>AAP</th>\n      <td>  4.747895</td>\n      <td> 111.8700</td>\n      <td> 114.736842</td>\n      <td>   5.526316</td>\n      <td> 28.527953</td>\n      <td> 0.042978</td>\n      <td> 0.068604</td>\n      <td> 0.400155</td>\n      <td> 0.575432</td>\n    </tr>\n    <tr>\n      <th>AAPL</th>\n      <td> 21.735500</td>\n      <td> 544.8500</td>\n      <td> 548.500000</td>\n      <td> 432.600000</td>\n      <td> 16.432372</td>\n      <td> 0.045491</td>\n      <td> 0.052190</td>\n      <td> 0.308568</td>\n      <td> 0.359631</td>\n    </tr>\n    <tr>\n      <th>AB</th>\n      <td>  1.458750</td>\n      <td>  22.0500</td>\n      <td>  21.250000</td>\n      <td>  78.250000</td>\n      <td>  8.243220</td>\n      <td> 0.084297</td>\n      <td> 0.048016</td>\n      <td> 0.783220</td>\n      <td> 0.313026</td>\n    </tr>\n    <tr>\n      <th>ABB</th>\n      <td>  1.096250</td>\n      <td>  25.7400</td>\n      <td>  25.750000</td>\n      <td>  21.875000</td>\n      <td>  9.031155</td>\n      <td> 0.049582</td>\n      <td> 0.049971</td>\n      <td> 0.491470</td>\n      <td> 0.313195</td>\n    </tr>\n    <tr>\n      <th>ABBV</th>\n      <td>  4.922000</td>\n      <td>  50.9250</td>\n      <td>  52.333333</td>\n      <td>  23.866667</td>\n      <td>  6.833588</td>\n      <td> 0.104507</td>\n      <td> 0.132162</td>\n      <td> 2.879628</td>\n      <td> 3.249249</td>\n    </tr>\n    <tr>\n      <th>ABC</th>\n      <td>  2.205000</td>\n      <td>  70.2800</td>\n      <td>  71.250000</td>\n      <td>  39.200000</td>\n      <td>  8.850327</td>\n      <td> 0.034718</td>\n      <td> 0.048520</td>\n      <td> 0.384425</td>\n      <td> 0.376921</td>\n    </tr>\n    <tr>\n      <th>ABFS</th>\n      <td>  2.290000</td>\n      <td>  34.1400</td>\n      <td>  35.500000</td>\n      <td>  21.350000</td>\n      <td> 21.704287</td>\n      <td> 0.067955</td>\n      <td> 0.107791</td>\n      <td> 0.510357</td>\n      <td> 0.852160</td>\n    </tr>\n    <tr>\n      <th>ABM</th>\n      <td>  1.875000</td>\n      <td>  28.4600</td>\n      <td>  27.500000</td>\n      <td>   2.500000</td>\n      <td>  8.107158</td>\n      <td> 0.071328</td>\n      <td> 0.037597</td>\n      <td> 1.214075</td>\n      <td> 0.153486</td>\n    </tr>\n    <tr>\n      <th>ABR</th>\n      <td>  1.650000</td>\n      <td>   6.8700</td>\n      <td>   5.000000</td>\n      <td>  12.000000</td>\n      <td> 11.202607</td>\n      <td> 0.259098</td>\n      <td>-0.013100</td>\n      <td> 5.436047</td>\n      <td>-0.023409</td>\n    </tr>\n    <tr>\n      <th>ABT</th>\n      <td>  0.746000</td>\n      <td>  39.2200</td>\n      <td>  39.900000</td>\n      <td>  81.733333</td>\n      <td> 10.402268</td>\n      <td> 0.024630</td>\n      <td> 0.041968</td>\n      <td> 0.783511</td>\n      <td> 1.600557</td>\n    </tr>\n    <tr>\n      <th>ABX</th>\n      <td>  0.957500</td>\n      <td>  18.0790</td>\n      <td>  18.650000</td>\n      <td> 115.650000</td>\n      <td>  9.205703</td>\n      <td> 0.055728</td>\n      <td> 0.087311</td>\n      <td> 0.439237</td>\n      <td> 0.643444</td>\n    </tr>\n    <tr>\n      <th>ACAT</th>\n      <td>  4.352667</td>\n      <td>  57.7300</td>\n      <td>  59.000000</td>\n      <td>   4.000000</td>\n      <td> 13.483919</td>\n      <td> 0.077129</td>\n      <td> 0.099128</td>\n      <td> 0.656080</td>\n      <td> 0.373682</td>\n    </tr>\n    <tr>\n      <th>ACC</th>\n      <td>  2.225000</td>\n      <td>  33.3350</td>\n      <td>  33.125000</td>\n      <td>  14.000000</td>\n      <td>  8.001001</td>\n      <td> 0.077546</td>\n      <td> 0.071246</td>\n      <td> 0.303885</td>\n      <td> 0.523226</td>\n    </tr>\n    <tr>\n      <th>ACE</th>\n      <td>  2.940909</td>\n      <td> 100.8500</td>\n      <td> 103.409091</td>\n      <td>  16.272727</td>\n      <td>  8.317192</td>\n      <td> 0.034218</td>\n      <td> 0.059593</td>\n      <td> 0.405661</td>\n      <td> 0.393308</td>\n    </tr>\n    <tr>\n      <th>ACET</th>\n      <td>  1.955455</td>\n      <td>  23.7075</td>\n      <td>  23.863636</td>\n      <td>  18.272727</td>\n      <td> 19.737273</td>\n      <td> 0.085013</td>\n      <td> 0.091599</td>\n      <td> 0.992716</td>\n      <td> 0.597713</td>\n    </tr>\n    <tr>\n      <th>ACFN</th>\n      <td>  1.125000</td>\n      <td>   4.3500</td>\n      <td>   3.750000</td>\n      <td>  95.500000</td>\n      <td> 18.952051</td>\n      <td> 0.269349</td>\n      <td> 0.131418</td>\n      <td> 1.301584</td>\n      <td> 0.596447</td>\n    </tr>\n    <tr>\n      <th>ACI</th>\n      <td>  0.482000</td>\n      <td>   4.4400</td>\n      <td>   4.500000</td>\n      <td>  75.533333</td>\n      <td>  7.360274</td>\n      <td> 0.115315</td>\n      <td> 0.128829</td>\n      <td> 1.528216</td>\n      <td> 1.745446</td>\n    </tr>\n    <tr>\n      <th>ACMP</th>\n      <td>  2.992500</td>\n      <td>  55.0500</td>\n      <td>  55.416667</td>\n      <td>  12.833333</td>\n      <td>  6.785223</td>\n      <td> 0.064078</td>\n      <td> 0.070739</td>\n      <td> 0.576360</td>\n      <td> 0.280063</td>\n    </tr>\n    <tr>\n      <th>ACN</th>\n      <td>  1.775333</td>\n      <td>  81.5600</td>\n      <td>  82.300000</td>\n      <td>  21.066667</td>\n      <td> 21.341627</td>\n      <td> 0.027469</td>\n      <td> 0.036542</td>\n      <td> 1.332757</td>\n      <td> 1.416040</td>\n    </tr>\n    <tr>\n      <th>ACO</th>\n      <td>  1.742500</td>\n      <td>  34.0000</td>\n      <td>  32.500000</td>\n      <td>  28.750000</td>\n      <td> 11.226938</td>\n      <td> 0.057132</td>\n      <td> 0.013015</td>\n      <td> 0.445867</td>\n      <td>-0.617116</td>\n    </tr>\n    <tr>\n      <th>ACRE</th>\n      <td>  0.950000</td>\n      <td>  13.4500</td>\n      <td>  12.500000</td>\n      <td>  13.000000</td>\n      <td> 14.957791</td>\n      <td> 0.089219</td>\n      <td> 0.018587</td>\n      <td> 1.272613</td>\n      <td> 0.155673</td>\n    </tr>\n    <tr>\n      <th>ACTG</th>\n      <td>  1.462000</td>\n      <td>  14.4450</td>\n      <td>  14.750000</td>\n      <td>  27.600000</td>\n      <td> 29.761776</td>\n      <td> 0.109865</td>\n      <td> 0.130980</td>\n      <td> 1.023489</td>\n      <td> 0.820108</td>\n    </tr>\n    <tr>\n      <th>ADC</th>\n      <td>  0.500000</td>\n      <td>  29.4000</td>\n      <td>  30.000000</td>\n      <td>   5.000000</td>\n      <td>  9.791159</td>\n      <td> 0.030952</td>\n      <td> 0.051361</td>\n      <td> 0.564881</td>\n      <td> 0.974206</td>\n    </tr>\n    <tr>\n      <th>ADI</th>\n      <td>  1.653077</td>\n      <td>  49.6800</td>\n      <td>  50.692308</td>\n      <td>   7.384615</td>\n      <td>  7.576730</td>\n      <td> 0.040118</td>\n      <td> 0.060495</td>\n      <td> 0.356110</td>\n      <td> 0.310205</td>\n    </tr>\n    <tr>\n      <th>ADM</th>\n      <td>  2.000000</td>\n      <td>  42.7200</td>\n      <td>  42.875000</td>\n      <td>  53.150000</td>\n      <td>  8.247265</td>\n      <td> 0.052434</td>\n      <td> 0.056063</td>\n      <td> 0.492660</td>\n      <td> 0.505924</td>\n    </tr>\n    <tr>\n      <th>ADP</th>\n      <td>  1.533846</td>\n      <td>  80.5350</td>\n      <td>  81.923077</td>\n      <td>   7.846154</td>\n      <td>  7.741370</td>\n      <td> 0.025006</td>\n      <td> 0.042242</td>\n      <td> 0.280654</td>\n      <td> 0.327369</td>\n    </tr>\n    <tr>\n      <th>ADT</th>\n      <td>  1.558571</td>\n      <td>  39.8600</td>\n      <td>  40.142857</td>\n      <td>  10.285714</td>\n      <td>  9.612963</td>\n      <td> 0.042237</td>\n      <td> 0.049333</td>\n      <td> 0.603266</td>\n      <td> 0.690794</td>\n    </tr>\n    <tr>\n      <th>ADTN</th>\n      <td>  1.536000</td>\n      <td>  26.7900</td>\n      <td>  26.933333</td>\n      <td>  23.200000</td>\n      <td> 14.916495</td>\n      <td> 0.060694</td>\n      <td> 0.066045</td>\n      <td> 0.675369</td>\n      <td> 0.765999</td>\n    </tr>\n    <tr>\n      <th>AEC</th>\n      <td>  1.540000</td>\n      <td>  15.9000</td>\n      <td>  15.000000</td>\n      <td>  14.600000</td>\n      <td> 14.832126</td>\n      <td> 0.108805</td>\n      <td> 0.052201</td>\n      <td> 0.881768</td>\n      <td> 0.217717</td>\n    </tr>\n    <tr>\n      <th>AEE</th>\n      <td>  1.466667</td>\n      <td>  36.0900</td>\n      <td>  36.666667</td>\n      <td>   2.333333</td>\n      <td>  7.351466</td>\n      <td> 0.051723</td>\n      <td> 0.067701</td>\n      <td> 0.175332</td>\n      <td> 0.182963</td>\n    </tr>\n    <tr>\n      <th>AEG</th>\n      <td>  1.907143</td>\n      <td>   9.2700</td>\n      <td>   7.500000</td>\n      <td>   9.857143</td>\n      <td>  8.359905</td>\n      <td> 0.212475</td>\n      <td> 0.021536</td>\n      <td> 3.733599</td>\n      <td>-0.089565</td>\n    </tr>\n    <tr>\n      <th>AEL</th>\n      <td>  1.808333</td>\n      <td>  26.2150</td>\n      <td>  25.833333</td>\n      <td>  10.000000</td>\n      <td>  7.488760</td>\n      <td> 0.070697</td>\n      <td> 0.056138</td>\n      <td> 0.379180</td>\n      <td> 0.299537</td>\n    </tr>\n    <tr>\n      <th>AEM</th>\n      <td>  2.340714</td>\n      <td>  27.0000</td>\n      <td>  27.142857</td>\n      <td>  62.214286</td>\n      <td>  9.716207</td>\n      <td> 0.094841</td>\n      <td> 0.100132</td>\n      <td> 1.130827</td>\n      <td> 0.913503</td>\n    </tr>\n    <tr>\n      <th>AEO</th>\n      <td>  0.964286</td>\n      <td>  15.0900</td>\n      <td>  15.571429</td>\n      <td> 116.857143</td>\n      <td> 15.689892</td>\n      <td> 0.072186</td>\n      <td> 0.104090</td>\n      <td> 1.156306</td>\n      <td> 0.984194</td>\n    </tr>\n    <tr>\n      <th>AEP</th>\n      <td>  0.907692</td>\n      <td>  46.3500</td>\n      <td>  46.769231</td>\n      <td>  38.692308</td>\n      <td>  8.728070</td>\n      <td> 0.030371</td>\n      <td> 0.039416</td>\n      <td> 0.339059</td>\n      <td> 0.334465</td>\n    </tr>\n    <tr>\n      <th>AES</th>\n      <td>  0.681818</td>\n      <td>  14.6350</td>\n      <td>  14.454545</td>\n      <td>  14.909091</td>\n      <td>  8.782301</td>\n      <td> 0.050005</td>\n      <td> 0.037674</td>\n      <td> 0.623974</td>\n      <td> 0.128149</td>\n    </tr>\n    <tr>\n      <th>AET</th>\n      <td>  2.706000</td>\n      <td>  68.1500</td>\n      <td>  68.566667</td>\n      <td>  34.266667</td>\n      <td>  6.488289</td>\n      <td> 0.043008</td>\n      <td> 0.049122</td>\n      <td> 0.346457</td>\n      <td> 0.380522</td>\n    </tr>\n    <tr>\n      <th>AF</th>\n      <td>  1.625000</td>\n      <td>  13.8800</td>\n      <td>  12.500000</td>\n      <td>  18.250000</td>\n      <td>  7.311913</td>\n      <td> 0.119957</td>\n      <td> 0.020533</td>\n      <td> 3.146377</td>\n      <td>-0.328580</td>\n    </tr>\n    <tr>\n      <th>AFG</th>\n      <td>  2.537500</td>\n      <td>  56.8500</td>\n      <td>  56.500000</td>\n      <td>   7.500000</td>\n      <td>  7.414744</td>\n      <td> 0.048505</td>\n      <td> 0.042348</td>\n      <td> 0.174934</td>\n      <td> 0.152691</td>\n    </tr>\n    <tr>\n      <th>AFL</th>\n      <td>  1.490000</td>\n      <td>  65.3050</td>\n      <td>  66.923077</td>\n      <td> 218.153846</td>\n      <td>  7.707753</td>\n      <td> 0.028482</td>\n      <td> 0.053259</td>\n      <td> 0.296101</td>\n      <td> 0.398196</td>\n    </tr>\n    <tr>\n      <th>AFOP</th>\n      <td>  1.668333</td>\n      <td>  15.5100</td>\n      <td>  16.458333</td>\n      <td>  12.750000</td>\n      <td>  8.692309</td>\n      <td> 0.109983</td>\n      <td> 0.171126</td>\n      <td> 1.312420</td>\n      <td> 1.334566</td>\n    </tr>\n    <tr>\n      <th>AFSI</th>\n      <td>  3.337368</td>\n      <td>  34.2500</td>\n      <td>  34.641053</td>\n      <td>  70.210526</td>\n      <td> 16.459949</td>\n      <td> 0.101529</td>\n      <td> 0.112947</td>\n      <td> 0.902404</td>\n      <td> 0.864621</td>\n    </tr>\n    <tr>\n      <th>AGCO</th>\n      <td>  4.005833</td>\n      <td>  57.7100</td>\n      <td>  57.916667</td>\n      <td>  12.416667</td>\n      <td> 10.595092</td>\n      <td> 0.071146</td>\n      <td> 0.074727</td>\n      <td> 0.912496</td>\n      <td> 0.520939</td>\n    </tr>\n    <tr>\n      <th>AGI</th>\n      <td>  1.603333</td>\n      <td>  12.0600</td>\n      <td>  13.333333</td>\n      <td>   6.000000</td>\n      <td>  8.020092</td>\n      <td> 0.137092</td>\n      <td> 0.242676</td>\n      <td> 0.357642</td>\n      <td> 0.722223</td>\n    </tr>\n    <tr>\n      <th>AGII</th>\n      <td>  0.500000</td>\n      <td>  46.5000</td>\n      <td>  45.000000</td>\n      <td>  10.000000</td>\n      <td> 10.749358</td>\n      <td> 0.013978</td>\n      <td>-0.018280</td>\n      <td> 0.510215</td>\n      <td>-0.667204</td>\n    </tr>\n    <tr>\n      <th>AGM</th>\n      <td>  4.904000</td>\n      <td>  32.2200</td>\n      <td>  30.000000</td>\n      <td>  16.400000</td>\n      <td>  7.789988</td>\n      <td> 0.155928</td>\n      <td> 0.087027</td>\n      <td> 0.843463</td>\n      <td> 0.430162</td>\n    </tr>\n    <tr>\n      <th>AGN</th>\n      <td>  3.634000</td>\n      <td> 112.2800</td>\n      <td> 115.000000</td>\n      <td>  18.266667</td>\n      <td> 24.716423</td>\n      <td> 0.032811</td>\n      <td> 0.057036</td>\n      <td> 0.407875</td>\n      <td> 0.797294</td>\n    </tr>\n    <tr>\n      <th>AGNC</th>\n      <td>  0.776500</td>\n      <td>  19.8690</td>\n      <td>  19.825000</td>\n      <td>  62.700000</td>\n      <td> 11.502495</td>\n      <td> 0.071795</td>\n      <td> 0.069581</td>\n      <td> 0.584392</td>\n      <td> 0.538837</td>\n    </tr>\n    <tr>\n      <th>AGO</th>\n      <td>  0.962000</td>\n      <td>  23.0900</td>\n      <td>  24.000000</td>\n      <td>  71.666667</td>\n      <td>  9.917154</td>\n      <td> 0.045994</td>\n      <td> 0.085405</td>\n      <td> 0.500109</td>\n      <td> 1.133641</td>\n    </tr>\n    <tr>\n      <th>AGU</th>\n      <td>  3.609333</td>\n      <td>  89.4900</td>\n      <td>  89.366667</td>\n      <td>  11.800000</td>\n      <td>  9.104863</td>\n      <td> 0.048713</td>\n      <td> 0.047335</td>\n      <td> 0.477585</td>\n      <td> 0.452935</td>\n    </tr>\n    <tr>\n      <th>AHGP</th>\n      <td>  1.733333</td>\n      <td>  55.6000</td>\n      <td>  56.666667</td>\n      <td>   3.333333</td>\n      <td> 17.705057</td>\n      <td> 0.045698</td>\n      <td> 0.064883</td>\n      <td> 0.718672</td>\n      <td> 0.669707</td>\n    </tr>\n    <tr>\n      <th>AHH</th>\n      <td>  1.166667</td>\n      <td>   9.7000</td>\n      <td>   9.166667</td>\n      <td>  16.666667</td>\n      <td>  6.343121</td>\n      <td> 0.136770</td>\n      <td> 0.081787</td>\n      <td> 0.980490</td>\n      <td> 0.480068</td>\n    </tr>\n    <tr>\n      <th>AHL</th>\n      <td>  4.290000</td>\n      <td>  40.3700</td>\n      <td>  38.333333</td>\n      <td>   7.666667</td>\n      <td>  9.738434</td>\n      <td> 0.110726</td>\n      <td> 0.060276</td>\n      <td> 0.246432</td>\n      <td> 0.134150</td>\n    </tr>\n    <tr>\n      <th>AHT</th>\n      <td>  1.707143</td>\n      <td>   8.5800</td>\n      <td>   8.928571</td>\n      <td>  28.428571</td>\n      <td> 10.406289</td>\n      <td> 0.212954</td>\n      <td> 0.253580</td>\n      <td> 0.851816</td>\n      <td> 0.973645</td>\n    </tr>\n    <tr>\n      <th>AI</th>\n      <td>  2.182500</td>\n      <td>  26.3800</td>\n      <td>  25.625000</td>\n      <td>   4.250000</td>\n      <td>  9.235001</td>\n      <td> 0.115902</td>\n      <td> 0.087282</td>\n      <td> 1.205791</td>\n      <td> 0.672273</td>\n    </tr>\n    <tr>\n      <th>AIG</th>\n      <td>  1.626000</td>\n      <td>  51.1400</td>\n      <td>  52.125000</td>\n      <td> 140.250000</td>\n      <td>  9.131700</td>\n      <td> 0.033750</td>\n      <td> 0.053011</td>\n      <td> 0.215802</td>\n      <td> 0.334357</td>\n    </tr>\n    <tr>\n      <th>AINV</th>\n      <td>  0.743750</td>\n      <td>   8.5600</td>\n      <td>   8.000000</td>\n      <td>  20.750000</td>\n      <td> 11.362113</td>\n      <td> 0.110251</td>\n      <td> 0.044831</td>\n      <td> 0.866021</td>\n      <td> 0.253074</td>\n    </tr>\n    <tr>\n      <th></th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1569 rows × 9 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": "      OptionPrice  StockPrice      Strike         Vol  Volatility  StaticRet  \\\nAA       0.585600     10.5300   10.960000  543.680000   13.847709   0.058462   \nAAN      3.650000     29.3600   25.625000   14.250000    7.175687   0.125000   \nAAON     3.275000     31.5200   28.750000    4.750000    8.840970   0.105489   \nAAP      4.747895    111.8700  114.736842    5.526316   28.527953   0.042978   \nAAPL    21.735500    544.8500  548.500000  432.600000   16.432372   0.045491   \nAB       1.458750     22.0500   21.250000   78.250000    8.243220   0.084297   \nABB      1.096250     25.7400   25.750000   21.875000    9.031155   0.049582   \nABBV     4.922000     50.9250   52.333333   23.866667    6.833588   0.104507   \nABC      2.205000     70.2800   71.250000   39.200000    8.850327   0.034718   \nABFS     2.290000     34.1400   35.500000   21.350000   21.704287   0.067955   \nABM      1.875000     28.4600   27.500000    2.500000    8.107158   0.071328   \nABR      1.650000      6.8700    5.000000   12.000000   11.202607   0.259098   \nABT      0.746000     39.2200   39.900000   81.733333   10.402268   0.024630   \nABX      0.957500     18.0790   18.650000  115.650000    9.205703   0.055728   \nACAT     4.352667     57.7300   59.000000    4.000000   13.483919   0.077129   \nACC      2.225000     33.3350   33.125000   14.000000    8.001001   0.077546   \nACE      2.940909    100.8500  103.409091   16.272727    8.317192   0.034218   \nACET     1.955455     23.7075   23.863636   18.272727   19.737273   0.085013   \nACFN     1.125000      4.3500    3.750000   95.500000   18.952051   0.269349   \nACI      0.482000      4.4400    4.500000   75.533333    7.360274   0.115315   \nACMP     2.992500     55.0500   55.416667   12.833333    6.785223   0.064078   \nACN      1.775333     81.5600   82.300000   21.066667   21.341627   0.027469   \nACO      1.742500     34.0000   32.500000   28.750000   11.226938   0.057132   \nACRE     0.950000     13.4500   12.500000   13.000000   14.957791   0.089219   \nACTG     1.462000     14.4450   14.750000   27.600000   29.761776   0.109865   \nADC      0.500000     29.4000   30.000000    5.000000    9.791159   0.030952   \nADI      1.653077     49.6800   50.692308    7.384615    7.576730   0.040118   \nADM      2.000000     42.7200   42.875000   53.150000    8.247265   0.052434   \nADP      1.533846     80.5350   81.923077    7.846154    7.741370   0.025006   \nADT      1.558571     39.8600   40.142857   10.285714    9.612963   0.042237   \nADTN     1.536000     26.7900   26.933333   23.200000   14.916495   0.060694   \nAEC      1.540000     15.9000   15.000000   14.600000   14.832126   0.108805   \nAEE      1.466667     36.0900   36.666667    2.333333    7.351466   0.051723   \nAEG      1.907143      9.2700    7.500000    9.857143    8.359905   0.212475   \nAEL      1.808333     26.2150   25.833333   10.000000    7.488760   0.070697   \nAEM      2.340714     27.0000   27.142857   62.214286    9.716207   0.094841   \nAEO      0.964286     15.0900   15.571429  116.857143   15.689892   0.072186   \nAEP      0.907692     46.3500   46.769231   38.692308    8.728070   0.030371   \nAES      0.681818     14.6350   14.454545   14.909091    8.782301   0.050005   \nAET      2.706000     68.1500   68.566667   34.266667    6.488289   0.043008   \nAF       1.625000     13.8800   12.500000   18.250000    7.311913   0.119957   \nAFG      2.537500     56.8500   56.500000    7.500000    7.414744   0.048505   \nAFL      1.490000     65.3050   66.923077  218.153846    7.707753   0.028482   \nAFOP     1.668333     15.5100   16.458333   12.750000    8.692309   0.109983   \nAFSI     3.337368     34.2500   34.641053   70.210526   16.459949   0.101529   \nAGCO     4.005833     57.7100   57.916667   12.416667   10.595092   0.071146   \nAGI      1.603333     12.0600   13.333333    6.000000    8.020092   0.137092   \nAGII     0.500000     46.5000   45.000000   10.000000   10.749358   0.013978   \nAGM      4.904000     32.2200   30.000000   16.400000    7.789988   0.155928   \nAGN      3.634000    112.2800  115.000000   18.266667   24.716423   0.032811   \nAGNC     0.776500     19.8690   19.825000   62.700000   11.502495   0.071795   \nAGO      0.962000     23.0900   24.000000   71.666667    9.917154   0.045994   \nAGU      3.609333     89.4900   89.366667   11.800000    9.104863   0.048713   \nAHGP     1.733333     55.6000   56.666667    3.333333   17.705057   0.045698   \nAHH      1.166667      9.7000    9.166667   16.666667    6.343121   0.136770   \nAHL      4.290000     40.3700   38.333333    7.666667    9.738434   0.110726   \nAHT      1.707143      8.5800    8.928571   28.428571   10.406289   0.212954   \nAI       2.182500     26.3800   25.625000    4.250000    9.235001   0.115902   \nAIG      1.626000     51.1400   52.125000  140.250000    9.131700   0.033750   \nAINV     0.743750      8.5600    8.000000   20.750000   11.362113   0.110251   \n              ...         ...         ...         ...         ...        ...   \n\n      CapitalRet  AnnStaticRet  AnnCapitalRet  \nAA      0.099297      0.407711       0.764060  \nAAN    -0.002214      0.952276      -0.108353  \nAAON    0.017608      1.541939      -0.872767  \nAAP     0.068604      0.400155       0.575432  \nAAPL    0.052190      0.308568       0.359631  \nAB      0.048016      0.783220       0.313026  \nABB     0.049971      0.491470       0.313195  \nABBV    0.132162      2.879628       3.249249  \nABC     0.048520      0.384425       0.376921  \nABFS    0.107791      0.510357       0.852160  \nABM     0.037597      1.214075       0.153486  \nABR    -0.013100      5.436047      -0.023409  \nABT     0.041968      0.783511       1.600557  \nABX     0.087311      0.439237       0.643444  \nACAT    0.099128      0.656080       0.373682  \nACC     0.071246      0.303885       0.523226  \nACE     0.059593      0.405661       0.393308  \nACET    0.091599      0.992716       0.597713  \nACFN    0.131418      1.301584       0.596447  \nACI     0.128829      1.528216       1.745446  \nACMP    0.070739      0.576360       0.280063  \nACN     0.036542      1.332757       1.416040  \nACO     0.013015      0.445867      -0.617116  \nACRE    0.018587      1.272613       0.155673  \nACTG    0.130980      1.023489       0.820108  \nADC     0.051361      0.564881       0.974206  \nADI     0.060495      0.356110       0.310205  \nADM     0.056063      0.492660       0.505924  \nADP     0.042242      0.280654       0.327369  \nADT     0.049333      0.603266       0.690794  \nADTN    0.066045      0.675369       0.765999  \nAEC     0.052201      0.881768       0.217717  \nAEE     0.067701      0.175332       0.182963  \nAEG     0.021536      3.733599      -0.089565  \nAEL     0.056138      0.379180       0.299537  \nAEM     0.100132      1.130827       0.913503  \nAEO     0.104090      1.156306       0.984194  \nAEP     0.039416      0.339059       0.334465  \nAES     0.037674      0.623974       0.128149  \nAET     0.049122      0.346457       0.380522  \nAF      0.020533      3.146377      -0.328580  \nAFG     0.042348      0.174934       0.152691  \nAFL     0.053259      0.296101       0.398196  \nAFOP    0.171126      1.312420       1.334566  \nAFSI    0.112947      0.902404       0.864621  \nAGCO    0.074727      0.912496       0.520939  \nAGI     0.242676      0.357642       0.722223  \nAGII   -0.018280      0.510215      -0.667204  \nAGM     0.087027      0.843463       0.430162  \nAGN     0.057036      0.407875       0.797294  \nAGNC    0.069581      0.584392       0.538837  \nAGO     0.085405      0.500109       1.133641  \nAGU     0.047335      0.477585       0.452935  \nAHGP    0.064883      0.718672       0.669707  \nAHH     0.081787      0.980490       0.480068  \nAHL     0.060276      0.246432       0.134150  \nAHT     0.253580      0.851816       0.973645  \nAI      0.087282      1.205791       0.672273  \nAIG     0.053011      0.215802       0.334357  \nAINV    0.044831      0.866021       0.253074  \n             ...           ...            ...  \n\n[1569 rows x 9 columns]"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "The `AnnCaptialRet` column is plagued by our assumption that the stock price will be equal to the strike price at expiry. In some cases this is not a realistic assumption to make. As an example, here are the top 5 positions (not averages by stock, but a single option) for the `AnnCaptialRet` column:"
    },
    {
     "metadata": {},
     "input": "crazy = cc.sort('AnnCapitalRet', ascending=0).head()\ncrazy",
     "cell_type": "code",
     "prompt_number": 28,
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ExDivDate</th>\n      <th>Expiry</th>\n      <th>OptionPrice</th>\n      <th>StockPrice</th>\n      <th>Strike</th>\n      <th>Vol</th>\n      <th>Volatility</th>\n      <th>StaticRet</th>\n      <th>CapitalRet</th>\n      <th>AnnStaticRet</th>\n      <th>AnnCapitalRet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>XLS</th>\n      <td> 11-13-2013</td>\n      <td> 01-18-14</td>\n      <td> 4.70</td>\n      <td> 19.125</td>\n      <td> 50.0</td>\n      <td>  20</td>\n      <td>  9.575011</td>\n      <td> 0.251111</td>\n      <td> 1.865490</td>\n      <td>  9.165556</td>\n      <td> 68.090392</td>\n    </tr>\n    <tr>\n      <th>XLS</th>\n      <td> 11-13-2013</td>\n      <td> 01-18-14</td>\n      <td> 9.50</td>\n      <td> 19.125</td>\n      <td> 40.0</td>\n      <td>   0</td>\n      <td>  9.575011</td>\n      <td> 0.502092</td>\n      <td> 1.593595</td>\n      <td> 18.326340</td>\n      <td> 58.166209</td>\n    </tr>\n    <tr>\n      <th>NRZ</th>\n      <td> 12-26-2013</td>\n      <td> 01-18-14</td>\n      <td> 2.95</td>\n      <td>  6.645</td>\n      <td> 10.0</td>\n      <td>   1</td>\n      <td>  6.817344</td>\n      <td> 0.470278</td>\n      <td> 0.975169</td>\n      <td> 17.165162</td>\n      <td> 35.593679</td>\n    </tr>\n    <tr>\n      <th>SRC</th>\n      <td> 12-27-2013</td>\n      <td> 01-18-14</td>\n      <td> 1.40</td>\n      <td>  9.840</td>\n      <td> 17.5</td>\n      <td>  10</td>\n      <td> 10.601199</td>\n      <td> 0.159299</td>\n      <td> 0.937754</td>\n      <td>  5.814405</td>\n      <td> 34.228023</td>\n    </tr>\n    <tr>\n      <th>NRZ</th>\n      <td> 12-26-2013</td>\n      <td> 01-18-14</td>\n      <td> 5.20</td>\n      <td>  6.645</td>\n      <td>  7.5</td>\n      <td> 140</td>\n      <td>  6.817344</td>\n      <td> 0.808879</td>\n      <td> 0.937547</td>\n      <td> 29.524078</td>\n      <td> 34.220467</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 11 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": "      ExDivDate    Expiry  OptionPrice  StockPrice  Strike  Vol  Volatility  \\\nXLS  11-13-2013  01-18-14         4.70      19.125    50.0   20    9.575011   \nXLS  11-13-2013  01-18-14         9.50      19.125    40.0    0    9.575011   \nNRZ  12-26-2013  01-18-14         2.95       6.645    10.0    1    6.817344   \nSRC  12-27-2013  01-18-14         1.40       9.840    17.5   10   10.601199   \nNRZ  12-26-2013  01-18-14         5.20       6.645     7.5  140    6.817344   \n\n     StaticRet  CapitalRet  AnnStaticRet  AnnCapitalRet  \nXLS   0.251111    1.865490      9.165556      68.090392  \nXLS   0.502092    1.593595     18.326340      58.166209  \nNRZ   0.470278    0.975169     17.165162      35.593679  \nSRC   0.159299    0.937754      5.814405      34.228023  \nNRZ   0.808879    0.937547     29.524078      34.220467  \n\n[5 rows x 11 columns]"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Now let's see how much the stock price would need to appreciate in order for that assumption to be correct"
    },
    {
     "metadata": {},
     "input": "crazy['Strike'] - crazy['StockPrice']",
     "cell_type": "code",
     "prompt_number": 32,
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": "XLS    30.875\nXLS    20.875\nNRZ     3.355\nSRC     7.660\nNRZ     0.855\ndtype: float64"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "That's pretty unreasonable, so we will focus on the `AnnStaticReturn` column that just picks up dividends and the option price."
    },
    {
     "metadata": {},
     "input": "# Get all the stocks above 10% annual returns\ngood = cc[cc.AnnStaticRet > 8]\n\n# Make sure the options are being traded in reasonable volume\ntraded = good[good.Vol > 30]\n\n# Of those, let's get the least volatile ones\nstable = traded[traded.Volatility < 12]\n\n# And now let's sort by the AnnStaticReturn column\nstable.sort('AnnStaticRet', ascending=0)",
     "cell_type": "code",
     "prompt_number": 71,
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ExDivDate</th>\n      <th>Expiry</th>\n      <th>OptionPrice</th>\n      <th>StockPrice</th>\n      <th>Strike</th>\n      <th>Vol</th>\n      <th>Volatility</th>\n      <th>StaticRet</th>\n      <th>CapitalRet</th>\n      <th>AnnStaticRet</th>\n      <th>AnnCapitalRet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>NRZ</th>\n      <td> 12-26-2013</td>\n      <td> 01-18-14</td>\n      <td> 5.20</td>\n      <td>  6.645</td>\n      <td>  7.5</td>\n      <td> 140</td>\n      <td>  6.817344</td>\n      <td> 0.808879</td>\n      <td> 0.937547</td>\n      <td> 29.524078</td>\n      <td> 34.220467</td>\n    </tr>\n    <tr>\n      <th>BSBR</th>\n      <td>  10-3-2013</td>\n      <td> 01-18-14</td>\n      <td> 2.20</td>\n      <td>  5.940</td>\n      <td>  4.0</td>\n      <td> 140</td>\n      <td>  8.701123</td>\n      <td> 0.382155</td>\n      <td> 0.055556</td>\n      <td> 13.948653</td>\n      <td>  2.027778</td>\n    </tr>\n    <tr>\n      <th>STM</th>\n      <td>  12-9-2013</td>\n      <td> 01-18-14</td>\n      <td> 2.60</td>\n      <td>  7.560</td>\n      <td>  5.0</td>\n      <td>  50</td>\n      <td> 10.344650</td>\n      <td> 0.357143</td>\n      <td> 0.018519</td>\n      <td> 13.035714</td>\n      <td>  0.675926</td>\n    </tr>\n    <tr>\n      <th>SID</th>\n      <td> 11-21-2013</td>\n      <td> 01-18-14</td>\n      <td> 1.95</td>\n      <td>  6.020</td>\n      <td>  4.0</td>\n      <td>  50</td>\n      <td>  7.493693</td>\n      <td> 0.333887</td>\n      <td>-0.001661</td>\n      <td> 12.186877</td>\n      <td> -0.060631</td>\n    </tr>\n    <tr>\n      <th>GA</th>\n      <td> 12-10-2013</td>\n      <td> 01-18-14</td>\n      <td> 3.40</td>\n      <td> 10.950</td>\n      <td>  7.5</td>\n      <td> 150</td>\n      <td> 11.766778</td>\n      <td> 0.321005</td>\n      <td> 0.005936</td>\n      <td> 11.716667</td>\n      <td>  0.216667</td>\n    </tr>\n    <tr>\n      <th>ACI</th>\n      <td> 11-26-2013</td>\n      <td> 01-18-14</td>\n      <td> 1.05</td>\n      <td>  4.440</td>\n      <td>  3.5</td>\n      <td>  80</td>\n      <td>  7.360274</td>\n      <td> 0.243243</td>\n      <td> 0.031532</td>\n      <td>  8.878378</td>\n      <td>  1.150901</td>\n    </tr>\n    <tr>\n      <th>AF</th>\n      <td> 11-13-2013</td>\n      <td> 01-18-14</td>\n      <td> 3.20</td>\n      <td> 13.880</td>\n      <td> 10.0</td>\n      <td>  50</td>\n      <td>  7.311913</td>\n      <td> 0.233429</td>\n      <td>-0.046110</td>\n      <td>  8.520173</td>\n      <td> -1.682997</td>\n    </tr>\n    <tr>\n      <th>XLS</th>\n      <td> 11-13-2013</td>\n      <td> 01-18-14</td>\n      <td> 4.10</td>\n      <td> 19.125</td>\n      <td> 15.0</td>\n      <td> 100</td>\n      <td>  9.575011</td>\n      <td> 0.219739</td>\n      <td> 0.004052</td>\n      <td>  8.020458</td>\n      <td>  0.147908</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 11 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": "       ExDivDate    Expiry  OptionPrice  StockPrice  Strike  Vol  Volatility  \\\nNRZ   12-26-2013  01-18-14         5.20       6.645     7.5  140    6.817344   \nBSBR   10-3-2013  01-18-14         2.20       5.940     4.0  140    8.701123   \nSTM    12-9-2013  01-18-14         2.60       7.560     5.0   50   10.344650   \nSID   11-21-2013  01-18-14         1.95       6.020     4.0   50    7.493693   \nGA    12-10-2013  01-18-14         3.40      10.950     7.5  150   11.766778   \nACI   11-26-2013  01-18-14         1.05       4.440     3.5   80    7.360274   \nAF    11-13-2013  01-18-14         3.20      13.880    10.0   50    7.311913   \nXLS   11-13-2013  01-18-14         4.10      19.125    15.0  100    9.575011   \n\n      StaticRet  CapitalRet  AnnStaticRet  AnnCapitalRet  \nNRZ    0.808879    0.937547     29.524078      34.220467  \nBSBR   0.382155    0.055556     13.948653       2.027778  \nSTM    0.357143    0.018519     13.035714       0.675926  \nSID    0.333887   -0.001661     12.186877      -0.060631  \nGA     0.321005    0.005936     11.716667       0.216667  \nACI    0.243243    0.031532      8.878378       1.150901  \nAF     0.233429   -0.046110      8.520173      -1.682997  \nXLS    0.219739    0.004052      8.020458       0.147908  \n\n[8 rows x 11 columns]"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "So, that gives us 8 stocks to look at. Let's get some of the fundamental information back on each of these stocks."
    },
    {
     "metadata": {},
     "input": "info_cols = ['Name', 'MarketCap', 'Sector', 'Industry']\ninfo_frame = pd.DataFrame(index=stable.index, columns=info_cols + ['Appreciate?'])\nfor sym in stable.index:\n    info_frame.ix[sym] = _cc.ix[sym][info_cols].ix[0]\n    app = stable.ix[sym, 'Strike'] > stable.ix[sym, 'StockPrice']\n    info_frame.ix[sym, 'Appreciate?'] = app\ninfo_frame",
     "cell_type": "code",
     "prompt_number": 75,
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>MarketCap</th>\n      <th>Sector</th>\n      <th>Industry</th>\n      <th>Appreciate?</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ACI</th>\n      <td>                  Arch Coal, Inc.</td>\n      <td> 9.382776e+08</td>\n      <td>            Energy</td>\n      <td>                   Coal Mining</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>AF</th>\n      <td>    Astoria Financial Corporation</td>\n      <td> 1.360384e+09</td>\n      <td>           Finance</td>\n      <td>          Savings Institutions</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>BSBR</th>\n      <td>        Banco Santander Brasil SA</td>\n      <td> 2.228673e+10</td>\n      <td>           Finance</td>\n      <td>              Commercial Banks</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>GA</th>\n      <td>      Giant Interactive Group Inc</td>\n      <td> 2.618984e+09</td>\n      <td>     Miscellaneous</td>\n      <td>             Business Services</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>NRZ</th>\n      <td> New Residential Investment Corp.</td>\n      <td> 1.678625e+09</td>\n      <td> Consumer Services</td>\n      <td> Real Estate Investment Trusts</td>\n      <td>  True</td>\n    </tr>\n    <tr>\n      <th>SID</th>\n      <td>           National Steel Company</td>\n      <td> 8.689502e+09</td>\n      <td>     Capital Goods</td>\n      <td>                Steel/Iron Ore</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>STM</th>\n      <td>          STMicroelectronics N.V.</td>\n      <td> 6.964383e+09</td>\n      <td>        Technology</td>\n      <td>                Semiconductors</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>XLS</th>\n      <td>                      Exelis Inc.</td>\n      <td> 3.584123e+09</td>\n      <td> Consumer Durables</td>\n      <td>  Telecommunications Equipment</td>\n      <td> False</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 5 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": "                                  Name     MarketCap             Sector  \\\nACI                    Arch Coal, Inc.  9.382776e+08             Energy   \nAF       Astoria Financial Corporation  1.360384e+09            Finance   \nBSBR         Banco Santander Brasil SA  2.228673e+10            Finance   \nGA         Giant Interactive Group Inc  2.618984e+09      Miscellaneous   \nNRZ   New Residential Investment Corp.  1.678625e+09  Consumer Services   \nSID             National Steel Company  8.689502e+09      Capital Goods   \nSTM            STMicroelectronics N.V.  6.964383e+09         Technology   \nXLS                        Exelis Inc.  3.584123e+09  Consumer Durables   \n\n                           Industry Appreciate?  \nACI                     Coal Mining       False  \nAF             Savings Institutions       False  \nBSBR               Commercial Banks       False  \nGA                Business Services       False  \nNRZ   Real Estate Investment Trusts        True  \nSID                  Steel/Iron Ore       False  \nSTM                  Semiconductors       False  \nXLS    Telecommunications Equipment       False  \n\n[8 rows x 5 columns]"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "The column `Appreciate?` tells us whether or not the current stock price is below the strike price (in other words, `False` above means the call is currently in the money). It is interesting that the only stock in our list of high-return, traded, low-volatility positions only one has an option that leaves room for the stock price to rise before being in the money: `NRZ`.\n\n\nAnother interesting thing to look for is a quick aribtrage opportunity where the current option price is greater than the spread between the stock price and the strike price for an option in the money. Unless there is momentum or some fundamental reason for investors to believe that the stock price in these cases will appriciate before expiry. This is not rational. People buying these calls shouldn't be willing to pay more today (OptionPrice) than what they can expect to get on the expiry date (StockPrice - Strike), but they are -- giving us an opportunity to make easy profits."
    },
    {
     "metadata": {},
     "input": "price_spread = pd.DataFrame(index=stable.index, \n                            columns=['Spread', 'OptionPrice', 'Difference'])\nprice_spread.OptionPrice = stable.OptionPrice\nprice_spread.Spread = stable.StockPrice - stable.Strike\nprice_spread.Difference = price_spread.OptionPrice - price_spread.Spread\nprice_spread",
     "cell_type": "code",
     "prompt_number": 80,
     "outputs": [
      {
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Spread</th>\n      <th>OptionPrice</th>\n      <th>Difference</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>ACI</th>\n      <td> 0.940</td>\n      <td> 1.05</td>\n      <td> 0.110</td>\n    </tr>\n    <tr>\n      <th>AF</th>\n      <td> 3.880</td>\n      <td> 3.20</td>\n      <td>-0.680</td>\n    </tr>\n    <tr>\n      <th>BSBR</th>\n      <td> 1.940</td>\n      <td> 2.20</td>\n      <td> 0.260</td>\n    </tr>\n    <tr>\n      <th>GA</th>\n      <td> 3.450</td>\n      <td> 3.40</td>\n      <td>-0.050</td>\n    </tr>\n    <tr>\n      <th>NRZ</th>\n      <td>-0.855</td>\n      <td> 5.20</td>\n      <td> 6.055</td>\n    </tr>\n    <tr>\n      <th>SID</th>\n      <td> 2.020</td>\n      <td> 1.95</td>\n      <td>-0.070</td>\n    </tr>\n    <tr>\n      <th>STM</th>\n      <td> 2.560</td>\n      <td> 2.60</td>\n      <td> 0.040</td>\n    </tr>\n    <tr>\n      <th>XLS</th>\n      <td> 4.125</td>\n      <td> 4.10</td>\n      <td>-0.025</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 3 columns</p>\n</div>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": "      Spread  OptionPrice  Difference\nACI    0.940         1.05       0.110\nAF     3.880         3.20      -0.680\nBSBR   1.940         2.20       0.260\nGA     3.450         3.40      -0.050\nNRZ   -0.855         5.20       6.055\nSID    2.020         1.95      -0.070\nSTM    2.560         2.60       0.040\nXLS    4.125         4.10      -0.025\n\n[8 rows x 3 columns]"
      }
     ],
     "language": "python",
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "Remember that `NRZ` is currently out of the money, so we discount that one. That leaves us with quick arbitrage in `ACI`, `BSBR`, and `STM`. Since all these options expire this month, it might not be a bad idea to try this idea out."
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "name": "CCanalysis010714"
 },
 "nbformat": 3
}