├── .gitignore
├── Bond_ETFs.ipynb
├── ETFs.csv
├── ETFs.ipynb
├── LICENSE
├── Lazy_Backtests.ipynb
├── OLPS_Comparison.ipynb
├── OLPS_algos.png
├── README.md
├── spy_100.py
├── swensen.py
├── vti_agg_5050.py
├── vti_agg_6040.py
└── vti_agg_7030.py


/.gitignore:
--------------------------------------------------------------------------------
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 2 | __pycache__/
 3 | *.py[cod]
 4 | 
 5 | # C extensions
 6 | *.so
 7 | 
 8 | # Distribution / packaging
 9 | .Python
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17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | *.egg-info/
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29 | *.manifest
30 | *.spec
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33 | pip-log.txt
34 | pip-delete-this-directory.txt
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36 | # Unit test / coverage reports
37 | htmlcov/
38 | .tox/
39 | .coverage
40 | .coverage.*
41 | .cache
42 | nosetests.xml
43 | coverage.xml
44 | *,cover
45 | 
46 | # Translations
47 | *.mo
48 | *.pot
49 | 
50 | # Django stuff:
51 | *.log
52 | 
53 | # Sphinx documentation
54 | docs/_build/
55 | 
56 | # PyBuilder
57 | target/
58 | 
59 | # emacs
60 | *~
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63 | 
64 | # ipython
65 | .ipynb_checkpoints
66 | 
67 | # Mac
68 | .DS_Store
69 | 
70 | 


--------------------------------------------------------------------------------
/ETFs.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Exploratory Data Analysis (EDA) of ETFs"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {
  13 |     "collapsed": true
  14 |    },
  15 |    "source": [
  16 |     "Original data from https://s3.amazonaws.com/zanran_storage/www.etftopics.com/ContentPages/13968652.xls , modified and available locally as ETFs.csv . \n",
  17 |     "\n",
  18 |     "This notebook only works in Quantopian Research, but one only needs to modify the loading of the file to work with it locally."
  19 |    ]
  20 |   },
  21 |   {
  22 |    "cell_type": "code",
  23 |    "execution_count": 193,
  24 |    "metadata": {
  25 |     "collapsed": false
  26 |    },
  27 |    "outputs": [],
  28 |    "source": [
  29 |     "import numpy as np\n",
  30 |     "import pandas as pd\n",
  31 |     "import matplotlib.pyplot as pyplot\n",
  32 |     "import seaborn as sns"
  33 |    ]
  34 |   },
  35 |   {
  36 |    "cell_type": "code",
  37 |    "execution_count": 194,
  38 |    "metadata": {
  39 |     "collapsed": false
  40 |    },
  41 |    "outputs": [
  42 |     {
  43 |      "data": {
  44 |       "text/html": [
  45 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
  46 |        "<table border=\"1\" class=\"dataframe\">\n",
  47 |        "  <thead>\n",
  48 |        "    <tr style=\"text-align: right;\">\n",
  49 |        "      <th></th>\n",
  50 |        "      <th>Name</th>\n",
  51 |        "      <th>Index</th>\n",
  52 |        "      <th>Description</th>\n",
  53 |        "      <th>Category</th>\n",
  54 |        "      <th>Provider</th>\n",
  55 |        "      <th>Expense_Ratio</th>\n",
  56 |        "      <th>Dividend</th>\n",
  57 |        "      <th>Div_Yield</th>\n",
  58 |        "      <th>Market_Cap</th>\n",
  59 |        "      <th>Shares</th>\n",
  60 |        "      <th>...</th>\n",
  61 |        "      <th>Beta</th>\n",
  62 |        "      <th>Asset</th>\n",
  63 |        "      <th>Size</th>\n",
  64 |        "      <th>Style</th>\n",
  65 |        "      <th>Region</th>\n",
  66 |        "      <th>Sector</th>\n",
  67 |        "      <th>Leveraged</th>\n",
  68 |        "      <th>Inverse</th>\n",
  69 |        "      <th>Top_Holdings</th>\n",
  70 |        "      <th>Chart</th>\n",
  71 |        "    </tr>\n",
  72 |        "    <tr>\n",
  73 |        "      <th>Symbol</th>\n",
  74 |        "      <th></th>\n",
  75 |        "      <th></th>\n",
  76 |        "      <th></th>\n",
  77 |        "      <th></th>\n",
  78 |        "      <th></th>\n",
  79 |        "      <th></th>\n",
  80 |        "      <th></th>\n",
  81 |        "      <th></th>\n",
  82 |        "      <th></th>\n",
  83 |        "      <th></th>\n",
  84 |        "      <th></th>\n",
  85 |        "      <th></th>\n",
  86 |        "      <th></th>\n",
  87 |        "      <th></th>\n",
  88 |        "      <th></th>\n",
  89 |        "      <th></th>\n",
  90 |        "      <th></th>\n",
  91 |        "      <th></th>\n",
  92 |        "      <th></th>\n",
  93 |        "      <th></th>\n",
  94 |        "      <th></th>\n",
  95 |        "    </tr>\n",
  96 |        "  </thead>\n",
  97 |        "  <tbody>\n",
  98 |        "    <tr>\n",
  99 |        "      <th>AAXJ</th>\n",
 100 |        "      <td>iShares MSCI All Country Asia ex Japan Index Fund</td>\n",
 101 |        "      <td>MSCI All Country Asia ex Japan Index</td>\n",
 102 |        "      <td>The index measures the performance of 11 devel...</td>\n",
 103 |        "      <td>Asia Pacific Equities</td>\n",
 104 |        "      <td>iShares</td>\n",
 105 |        "      <td>0.7400%</td>\n",
 106 |        "      <td>$0.36</td>\n",
 107 |        "      <td>1.8400%</td>\n",
 108 |        "      <td>$1,687.89</td>\n",
 109 |        "      <td>31</td>\n",
 110 |        "      <td>...</td>\n",
 111 |        "      <td>1</td>\n",
 112 |        "      <td>Equity</td>\n",
 113 |        "      <td>NaN</td>\n",
 114 |        "      <td>NaN</td>\n",
 115 |        "      <td>Pacific ex-Japan</td>\n",
 116 |        "      <td>NaN</td>\n",
 117 |        "      <td>NaN</td>\n",
 118 |        "      <td>NaN</td>\n",
 119 |        "      <td>NaN</td>\n",
 120 |        "      <td>NaN</td>\n",
 121 |        "    </tr>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>ACWI</th>\n",
 124 |        "      <td>MSCI All Country World Index Fund</td>\n",
 125 |        "      <td>MSCI All Country World Index</td>\n",
 126 |        "      <td>The Index is designed to measure the performan...</td>\n",
 127 |        "      <td>Global Equities</td>\n",
 128 |        "      <td>iShares</td>\n",
 129 |        "      <td>0.3500%</td>\n",
 130 |        "      <td>$0.36</td>\n",
 131 |        "      <td>1.4100%</td>\n",
 132 |        "      <td>$1,176.64</td>\n",
 133 |        "      <td>30</td>\n",
 134 |        "      <td>...</td>\n",
 135 |        "      <td>1</td>\n",
 136 |        "      <td>Equity</td>\n",
 137 |        "      <td>Large-Cap</td>\n",
 138 |        "      <td>Blend</td>\n",
 139 |        "      <td>Global/Multi-Region</td>\n",
 140 |        "      <td>NaN</td>\n",
 141 |        "      <td>NaN</td>\n",
 142 |        "      <td>NaN</td>\n",
 143 |        "      <td>NaN</td>\n",
 144 |        "      <td>NaN</td>\n",
 145 |        "    </tr>\n",
 146 |        "  </tbody>\n",
 147 |        "</table>\n",
 148 |        "<p>2 rows × 21 columns</p>\n",
 149 |        "</div>"
 150 |       ],
 151 |       "text/plain": [
 152 |        "                                                     Name  \\\n",
 153 |        "Symbol                                                      \n",
 154 |        "AAXJ    iShares MSCI All Country Asia ex Japan Index Fund   \n",
 155 |        "ACWI                    MSCI All Country World Index Fund   \n",
 156 |        "\n",
 157 |        "                                       Index  \\\n",
 158 |        "Symbol                                         \n",
 159 |        "AAXJ    MSCI All Country Asia ex Japan Index   \n",
 160 |        "ACWI            MSCI All Country World Index   \n",
 161 |        "\n",
 162 |        "                                              Description  \\\n",
 163 |        "Symbol                                                      \n",
 164 |        "AAXJ    The index measures the performance of 11 devel...   \n",
 165 |        "ACWI    The Index is designed to measure the performan...   \n",
 166 |        "\n",
 167 |        "                     Category Provider Expense_Ratio Dividend Div_Yield  \\\n",
 168 |        "Symbol                                                                    \n",
 169 |        "AAXJ    Asia Pacific Equities  iShares       0.7400%    $0.36   1.8400%   \n",
 170 |        "ACWI          Global Equities  iShares       0.3500%    $0.36   1.4100%   \n",
 171 |        "\n",
 172 |        "       Market_Cap  Shares  ...    Beta   Asset       Size  Style  \\\n",
 173 |        "Symbol                     ...                                     \n",
 174 |        "AAXJ    $1,687.89      31  ...       1  Equity        NaN    NaN   \n",
 175 |        "ACWI    $1,176.64      30  ...       1  Equity  Large-Cap  Blend   \n",
 176 |        "\n",
 177 |        "                     Region Sector Leveraged Inverse Top_Holdings  Chart  \n",
 178 |        "Symbol                                                                    \n",
 179 |        "AAXJ       Pacific ex-Japan    NaN       NaN     NaN          NaN    NaN  \n",
 180 |        "ACWI    Global/Multi-Region    NaN       NaN     NaN          NaN    NaN  \n",
 181 |        "\n",
 182 |        "[2 rows x 21 columns]"
 183 |       ]
 184 |      },
 185 |      "execution_count": 194,
 186 |      "metadata": {},
 187 |      "output_type": "execute_result"
 188 |     }
 189 |    ],
 190 |    "source": [
 191 |     "ETFs = local_csv('ETFs.csv', skipinitialspace=True, \n",
 192 |     "                    na_values=['n/a','NaN'], thousands=',')\n",
 193 |     "ETFs = ETFs.set_index('Symbol')\n",
 194 |     "ETFs[:2]"
 195 |    ]
 196 |   },
 197 |   {
 198 |    "cell_type": "code",
 199 |    "execution_count": 195,
 200 |    "metadata": {
 201 |     "collapsed": false
 202 |    },
 203 |    "outputs": [],
 204 |    "source": [
 205 |     "del ETFs['Top_Holdings']\n",
 206 |     "del ETFs['Chart']"
 207 |    ]
 208 |   },
 209 |   {
 210 |    "cell_type": "code",
 211 |    "execution_count": 196,
 212 |    "metadata": {
 213 |     "collapsed": false
 214 |    },
 215 |    "outputs": [
 216 |     {
 217 |      "data": {
 218 |       "text/plain": [
 219 |        "Index([u'Name', u'Index', u'Description', u'Category', u'Provider', u'Expense_Ratio', u'Dividend', u'Div_Yield', u'Market_Cap', u'Shares', u'Avg_Vol', u'Beta', u'Asset', u'Size', u'Style', u'Region', u'Sector', u'Leveraged', u'Inverse'], dtype='object')"
 220 |       ]
 221 |      },
 222 |      "execution_count": 196,
 223 |      "metadata": {},
 224 |      "output_type": "execute_result"
 225 |     }
 226 |    ],
 227 |    "source": [
 228 |     "ETFs.columns"
 229 |    ]
 230 |   },
 231 |   {
 232 |    "cell_type": "code",
 233 |    "execution_count": 197,
 234 |    "metadata": {
 235 |     "collapsed": false
 236 |    },
 237 |    "outputs": [],
 238 |    "source": [
 239 |     "def p2f(x):\n",
 240 |     "    if type(x) is str:\n",
 241 |     "        return float(x.strip('%'))/100\n",
 242 |     "    else:\n",
 243 |     "        return x / 100\n",
 244 |     "\n",
 245 |     "def d2f(x):\n",
 246 |     "    if type(x) is str:\n",
 247 |     "        return float(x.strip('$').replace(',',''))\n",
 248 |     "    else:\n",
 249 |     "        return x\n",
 250 |     "\n",
 251 |     "def s2b(x):\n",
 252 |     "    if type(x) is str:\n",
 253 |     "        if x == 'Yes':\n",
 254 |     "            return True\n",
 255 |     "        else:\n",
 256 |     "            return False\n",
 257 |     "    else:\n",
 258 |     "        return False        "
 259 |    ]
 260 |   },
 261 |   {
 262 |    "cell_type": "code",
 263 |    "execution_count": 198,
 264 |    "metadata": {
 265 |     "collapsed": false
 266 |    },
 267 |    "outputs": [
 268 |     {
 269 |      "data": {
 270 |       "text/html": [
 271 |        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
 272 |        "<table border=\"1\" class=\"dataframe\">\n",
 273 |        "  <thead>\n",
 274 |        "    <tr style=\"text-align: right;\">\n",
 275 |        "      <th></th>\n",
 276 |        "      <th>Name</th>\n",
 277 |        "      <th>Index</th>\n",
 278 |        "      <th>Description</th>\n",
 279 |        "      <th>Category</th>\n",
 280 |        "      <th>Provider</th>\n",
 281 |        "      <th>Expense_Ratio</th>\n",
 282 |        "      <th>Dividend</th>\n",
 283 |        "      <th>Div_Yield</th>\n",
 284 |        "      <th>Market_Cap</th>\n",
 285 |        "      <th>Shares</th>\n",
 286 |        "      <th>Avg_Vol</th>\n",
 287 |        "      <th>Beta</th>\n",
 288 |        "      <th>Asset</th>\n",
 289 |        "      <th>Size</th>\n",
 290 |        "      <th>Style</th>\n",
 291 |        "      <th>Region</th>\n",
 292 |        "      <th>Sector</th>\n",
 293 |        "      <th>Leveraged</th>\n",
 294 |        "      <th>Inverse</th>\n",
 295 |        "    </tr>\n",
 296 |        "    <tr>\n",
 297 |        "      <th>Symbol</th>\n",
 298 |        "      <th></th>\n",
 299 |        "      <th></th>\n",
 300 |        "      <th></th>\n",
 301 |        "      <th></th>\n",
 302 |        "      <th></th>\n",
 303 |        "      <th></th>\n",
 304 |        "      <th></th>\n",
 305 |        "      <th></th>\n",
 306 |        "      <th></th>\n",
 307 |        "      <th></th>\n",
 308 |        "      <th></th>\n",
 309 |        "      <th></th>\n",
 310 |        "      <th></th>\n",
 311 |        "      <th></th>\n",
 312 |        "      <th></th>\n",
 313 |        "      <th></th>\n",
 314 |        "      <th></th>\n",
 315 |        "      <th></th>\n",
 316 |        "      <th></th>\n",
 317 |        "    </tr>\n",
 318 |        "  </thead>\n",
 319 |        "  <tbody>\n",
 320 |        "    <tr>\n",
 321 |        "      <th>AAXJ</th>\n",
 322 |        "      <td>iShares MSCI All Country Asia ex Japan Index Fund</td>\n",
 323 |        "      <td>MSCI All Country Asia ex Japan Index</td>\n",
 324 |        "      <td>The index measures the performance of 11 devel...</td>\n",
 325 |        "      <td>Asia Pacific Equities</td>\n",
 326 |        "      <td>iShares</td>\n",
 327 |        "      <td>0.0074</td>\n",
 328 |        "      <td>0.36</td>\n",
 329 |        "      <td>0.0184</td>\n",
 330 |        "      <td>1687.89</td>\n",
 331 |        "      <td>31</td>\n",
 332 |        "      <td>527890</td>\n",
 333 |        "      <td>1</td>\n",
 334 |        "      <td>Equity</td>\n",
 335 |        "      <td>NaN</td>\n",
 336 |        "      <td>NaN</td>\n",
 337 |        "      <td>Pacific ex-Japan</td>\n",
 338 |        "      <td>NaN</td>\n",
 339 |        "      <td>NaN</td>\n",
 340 |        "      <td>False</td>\n",
 341 |        "    </tr>\n",
 342 |        "    <tr>\n",
 343 |        "      <th>ACWI</th>\n",
 344 |        "      <td>MSCI All Country World Index Fund</td>\n",
 345 |        "      <td>MSCI All Country World Index</td>\n",
 346 |        "      <td>The Index is designed to measure the performan...</td>\n",
 347 |        "      <td>Global Equities</td>\n",
 348 |        "      <td>iShares</td>\n",
 349 |        "      <td>0.0035</td>\n",
 350 |        "      <td>0.36</td>\n",
 351 |        "      <td>0.0141</td>\n",
 352 |        "      <td>1176.64</td>\n",
 353 |        "      <td>30</td>\n",
 354 |        "      <td>379781</td>\n",
 355 |        "      <td>1</td>\n",
 356 |        "      <td>Equity</td>\n",
 357 |        "      <td>Large-Cap</td>\n",
 358 |        "      <td>Blend</td>\n",
 359 |        "      <td>Global/Multi-Region</td>\n",
 360 |        "      <td>NaN</td>\n",
 361 |        "      <td>NaN</td>\n",
 362 |        "      <td>False</td>\n",
 363 |        "    </tr>\n",
 364 |        "    <tr>\n",
 365 |        "      <th>ACWX</th>\n",
 366 |        "      <td>iShares MSCI ACWI ex US Index Fund</td>\n",
 367 |        "      <td>MSCI All Country World ex-U.S. Index</td>\n",
 368 |        "      <td>The index measures the performance of global e...</td>\n",
 369 |        "      <td>Global Equities</td>\n",
 370 |        "      <td>iShares</td>\n",
 371 |        "      <td>0.0035</td>\n",
 372 |        "      <td>0.52</td>\n",
 373 |        "      <td>0.0147</td>\n",
 374 |        "      <td>654.07</td>\n",
 375 |        "      <td>17</td>\n",
 376 |        "      <td>161924</td>\n",
 377 |        "      <td>1</td>\n",
 378 |        "      <td>Equity</td>\n",
 379 |        "      <td>Large-Cap</td>\n",
 380 |        "      <td>Blend</td>\n",
 381 |        "      <td>Ex-U.S.</td>\n",
 382 |        "      <td>NaN</td>\n",
 383 |        "      <td>NaN</td>\n",
 384 |        "      <td>False</td>\n",
 385 |        "    </tr>\n",
 386 |        "  </tbody>\n",
 387 |        "</table>\n",
 388 |        "</div>"
 389 |       ],
 390 |       "text/plain": [
 391 |        "                                                     Name  \\\n",
 392 |        "Symbol                                                      \n",
 393 |        "AAXJ    iShares MSCI All Country Asia ex Japan Index Fund   \n",
 394 |        "ACWI                    MSCI All Country World Index Fund   \n",
 395 |        "ACWX                   iShares MSCI ACWI ex US Index Fund   \n",
 396 |        "\n",
 397 |        "                                       Index  \\\n",
 398 |        "Symbol                                         \n",
 399 |        "AAXJ    MSCI All Country Asia ex Japan Index   \n",
 400 |        "ACWI            MSCI All Country World Index   \n",
 401 |        "ACWX    MSCI All Country World ex-U.S. Index   \n",
 402 |        "\n",
 403 |        "                                              Description  \\\n",
 404 |        "Symbol                                                      \n",
 405 |        "AAXJ    The index measures the performance of 11 devel...   \n",
 406 |        "ACWI    The Index is designed to measure the performan...   \n",
 407 |        "ACWX    The index measures the performance of global e...   \n",
 408 |        "\n",
 409 |        "                     Category Provider  Expense_Ratio  Dividend  Div_Yield  \\\n",
 410 |        "Symbol                                                                       \n",
 411 |        "AAXJ    Asia Pacific Equities  iShares         0.0074      0.36     0.0184   \n",
 412 |        "ACWI          Global Equities  iShares         0.0035      0.36     0.0141   \n",
 413 |        "ACWX          Global Equities  iShares         0.0035      0.52     0.0147   \n",
 414 |        "\n",
 415 |        "        Market_Cap  Shares  Avg_Vol  Beta   Asset       Size  Style  \\\n",
 416 |        "Symbol                                                                \n",
 417 |        "AAXJ       1687.89      31   527890     1  Equity        NaN    NaN   \n",
 418 |        "ACWI       1176.64      30   379781     1  Equity  Large-Cap  Blend   \n",
 419 |        "ACWX        654.07      17   161924     1  Equity  Large-Cap  Blend   \n",
 420 |        "\n",
 421 |        "                     Region Sector Leveraged Inverse  \n",
 422 |        "Symbol                                                \n",
 423 |        "AAXJ       Pacific ex-Japan    NaN       NaN   False  \n",
 424 |        "ACWI    Global/Multi-Region    NaN       NaN   False  \n",
 425 |        "ACWX                Ex-U.S.    NaN       NaN   False  "
 426 |       ]
 427 |      },
 428 |      "execution_count": 198,
 429 |      "metadata": {},
 430 |      "output_type": "execute_result"
 431 |     }
 432 |    ],
 433 |    "source": [
 434 |     "# Apply conversions\n",
 435 |     "ETFs.Expense_Ratio = ETFs.Expense_Ratio.apply(p2f) \n",
 436 |     "ETFs.Dividend = ETFs.Dividend.apply(d2f)\n",
 437 |     "ETFs.Div_Yield = ETFs.Div_Yield.apply(p2f)\n",
 438 |     "ETFs.Market_Cap = ETFs.Market_Cap.apply(d2f)\n",
 439 |     "ETFs.Inverse = ETFs.Inverse.apply(s2b)\n",
 440 |     "ETFs[:3]"
 441 |    ]
 442 |   },
 443 |   {
 444 |    "cell_type": "code",
 445 |    "execution_count": 199,
 446 |    "metadata": {
 447 |     "collapsed": false
 448 |    },
 449 |    "outputs": [
 450 |     {
 451 |      "data": {
 452 |       "text/plain": [
 453 |        "1020"
 454 |       ]
 455 |      },
 456 |      "execution_count": 199,
 457 |      "metadata": {},
 458 |      "output_type": "execute_result"
 459 |     }
 460 |    ],
 461 |    "source": [
 462 |     "len(ETFs.index)"
 463 |    ]
 464 |   },
 465 |   {
 466 |    "cell_type": "code",
 467 |    "execution_count": 223,
 468 |    "metadata": {
 469 |     "collapsed": false
 470 |    },
 471 |    "outputs": [
 472 |     {
 473 |      "data": {
 474 |       "text/plain": [
 475 |        "Leveraged Equities                   120\n",
 476 |        "Emerging Markets Equities             46\n",
 477 |        "Energy Equities                       40\n",
 478 |        "Large Cap Value Equities              36\n",
 479 |        "Technology Equities                   32\n",
 480 |        "Large Cap Blend Equities              30\n",
 481 |        "Currency                              29\n",
 482 |        "Financials Equities                   28\n",
 483 |        "Europe Equities                       25\n",
 484 |        "Foreign Large Cap Equities            25\n",
 485 |        "Health & Biotech Equities             22\n",
 486 |        "National Munis                        21\n",
 487 |        "Commodities                           19\n",
 488 |        "All Cap Equities                      19\n",
 489 |        "Commodity Producers Equities          18\n",
 490 |        "Precious Metals                       18\n",
 491 |        "Government Bonds                      18\n",
 492 |        "Leveraged Commodities                 18\n",
 493 |        "Large Cap Growth Equities             18\n",
 494 |        "Asia Pacific Equities                 17\n",
 495 |        "China Equities                        16\n",
 496 |        "Quantitative Methodology Indexes      16\n",
 497 |        "Global Equities                       16\n",
 498 |        "Oil & Gas                             15\n",
 499 |        "Inverse Equities                      15\n",
 500 |        "Small Cap Blend Equities              15\n",
 501 |        "Consumer Discretionary Equities       14\n",
 502 |        "Miscellaneous Sector Equities         14\n",
 503 |        "Utilities Equities                    14\n",
 504 |        "Real Estate                           12\n",
 505 |        "                                    ... \n",
 506 |        "Consumer Staples Equities             11\n",
 507 |        "Corporate Bonds                       11\n",
 508 |        "Latin America Equities                10\n",
 509 |        "Materials                             10\n",
 510 |        "Foreign Small & Mid Cap Equities      10\n",
 511 |        "Diversified Portfolio                 10\n",
 512 |        "Small Cap Value Equities              10\n",
 513 |        "Global Real Estate                     9\n",
 514 |        "Mid Cap Value Equities                 9\n",
 515 |        "Metals                                 9\n",
 516 |        "Communications Equities                8\n",
 517 |        "Inverse Commodities                    8\n",
 518 |        "Mid Cap Growth Equities                8\n",
 519 |        "Japan Equities                         8\n",
 520 |        "Leveraged Real Estate                  7\n",
 521 |        "Small Cap Growth Equities              7\n",
 522 |        "Money Market                           7\n",
 523 |        "Leveraged Currency                     6\n",
 524 |        "Inflation-Protected Bonds              6\n",
 525 |        "Hedge Fund                             6\n",
 526 |        "Long-Short                             6\n",
 527 |        "Preferred Stock/Convertible Bonds      5\n",
 528 |        "International Government Bonds         4\n",
 529 |        "Transportation Equities                4\n",
 530 |        "Mortgage Backed Securities             3\n",
 531 |        "High Yield Bonds                       3\n",
 532 |        "California Munis                       3\n",
 533 |        "New York Munis                         3\n",
 534 |        "Volatility                             2\n",
 535 |        "Emerging Markets Bonds                 2\n",
 536 |        "dtype: int64"
 537 |       ]
 538 |      },
 539 |      "execution_count": 223,
 540 |      "metadata": {},
 541 |      "output_type": "execute_result"
 542 |     }
 543 |    ],
 544 |    "source": [
 545 |     "ETFs.Category.value_counts()"
 546 |    ]
 547 |   },
 548 |   {
 549 |    "cell_type": "code",
 550 |    "execution_count": 222,
 551 |    "metadata": {
 552 |     "collapsed": false
 553 |    },
 554 |    "outputs": [
 555 |     {
 556 |      "data": {
 557 |       "text/plain": [
 558 |        "iShares                    201\n",
 559 |        "Invesco PowerShares        146\n",
 560 |        "ProShares                  111\n",
 561 |        "State Street                89\n",
 562 |        "Wisdom Tree                 52\n",
 563 |        "Vanguard                    46\n",
 564 |        "First Trust                 44\n",
 565 |        "Rydex                       40\n",
 566 |        "Barclays iPath              38\n",
 567 |        "Claymore                    36\n",
 568 |        "Direxion                    34\n",
 569 |        "Van Eck                     29\n",
 570 |        "Merrill Lynch HOLDRs        17\n",
 571 |        "ELEMENTS                    16\n",
 572 |        "UBS                         13\n",
 573 |        "PIMCO                       10\n",
 574 |        "Global X                    10\n",
 575 |        "IndexIQ                      9\n",
 576 |        "Charles Schwab               8\n",
 577 |        "Grail Advisors               7\n",
 578 |        "US Commodity Funds           7\n",
 579 |        "EG Shares                    6\n",
 580 |        "XShares                      6\n",
 581 |        "RevenueShares                6\n",
 582 |        "GlobalShares                 5\n",
 583 |        "FaithShares                  5\n",
 584 |        "ETF Securities               4\n",
 585 |        "MacroMarkets                 4\n",
 586 |        "Jefferies                    4\n",
 587 |        "Credit Suisse                2\n",
 588 |        "JP Morgan                    2\n",
 589 |        "Javelin Funds                2\n",
 590 |        "Geary Advisors               2\n",
 591 |        "ALPS                         2\n",
 592 |        "Goldman Sachs                2\n",
 593 |        "GreenHaven                   1\n",
 594 |        "Fidelity                     1\n",
 595 |        "AdvisorShares                1\n",
 596 |        "Sprott Asset Management      1\n",
 597 |        "U.S. One                     1\n",
 598 |        "dtype: int64"
 599 |       ]
 600 |      },
 601 |      "execution_count": 222,
 602 |      "metadata": {},
 603 |      "output_type": "execute_result"
 604 |     }
 605 |    ],
 606 |    "source": [
 607 |     "ETFs.Provider.value_counts()"
 608 |    ]
 609 |   },
 610 |   {
 611 |    "cell_type": "code",
 612 |    "execution_count": 202,
 613 |    "metadata": {
 614 |     "collapsed": false
 615 |    },
 616 |    "outputs": [
 617 |     {
 618 |      "data": {
 619 |       "text/plain": [
 620 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f85b3e2c3d0>"
 621 |       ]
 622 |      },
 623 |      "execution_count": 202,
 624 |      "metadata": {},
 625 |      "output_type": "execute_result"
 626 |     },
 627 |     {
 628 |      "data": {
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 756 |        "AAAASUVORK5CYII=\n"
 757 |       ],
 758 |       "text/plain": [
 759 |        "<matplotlib.figure.Figure at 0x7f85b7ce6210>"
 760 |       ]
 761 |      },
 762 |      "metadata": {},
 763 |      "output_type": "display_data"
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "ETFs.Expense_Ratio.plot(kind='hist')"
 768 |    ]
 769 |   },
 770 |   {
 771 |    "cell_type": "code",
 772 |    "execution_count": 203,
 773 |    "metadata": {
 774 |     "collapsed": false
 775 |    },
 776 |    "outputs": [
 777 |     {
 778 |      "data": {
 779 |       "text/plain": [
 780 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f85b3bd19d0>"
 781 |       ]
 782 |      },
 783 |      "execution_count": 203,
 784 |      "metadata": {},
 785 |      "output_type": "execute_result"
 786 |     },
 787 |     {
 788 |      "data": {
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 924 |        "RPvNJK9Ict+yB2EY+5O8t6o+VFUvPthOh2sUudwKHNGq6lFJ3pbk5dMVI5hFd9833T53cpL/UFU/\n",
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 929 |        "VVcm+Ysk26vqU1X1wmXPxBHtmUmen7W3gH14+sfbD5nLCUneNz1T9MEk7+ruG5Y8E+PwOARz25bk\n",
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 932 |       ],
 933 |       "text/plain": [
 934 |        "<matplotlib.figure.Figure at 0x7f85b3c04190>"
 935 |       ]
 936 |      },
 937 |      "metadata": {},
 938 |      "output_type": "display_data"
 939 |     }
 940 |    ],
 941 |    "source": [
 942 |     "ETFs.Dividend.plot(kind='hist', bins=20)"
 943 |    ]
 944 |   },
 945 |   {
 946 |    "cell_type": "code",
 947 |    "execution_count": 204,
 948 |    "metadata": {
 949 |     "collapsed": false
 950 |    },
 951 |    "outputs": [
 952 |     {
 953 |      "data": {
 954 |       "text/plain": [
 955 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f85b3adf390>"
 956 |       ]
 957 |      },
 958 |      "execution_count": 204,
 959 |      "metadata": {},
 960 |      "output_type": "execute_result"
 961 |     },
 962 |     {
 963 |      "data": {
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1038 |        "d++eHLI7yfrJ9llJdq04fVeSs6c5HwAAwKFM7TNNSdLdjyR5flWdluR9VfXi/Z5frqrlwY8YPbfQ\n",
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1101 |        "/Yaq+tdJ0t2/NDnm0btlfCnJ93X3Hx/s3Dn8K7CGHOl6q6qvzL4/iMm+v4T4deuNQznUepvc1Oaj\n",
1102 |        "Sb4iySPZd4vUTd19v9c3DteRrrckp8frG0dgFWvubUm+PcnfTE557FbPXuM4XEe63g73/+EWNpoA\n",
1103 |        "AAAWwaJ+pgkAAGAhiCYAAIAB0QQAADAgmgAAAAZEEwAAwIBoAgAAGBBNAAAAA/8PpsFEcPB1IGoA\n",
1104 |        "AAAASUVORK5CYII=\n"
1105 |       ],
1106 |       "text/plain": [
1107 |        "<matplotlib.figure.Figure at 0x7f85b3b62c90>"
1108 |       ]
1109 |      },
1110 |      "metadata": {},
1111 |      "output_type": "display_data"
1112 |     }
1113 |    ],
1114 |    "source": [
1115 |     "ETFs.Div_Yield.plot(kind='hist', bins=20)"
1116 |    ]
1117 |   },
1118 |   {
1119 |    "cell_type": "code",
1120 |    "execution_count": 205,
1121 |    "metadata": {
1122 |     "collapsed": false
1123 |    },
1124 |    "outputs": [
1125 |     {
1126 |      "data": {
1127 |       "text/plain": [
1128 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f85b3998510>"
1129 |       ]
1130 |      },
1131 |      "execution_count": 205,
1132 |      "metadata": {},
1133 |      "output_type": "execute_result"
1134 |     },
1135 |     {
1136 |      "data": {
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1277 |       ],
1278 |       "text/plain": [
1279 |        "<matplotlib.figure.Figure at 0x7f85b3a06290>"
1280 |       ]
1281 |      },
1282 |      "metadata": {},
1283 |      "output_type": "display_data"
1284 |     }
1285 |    ],
1286 |    "source": [
1287 |     "ETFs.Market_Cap.plot(kind='hist',bins=40)"
1288 |    ]
1289 |   },
1290 |   {
1291 |    "cell_type": "code",
1292 |    "execution_count": 221,
1293 |    "metadata": {
1294 |     "collapsed": false
1295 |    },
1296 |    "outputs": [
1297 |     {
1298 |      "data": {
1299 |       "text/plain": [
1300 |        "Equity         725\n",
1301 |        "Bond           106\n",
1302 |        "Commodity      100\n",
1303 |        "Currency        35\n",
1304 |        "Real Estate     28\n",
1305 |        "Multi-Asset     26\n",
1306 |        "dtype: int64"
1307 |       ]
1308 |      },
1309 |      "execution_count": 221,
1310 |      "metadata": {},
1311 |      "output_type": "execute_result"
1312 |     }
1313 |    ],
1314 |    "source": [
1315 |     "ETFs.Asset.value_counts()"
1316 |    ]
1317 |   },
1318 |   {
1319 |    "cell_type": "code",
1320 |    "execution_count": 220,
1321 |    "metadata": {
1322 |     "collapsed": false
1323 |    },
1324 |    "outputs": [
1325 |     {
1326 |      "data": {
1327 |       "text/plain": [
1328 |        "Large-Cap    205\n",
1329 |        "Small-Cap     79\n",
1330 |        "Mid-Cap       53\n",
1331 |        "Multi-Cap     24\n",
1332 |        "dtype: int64"
1333 |       ]
1334 |      },
1335 |      "execution_count": 220,
1336 |      "metadata": {},
1337 |      "output_type": "execute_result"
1338 |     }
1339 |    ],
1340 |    "source": [
1341 |     "ETFs.Size.value_counts()"
1342 |    ]
1343 |   },
1344 |   {
1345 |    "cell_type": "code",
1346 |    "execution_count": 219,
1347 |    "metadata": {
1348 |     "collapsed": false
1349 |    },
1350 |    "outputs": [
1351 |     {
1352 |      "data": {
1353 |       "text/plain": [
1354 |        "Blend     177\n",
1355 |        "Value      85\n",
1356 |        "Growth     59\n",
1357 |        "dtype: int64"
1358 |       ]
1359 |      },
1360 |      "execution_count": 219,
1361 |      "metadata": {},
1362 |      "output_type": "execute_result"
1363 |     }
1364 |    ],
1365 |    "source": [
1366 |     "ETFs.Style.value_counts()"
1367 |    ]
1368 |   },
1369 |   {
1370 |    "cell_type": "code",
1371 |    "execution_count": 209,
1372 |    "metadata": {
1373 |     "collapsed": false
1374 |    },
1375 |    "outputs": [
1376 |     {
1377 |      "data": {
1378 |       "text/plain": [
1379 |        "<matplotlib.axes._subplots.AxesSubplot at 0x7f85b38f3690>"
1380 |       ]
1381 |      },
1382 |      "execution_count": 209,
1383 |      "metadata": {},
1384 |      "output_type": "execute_result"
1385 |     },
1386 |     {
1387 |      "data": {
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1505 |        "//1nb81/O2DDWIfNwTrMnjXYHKzD5mAd5slmC6M7kuw86vHOrJ41eli7d+/2TyEAAMBJ22xh9NEk\n",
1506 |        "F1fVhUn+KslVSX5wphMBAABb3qb6gNfuvj/JjyV5b5J9Sd651h3pAAAAToXTjhw54bf1AAAAbCmb\n",
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1509 |        "DyemqnYm+dUkZ2f195U3d/cvznYq1qOqHpPk/UkenWRbknd1957ZTsWJqqrTs3r36wPd/f3H2m8r\n",
1510 |        "nDH62e7+pu5+epKbkvzkrAdi3W5N8tTu/qYkn07iB818+USSH0jyh7MehOOb/J/CLyW5PMklSX6w\n",
1511 |        "qp4y26k4AW/L6toxn+5L8uPd/dQk357kZf7+zYfu/nKSZ01+z3xakmdV1XfNeCxO3CuyesfrNe86\n",
1512 |        "N/dh1N2Hj3p4VpK/mdUsnJjuvq27H5w8/HCS82c5Dyemuz/V3Z+e9Rys26VJbu/u5e6+L8mvJ7ly\n",
1513 |        "xjOxTt39gSRfnPUcPDLdfbC7Pz7Z/lKSTyY5d7ZTsV7dfc9kc1tWz7h/YYbjcIKq6vwkz03yliSn\n",
1514 |        "rbXv3F9KlyRV9TNJXpTknqz+Swzz55okN8x6CNjCzkty9GXGB5J824xmgWFNPsT+m7P6D4LMgap6\n",
1515 |        "VJI/SfL1Sd7U3ftmPBIn5ueTvDLJ446341yEUVXdluTJD/PSq7r7d7r71UleXVXXZvU//kc2dECO\n",
1516 |        "6XhrN9nn1Unu7e53bOhwHNd61o+54UPrYMaq6qwkv5HkFZMzR8yBydUtT6+q7UneW1XP7O4/mPFY\n",
1517 |        "rENVfV9W35v5sap65vH2n4sw6u7nrHPXdyT53WnOwok53tpV1Yuzenrzn27IQJyQE/i7x+Z3R5Kd\n",
1518 |        "Rz3emdWzRsAGqKqvSfKbSX6tu2+a9TycuO6+u6reneRbkvzBjMdhfb4jyRVV9dwkj0nyuKr61e7+\n",
1519 |        "4Yfbee7fY1RVFx/18MokH5vVLJyYyR2yXpnkysmbG5lfa16zy6bw0SQXV9WFVbUtyVVJbp7xTDCE\n",
1520 |        "qjotyVuT7OvuN8x6Htavqp5UVY+fbD82yXPid8250d2v6u6d3X1Rkhck+f1jRVEyJ2eMjuO1VVVJ\n",
1521 |        "HsjqLZ9/dMbzsH7/NatvZLxtdQnzoe5+6WxHYr2q6geS/GKSJyV5d1V9rLv/xYzH4hi6+/6q+rEk\n",
1522 |        "783qm4ff2t2fnPFYrFNV3ZDke5J8XVV9NslruvttMx6L9fvOJD+U5M+q6qFfqvd09/+c4UyszzlJ\n",
1523 |        "rp+8z+hRSd7e3e+b8Uw8cmteVn7akSMuOwcAAMY295fSAQAAnCxhBAAADE8YAQAAwxNGAADA8IQR\n",
1524 |        "AAAwPGEEAAAMTxgBAADD+79lB14xV812IQAAAABJRU5ErkJggg==\n"
1525 |       ],
1526 |       "text/plain": [
1527 |        "<matplotlib.figure.Figure at 0x7f85b3915cd0>"
1528 |       ]
1529 |      },
1530 |      "metadata": {},
1531 |      "output_type": "display_data"
1532 |     }
1533 |    ],
1534 |    "source": [
1535 |     "ETFs.Beta.plot(kind='hist',bins=8)"
1536 |    ]
1537 |   },
1538 |   {
1539 |    "cell_type": "code",
1540 |    "execution_count": 218,
1541 |    "metadata": {
1542 |     "collapsed": false
1543 |    },
1544 |    "outputs": [
1545 |     {
1546 |      "data": {
1547 |       "text/plain": [
1548 |        "U.S.                   460\n",
1549 |        "Global/Multi-Region    107\n",
1550 |        "Emerging Markets        28\n",
1551 |        "China                   20\n",
1552 |        "Europe                  15\n",
1553 |        "Japan                   10\n",
1554 |        "Developed Markets        9\n",
1555 |        "Ex-U.S.                  9\n",
1556 |        "EAFE                     8\n",
1557 |        "Asia                     7\n",
1558 |        "India                    6\n",
1559 |        "Pacific ex-Japan         6\n",
1560 |        "BRIC                     5\n",
1561 |        "Brazil                   5\n",
1562 |        "Latin America            5\n",
1563 |        "Canada                   3\n",
1564 |        "Euro-Pacific             3\n",
1565 |        "Middle East              3\n",
1566 |        "Mexico                   3\n",
1567 |        "South Korea              2\n",
1568 |        "Africa                   2\n",
1569 |        "Taiwan                   2\n",
1570 |        "Australia                2\n",
1571 |        "Russia                   2\n",
1572 |        "Indonesia                2\n",
1573 |        "Middle East/Africa       2\n",
1574 |        "Israel                   1\n",
1575 |        "Colombia                 1\n",
1576 |        "Austria                  1\n",
1577 |        "France                   1\n",
1578 |        "Turkey                   1\n",
1579 |        "Ireland                  1\n",
1580 |        "Italy                    1\n",
1581 |        "Switzerland              1\n",
1582 |        "Malaysia                 1\n",
1583 |        "United Kingdom           1\n",
1584 |        "Germany                  1\n",
1585 |        "Pacific                  1\n",
1586 |        "Singapore                1\n",
1587 |        "Chindia                  1\n",
1588 |        "Nordic                   1\n",
1589 |        "Poland                   1\n",
1590 |        "Chile                    1\n",
1591 |        "Belgium                  1\n",
1592 |        "Thailand                 1\n",
1593 |        "Hong Kong                1\n",
1594 |        "Netherlands              1\n",
1595 |        "South Africa             1\n",
1596 |        "Peru                     1\n",
1597 |        "North America            1\n",
1598 |        "Sweden                   1\n",
1599 |        "Vietnam                  1\n",
1600 |        "Spain                    1\n",
1601 |        "dtype: int64"
1602 |       ]
1603 |      },
1604 |      "execution_count": 218,
1605 |      "metadata": {},
1606 |      "output_type": "execute_result"
1607 |     }
1608 |    ],
1609 |    "source": [
1610 |     "ETFs.Region.value_counts()"
1611 |    ]
1612 |   },
1613 |   {
1614 |    "cell_type": "code",
1615 |    "execution_count": 217,
1616 |    "metadata": {
1617 |     "collapsed": false
1618 |    },
1619 |    "outputs": [
1620 |     {
1621 |      "data": {
1622 |       "text/plain": [
1623 |        "Energy                     45\n",
1624 |        "Technology                 41\n",
1625 |        "Financial                  41\n",
1626 |        "Industrial                 32\n",
1627 |        "Health & Biotech           25\n",
1628 |        "Utilities                  17\n",
1629 |        "Retail & Consumer          15\n",
1630 |        "Materials                  12\n",
1631 |        "Communications             11\n",
1632 |        "Consumer Staples            6\n",
1633 |        "Consumer Discretionary      6\n",
1634 |        "Transportation              4\n",
1635 |        "Building & Construction     3\n",
1636 |        "Agriculture                 3\n",
1637 |        "Aerospace & Defense         2\n",
1638 |        "Environmental               1\n",
1639 |        "dtype: int64"
1640 |       ]
1641 |      },
1642 |      "execution_count": 217,
1643 |      "metadata": {},
1644 |      "output_type": "execute_result"
1645 |     }
1646 |    ],
1647 |    "source": [
1648 |     "ETFs.Sector.value_counts()"
1649 |    ]
1650 |   },
1651 |   {
1652 |    "cell_type": "code",
1653 |    "execution_count": 216,
1654 |    "metadata": {
1655 |     "collapsed": false
1656 |    },
1657 |    "outputs": [
1658 |     {
1659 |      "data": {
1660 |       "text/plain": [
1661 |        "2x    116\n",
1662 |        "3x     44\n",
1663 |        "dtype: int64"
1664 |       ]
1665 |      },
1666 |      "execution_count": 216,
1667 |      "metadata": {},
1668 |      "output_type": "execute_result"
1669 |     }
1670 |    ],
1671 |    "source": [
1672 |     "ETFs.Leveraged.value_counts()"
1673 |    ]
1674 |   },
1675 |   {
1676 |    "cell_type": "code",
1677 |    "execution_count": 215,
1678 |    "metadata": {
1679 |     "collapsed": false
1680 |    },
1681 |    "outputs": [
1682 |     {
1683 |      "data": {
1684 |       "text/plain": [
1685 |        "False    914\n",
1686 |        "True     106\n",
1687 |        "dtype: int64"
1688 |       ]
1689 |      },
1690 |      "execution_count": 215,
1691 |      "metadata": {},
1692 |      "output_type": "execute_result"
1693 |     }
1694 |    ],
1695 |    "source": [
1696 |     "ETFs.Inverse.value_counts()"
1697 |    ]
1698 |   },
1699 |   {
1700 |    "cell_type": "markdown",
1701 |    "metadata": {},
1702 |    "source": [
1703 |     "## Places to collect holdings data "
1704 |    ]
1705 |   },
1706 |   {
1707 |    "cell_type": "code",
1708 |    "execution_count": 213,
1709 |    "metadata": {
1710 |     "collapsed": true
1711 |    },
1712 |    "outputs": [],
1713 |    "source": [
1714 |     "# http://www.etfreplay.com/"
1715 |    ]
1716 |   },
1717 |   {
1718 |    "cell_type": "code",
1719 |    "execution_count": null,
1720 |    "metadata": {
1721 |     "collapsed": true
1722 |    },
1723 |    "outputs": [],
1724 |    "source": [
1725 |     "# http://etfdb.com/etf/###\n",
1726 |     "# http://finance.yahoo.com/q/hl?s=### ETF holdings"
1727 |    ]
1728 |   },
1729 |   {
1730 |    "cell_type": "code",
1731 |    "execution_count": null,
1732 |    "metadata": {
1733 |     "collapsed": true
1734 |    },
1735 |    "outputs": [],
1736 |    "source": [
1737 |     "# https://accounts.profunds.com/etfdata/ByFund/EFAD-psdlyhld.csv\n",
1738 |     "# http://www.ishares.com/us/products/239709/ishares-russell-2000-growth-etf"
1739 |    ]
1740 |   },
1741 |   {
1742 |    "cell_type": "code",
1743 |    "execution_count": null,
1744 |    "metadata": {
1745 |     "collapsed": true
1746 |    },
1747 |    "outputs": [],
1748 |    "source": []
1749 |   }
1750 |  ],
1751 |  "metadata": {
1752 |   "kernelspec": {
1753 |    "display_name": "Python 2",
1754 |    "language": "python",
1755 |    "name": "python2"
1756 |   },
1757 |   "language_info": {
1758 |    "codemirror_mode": {
1759 |     "name": "ipython",
1760 |     "version": 2
1761 |    },
1762 |    "file_extension": ".py",
1763 |    "mimetype": "text/x-python",
1764 |    "name": "python",
1765 |    "nbconvert_exporter": "python",
1766 |    "pygments_lexer": "ipython2",
1767 |    "version": "2.7.9"
1768 |   }
1769 |  },
1770 |  "nbformat": 4,
1771 |  "nbformat_minor": 0
1772 | }
1773 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | The Feedback MIT License (FMIT) 
 2 | 
 3 | Copyright (c) 2014, Paul O. Perry
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of
 6 | this software and associated documentation files (the "Software"), to deal in
 7 | the Software without restriction, including without limitation the rights to
 8 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
 9 | the Software, and to permit persons to whom the Software is furnished to do so,
10 | subject to the following conditions:
11 | 
12 | 1. The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | 2. Person obtaining a copy must return feedback to the authors.
16 | 
17 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
18 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
19 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
20 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
21 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
22 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
23 | 


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/OLPS_algos.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/paulperry/quant/a94ff7ea34aa0319e81307390a238e041af56dff/OLPS_algos.png


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/README.md:
--------------------------------------------------------------------------------
 1 | # quant
 2 | 
 3 | Here I collect some Quantitative Finance and Algorithmic Trading
 4 | exhaust; mostly ipython notebooks based on
 5 | [Quantopian](https://www.quantopian.com),
 6 | [Zipline](https://github.com/quantopian/zipline), or
 7 | [Pandas](http://pandas.pydata.org/).
 8 | 
 9 | 1. [Comparing some Lazy Portfolios](https://github.com/paulperry/quant/blob/master/Lazy_Backtests.ipynb)
10 | 2. [Exploratory Data Analysis (EDA) of ETFs](https://github.com/paulperry/quant/blob/master/ETFs.ipynb)
11 | 3. [Comparing OLPS Algorithms on ETFs](https://github.com/paulperry/quant/blob/master/OLPS_Comparison.ipynb)
12 | 4. [Comparing Bond ETF's in the 2008 downturn](https://github.com/paulperry/quant/blob/master/Bond_ETFs.ipynb)
13 | 
14 | More to come ... 
15 | 


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/spy_100.py:
--------------------------------------------------------------------------------
 1 | '''
 2 |     A benchmark comparison to buying and holding SPY at 100%.
 3 | 
 4 |     NOTE: This algo can run in minute-mode simulation and is compatible with LIVE TRADING.
 5 | '''
 6 | 
 7 | import pandas as pd
 8 | from zipline.api import order_target_percent
 9 | 
10 | def initialize(context):
11 |     set_long_only()
12 |     set_symbol_lookup_date('2008-01-01') 
13 |     schedule_function(trade,                      
14 |                       date_rule=date_rules.every_day(),
15 |                       time_rule=time_rules.market_open())
16 | 
17 |     context.secs = [symbol('SPY')]
18 |     context.pcts = [1.0]
19 |     context.ETFs = zip(context.secs, context.pcts) # list of tuples
20 |  
21 | def handle_data(context, data):
22 |     pass
23 | 
24 | def trade(context, data):  
25 |     """
26 |     Make sure the porfolio is fully invested every day.
27 |     """
28 |     threshold = 0.05
29 |     
30 |     need_full_rebalance = False
31 |     # rebalance if we have too much cash
32 |     if context.portfolio.cash / context.portfolio.portfolio_value > threshold:
33 |         need_full_rebalance = True
34 |     
35 |     # What we should do is first sell the overs and then buy the unders.
36 |     if need_full_rebalance:
37 |         # Get the current exchange time, in the exchange timezone 
38 |         exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
39 |         # perform the full rebalance if we flagged the need to do so
40 |         for sid, target in context.ETFs:
41 |             order_target_percent(sid, target)
42 |         log.info("Rebalanced at %s" %  str(exchange_time))
43 | 
44 | 


--------------------------------------------------------------------------------
/swensen.py:
--------------------------------------------------------------------------------
  1 | '''
  2 |     This algorithm defines a long-only portfolio of 6 ETF's and rebalances all of 
  3 |     them when any one of them is off the target by a threshold of 5%.
  4 |     It is based on David Swensen's rationale for portfolio construction as defined in
  5 |     his book: "Unconventional Success: A Fundamental Approach to Personal Investment":
  6 |     http://www.amazon.com/Unconventional-Success-Fundamental-Approach-Investment/dp/0743228383 .
  7 |     The representative ETF's are defined here:
  8 |     http://seekingalpha.com/article/531591-swensens-6-etf-portfolio
  9 |     The target percents are defined here:
 10 |     https://www.yalealumnimagazine.com/articles/2398/david-swensen-s-guide-to-sleeping-soundly
 11 |     The rebalancing strategy is defined in the book and here:
 12 |     http://socialize.morningstar.com/NewSocialize/forums/p/102207/102207.aspx
 13 | 
 14 |     This is effectively a passive managment strategy or Lazy portfolio:
 15 |     http://en.wikipedia.org/wiki/Passive_management
 16 |     http://www.bogleheads.org/wiki/Lazy_portfolios
 17 | 
 18 |     Taxes are not modelled.
 19 |     
 20 |     NOTE: This algo can run in minute-mode simulation and is compatible with LIVE TRADING.
 21 | '''
 22 | 
 23 | from __future__ import division
 24 | import datetime
 25 | import pytz
 26 | import pandas as pd
 27 | from zipline.api import order_target_percent
 28 | 
 29 | def initialize(context):
 30 |     
 31 |     set_long_only()
 32 |     set_symbol_lookup_date('2005-01-01') # because EEM has multiple sid's. 
 33 |  
 34 |     context.secs = symbols('TIP', 'TLT', 'VNQ', 'EEM', 'EFA', 'VTI')  # Securities
 35 |     context.pcts =        [ 0.15,  0.15,   0.15,  0.1,  0.15,  0.3 ]  # Percentages
 36 |     context.ETFs = zip(context.secs, context.pcts)                    # list of tuples
 37 |     
 38 |     # Change this variable if you want to rebalance less frequently
 39 |     context.rebalance_days = 20    # 1 = can rebalance any day, 20 = every month
 40 | 
 41 |     # Set the trade time, if in minute mode, we trade between 10am and 3pm.
 42 |     context.rebalance_date = None
 43 |     context.rebalance_hour_start = 10
 44 |     context.rebalance_hour_end = 15
 45 | 
 46 | def handle_data(context, data):
 47 | 
 48 |     # Get the current exchange time, in the exchange timezone 
 49 |     exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
 50 | 
 51 |     # If it's a rebalance day (defined in intialize()) then rebalance:
 52 |     if  context.rebalance_date == None or \
 53 |         exchange_time >= context.rebalance_date + datetime.timedelta(days=context.rebalance_days):
 54 | 
 55 |         # Do nothing if there are open orders:
 56 |         if has_orders(context):
 57 |             print('has open orders - doing nothing!')
 58 |             return
 59 | 
 60 |         rebalance(context, data, exchange_time)
 61 |     
 62 | def rebalance(context, data, exchange_time, threshold = 0.05):  
 63 |     """
 64 |     For every stock or cash position, if the target percent is off by the threshold
 65 |     amount (5% as a default), then place orders to adjust all positions to the target   
 66 |     percent of the current portfolio value.
 67 |     """
 68 |     
 69 |     #  if the backtest is in minute mode
 70 |     if get_environment('data_frequency') == 'minute':
 71 |         # rebalance if we are in the user specified rebalance time-of-day window
 72 |         if exchange_time.hour < context.rebalance_hour_start or \
 73 |             exchange_time.hour > context.rebalance_hour_end:
 74 |                 return
 75 | 
 76 |     need_full_rebalance = False
 77 |     portfolio_value = context.portfolio.portfolio_value
 78 |     
 79 |     # rebalance if we have too much cash
 80 |     if context.portfolio.cash / portfolio_value > threshold:
 81 |         need_full_rebalance = True
 82 |     
 83 |     # or rebalance if an ETF is off by the given threshold
 84 |     for sid, target in context.ETFs:
 85 |         pos = context.portfolio.positions[sid]
 86 |         position_pct = (pos.amount * pos.last_sale_price) / portfolio_value
 87 |         # if any position is out of range then rebalance the whole portfolio
 88 |         if abs(position_pct - target) > threshold:
 89 |             need_full_rebalance = True
 90 |             break # don't bother checking the rest
 91 |     
 92 |     # perform the full rebalance if we flagged the need to do so
 93 |     # What we should do is first sell the overs and then buy the unders.
 94 |     if need_full_rebalance:
 95 |         for sid, target in context.ETFs:
 96 |             order_target_percent(sid, target)
 97 |         log.info("Rebalanced at %s" %  str(exchange_time))
 98 |         context.rebalance_date = exchange_time
 99 | 
100 | 
101 | def has_orders(context):
102 |     # Return true if there are pending orders.
103 |     has_orders = False
104 |     for sec in context.secs:
105 |         orders = get_open_orders(sec)
106 |         if orders:
107 |             for oo in orders:                  
108 |                 message = 'Open order for {amount} shares in {stock}'  
109 |                 message = message.format(amount=oo.amount, stock=sec)  
110 |                 log.info(message)
111 | 
112 |             has_orders = True
113 |     return has_orders
114 | 


--------------------------------------------------------------------------------
/vti_agg_5050.py:
--------------------------------------------------------------------------------
 1 | '''
 2 | A Basic Markowitz portfolio of Stocks and Bonds.  
 3 | Change it from 50/50 to 60/40 or 70/30.
 4 | '''
 5 | 
 6 | from __future__ import division
 7 | import datetime
 8 | import pytz
 9 | import pandas as pd
10 | from zipline.api import order_target_percent
11 | 
12 | def initialize(context):
13 |     set_long_only()
14 |     set_symbol_lookup_date('2005-01-01') 
15 |  
16 |     context.secs = symbols( 'VTI', 'AGG')  # Securities
17 |     context.pcts =        [ 0.5,  0.5 ]   # Percentages
18 |     context.ETFs = zip(context.secs, context.pcts)
19 |     # Check to rebalance every month, but only do it in December
20 |     schedule_function(rebalance,
21 |         date_rules.month_end(days_offset=5), # trade before EOY settlment dates
22 |         time_rules.market_open(minutes=45)) # trade after 10:15am
23 |     return
24 | 
25 | def rebalance(context, data):
26 |     threshold = 0.05   # trigger a rebalance if we are off by this threshold (5%)
27 |     # Get the current exchange time, in the exchange timezone 
28 |     exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
29 |     if exchange_time.month < 12:
30 |         return # bail if it's not December
31 |     
32 |     need_full_rebalance = False
33 |     portfolio_value = context.portfolio.portfolio_value
34 |     
35 |     # rebalance if we have too much cash
36 |     if context.portfolio.cash / portfolio_value > threshold:
37 |         need_full_rebalance = True
38 |     
39 |     # or rebalance if an ETF is off by the given threshold
40 |     for sid, target in context.ETFs:
41 |         pos = context.portfolio.positions[sid]
42 |         position_pct = (pos.amount * pos.last_sale_price) / portfolio_value
43 |         # if any position is out of range then rebalance the whole portfolio
44 |         if abs(position_pct - target) > threshold:
45 |             need_full_rebalance = True
46 |             break # don't bother checking the rest
47 |     # perform the full rebalance if we flagged the need to do so
48 |     if need_full_rebalance:
49 |         for sid, target in context.ETFs:
50 |             order_target_percent(sid, target)
51 |         log.info("Rebalanced at %s" %  str(exchange_time))
52 |         context.rebalance_date = exchange_time
53 | 
54 | def handle_data(context, data):
55 |     pass
56 | 
57 | 
58 | 


--------------------------------------------------------------------------------
/vti_agg_6040.py:
--------------------------------------------------------------------------------
 1 | '''
 2 | A Basic Markowitz portfolio of Stocks and Bonds.  
 3 | Change it from 50/50 to 60/40 or 70/30.
 4 | '''
 5 | 
 6 | from __future__ import division
 7 | import datetime
 8 | import pytz
 9 | import pandas as pd
10 | from zipline.api import order_target_percent
11 | 
12 | def initialize(context):
13 |     set_long_only()
14 |     set_symbol_lookup_date('2005-01-01') 
15 |  
16 |     context.secs = symbols( 'VTI', 'AGG')  # Securities
17 |     context.pcts =        [ 0.6,  0.4 ]   # Percentages
18 |     context.ETFs = zip(context.secs, context.pcts)
19 |     # Check to rebalance every month, but only do it in December
20 |     schedule_function(rebalance,
21 |         date_rules.month_end(days_offset=5), # trade before EOY settlment dates
22 |         time_rules.market_open(minutes=45)) # trade after 10:15am
23 |     return
24 | 
25 | def rebalance(context, data):
26 |     threshold = 0.05   # trigger a rebalance if we are off by this threshold (5%)
27 |     # Get the current exchange time, in the exchange timezone 
28 |     exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
29 |     if exchange_time.month < 12:
30 |         return # bail if it's not December
31 |     
32 |     need_full_rebalance = False
33 |     portfolio_value = context.portfolio.portfolio_value
34 |     
35 |     # rebalance if we have too much cash
36 |     if context.portfolio.cash / portfolio_value > threshold:
37 |         need_full_rebalance = True
38 |     
39 |     # or rebalance if an ETF is off by the given threshold
40 |     for sid, target in context.ETFs:
41 |         pos = context.portfolio.positions[sid]
42 |         position_pct = (pos.amount * pos.last_sale_price) / portfolio_value
43 |         # if any position is out of range then rebalance the whole portfolio
44 |         if abs(position_pct - target) > threshold:
45 |             need_full_rebalance = True
46 |             break # don't bother checking the rest
47 |     # perform the full rebalance if we flagged the need to do so
48 |     if need_full_rebalance:
49 |         for sid, target in context.ETFs:
50 |             order_target_percent(sid, target)
51 |         log.info("Rebalanced at %s" %  str(exchange_time))
52 |         context.rebalance_date = exchange_time
53 | 
54 | def handle_data(context, data):
55 |     pass
56 | 
57 | 
58 | 


--------------------------------------------------------------------------------
/vti_agg_7030.py:
--------------------------------------------------------------------------------
 1 | '''
 2 | A Basic Markowitz portfolio of Stocks and Bonds.  
 3 | Change it from 50/50 to 60/40 or 70/30.
 4 | '''
 5 | 
 6 | from __future__ import division
 7 | import datetime
 8 | import pytz
 9 | import pandas as pd
10 | from zipline.api import order_target_percent
11 | 
12 | def initialize(context):
13 |     set_long_only()
14 |     set_symbol_lookup_date('2005-01-01') 
15 |  
16 |     context.secs = symbols( 'VTI', 'AGG')  # Securities
17 |     context.pcts =        [ 0.7,  0.3 ]   # Percentages
18 |     context.ETFs = zip(context.secs, context.pcts)
19 |     # Check to rebalance every month, but only do it in December
20 |     schedule_function(rebalance,
21 |         date_rules.month_end(days_offset=5), # trade before EOY settlment dates
22 |         time_rules.market_open(minutes=45)) # trade after 10:15am
23 |     return
24 | 
25 | def rebalance(context, data):
26 |     threshold = 0.05   # trigger a rebalance if we are off by this threshold (5%)
27 |     # Get the current exchange time, in the exchange timezone 
28 |     exchange_time = pd.Timestamp(get_datetime()).tz_convert('US/Eastern')
29 |     if exchange_time.month < 12:
30 |         return # bail if it's not December
31 |     
32 |     need_full_rebalance = False
33 |     portfolio_value = context.portfolio.portfolio_value
34 |     
35 |     # rebalance if we have too much cash
36 |     if context.portfolio.cash / portfolio_value > threshold:
37 |         need_full_rebalance = True
38 |     
39 |     # or rebalance if an ETF is off by the given threshold
40 |     for sid, target in context.ETFs:
41 |         pos = context.portfolio.positions[sid]
42 |         position_pct = (pos.amount * pos.last_sale_price) / portfolio_value
43 |         # if any position is out of range then rebalance the whole portfolio
44 |         if abs(position_pct - target) > threshold:
45 |             need_full_rebalance = True
46 |             break # don't bother checking the rest
47 |     # perform the full rebalance if we flagged the need to do so
48 |     if need_full_rebalance:
49 |         for sid, target in context.ETFs:
50 |             order_target_percent(sid, target)
51 |         log.info("Rebalanced at %s" %  str(exchange_time))
52 |         context.rebalance_date = exchange_time
53 | 
54 | def handle_data(context, data):
55 |     pass
56 | 
57 | 
58 | 


--------------------------------------------------------------------------------