0.+API+Reference.ipynb

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   "source": [
    "# Quantopian Research \n",
    "\n",
    "The Quantopian research platform is a hosted research environment. Our aim is to make it simple to get started on your research without a lot of setup. Libraries are pre-installed and data is made readily accessible. \n",
    "\n",
    "## How do I use this? \n",
    "\n",
    "The Quantopian research platform is built using IPython, an open source project that provides an interactive notebook with executable cells. Each cell can be executed individually, but they all share the same variables (more technically the same namespace). \n",
    "\n",
    "### To execute a cell, click on the cell and press `shift + enter`.\n",
    "\n",
    "\n",
    "##Researching Investment Algorithms\n",
    "\n",
    "We've added the following functions to optimize the platform for researching and developing investment algorithms.  \n",
    "\n",
    "* get_pricing()\n",
    "* symbols()\n",
    "* local_csv()\n",
    "* get_backtest()\n",
    "* get_fundamentals()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##get_pricing\n",
    "The `get_pricing` function provides access to 12 years of US Equity pricing data: the same data used by the Quantopian backtester.\n",
    "\n",
    "`get_pricing` returns a <b>pandas object</b>. This could be a panel, dataframe or series depending on the input values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_price</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close_price</th>\n",
       "      <th>volume</th>\n",
       "      <th>price</th>\n",
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       "      <th>2013-01-03 00:00:00+00:00</th>\n",
       "      <td>78.270</td>\n",
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       "      <th>2013-01-04 00:00:00+00:00</th>\n",
       "      <td>76.711</td>\n",
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       "      <td>75.163</td>\n",
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       "      <th>2013-01-07 00:00:00+00:00</th>\n",
       "      <td>74.573</td>\n",
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       "      <th>2013-01-08 00:00:00+00:00</th>\n",
       "      <td>75.603</td>\n",
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       "      <th>2013-01-09 00:00:00+00:00</th>\n",
       "      <td>74.644</td>\n",
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       "      <th>2013-01-10 00:00:00+00:00</th>\n",
       "      <td>75.509</td>\n",
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       "      <th>2013-01-11 00:00:00+00:00</th>\n",
       "      <td>74.430</td>\n",
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       "      <th>2013-01-14 00:00:00+00:00</th>\n",
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       "      <th>2013-01-15 00:00:00+00:00</th>\n",
       "      <td>71.187</td>\n",
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       "      <th>2013-01-16 00:00:00+00:00</th>\n",
       "      <td>70.664</td>\n",
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       "      <td>72.299</td>\n",
       "      <td>159155725</td>\n",
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       "      <th>2013-01-17 00:00:00+00:00</th>\n",
       "      <td>72.903</td>\n",
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       "      <th>2013-01-18 00:00:00+00:00</th>\n",
       "      <td>71.218</td>\n",
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       "      <td>71.424</td>\n",
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       "      <th>2013-01-22 00:00:00+00:00</th>\n",
       "      <td>72.081</td>\n",
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       "      <th>2013-01-23 00:00:00+00:00</th>\n",
       "      <td>72.689</td>\n",
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       "      <td>73.430</td>\n",
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       "      <th>2013-01-24 00:00:00+00:00</th>\n",
       "      <td>65.716</td>\n",
       "      <td>66.534</td>\n",
       "      <td>64.323</td>\n",
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       "      <th>2013-01-25 00:00:00+00:00</th>\n",
       "      <td>64.528</td>\n",
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       "      <th>2013-01-28 00:00:00+00:00</th>\n",
       "      <td>62.548</td>\n",
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       "      <th>2013-01-29 00:00:00+00:00</th>\n",
       "      <td>65.501</td>\n",
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       "      <td>64.590</td>\n",
       "      <td>65.491</td>\n",
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       "      <th>2013-01-30 00:00:00+00:00</th>\n",
       "      <td>65.287</td>\n",
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       "      <th>2013-01-31 00:00:00+00:00</th>\n",
       "      <td>65.284</td>\n",
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       "      <th>2013-02-01 00:00:00+00:00</th>\n",
       "      <td>65.588</td>\n",
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       "      <th>2013-02-04 00:00:00+00:00</th>\n",
       "      <td>64.846</td>\n",
       "      <td>65.136</td>\n",
       "      <td>63.144</td>\n",
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       "      <td>63.176</td>\n",
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       "      <th>2013-02-05 00:00:00+00:00</th>\n",
       "      <td>63.437</td>\n",
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       "      <td>63.176</td>\n",
       "      <td>65.404</td>\n",
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       "      <th>2013-02-06 00:00:00+00:00</th>\n",
       "      <td>65.211</td>\n",
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       "      <th>2013-02-07 00:00:00+00:00</th>\n",
       "      <td>66.180</td>\n",
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       "      <td>64.876</td>\n",
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       "      <th>2013-02-08 00:00:00+00:00</th>\n",
       "      <td>67.716</td>\n",
       "      <td>68.403</td>\n",
       "      <td>67.180</td>\n",
       "      <td>67.854</td>\n",
       "      <td>147639465</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-11 00:00:00+00:00</th>\n",
       "      <td>68.073</td>\n",
       "      <td>69.279</td>\n",
       "      <td>67.608</td>\n",
       "      <td>68.566</td>\n",
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       "      <td>68.566</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-12 00:00:00+00:00</th>\n",
       "      <td>68.503</td>\n",
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       "      <td>66.822</td>\n",
       "      <td>66.836</td>\n",
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       "      <td>66.836</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-13 00:00:00+00:00</th>\n",
       "      <td>66.746</td>\n",
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       "      <td>66.176</td>\n",
       "      <td>66.716</td>\n",
       "      <td>111614622</td>\n",
       "      <td>66.716</td>\n",
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       "    <tr>\n",
       "      <th>2013-02-14 00:00:00+00:00</th>\n",
       "      <td>66.361</td>\n",
       "      <td>67.378</td>\n",
       "      <td>66.290</td>\n",
       "      <td>66.658</td>\n",
       "      <td>77903842</td>\n",
       "      <td>66.658</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>2013-11-20 00:00:00+00:00</th>\n",
       "      <td>74.177</td>\n",
       "      <td>74.347</td>\n",
       "      <td>73.477</td>\n",
       "      <td>73.574</td>\n",
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       "      <td>73.574</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-21 00:00:00+00:00</th>\n",
       "      <td>73.944</td>\n",
       "      <td>74.460</td>\n",
       "      <td>73.383</td>\n",
       "      <td>74.433</td>\n",
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       "      <td>74.433</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-22 00:00:00+00:00</th>\n",
       "      <td>74.219</td>\n",
       "      <td>74.596</td>\n",
       "      <td>74.077</td>\n",
       "      <td>74.243</td>\n",
       "      <td>47004108</td>\n",
       "      <td>74.243</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-25 00:00:00+00:00</th>\n",
       "      <td>74.433</td>\n",
       "      <td>75.126</td>\n",
       "      <td>74.430</td>\n",
       "      <td>74.823</td>\n",
       "      <td>53836245</td>\n",
       "      <td>74.823</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-26 00:00:00+00:00</th>\n",
       "      <td>74.876</td>\n",
       "      <td>76.593</td>\n",
       "      <td>74.859</td>\n",
       "      <td>76.230</td>\n",
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       "      <td>76.230</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-27 00:00:00+00:00</th>\n",
       "      <td>76.617</td>\n",
       "      <td>78.002</td>\n",
       "      <td>76.597</td>\n",
       "      <td>77.999</td>\n",
       "      <td>85159225</td>\n",
       "      <td>77.999</td>\n",
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       "    <tr>\n",
       "      <th>2013-11-29 00:00:00+00:00</th>\n",
       "      <td>78.499</td>\n",
       "      <td>79.763</td>\n",
       "      <td>78.260</td>\n",
       "      <td>79.440</td>\n",
       "      <td>75610142</td>\n",
       "      <td>79.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-02 00:00:00+00:00</th>\n",
       "      <td>79.716</td>\n",
       "      <td>80.620</td>\n",
       "      <td>78.690</td>\n",
       "      <td>78.759</td>\n",
       "      <td>111696164</td>\n",
       "      <td>78.759</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-03 00:00:00+00:00</th>\n",
       "      <td>79.759</td>\n",
       "      <td>80.913</td>\n",
       "      <td>79.670</td>\n",
       "      <td>80.913</td>\n",
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       "      <td>80.913</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-04 00:00:00+00:00</th>\n",
       "      <td>80.787</td>\n",
       "      <td>81.314</td>\n",
       "      <td>80.119</td>\n",
       "      <td>80.696</td>\n",
       "      <td>86112711</td>\n",
       "      <td>80.696</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-05 00:00:00+00:00</th>\n",
       "      <td>81.809</td>\n",
       "      <td>82.466</td>\n",
       "      <td>80.917</td>\n",
       "      <td>81.159</td>\n",
       "      <td>97639241</td>\n",
       "      <td>81.159</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-06 00:00:00+00:00</th>\n",
       "      <td>80.829</td>\n",
       "      <td>80.966</td>\n",
       "      <td>79.940</td>\n",
       "      <td>79.992</td>\n",
       "      <td>79291103</td>\n",
       "      <td>79.992</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-09 00:00:00+00:00</th>\n",
       "      <td>80.223</td>\n",
       "      <td>81.370</td>\n",
       "      <td>80.130</td>\n",
       "      <td>80.944</td>\n",
       "      <td>74383060</td>\n",
       "      <td>80.944</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-10 00:00:00+00:00</th>\n",
       "      <td>80.517</td>\n",
       "      <td>81.127</td>\n",
       "      <td>80.173</td>\n",
       "      <td>80.807</td>\n",
       "      <td>61224730</td>\n",
       "      <td>80.807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-11 00:00:00+00:00</th>\n",
       "      <td>80.939</td>\n",
       "      <td>81.570</td>\n",
       "      <td>79.957</td>\n",
       "      <td>80.173</td>\n",
       "      <td>81888933</td>\n",
       "      <td>80.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-12 00:00:00+00:00</th>\n",
       "      <td>80.308</td>\n",
       "      <td>80.764</td>\n",
       "      <td>80.002</td>\n",
       "      <td>80.097</td>\n",
       "      <td>60993840</td>\n",
       "      <td>80.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-13 00:00:00+00:00</th>\n",
       "      <td>80.370</td>\n",
       "      <td>80.413</td>\n",
       "      <td>79.096</td>\n",
       "      <td>79.214</td>\n",
       "      <td>75239577</td>\n",
       "      <td>79.214</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-16 00:00:00+00:00</th>\n",
       "      <td>79.300</td>\n",
       "      <td>80.379</td>\n",
       "      <td>79.289</td>\n",
       "      <td>79.637</td>\n",
       "      <td>62266330</td>\n",
       "      <td>79.637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-17 00:00:00+00:00</th>\n",
       "      <td>79.409</td>\n",
       "      <td>79.922</td>\n",
       "      <td>79.054</td>\n",
       "      <td>79.284</td>\n",
       "      <td>51595695</td>\n",
       "      <td>79.284</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-18 00:00:00+00:00</th>\n",
       "      <td>78.533</td>\n",
       "      <td>78.780</td>\n",
       "      <td>76.973</td>\n",
       "      <td>78.672</td>\n",
       "      <td>131173155</td>\n",
       "      <td>78.672</td>\n",
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       "    <tr>\n",
       "      <th>2013-12-19 00:00:00+00:00</th>\n",
       "      <td>78.502</td>\n",
       "      <td>78.577</td>\n",
       "      <td>77.677</td>\n",
       "      <td>77.780</td>\n",
       "      <td>73579994</td>\n",
       "      <td>77.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-20 00:00:00+00:00</th>\n",
       "      <td>77.920</td>\n",
       "      <td>78.806</td>\n",
       "      <td>77.832</td>\n",
       "      <td>78.392</td>\n",
       "      <td>86285363</td>\n",
       "      <td>78.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-23 00:00:00+00:00</th>\n",
       "      <td>81.144</td>\n",
       "      <td>81.533</td>\n",
       "      <td>80.396</td>\n",
       "      <td>81.463</td>\n",
       "      <td>115533067</td>\n",
       "      <td>81.463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-24 00:00:00+00:00</th>\n",
       "      <td>81.407</td>\n",
       "      <td>81.699</td>\n",
       "      <td>80.862</td>\n",
       "      <td>81.097</td>\n",
       "      <td>40526130</td>\n",
       "      <td>81.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-26 00:00:00+00:00</th>\n",
       "      <td>81.129</td>\n",
       "      <td>81.359</td>\n",
       "      <td>80.480</td>\n",
       "      <td>80.559</td>\n",
       "      <td>46914818</td>\n",
       "      <td>80.559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-27 00:00:00+00:00</th>\n",
       "      <td>80.560</td>\n",
       "      <td>80.632</td>\n",
       "      <td>79.930</td>\n",
       "      <td>80.014</td>\n",
       "      <td>54221769</td>\n",
       "      <td>80.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-30 00:00:00+00:00</th>\n",
       "      <td>79.707</td>\n",
       "      <td>79.729</td>\n",
       "      <td>78.905</td>\n",
       "      <td>79.216</td>\n",
       "      <td>59118822</td>\n",
       "      <td>79.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 00:00:00+00:00</th>\n",
       "      <td>79.154</td>\n",
       "      <td>80.186</td>\n",
       "      <td>79.144</td>\n",
       "      <td>80.167</td>\n",
       "      <td>51011087</td>\n",
       "      <td>80.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02 00:00:00+00:00</th>\n",
       "      <td>79.430</td>\n",
       "      <td>79.584</td>\n",
       "      <td>78.862</td>\n",
       "      <td>79.034</td>\n",
       "      <td>54363271</td>\n",
       "      <td>79.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 00:00:00+00:00</th>\n",
       "      <td>78.983</td>\n",
       "      <td>79.102</td>\n",
       "      <td>77.206</td>\n",
       "      <td>77.284</td>\n",
       "      <td>92325248</td>\n",
       "      <td>77.284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open_price    high     low  close_price     volume  \\\n",
       "2013-01-03 00:00:00+00:00      78.270  78.526  77.287       77.484   82090158   \n",
       "2013-01-04 00:00:00+00:00      76.711  76.949  75.163       75.289  141288863   \n",
       "2013-01-07 00:00:00+00:00      74.573  75.616  73.601       74.854  114493553   \n",
       "2013-01-08 00:00:00+00:00      75.603  75.986  74.466       75.029  106374926   \n",
       "2013-01-09 00:00:00+00:00      74.644  75.002  73.714       73.873   93267415   \n",
       "2013-01-10 00:00:00+00:00      75.509  75.533  73.647       74.784  139591313   \n",
       "2013-01-11 00:00:00+00:00      74.430  75.047  74.147       74.304   78800349   \n",
       "2013-01-14 00:00:00+00:00      71.813  72.501  71.217       71.701  163209526   \n",
       "2013-01-15 00:00:00+00:00      71.187  71.286  69.056       69.403  208326893   \n",
       "2013-01-16 00:00:00+00:00      70.664  72.779  69.861       72.299  159155725   \n",
       "2013-01-17 00:00:00+00:00      72.903  72.966  71.720       71.816  105699461   \n",
       "2013-01-18 00:00:00+00:00      71.218  71.747  70.916       71.424  108352365   \n",
       "2013-01-22 00:00:00+00:00      72.081  72.556  70.949       72.103  103487778   \n",
       "2013-01-23 00:00:00+00:00      72.689  73.571  72.111       73.430  150399608   \n",
       "2013-01-24 00:00:00+00:00      65.716  66.534  64.323       64.358  339368906   \n",
       "2013-01-25 00:00:00+00:00      64.528  65.177  62.144       62.837  285973953   \n",
       "2013-01-28 00:00:00+00:00      62.548  64.746  62.267       64.248  181013747   \n",
       "2013-01-29 00:00:00+00:00      65.501  65.744  64.590       65.491  134268983   \n",
       "2013-01-30 00:00:00+00:00      65.287  66.087  64.930       65.274   97733858   \n",
       "2013-01-31 00:00:00+00:00      65.284  65.613  64.998       65.056   66514496   \n",
       "2013-02-01 00:00:00+00:00      65.588  65.641  64.051       64.824  125669970   \n",
       "2013-02-04 00:00:00+00:00      64.846  65.136  63.144       63.176  111873694   \n",
       "2013-02-05 00:00:00+00:00      63.437  65.678  63.176       65.404  136330974   \n",
       "2013-02-06 00:00:00+00:00      65.211  66.644  64.656       65.328  140355067   \n",
       "2013-02-07 00:00:00+00:00      66.180  67.144  64.876       66.894  160811101   \n",
       "2013-02-08 00:00:00+00:00      67.716  68.403  67.180       67.854  147639465   \n",
       "2013-02-11 00:00:00+00:00      68.073  69.279  67.608       68.566  118627131   \n",
       "2013-02-12 00:00:00+00:00      68.503  68.913  66.822       66.836  145834950   \n",
       "2013-02-13 00:00:00+00:00      66.746  67.664  66.176       66.716  111614622   \n",
       "2013-02-14 00:00:00+00:00      66.361  67.378  66.290       66.658   77903842   \n",
       "...                               ...     ...     ...          ...        ...   \n",
       "2013-11-20 00:00:00+00:00      74.177  74.347  73.477       73.574   45875325   \n",
       "2013-11-21 00:00:00+00:00      73.944  74.460  73.383       74.433   60442580   \n",
       "2013-11-22 00:00:00+00:00      74.219  74.596  74.077       74.243   47004108   \n",
       "2013-11-25 00:00:00+00:00      74.433  75.126  74.430       74.823   53836245   \n",
       "2013-11-26 00:00:00+00:00      74.876  76.593  74.859       76.230   86500678   \n",
       "2013-11-27 00:00:00+00:00      76.617  78.002  76.597       77.999   85159225   \n",
       "2013-11-29 00:00:00+00:00      78.499  79.763  78.260       79.440   75610142   \n",
       "2013-12-02 00:00:00+00:00      79.716  80.620  78.690       78.759  111696164   \n",
       "2013-12-03 00:00:00+00:00      79.759  80.913  79.670       80.913  102825346   \n",
       "2013-12-04 00:00:00+00:00      80.787  81.314  80.119       80.696   86112711   \n",
       "2013-12-05 00:00:00+00:00      81.809  82.466  80.917       81.159   97639241   \n",
       "2013-12-06 00:00:00+00:00      80.829  80.966  79.940       79.992   79291103   \n",
       "2013-12-09 00:00:00+00:00      80.223  81.370  80.130       80.944   74383060   \n",
       "2013-12-10 00:00:00+00:00      80.517  81.127  80.173       80.807   61224730   \n",
       "2013-12-11 00:00:00+00:00      80.939  81.570  79.957       80.173   81888933   \n",
       "2013-12-12 00:00:00+00:00      80.308  80.764  80.002       80.097   60993840   \n",
       "2013-12-13 00:00:00+00:00      80.370  80.413  79.096       79.214   75239577   \n",
       "2013-12-16 00:00:00+00:00      79.300  80.379  79.289       79.637   62266330   \n",
       "2013-12-17 00:00:00+00:00      79.409  79.922  79.054       79.284   51595695   \n",
       "2013-12-18 00:00:00+00:00      78.533  78.780  76.973       78.672  131173155   \n",
       "2013-12-19 00:00:00+00:00      78.502  78.577  77.677       77.780   73579994   \n",
       "2013-12-20 00:00:00+00:00      77.920  78.806  77.832       78.392   86285363   \n",
       "2013-12-23 00:00:00+00:00      81.144  81.533  80.396       81.463  115533067   \n",
       "2013-12-24 00:00:00+00:00      81.407  81.699  80.862       81.097   40526130   \n",
       "2013-12-26 00:00:00+00:00      81.129  81.359  80.480       80.559   46914818   \n",
       "2013-12-27 00:00:00+00:00      80.560  80.632  79.930       80.014   54221769   \n",
       "2013-12-30 00:00:00+00:00      79.707  79.729  78.905       79.216   59118822   \n",
       "2013-12-31 00:00:00+00:00      79.154  80.186  79.144       80.167   51011087   \n",
       "2014-01-02 00:00:00+00:00      79.430  79.584  78.862       79.034   54363271   \n",
       "2014-01-03 00:00:00+00:00      78.983  79.102  77.206       77.284   92325248   \n",
       "\n",
       "                            price  \n",
       "2013-01-03 00:00:00+00:00  77.484  \n",
       "2013-01-04 00:00:00+00:00  75.289  \n",
       "2013-01-07 00:00:00+00:00  74.854  \n",
       "2013-01-08 00:00:00+00:00  75.029  \n",
       "2013-01-09 00:00:00+00:00  73.873  \n",
       "2013-01-10 00:00:00+00:00  74.784  \n",
       "2013-01-11 00:00:00+00:00  74.304  \n",
       "2013-01-14 00:00:00+00:00  71.701  \n",
       "2013-01-15 00:00:00+00:00  69.403  \n",
       "2013-01-16 00:00:00+00:00  72.299  \n",
       "2013-01-17 00:00:00+00:00  71.816  \n",
       "2013-01-18 00:00:00+00:00  71.424  \n",
       "2013-01-22 00:00:00+00:00  72.103  \n",
       "2013-01-23 00:00:00+00:00  73.430  \n",
       "2013-01-24 00:00:00+00:00  64.358  \n",
       "2013-01-25 00:00:00+00:00  62.837  \n",
       "2013-01-28 00:00:00+00:00  64.248  \n",
       "2013-01-29 00:00:00+00:00  65.491  \n",
       "2013-01-30 00:00:00+00:00  65.274  \n",
       "2013-01-31 00:00:00+00:00  65.056  \n",
       "2013-02-01 00:00:00+00:00  64.824  \n",
       "2013-02-04 00:00:00+00:00  63.176  \n",
       "2013-02-05 00:00:00+00:00  65.404  \n",
       "2013-02-06 00:00:00+00:00  65.328  \n",
       "2013-02-07 00:00:00+00:00  66.894  \n",
       "2013-02-08 00:00:00+00:00  67.854  \n",
       "2013-02-11 00:00:00+00:00  68.566  \n",
       "2013-02-12 00:00:00+00:00  66.836  \n",
       "2013-02-13 00:00:00+00:00  66.716  \n",
       "2013-02-14 00:00:00+00:00  66.658  \n",
       "...                           ...  \n",
       "2013-11-20 00:00:00+00:00  73.574  \n",
       "2013-11-21 00:00:00+00:00  74.433  \n",
       "2013-11-22 00:00:00+00:00  74.243  \n",
       "2013-11-25 00:00:00+00:00  74.823  \n",
       "2013-11-26 00:00:00+00:00  76.230  \n",
       "2013-11-27 00:00:00+00:00  77.999  \n",
       "2013-11-29 00:00:00+00:00  79.440  \n",
       "2013-12-02 00:00:00+00:00  78.759  \n",
       "2013-12-03 00:00:00+00:00  80.913  \n",
       "2013-12-04 00:00:00+00:00  80.696  \n",
       "2013-12-05 00:00:00+00:00  81.159  \n",
       "2013-12-06 00:00:00+00:00  79.992  \n",
       "2013-12-09 00:00:00+00:00  80.944  \n",
       "2013-12-10 00:00:00+00:00  80.807  \n",
       "2013-12-11 00:00:00+00:00  80.173  \n",
       "2013-12-12 00:00:00+00:00  80.097  \n",
       "2013-12-13 00:00:00+00:00  79.214  \n",
       "2013-12-16 00:00:00+00:00  79.637  \n",
       "2013-12-17 00:00:00+00:00  79.284  \n",
       "2013-12-18 00:00:00+00:00  78.672  \n",
       "2013-12-19 00:00:00+00:00  77.780  \n",
       "2013-12-20 00:00:00+00:00  78.392  \n",
       "2013-12-23 00:00:00+00:00  81.463  \n",
       "2013-12-24 00:00:00+00:00  81.097  \n",
       "2013-12-26 00:00:00+00:00  80.559  \n",
       "2013-12-27 00:00:00+00:00  80.014  \n",
       "2013-12-30 00:00:00+00:00  79.216  \n",
       "2013-12-31 00:00:00+00:00  80.167  \n",
       "2014-01-02 00:00:00+00:00  79.034  \n",
       "2014-01-03 00:00:00+00:00  77.284  \n",
       "\n",
       "[253 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_pricing('AAPL') #returns a pandas dataframe when run for 1 security"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Customize get_pricing\n",
    "By default, `get_pricing` returns OHLCV data at daily frequency for all of 2013.  You can change the default behavior by passing the following parameters:\n",
    "- Specify start and end dates by passing values to `start_date` and `end_date`.  Dates can be passed as python `datetime` objects, pandas `Timestamp`s, or strings that can be parsed by pandas.\n",
    "- Narrow the set of returned fields by passing strings or lists of strings as `fields`.  Valid fields are:\n",
    "  - `price`\n",
    "  - `close_price` (an alias of price)\n",
    "  - `open_price`\n",
    "  - `high`\n",
    "  - `low`\n",
    "  - `volume`\n",
    "- Access minutely or daily data by setting frequency to `frequency = daily` or  `frequency='minute'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f634f121d10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ld12Pzzt1pBLiYvTmQ5coxsR31YhcvHoBAAB6qdU5+a7typpG3fbomnbH3PVk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\nmKj2/zhUvvVM+k0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f635c09efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aapl_minute_closes = get_pricing(\n",
    "    'AAPL', \n",
    "    fields='close_price', #modify to price, open_price, high, low or volume to change the field\n",
    "    start_date='2014-01-01', #customize your pricing date range\n",
    "    end_date = '2014-07-01', \n",
    "    frequency='minute', #change to daily for daily pricing\n",
    ")\n",
    "\n",
    "# matplotlib is installed for easy plotting\n",
    "aapl_minute_closes.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##symbols()\n",
    "\n",
    "By default `symbols` returns the security object for a ticker symbol. Specify a ticker symbol, or list of symbols, as a string and get a list of security objects back. \n",
    "\n",
    "- Use `symbol_reference_date` to identify which date you want the symbol back for a particular ticker symbol. \n",
    "- Specify how you would like missing results to be handled with `handle_missing`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Security(24, symbol=u'AAPL', security_name=u'APPLE INC', exchange=u'NASDAQ GLOBAL SELECT MARKET', start_date=u'Mon, 04 Jan 1993 00:00:00 GMT', end_date=u'Mon, 15 Jun 2015 00:00:00 GMT', first_traded=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symbols('AAPL')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##local_csv()\n",
    "If you want to work with your own data in our environment, you can upload a csv to your `data` directory and load the file with `local_csv`. By default, `local_csv` returns a DataFrame with the data from the loaded file. \n",
    "\n",
    "You can customize the resulting DataFrame by passing the following parameters: \n",
    "- Automatically convert strings to security objects by indicating a `symbol_column`\n",
    "- Automatically parse dates as Datetime object by indicating a `date_column`\n",
    "- Specify what timezone to use with `timezone`\n",
    "- Use the date column as the index with `use_date_column_as_index`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>Revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-06-28 00:00:00+00:00</th>\n",
       "      <td>37432000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-29 00:00:00+00:00</th>\n",
       "      <td>45646000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-28 00:00:00+00:00</th>\n",
       "      <td>57594000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-09-28 00:00:00+00:00</th>\n",
       "      <td>37472000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-06-29 00:00:00+00:00</th>\n",
       "      <td>35323000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-03-30 00:00:00+00:00</th>\n",
       "      <td>43603000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-29 00:00:00+00:00</th>\n",
       "      <td>54512000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-29 00:00:00+00:00</th>\n",
       "      <td>35966000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-30 00:00:00+00:00</th>\n",
       "      <td>35023000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-31 00:00:00+00:00</th>\n",
       "      <td>39186000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-31 00:00:00+00:00</th>\n",
       "      <td>46333000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-24 00:00:00+00:00</th>\n",
       "      <td>28270000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-25 00:00:00+00:00</th>\n",
       "      <td>28571000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-26 00:00:00+00:00</th>\n",
       "      <td>24667000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-25 00:00:00+00:00</th>\n",
       "      <td>26741000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-25 00:00:00+00:00</th>\n",
       "      <td>20343000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-26 00:00:00+00:00</th>\n",
       "      <td>15700000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-27 00:00:00+00:00</th>\n",
       "      <td>13499000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-26 00:00:00+00:00</th>\n",
       "      <td>15683000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-27 00:00:00+00:00</th>\n",
       "      <td>8337000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Revenue\n",
       "Date                                  \n",
       "2014-06-28 00:00:00+00:00  37432000000\n",
       "2014-03-29 00:00:00+00:00  45646000000\n",
       "2013-12-28 00:00:00+00:00  57594000000\n",
       "2013-09-28 00:00:00+00:00  37472000000\n",
       "2013-06-29 00:00:00+00:00  35323000000\n",
       "2013-03-30 00:00:00+00:00  43603000000\n",
       "2012-12-29 00:00:00+00:00  54512000000\n",
       "2012-09-29 00:00:00+00:00  35966000000\n",
       "2012-06-30 00:00:00+00:00  35023000000\n",
       "2012-03-31 00:00:00+00:00  39186000000\n",
       "2011-12-31 00:00:00+00:00  46333000000\n",
       "2011-09-24 00:00:00+00:00  28270000000\n",
       "2011-06-25 00:00:00+00:00  28571000000\n",
       "2011-03-26 00:00:00+00:00  24667000000\n",
       "2010-12-25 00:00:00+00:00  26741000000\n",
       "2010-09-25 00:00:00+00:00  20343000000\n",
       "2010-06-26 00:00:00+00:00  15700000000\n",
       "2010-03-27 00:00:00+00:00  13499000000\n",
       "2009-12-26 00:00:00+00:00  15683000000\n",
       "2009-06-27 00:00:00+00:00   8337000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apple_Rev is a CSV from Quandl showing the quarterly revenue for Apple. \n",
    "# It is pre-loaded in your data folder. \n",
    "\n",
    "AAPLRev = local_csv('AAPL_Rev.csv', date_column = 'Date', use_date_column_as_index = True)\n",
    "AAPLRev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f634eda37d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6kup2aNQqZKXYdoTwbK5aE49APx3eP3QKw1wPxeKEiIiIXE9tQy/6BsewOjMSatXcW280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\nFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f634f062b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot with matplotlib\n",
    "\n",
    "AAPLRev.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##get_backtest()\n",
    "The `get_backtest` function provides programmatic access to the results of backtests run on the Quantopian platform.  It takes a single parameter, the ID of a backtest for which results are desired.\n",
    "\n",
    "You can find the ID of a backtest in the URL of its full results page, which will be of the form:\n",
    "\n",
    "`https://www.quantopian.com/algorithms/<algorithm_id>/<backtest_id>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% Time: 0:00:08|###########################################################|\n"
     ]
    }
   ],
   "source": [
    "result = get_backtest('536a6d181a4f090716c383b7')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###The `BacktestResult` Object\n",
    "The `BacktestResult` object returned by `get_backtest` contains all the information stored by Quantopian about the performance of a given backtest run, as well as some additional metadata about the backtest. \n",
    "\n",
    "Get_backtest returns a backtest results object that includes 11 different data sets. If you type result.`<tab>` you will see a list of all the things that are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-10-9c21d2297c6a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-10-9c21d2297c6a>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    result.\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there are so many fields on `BacktestResult`, the class provides helper attributes for easy reference.\n",
    "- `scalars` contains a list of all the scalar attributes of `BacktestResult`.\n",
    "- `frames` contains a list of all the `DataFrame` attributes of `BacktestResult`.\n",
    "- `attrs` contains a list off all the public attributes of `BacktestResult`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print \"Scalars:\"\n",
    "print result.scalars\n",
    "print \"\"\n",
    "print \"Frames:\"\n",
    "print result.frames\n",
    "print \"\"\n",
    "print \"All Attributes:\"\n",
    "print result.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Preview Backtest Contents\n",
    "For easy inspection of the contents of frame contents, `BacktestResult` provides a `preview` method that returns a transposed version of the first few columns of a frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result.preview('risk')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##get_fundamentals()\n",
    "The `get_fundamentals` function provides programmatic access to the Quantopian fundamental database. Based on data provided by Morningstar, `get_fundamentals` provides over 600 corporate metrics dating back to 2002 (to match Quantopian's pricing data). The data used by this research function is the same data used by the `get_fundamentals` function used in the Quantopian IDE. The fields are described in the Quantopian help documents: http://www.quantopian.com/help/fundamentals.\n",
    "\n",
    "This function is very similar to the `get_fundamentals` function used in the Quantopian IDE. It provides 2 further functions not available there, however.\n",
    "\n",
    "First, there is an added required parameter that sets the date for querying the database. Whereas the backtester simulations enforce the date upon which you are querying for data, you have flexibility here in the research environment to simulate any date in the database, from 2002 to present day.\n",
    "\n",
    "N.B., prior to running a `get_fundamentals` call in research, you must run `init_fundamentals` to load the data schema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>security</th>\n",
       "      <th>Security(18693 [EBIX])</th>\n",
       "      <th>Security(23908 [RUSH_B])</th>\n",
       "      <th>Security(47785 [CNNX])</th>\n",
       "      <th>Security(26381 [MNTA])</th>\n",
       "      <th>Security(32143 [VG])</th>\n",
       "      <th>Security(41372 [RENN])</th>\n",
       "      <th>Security(45957 [KFX])</th>\n",
       "      <th>Security(13777 [AEIS])</th>\n",
       "      <th>Security(21457 [VDSI])</th>\n",
       "      <th>Security(27418 [SIMO])</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pe_ratio</th>\n",
       "      <td>16.9737</td>\n",
       "      <td>12.8272</td>\n",
       "      <td>17.6916</td>\n",
       "      <td>16.2602</td>\n",
       "      <td>51.9694</td>\n",
       "      <td>33</td>\n",
       "      <td>112.3917</td>\n",
       "      <td>21.9123</td>\n",
       "      <td>30.8675</td>\n",
       "      <td>19.0019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "security  Security(18693 [EBIX])  Security(23908 [RUSH_B])  \\\n",
       "pe_ratio                 16.9737                   12.8272   \n",
       "\n",
       "security  Security(47785 [CNNX])  Security(26381 [MNTA])  \\\n",
       "pe_ratio                 17.6916                 16.2602   \n",
       "\n",
       "security  Security(32143 [VG])  Security(41372 [RENN])  Security(45957 [KFX])  \\\n",
       "pe_ratio               51.9694                      33               112.3917   \n",
       "\n",
       "security  Security(13777 [AEIS])  Security(21457 [VDSI])  \\\n",
       "pe_ratio                 21.9123                 30.8675   \n",
       "\n",
       "security  Security(27418 [SIMO])  \n",
       "pe_ratio                 19.0019  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fundamentals = init_fundamentals()\n",
    "fund_df = get_fundamentals(query(fundamentals.valuation_ratios.pe_ratio)\n",
    "                             .filter(fundamentals.valuation.market_cap > 1e9)\n",
    "                             .filter(fundamentals.valuation_ratios.pe_ratio > 1)\n",
    "                             .order_by(fundamentals.valuation.market_cap)\n",
    "                             .limit(10),\n",
    "                             '2015-05-01')\n",
    "fund_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, `get_fundamentals` optionally provides the ability to build out time series for each requested metric.\n",
    "\n",
    "In these cases, the method returns a panel rather than a dataframe.\n",
    "\n",
    "Here's a simple example using a predefined company and market cap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 1 (items) x 6 (major_axis) x 1 (minor_axis)\n",
       "Items axis: pe_ratio to pe_ratio\n",
       "Major_axis axis: 2014-01-30 00:00:00+00:00 to 2015-04-30 00:00:00+00:00\n",
       "Minor_axis axis: Security(24 [AAPL]) to Security(24 [AAPL])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_fund_panel = get_fundamentals(query(fundamentals.valuation_ratios.pe_ratio)\n",
    "                             .filter(fundamentals.valuation.sid == 24),\n",
    "                             '2015-05-01', '6q')\n",
    "simple_fund_panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>security</th>\n",
       "      <th>Security(24 [AAPL])</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-30 00:00:00+00:00</th>\n",
       "      <td>13.9470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01 00:00:00+00:00</th>\n",
       "      <td>12.8700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-07-31 00:00:00+00:00</th>\n",
       "      <td>15.8479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-30 00:00:00+00:00</th>\n",
       "      <td>16.6339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29 00:00:00+00:00</th>\n",
       "      <td>17.8690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30 00:00:00+00:00</th>\n",
       "      <td>15.9700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "security                   Security(24 [AAPL])\n",
       "date                                          \n",
       "2014-01-30 00:00:00+00:00              13.9470\n",
       "2014-05-01 00:00:00+00:00              12.8700\n",
       "2014-07-31 00:00:00+00:00              15.8479\n",
       "2014-10-30 00:00:00+00:00              16.6339\n",
       "2015-01-29 00:00:00+00:00              17.8690\n",
       "2015-04-30 00:00:00+00:00              15.9700"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_fund_panel['pe_ratio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a simple example for a defined set of companies. This results in a dense time series because the inclusion criteria is essentially static. Below is a slightly more complex example in which the inclusion criteria is dynamic, i.e. because the query is evaluated on each day requested, the resulting response will include the set of companies which meet the criteria on each requested day.\n",
    "\n",
    "In the below example, we have 6 companies that meet the criteria for the top 5 companies over a 5 day time period because the top 5 are different, on different days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fund_panel = get_fundamentals(query(fundamentals.valuation_ratios.pe_ratio)\n",
    "                             .filter(fundamentals.valuation.market_cap > 1e9)\n",
    "                             .filter(fundamentals.valuation_ratios.pe_ratio > 5)\n",
    "                             .order_by(fundamentals.valuation_ratios.pe_ratio)\n",
    "                             .limit(5),\n",
    "                             '2015-05-01', '5d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>security</th>\n",
       "      <th>Security(4664 [SM])</th>\n",
       "      <th>Security(26898 [MIC])</th>\n",
       "      <th>Security(32325 [CSII])</th>\n",
       "      <th>Security(33996 [CIG_C])</th>\n",
       "      <th>Security(35081 [CIM])</th>\n",
       "      <th>Security(44336 [BPY])</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-04-24 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.2182</td>\n",
       "      <td>5.0302</td>\n",
       "      <td>5.3001</td>\n",
       "      <td>5.4062</td>\n",
       "      <td>5.1881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-27 00:00:00+00:00</th>\n",
       "      <td>5.5534</td>\n",
       "      <td>5.2276</td>\n",
       "      <td>5.0302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.4237</td>\n",
       "      <td>5.1624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-28 00:00:00+00:00</th>\n",
       "      <td>5.5871</td>\n",
       "      <td>5.1534</td>\n",
       "      <td>5.0302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3853</td>\n",
       "      <td>5.1538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-29 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1466</td>\n",
       "      <td>5.0302</td>\n",
       "      <td>5.5976</td>\n",
       "      <td>5.3574</td>\n",
       "      <td>5.1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-30 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1908</td>\n",
       "      <td>5.0302</td>\n",
       "      <td>5.5596</td>\n",
       "      <td>5.3225</td>\n",
       "      <td>5.0809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "security                   Security(4664 [SM])  Security(26898 [MIC])  \\\n",
       "date                                                                    \n",
       "2015-04-24 00:00:00+00:00                  NaN                 5.2182   \n",
       "2015-04-27 00:00:00+00:00               5.5534                 5.2276   \n",
       "2015-04-28 00:00:00+00:00               5.5871                 5.1534   \n",
       "2015-04-29 00:00:00+00:00                  NaN                 5.1466   \n",
       "2015-04-30 00:00:00+00:00                  NaN                 5.1908   \n",
       "\n",
       "security                   Security(32325 [CSII])  Security(33996 [CIG_C])  \\\n",
       "date                                                                         \n",
       "2015-04-24 00:00:00+00:00                  5.0302                   5.3001   \n",
       "2015-04-27 00:00:00+00:00                  5.0302                      NaN   \n",
       "2015-04-28 00:00:00+00:00                  5.0302                      NaN   \n",
       "2015-04-29 00:00:00+00:00                  5.0302                   5.5976   \n",
       "2015-04-30 00:00:00+00:00                  5.0302                   5.5596   \n",
       "\n",
       "security                   Security(35081 [CIM])  Security(44336 [BPY])  \n",
       "date                                                                     \n",
       "2015-04-24 00:00:00+00:00                 5.4062                 5.1881  \n",
       "2015-04-27 00:00:00+00:00                 5.4237                 5.1624  \n",
       "2015-04-28 00:00:00+00:00                 5.3853                 5.1538  \n",
       "2015-04-29 00:00:00+00:00                 5.3574                 5.1645  \n",
       "2015-04-30 00:00:00+00:00                 5.3225                 5.0809  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fund_panel.loc['pe_ratio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Get Interactive Documentation\n",
    "View documentation for any `callable` python object with the `?` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "get_pricing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
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