5 Regressions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as sm\n",
    "df = pd.DataFrame.from_csv(\"StockData.csv\", encoding=\"UTF-8\")\n",
    "model = sm.ols(formula=\"PXD ~ Index + Oil + Gold + NaturalGas\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    PXD   R-squared:                       0.513\n",
      "Model:                            OLS   Adj. R-squared:                  0.512\n",
      "Method:                 Least Squares   F-statistic:                     453.1\n",
      "Date:                Sat, 22 Jul 2017   Prob (F-statistic):          6.19e-267\n",
      "Time:                        18:39:30   Log-Likelihood:                 4621.8\n",
      "No. Observations:                1727   AIC:                            -9234.\n",
      "Df Residuals:                    1722   BIC:                            -9206.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0005      0.000      1.168      0.243      -0.000       0.001\n",
      "Index          1.2220      0.044     27.529      0.000       1.135       1.309\n",
      "Oil            0.3862      0.020     19.313      0.000       0.347       0.425\n",
      "Gold           0.0527      0.038      1.392      0.164      -0.022       0.127\n",
      "NaturalGas     0.0054      0.011      0.500      0.617      -0.016       0.027\n",
      "==============================================================================\n",
      "Omnibus:                      171.149   Durbin-Watson:                   2.047\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              723.613\n",
      "Skew:                           0.391   Prob(JB):                    7.40e-158\n",
      "Kurtosis:                       6.073   Cond. No.                         113.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      F   R-squared:                       0.474\n",
      "Model:                            OLS   Adj. R-squared:                  0.473\n",
      "Method:                 Least Squares   F-statistic:                     388.7\n",
      "Date:                Sat, 22 Jul 2017   Prob (F-statistic):          1.13e-238\n",
      "Time:                        18:39:30   Log-Likelihood:                 5015.0\n",
      "No. Observations:                1727   AIC:                        -1.002e+04\n",
      "Df Residuals:                    1722   BIC:                            -9993.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0003      0.000     -0.969      0.333      -0.001       0.000\n",
      "Index          1.2920      0.035     36.549      0.000       1.223       1.361\n",
      "Oil           -0.0077      0.016     -0.485      0.628      -0.039       0.024\n",
      "Gold           0.0338      0.030      1.122      0.262      -0.025       0.093\n",
      "NaturalGas     0.0045      0.009      0.524      0.601      -0.012       0.021\n",
      "==============================================================================\n",
      "Omnibus:                      322.554   Durbin-Watson:                   1.856\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4855.417\n",
      "Skew:                          -0.411   Prob(JB):                         0.00\n",
      "Kurtosis:                      11.173   Cond. No.                         113.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    print(result.summary())\n",
    "regress(\"F\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept      1.946173e-01\n",
      "Index         4.033200e-133\n",
      "Oil            1.547053e-02\n",
      "Gold           7.595522e-02\n",
      "NaturalGas     4.721861e-03\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    print(result.pvalues)\n",
    "regress(\"AAPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept     False\n",
      "Index          True\n",
      "Oil            True\n",
      "Gold          False\n",
      "NaturalGas    False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    print(result.pvalues<.05)\n",
    "regress(\"PXD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept     False\n",
      "Index          True\n",
      "Oil            True\n",
      "Gold          False\n",
      "NaturalGas    False\n",
      "dtype: bool\n",
      "Intercept     0.000469\n",
      "Index         1.222012\n",
      "Oil           0.386156\n",
      "Gold          0.052714\n",
      "NaturalGas    0.005414\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    print(result.pvalues<.05)\n",
    "    print(result.params)\n",
    "regress(\"PXD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index    1.222012\n",
      "Oil      0.386156\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    truthSeries = result.pvalues<.05\n",
    "    print(result.params[truthSeries])\n",
    "regress(\"PXD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PXD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <td>1.222012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oil</th>\n",
       "      <td>0.386156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PXD\n",
       "Index  1.222012\n",
       "Oil    0.386156"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def regress(stock):\n",
    "    result = sm.ols(formula=stock+\" ~ Index + Oil + Gold + NaturalGas\", data=df).fit()\n",
    "    return pd.DataFrame(result.params[result.pvalues<.05],columns=[stock])\n",
    "regress(\"PXD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "PatsyError",
     "evalue": "Error evaluating factor: NameError: name 'BRK' is not defined\n    BRK.B ~ Index + Oil + Gold + NaturalGas\n    ^^^^^",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/compat.py\u001b[0m in \u001b[0;36mcall_and_wrap_exc\u001b[0;34m(msg, origin, f, *args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, expr, source_name, inner_namespace)\u001b[0m\n\u001b[1;32m    165\u001b[0m         return eval(code, {}, VarLookupDict([inner_namespace]\n\u001b[0;32m--> 166\u001b[0;31m                                             + self._namespaces))\n\u001b[0m\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<string>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'BRK' is not defined",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mPatsyError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-68a1951bf866>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"BRK.B\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-9-95a51baf8b83>\u001b[0m in \u001b[0;36mregress\u001b[0;34m(stock)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformula\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstock\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\" ~ Index + Oil + Gold + NaturalGas\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpvalues\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m.05\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstock\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mregress\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"PXD\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py\u001b[0m in \u001b[0;36mfrom_formula\u001b[0;34m(cls, formula, data, subset, drop_cols, *args, **kwargs)\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         tmp = handle_formula_data(data, None, formula, depth=eval_env,\n\u001b[0;32m--> 155\u001b[0;31m                                   missing=missing)\n\u001b[0m\u001b[1;32m    156\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdesign_info\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/statsmodels/formula/formulatools.py\u001b[0m in \u001b[0;36mhandle_formula_data\u001b[0;34m(Y, X, formula, depth, missing)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata_util\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_using_pandas\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m             result = dmatrices(formula, Y, depth, return_type='dataframe',\n\u001b[0;32m---> 65\u001b[0;31m                                NA_action=na_action)\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             result = dmatrices(formula, Y, depth, return_type='dataframe',\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/highlevel.py\u001b[0m in \u001b[0;36mdmatrices\u001b[0;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[1;32m    308\u001b[0m     \u001b[0meval_env\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcapture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_env\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreference\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,\n\u001b[0;32m--> 310\u001b[0;31m                                       NA_action, return_type)\n\u001b[0m\u001b[1;32m    311\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlhs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mPatsyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model is missing required outcome variables\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/highlevel.py\u001b[0m in \u001b[0;36m_do_highlevel_design\u001b[0;34m(formula_like, data, eval_env, NA_action, return_type)\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m     design_infos = _try_incr_builders(formula_like, data_iter_maker, eval_env,\n\u001b[0;32m--> 165\u001b[0;31m                                       NA_action)\n\u001b[0m\u001b[1;32m    166\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdesign_infos\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m         return build_design_matrices(design_infos, data,\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/highlevel.py\u001b[0m in \u001b[0;36m_try_incr_builders\u001b[0;34m(formula_like, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[1;32m     68\u001b[0m                                       \u001b[0mdata_iter_maker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m                                       \u001b[0meval_env\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m                                       NA_action)\n\u001b[0m\u001b[1;32m     71\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/build.py\u001b[0m in \u001b[0;36mdesign_matrix_builders\u001b[0;34m(termlists, data_iter_maker, eval_env, NA_action)\u001b[0m\n\u001b[1;32m    694\u001b[0m                                                    \u001b[0mfactor_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m                                                    \u001b[0mdata_iter_maker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m                                                    NA_action)\n\u001b[0m\u001b[1;32m    697\u001b[0m     \u001b[0;31m# Now we need the factor infos, which encapsulate the knowledge of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m     \u001b[0;31m# how to turn any given factor into a chunk of data:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/build.py\u001b[0m in \u001b[0;36m_examine_factor_types\u001b[0;34m(factors, factor_states, data_iter_maker, NA_action)\u001b[0m\n\u001b[1;32m    441\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_iter_maker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfactor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamine_needed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfactor_states\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfactor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfactor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat_sniffers\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mguess_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfactor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcat_sniffers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/eval.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, memorize_state, data)\u001b[0m\n\u001b[1;32m    564\u001b[0m         return self._eval(memorize_state[\"eval_code\"],\n\u001b[1;32m    565\u001b[0m                           \u001b[0mmemorize_state\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m                           data)\n\u001b[0m\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0m__getstate__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mno_pickling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/eval.py\u001b[0m in \u001b[0;36m_eval\u001b[0;34m(self, code, memorize_state, data)\u001b[0m\n\u001b[1;32m    549\u001b[0m                                  \u001b[0mmemorize_state\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"eval_env\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m                                  \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                                  inner_namespace=inner_namespace)\n\u001b[0m\u001b[1;32m    552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmemorize_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich_pass\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/compat.py\u001b[0m in \u001b[0;36mcall_and_wrap_exc\u001b[0;34m(msg, origin, f, *args, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m                                  origin)\n\u001b[1;32m    123\u001b[0m             \u001b[0;31m# Use 'exec' to hide this syntax from the Python 2 parser:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"raise new_exc from e\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0;31m# In python 2, we just let the original exception escape -- better\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/seanmcowen/anaconda/lib/python3.6/site-packages/patsy/compat.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mPatsyError\u001b[0m: Error evaluating factor: NameError: name 'BRK' is not defined\n    BRK.B ~ Index + Oil + Gold + NaturalGas\n    ^^^^^"
     ]
    }
   ],
   "source": [
    "regress(\"BRK.B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = []\n",
    "for x in df.columns:\n",
    "    if \".\" in x:\n",
    "        x = x.replace(\".\",\"\")\n",
    "    if \":\" in x:\n",
    "        x = x.replace(\":\",\"\")\n",
    "    cols.append(x)\n",
    "df.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df[\"FTI\"]\n",
    "del df[\"COL\"]\n",
    "del df['DXC']\n",
    "del df[\"BHGE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 MMM       ABT      ABBV       ACN      ATVI       AYI  \\\n",
      "Gold             NaN       NaN       NaN       NaN       NaN  0.078922   \n",
      "Index       0.958352  0.769490  1.098795  0.988583  0.904080  1.317917   \n",
      "Intercept        NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "NaturalGas       NaN       NaN       NaN       NaN -0.022059       NaN   \n",
      "Oil              NaN -0.034686       NaN       NaN -0.046367       NaN   \n",
      "\n",
      "                ADBE       AMD       AAP       AES    ...         WYNN  \\\n",
      "Gold             NaN       NaN       NaN       NaN    ...     0.150433   \n",
      "Index       1.171635  1.559588  0.810991  1.121761    ...     1.446935   \n",
      "Intercept        NaN       NaN       NaN       NaN    ...          NaN   \n",
      "NaturalGas       NaN       NaN       NaN       NaN    ...          NaN   \n",
      "Oil              NaN  0.079827 -0.060120  0.081796    ...     0.052993   \n",
      "\n",
      "                 XEL       XRX      XLNX        XL       XYL       YUM  \\\n",
      "Gold             NaN -0.079305       NaN       NaN       NaN       NaN   \n",
      "Index       0.571913  1.288955  1.022487  0.935746  0.956918  0.960295   \n",
      "Intercept        NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "NaturalGas       NaN       NaN       NaN       NaN       NaN       NaN   \n",
      "Oil        -0.040756       NaN       NaN       NaN  0.065023       NaN   \n",
      "\n",
      "                 ZBH      ZION       ZTS  \n",
      "Gold             NaN -0.123886       NaN  \n",
      "Index       0.923686  1.450939  0.937724  \n",
      "Intercept        NaN       NaN       NaN  \n",
      "NaturalGas       NaN       NaN       NaN  \n",
      "Oil              NaN  0.076283 -0.039546  \n",
      "\n",
      "[5 rows x 501 columns]\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame()\n",
    "for stock in df.columns[4:]:\n",
    "    frame = regress(stock)\n",
    "    df2 = pd.concat([df2,frame], axis=1)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2.to_csv(\"RegressionMatrix1.csv\",encoding=\"UTF-8\")"
   ]
  }
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