Machine Learning for Investment - Part 1 - Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference https://youtu.be/KTeVOb8gaD4?list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3\n",
    "\n",
    "###Basic Setup and Test - Character Recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('dark_background')\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn import svm\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = svm.SVC(C = 100, gamma = 0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n",
       "  gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n",
       "  random_state=None, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y = digits.data[:-1], digits.target[:-1]\n",
    "# The last instance is held out for testing\n",
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [8]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10a6cd128>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ri3jyy6MXwqjx/LkIU9epWP9WKSZbAorh9cKs87Nsmzekw3NUtMfYODx0GXYgEaFwV6+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\n1WDXqjWEqsGuVWsIVYNdq9YQ6v8BT5YjCNDIdx0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a977eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Prediction:',clf.predict(digits.data[-1]))\n",
    "plt.imshow(digits.images[-1], cmap=plt.cm.gray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n",
       "  gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n",
       "  random_state=None, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train,y_train = digits.data[:-10], digits.target[:-10]\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 4, 8, 8, 4, 9, 0, 8, 9, 8])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test,y_test = digits.data[-10:], digits.target[-10:]\n",
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 4 8 8 4 9 0 8 9 8]\n"
     ]
    }
   ],
   "source": [
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(clf.predict(X_test) == y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Obtaining data\n",
    "\n",
    "If you scrape super fast, the company might sue you for doing DoS attack. So, be careful. Even ask the company for data\n",
    "\n",
    "Important Features  \n",
    "P/E Ratio - Price to earnings ratio  \n",
    "Debt to Equity Ratio  \n",
    "Stock price\n",
    "\n",
    "Import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import time\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "underperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "underperform\n",
      "outperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "underperform\n",
      "TotalDebtEquitymrq.csv\n"
     ]
    },
    {
     "data": {
      "image/png": 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6SN1d3j++AdYBt1MejtgE5ZG2o8T71dRqLHkSeBSNuX8fdUVwBup4CAv3FCL2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\naX/RyABfI6SL3RHpIXxpyVfkz3AIR3z671rPXoe4VHYifv5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KEu3TNbjrCn53FqhcgWyA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d7ba3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path ='IntraQuarter'\n",
    "def Key_Stats(gather='Total Debt/Equity (mrq)'):\n",
    "    statspath = path+'/_KeyStats'\n",
    "    stock_list = [x[0] for x in os.walk(statspath)]\n",
    "    df = pd.DataFrame(columns = ['Date','Unix','Ticker','DE Ratio','Price',\n",
    "                                 'stock_p_change','SP500','sp500_p_change',\n",
    "                                 'difference','status'])\n",
    "    sp500_df = pd.DataFrame.from_csv('YAHOO-INDEX_GSPC.csv')\n",
    "    ticker_list=[]\n",
    "    \n",
    "    for each_dir in stock_list[1:25]:\n",
    "        each_file = os.listdir(each_dir)\n",
    "        ticker = each_dir.split('/')[2]\n",
    "        ticker_list.append(ticker)\n",
    "        \n",
    "        starting_stock_value = False\n",
    "        starting_sp500_value = False\n",
    "        \n",
    "        if len(each_file) > 0:\n",
    "            for file in each_file:\n",
    "                date_stamp = datetime.strptime(file,'%Y%m%d%H%M%S.html')\n",
    "                unix_time = time.mktime(date_stamp.timetuple())\n",
    "                source = open(each_dir+'/'+file).read()\n",
    "                try:\n",
    "                    try:\n",
    "                        value = float(source.split(gather+':</td><td class=\"yfnc_tabledata1\">')[1].split('</td>')[0])\n",
    "                    except Exception as e:\n",
    "                        #print(e,ticker,file)\n",
    "                        value = float(source.split(gather+':</td>\\n<td class=\"yfnc_tabledata1\">')[1].split('</td>')[0])\n",
    "                        #print(value)\n",
    "                    \n",
    "                    try:\n",
    "                        sp500_date = datetime.fromtimestamp(unix_time).strftime('%Y-%m-%d')\n",
    "                        row = sp500_df.ix[sp500_date]\n",
    "                        sp500_value = float(row[\"Adjusted Close\"])\n",
    "                    except:\n",
    "                        sp500_date = datetime.fromtimestamp(unix_time - 259200).strftime('%Y-%m-%d')\n",
    "                        row = sp500_df.ix[sp500_date]\n",
    "                        sp500_value = float(row[\"Adjusted Close\"])\n",
    "                    \n",
    "                    try:\n",
    "                        stock_price = float(source.split('</small><big><b>')[1].split('</b')[0])\n",
    "                    except Exception as e:\n",
    "                        try:\n",
    "                            stock_price = (source.split('</small><big><b>')[1].split('</b')[0])\n",
    "                            stock_price = re.search(r'(\\d{1,8}\\.\\d{1,8})',stock_price)\n",
    "                            stock_price = float(stock_price.group(1))\n",
    "                            #print(e, ticker, file)\n",
    "                            #print(stock_price)\n",
    "                        except:\n",
    "                            pass\n",
    "                        \n",
    "                    if not starting_stock_value:\n",
    "                        starting_stock_value = stock_price\n",
    "                    if not starting_sp500_value:\n",
    "                        starting_sp500_value = sp500_value\n",
    "                    \n",
    "                    stock_p_change = 100 * (stock_price - starting_stock_value)/starting_stock_value\n",
    "                    sp500_p_change = 100 * (sp500_value - starting_sp500_value)/starting_sp500_value\n",
    "                    \n",
    "                    difference = stock_p_change - sp500_p_change\n",
    "                    status = \"outperform\" if difference > 0 else \"underperform\"\n",
    "                    \n",
    "                    df = df.append({'Date':date_stamp, \n",
    "                                    'Unix':unix_time, \n",
    "                                    'Ticker':ticker, \n",
    "                                    'DE Ratio':value,\n",
    "                                    'Price':stock_price,\n",
    "                                    'stock_p_change':stock_p_change,\n",
    "                                    'SP500':sp500_value,\n",
    "                                    'sp500_p_change':sp500_p_change,\n",
    "                                    'difference':difference,\n",
    "                                    'status':status}, ignore_index = True)\n",
    "                except Exception as e:\n",
    "                    pass\n",
    "                \n",
    "                #print(ticker,':',value)\n",
    "    for each_ticker in ticker_list:\n",
    "        try:\n",
    "            plot_df = df[df['Ticker'] == each_ticker]\n",
    "            plot_df = plot_df.set_index(['Date'])\n",
    "            print(plot_df['status'][-1])\n",
    "            if plot_df['status'][-1] == \"underperform\":\n",
    "                color = 'r'\n",
    "            else:\n",
    "                color = 'g'\n",
    "            plot_df['difference'].plot(label=each_ticker,color=color)\n",
    "            plt.legend()\n",
    "        except:\n",
    "            pass\n",
    "    save = gather.replace('/','').replace(' ','').replace(')','').replace('(','')+('.csv')\n",
    "    print(save)\n",
    "    df.to_csv(save)\n",
    "Key_Stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "0  1  2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(columns=['a','b'])\n",
    "df1 = df1.append({'a':1,'b':2}, ignore_index=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IntraQuarter/_KeyStats', 'IntraQuarter/_KeyStats/a', 'IntraQuarter/_KeyStats/aa', 'IntraQuarter/_KeyStats/aapl', 'IntraQuarter/_KeyStats/abbv', 'IntraQuarter/_KeyStats/abc', 'IntraQuarter/_KeyStats/abt', 'IntraQuarter/_KeyStats/ace', 'IntraQuarter/_KeyStats/aci', 'IntraQuarter/_KeyStats/acn', 'IntraQuarter/_KeyStats/act', 'IntraQuarter/_KeyStats/adbe', 'IntraQuarter/_KeyStats/adi', 'IntraQuarter/_KeyStats/adm', 'IntraQuarter/_KeyStats/adp', 'IntraQuarter/_KeyStats/adsk', 'IntraQuarter/_KeyStats/adt', 'IntraQuarter/_KeyStats/aee', 'IntraQuarter/_KeyStats/aeo', 'IntraQuarter/_KeyStats/aep', 'IntraQuarter/_KeyStats/aes', 'IntraQuarter/_KeyStats/aet', 'IntraQuarter/_KeyStats/afl', 'IntraQuarter/_KeyStats/agn', 'IntraQuarter/_KeyStats/aig', 'IntraQuarter/_KeyStats/aiv', 'IntraQuarter/_KeyStats/aiz', 'IntraQuarter/_KeyStats/akam', 'IntraQuarter/_KeyStats/all', 'IntraQuarter/_KeyStats/altr', 'IntraQuarter/_KeyStats/alxn', 'IntraQuarter/_KeyStats/amat', 'IntraQuarter/_KeyStats/amd', 'IntraQuarter/_KeyStats/amgn', 'IntraQuarter/_KeyStats/amp', 'IntraQuarter/_KeyStats/amt', 'IntraQuarter/_KeyStats/amzn', 'IntraQuarter/_KeyStats/an', 'IntraQuarter/_KeyStats/anf', 'IntraQuarter/_KeyStats/ann', 'IntraQuarter/_KeyStats/aon', 'IntraQuarter/_KeyStats/apa', 'IntraQuarter/_KeyStats/apc', 'IntraQuarter/_KeyStats/apd', 'IntraQuarter/_KeyStats/aph', 'IntraQuarter/_KeyStats/apol', 'IntraQuarter/_KeyStats/arg', 'IntraQuarter/_KeyStats/arna', 'IntraQuarter/_KeyStats/aro', 'IntraQuarter/_KeyStats/ati', 'IntraQuarter/_KeyStats/atvi', 'IntraQuarter/_KeyStats/avb', 'IntraQuarter/_KeyStats/avp', 'IntraQuarter/_KeyStats/avy', 'IntraQuarter/_KeyStats/axp', 'IntraQuarter/_KeyStats/azo', 'IntraQuarter/_KeyStats/ba', 'IntraQuarter/_KeyStats/bac', 'IntraQuarter/_KeyStats/bax', 'IntraQuarter/_KeyStats/bbby', 'IntraQuarter/_KeyStats/bbry', 'IntraQuarter/_KeyStats/bbt', 'IntraQuarter/_KeyStats/bby', 'IntraQuarter/_KeyStats/bcr', 'IntraQuarter/_KeyStats/bdx', 'IntraQuarter/_KeyStats/beam', 'IntraQuarter/_KeyStats/ben', 'IntraQuarter/_KeyStats/bf-b', 'IntraQuarter/_KeyStats/bhi', 'IntraQuarter/_KeyStats/big', 'IntraQuarter/_KeyStats/biib', 'IntraQuarter/_KeyStats/bk', 'IntraQuarter/_KeyStats/bks', 'IntraQuarter/_KeyStats/blk', 'IntraQuarter/_KeyStats/bll', 'IntraQuarter/_KeyStats/bmc', 'IntraQuarter/_KeyStats/bms', 'IntraQuarter/_KeyStats/bmy', 'IntraQuarter/_KeyStats/brcm', 'IntraQuarter/_KeyStats/brk-b', 'IntraQuarter/_KeyStats/bsx', 'IntraQuarter/_KeyStats/btu', 'IntraQuarter/_KeyStats/bwa', 'IntraQuarter/_KeyStats/bxp', 'IntraQuarter/_KeyStats/c', 'IntraQuarter/_KeyStats/ca', 'IntraQuarter/_KeyStats/cab', 'IntraQuarter/_KeyStats/cag', 'IntraQuarter/_KeyStats/cah', 'IntraQuarter/_KeyStats/cam', 'IntraQuarter/_KeyStats/camp', 'IntraQuarter/_KeyStats/cat', 'IntraQuarter/_KeyStats/cb', 'IntraQuarter/_KeyStats/cbg', 'IntraQuarter/_KeyStats/cbs', 'IntraQuarter/_KeyStats/cce', 'IntraQuarter/_KeyStats/cci', 'IntraQuarter/_KeyStats/ccl', 'IntraQuarter/_KeyStats/celg', 'IntraQuarter/_KeyStats/cern', 'IntraQuarter/_KeyStats/cf', 'IntraQuarter/_KeyStats/cfn', 'IntraQuarter/_KeyStats/chk', 'IntraQuarter/_KeyStats/chrw', 'IntraQuarter/_KeyStats/ci', 'IntraQuarter/_KeyStats/cim', 'IntraQuarter/_KeyStats/cinf', 'IntraQuarter/_KeyStats/cl', 'IntraQuarter/_KeyStats/cldx', 'IntraQuarter/_KeyStats/clf', 'IntraQuarter/_KeyStats/clx', 'IntraQuarter/_KeyStats/cma', 'IntraQuarter/_KeyStats/cmcsa', 'IntraQuarter/_KeyStats/cme', 'IntraQuarter/_KeyStats/cmg', 'IntraQuarter/_KeyStats/cmi', 'IntraQuarter/_KeyStats/cms', 'IntraQuarter/_KeyStats/cnp', 'IntraQuarter/_KeyStats/cnx', 'IntraQuarter/_KeyStats/cof', 'IntraQuarter/_KeyStats/cog', 'IntraQuarter/_KeyStats/coh', 'IntraQuarter/_KeyStats/col', 'IntraQuarter/_KeyStats/cop', 'IntraQuarter/_KeyStats/cost', 'IntraQuarter/_KeyStats/cov', 'IntraQuarter/_KeyStats/cpb', 'IntraQuarter/_KeyStats/crm', 'IntraQuarter/_KeyStats/csc', 'IntraQuarter/_KeyStats/csco', 'IntraQuarter/_KeyStats/csx', 'IntraQuarter/_KeyStats/ctas', 'IntraQuarter/_KeyStats/ctl', 'IntraQuarter/_KeyStats/ctsh', 'IntraQuarter/_KeyStats/ctxs', 'IntraQuarter/_KeyStats/cvc', 'IntraQuarter/_KeyStats/cvs', 'IntraQuarter/_KeyStats/cvx', 'IntraQuarter/_KeyStats/d', 'IntraQuarter/_KeyStats/dal', 'IntraQuarter/_KeyStats/dd', 'IntraQuarter/_KeyStats/dds', 'IntraQuarter/_KeyStats/de', 'IntraQuarter/_KeyStats/dell', 'IntraQuarter/_KeyStats/df', 'IntraQuarter/_KeyStats/dfs', 'IntraQuarter/_KeyStats/dg', 'IntraQuarter/_KeyStats/dgx', 'IntraQuarter/_KeyStats/dhi', 'IntraQuarter/_KeyStats/dhr', 'IntraQuarter/_KeyStats/dis', 'IntraQuarter/_KeyStats/disca', 'IntraQuarter/_KeyStats/dks', 'IntraQuarter/_KeyStats/dlph', 'IntraQuarter/_KeyStats/dltr', 'IntraQuarter/_KeyStats/dlx', 'IntraQuarter/_KeyStats/dnb', 'IntraQuarter/_KeyStats/dnr', 'IntraQuarter/_KeyStats/do', 'IntraQuarter/_KeyStats/dov', 'IntraQuarter/_KeyStats/dow', 'IntraQuarter/_KeyStats/dps', 'IntraQuarter/_KeyStats/dri', 'IntraQuarter/_KeyStats/dsw', 'IntraQuarter/_KeyStats/dte', 'IntraQuarter/_KeyStats/dtv', 'IntraQuarter/_KeyStats/duk', 'IntraQuarter/_KeyStats/dva', 'IntraQuarter/_KeyStats/dvn', 'IntraQuarter/_KeyStats/ea', 'IntraQuarter/_KeyStats/ebay', 'IntraQuarter/_KeyStats/ecl', 'IntraQuarter/_KeyStats/ecyt', 'IntraQuarter/_KeyStats/ed', 'IntraQuarter/_KeyStats/efx', 'IntraQuarter/_KeyStats/eix', 'IntraQuarter/_KeyStats/el', 'IntraQuarter/_KeyStats/emc', 'IntraQuarter/_KeyStats/emn', 'IntraQuarter/_KeyStats/emr', 'IntraQuarter/_KeyStats/eog', 'IntraQuarter/_KeyStats/eqr', 'IntraQuarter/_KeyStats/eqt', 'IntraQuarter/_KeyStats/esrx', 'IntraQuarter/_KeyStats/esv', 'IntraQuarter/_KeyStats/etfc', 'IntraQuarter/_KeyStats/etn', 'IntraQuarter/_KeyStats/etr', 'IntraQuarter/_KeyStats/ew', 'IntraQuarter/_KeyStats/exc', 'IntraQuarter/_KeyStats/expd', 'IntraQuarter/_KeyStats/expe', 'IntraQuarter/_KeyStats/expr', 'IntraQuarter/_KeyStats/f', 'IntraQuarter/_KeyStats/fast', 'IntraQuarter/_KeyStats/fb', 'IntraQuarter/_KeyStats/fcx', 'IntraQuarter/_KeyStats/fdo', 'IntraQuarter/_KeyStats/fdx', 'IntraQuarter/_KeyStats/fe', 'IntraQuarter/_KeyStats/ffiv', 'IntraQuarter/_KeyStats/fhn', 'IntraQuarter/_KeyStats/fis', 'IntraQuarter/_KeyStats/fisv', 'IntraQuarter/_KeyStats/fitb', 'IntraQuarter/_KeyStats/fl', 'IntraQuarter/_KeyStats/flir', 'IntraQuarter/_KeyStats/flr', 'IntraQuarter/_KeyStats/fls', 'IntraQuarter/_KeyStats/flws', 'IntraQuarter/_KeyStats/fmc', 'IntraQuarter/_KeyStats/fosl', 'IntraQuarter/_KeyStats/frx', 'IntraQuarter/_KeyStats/fslr', 'IntraQuarter/_KeyStats/fti', 'IntraQuarter/_KeyStats/ftr', 'IntraQuarter/_KeyStats/gas', 'IntraQuarter/_KeyStats/gci', 'IntraQuarter/_KeyStats/gd', 'IntraQuarter/_KeyStats/ge', 'IntraQuarter/_KeyStats/ges', 'IntraQuarter/_KeyStats/gild', 'IntraQuarter/_KeyStats/gis', 'IntraQuarter/_KeyStats/glw', 'IntraQuarter/_KeyStats/gm', 'IntraQuarter/_KeyStats/gmcr', 'IntraQuarter/_KeyStats/gme', 'IntraQuarter/_KeyStats/gnw', 'IntraQuarter/_KeyStats/goog', 'IntraQuarter/_KeyStats/gpc', 'IntraQuarter/_KeyStats/gps', 'IntraQuarter/_KeyStats/grmn', 'IntraQuarter/_KeyStats/grpn', 'IntraQuarter/_KeyStats/gs', 'IntraQuarter/_KeyStats/gt', 'IntraQuarter/_KeyStats/gtn', 'IntraQuarter/_KeyStats/gww', 'IntraQuarter/_KeyStats/hal', 'IntraQuarter/_KeyStats/har', 'IntraQuarter/_KeyStats/has', 'IntraQuarter/_KeyStats/hban', 'IntraQuarter/_KeyStats/hcbk', 'IntraQuarter/_KeyStats/hcn', 'IntraQuarter/_KeyStats/hcp', 'IntraQuarter/_KeyStats/hd', 'IntraQuarter/_KeyStats/hes', 'IntraQuarter/_KeyStats/hig', 'IntraQuarter/_KeyStats/hog', 'IntraQuarter/_KeyStats/hon', 'IntraQuarter/_KeyStats/hot', 'IntraQuarter/_KeyStats/hov', 'IntraQuarter/_KeyStats/hp', 'IntraQuarter/_KeyStats/hpq', 'IntraQuarter/_KeyStats/hrb', 'IntraQuarter/_KeyStats/hrl', 'IntraQuarter/_KeyStats/hrs', 'IntraQuarter/_KeyStats/hsp', 'IntraQuarter/_KeyStats/hst', 'IntraQuarter/_KeyStats/hsy', 'IntraQuarter/_KeyStats/htz', 'IntraQuarter/_KeyStats/hum', 'IntraQuarter/_KeyStats/ibm', 'IntraQuarter/_KeyStats/ice', 'IntraQuarter/_KeyStats/iff', 'IntraQuarter/_KeyStats/igt', 'IntraQuarter/_KeyStats/intc', 'IntraQuarter/_KeyStats/intu', 'IntraQuarter/_KeyStats/ip', 'IntraQuarter/_KeyStats/ipg', 'IntraQuarter/_KeyStats/ir', 'IntraQuarter/_KeyStats/irm', 'IntraQuarter/_KeyStats/isrg', 'IntraQuarter/_KeyStats/itw', 'IntraQuarter/_KeyStats/ivz', 'IntraQuarter/_KeyStats/jbl', 'IntraQuarter/_KeyStats/jci', 'IntraQuarter/_KeyStats/jcp', 'IntraQuarter/_KeyStats/jdsu', 'IntraQuarter/_KeyStats/jec', 'IntraQuarter/_KeyStats/jnj', 'IntraQuarter/_KeyStats/jnpr', 'IntraQuarter/_KeyStats/josb', 'IntraQuarter/_KeyStats/joy', 'IntraQuarter/_KeyStats/jpm', 'IntraQuarter/_KeyStats/jwn', 'IntraQuarter/_KeyStats/k', 'IntraQuarter/_KeyStats/key', 'IntraQuarter/_KeyStats/kim', 'IntraQuarter/_KeyStats/klac', 'IntraQuarter/_KeyStats/kmb', 'IntraQuarter/_KeyStats/kmi', 'IntraQuarter/_KeyStats/kmx', 'IntraQuarter/_KeyStats/ko', 'IntraQuarter/_KeyStats/kr', 'IntraQuarter/_KeyStats/krft', 'IntraQuarter/_KeyStats/kss', 'IntraQuarter/_KeyStats/ksu', 'IntraQuarter/_KeyStats/l', 'IntraQuarter/_KeyStats/leg', 'IntraQuarter/_KeyStats/len', 'IntraQuarter/_KeyStats/lh', 'IntraQuarter/_KeyStats/life', 'IntraQuarter/_KeyStats/lll', 'IntraQuarter/_KeyStats/lltc', 'IntraQuarter/_KeyStats/lly', 'IntraQuarter/_KeyStats/lm', 'IntraQuarter/_KeyStats/lmt', 'IntraQuarter/_KeyStats/lnc', 'IntraQuarter/_KeyStats/lo', 'IntraQuarter/_KeyStats/low', 'IntraQuarter/_KeyStats/lrcx', 'IntraQuarter/_KeyStats/lsi', 'IntraQuarter/_KeyStats/ltd', 'IntraQuarter/_KeyStats/luk', 'IntraQuarter/_KeyStats/luv', 'IntraQuarter/_KeyStats/lyb', 'IntraQuarter/_KeyStats/m', 'IntraQuarter/_KeyStats/ma', 'IntraQuarter/_KeyStats/mac', 'IntraQuarter/_KeyStats/mar', 'IntraQuarter/_KeyStats/mas', 'IntraQuarter/_KeyStats/mat', 'IntraQuarter/_KeyStats/mcd', 'IntraQuarter/_KeyStats/mchp', 'IntraQuarter/_KeyStats/mck', 'IntraQuarter/_KeyStats/mco', 'IntraQuarter/_KeyStats/mcp', 'IntraQuarter/_KeyStats/mdlz', 'IntraQuarter/_KeyStats/mdt', 'IntraQuarter/_KeyStats/met', 'IntraQuarter/_KeyStats/mgm', 'IntraQuarter/_KeyStats/mhfi', 'IntraQuarter/_KeyStats/mjn', 'IntraQuarter/_KeyStats/mkc', 'IntraQuarter/_KeyStats/mmc', 'IntraQuarter/_KeyStats/mmm', 'IntraQuarter/_KeyStats/mnst', 'IntraQuarter/_KeyStats/mo', 'IntraQuarter/_KeyStats/molx', 'IntraQuarter/_KeyStats/mon', 'IntraQuarter/_KeyStats/mos', 'IntraQuarter/_KeyStats/mpc', 'IntraQuarter/_KeyStats/mrk', 'IntraQuarter/_KeyStats/mro', 'IntraQuarter/_KeyStats/ms', 'IntraQuarter/_KeyStats/msft', 'IntraQuarter/_KeyStats/msi', 'IntraQuarter/_KeyStats/mtb', 'IntraQuarter/_KeyStats/mu', 'IntraQuarter/_KeyStats/mur', 'IntraQuarter/_KeyStats/mwv', 'IntraQuarter/_KeyStats/myl', 'IntraQuarter/_KeyStats/nbl', 'IntraQuarter/_KeyStats/nbr', 'IntraQuarter/_KeyStats/ndaq', 'IntraQuarter/_KeyStats/ne', 'IntraQuarter/_KeyStats/nee', 'IntraQuarter/_KeyStats/nem', 'IntraQuarter/_KeyStats/nflx', 'IntraQuarter/_KeyStats/nfx', 'IntraQuarter/_KeyStats/ni', 'IntraQuarter/_KeyStats/nile', 'IntraQuarter/_KeyStats/nke', 'IntraQuarter/_KeyStats/nly', 'IntraQuarter/_KeyStats/noc', 'IntraQuarter/_KeyStats/nok', 'IntraQuarter/_KeyStats/nov', 'IntraQuarter/_KeyStats/nrg', 'IntraQuarter/_KeyStats/nsc', 'IntraQuarter/_KeyStats/ntap', 'IntraQuarter/_KeyStats/ntri', 'IntraQuarter/_KeyStats/ntrs', 'IntraQuarter/_KeyStats/nu', 'IntraQuarter/_KeyStats/nue', 'IntraQuarter/_KeyStats/nus', 'IntraQuarter/_KeyStats/nvda', 'IntraQuarter/_KeyStats/nwl', 'IntraQuarter/_KeyStats/nwsa', 'IntraQuarter/_KeyStats/nyx', 'IntraQuarter/_KeyStats/oi', 'IntraQuarter/_KeyStats/oke', 'IntraQuarter/_KeyStats/omc', 'IntraQuarter/_KeyStats/orcl', 'IntraQuarter/_KeyStats/orly', 'IntraQuarter/_KeyStats/oxy', 'IntraQuarter/_KeyStats/p', 'IntraQuarter/_KeyStats/payx', 'IntraQuarter/_KeyStats/pbct', 'IntraQuarter/_KeyStats/pbi', 'IntraQuarter/_KeyStats/pcar', 'IntraQuarter/_KeyStats/pcg', 'IntraQuarter/_KeyStats/pcl', 'IntraQuarter/_KeyStats/pcln', 'IntraQuarter/_KeyStats/pcp', 'IntraQuarter/_KeyStats/pdco', 'IntraQuarter/_KeyStats/peg', 'IntraQuarter/_KeyStats/pep', 'IntraQuarter/_KeyStats/petm', 'IntraQuarter/_KeyStats/pets', 'IntraQuarter/_KeyStats/pfe', 'IntraQuarter/_KeyStats/pfg', 'IntraQuarter/_KeyStats/pg', 'IntraQuarter/_KeyStats/pgr', 'IntraQuarter/_KeyStats/ph', 'IntraQuarter/_KeyStats/phm', 'IntraQuarter/_KeyStats/pki', 'IntraQuarter/_KeyStats/pld', 'IntraQuarter/_KeyStats/pll', 'IntraQuarter/_KeyStats/pm', 'IntraQuarter/_KeyStats/pnc', 'IntraQuarter/_KeyStats/pnr', 'IntraQuarter/_KeyStats/pnw', 'IntraQuarter/_KeyStats/pom', 'IntraQuarter/_KeyStats/ppg', 'IntraQuarter/_KeyStats/ppl', 'IntraQuarter/_KeyStats/prgo', 'IntraQuarter/_KeyStats/pru', 'IntraQuarter/_KeyStats/psa', 'IntraQuarter/_KeyStats/psx', 'IntraQuarter/_KeyStats/pvtb', 'IntraQuarter/_KeyStats/pwr', 'IntraQuarter/_KeyStats/px', 'IntraQuarter/_KeyStats/pxd', 'IntraQuarter/_KeyStats/qcom', 'IntraQuarter/_KeyStats/qdel', 'IntraQuarter/_KeyStats/qep', 'IntraQuarter/_KeyStats/r', 'IntraQuarter/_KeyStats/rai', 'IntraQuarter/_KeyStats/rdc', 'IntraQuarter/_KeyStats/rf', 'IntraQuarter/_KeyStats/rhi', 'IntraQuarter/_KeyStats/rht', 'IntraQuarter/_KeyStats/rl', 'IntraQuarter/_KeyStats/rok', 'IntraQuarter/_KeyStats/rop', 'IntraQuarter/_KeyStats/rost', 'IntraQuarter/_KeyStats/rrc', 'IntraQuarter/_KeyStats/rsg', 'IntraQuarter/_KeyStats/rsh', 'IntraQuarter/_KeyStats/rtn', 'IntraQuarter/_KeyStats/s', 'IntraQuarter/_KeyStats/sai', 'IntraQuarter/_KeyStats/sbux', 'IntraQuarter/_KeyStats/scg', 'IntraQuarter/_KeyStats/schl', 'IntraQuarter/_KeyStats/schw', 'IntraQuarter/_KeyStats/sd', 'IntraQuarter/_KeyStats/se', 'IntraQuarter/_KeyStats/see', 'IntraQuarter/_KeyStats/sfly', 'IntraQuarter/_KeyStats/shld', 'IntraQuarter/_KeyStats/shw', 'IntraQuarter/_KeyStats/sial', 'IntraQuarter/_KeyStats/siri', 'IntraQuarter/_KeyStats/sjm', 'IntraQuarter/_KeyStats/sks', 'IntraQuarter/_KeyStats/slb', 'IntraQuarter/_KeyStats/slm', 'IntraQuarter/_KeyStats/sna', 'IntraQuarter/_KeyStats/sndk', 'IntraQuarter/_KeyStats/sne', 'IntraQuarter/_KeyStats/sni', 'IntraQuarter/_KeyStats/snts', 'IntraQuarter/_KeyStats/so', 'IntraQuarter/_KeyStats/spg', 'IntraQuarter/_KeyStats/spls', 'IntraQuarter/_KeyStats/srcl', 'IntraQuarter/_KeyStats/sre', 'IntraQuarter/_KeyStats/sti', 'IntraQuarter/_KeyStats/stj', 'IntraQuarter/_KeyStats/stt', 'IntraQuarter/_KeyStats/stx', 'IntraQuarter/_KeyStats/stz', 'IntraQuarter/_KeyStats/swk', 'IntraQuarter/_KeyStats/swn', 'IntraQuarter/_KeyStats/swy', 'IntraQuarter/_KeyStats/syk', 'IntraQuarter/_KeyStats/symc', 'IntraQuarter/_KeyStats/syy', 'IntraQuarter/_KeyStats/t', 'IntraQuarter/_KeyStats/tap', 'IntraQuarter/_KeyStats/tdc', 'IntraQuarter/_KeyStats/te', 'IntraQuarter/_KeyStats/teg', 'IntraQuarter/_KeyStats/tel', 'IntraQuarter/_KeyStats/ter', 'IntraQuarter/_KeyStats/tgt', 'IntraQuarter/_KeyStats/thc', 'IntraQuarter/_KeyStats/tibx', 'IntraQuarter/_KeyStats/tif', 'IntraQuarter/_KeyStats/tjx', 'IntraQuarter/_KeyStats/tm', 'IntraQuarter/_KeyStats/tmk', 'IntraQuarter/_KeyStats/tmo', 'IntraQuarter/_KeyStats/trip', 'IntraQuarter/_KeyStats/trow', 'IntraQuarter/_KeyStats/trv', 'IntraQuarter/_KeyStats/tsla', 'IntraQuarter/_KeyStats/tsn', 'IntraQuarter/_KeyStats/tso', 'IntraQuarter/_KeyStats/tss', 'IntraQuarter/_KeyStats/twc', 'IntraQuarter/_KeyStats/twx', 'IntraQuarter/_KeyStats/txn', 'IntraQuarter/_KeyStats/txt', 'IntraQuarter/_KeyStats/tyc', 'IntraQuarter/_KeyStats/ua', 'IntraQuarter/_KeyStats/unh', 'IntraQuarter/_KeyStats/unm', 'IntraQuarter/_KeyStats/unp', 'IntraQuarter/_KeyStats/ups', 'IntraQuarter/_KeyStats/urbn', 'IntraQuarter/_KeyStats/usb', 'IntraQuarter/_KeyStats/utx', 'IntraQuarter/_KeyStats/v', 'IntraQuarter/_KeyStats/vale', 'IntraQuarter/_KeyStats/var', 'IntraQuarter/_KeyStats/vfc', 'IntraQuarter/_KeyStats/viab', 'IntraQuarter/_KeyStats/vitc', 'IntraQuarter/_KeyStats/vlo', 'IntraQuarter/_KeyStats/vmc', 'IntraQuarter/_KeyStats/vno', 'IntraQuarter/_KeyStats/vprt', 'IntraQuarter/_KeyStats/vrsn', 'IntraQuarter/_KeyStats/vtr', 'IntraQuarter/_KeyStats/vz', 'IntraQuarter/_KeyStats/wag', 'IntraQuarter/_KeyStats/wat', 'IntraQuarter/_KeyStats/wdc', 'IntraQuarter/_KeyStats/wec', 'IntraQuarter/_KeyStats/wfc', 'IntraQuarter/_KeyStats/wfm', 'IntraQuarter/_KeyStats/wgo', 'IntraQuarter/_KeyStats/whr', 'IntraQuarter/_KeyStats/win', 'IntraQuarter/_KeyStats/wlp', 'IntraQuarter/_KeyStats/wm', 'IntraQuarter/_KeyStats/wmb', 'IntraQuarter/_KeyStats/wmt', 'IntraQuarter/_KeyStats/wpo', 'IntraQuarter/_KeyStats/wpx', 'IntraQuarter/_KeyStats/wtw', 'IntraQuarter/_KeyStats/wu', 'IntraQuarter/_KeyStats/wy', 'IntraQuarter/_KeyStats/wyn', 'IntraQuarter/_KeyStats/wynn', 'IntraQuarter/_KeyStats/x', 'IntraQuarter/_KeyStats/xel', 'IntraQuarter/_KeyStats/xl', 'IntraQuarter/_KeyStats/xlnx', 'IntraQuarter/_KeyStats/xom', 'IntraQuarter/_KeyStats/xray', 'IntraQuarter/_KeyStats/xrx', 'IntraQuarter/_KeyStats/xyl', 'IntraQuarter/_KeyStats/yhoo', 'IntraQuarter/_KeyStats/yum', 'IntraQuarter/_KeyStats/zion', 'IntraQuarter/_KeyStats/zlc', 'IntraQuarter/_KeyStats/zmh', 'IntraQuarter/_KeyStats/znga']\n"
     ]
    }
   ],
   "source": [
    "print([x[0] for x in os.walk(path+'/_KeyStats')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nah'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ternary conditional syntax\n",
    "a = \"hey\" if False else \"nah\"\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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