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January 7, 2014 17:14
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| { | |
| "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 | |
| } |
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