Test File.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "from B3_Reader import *\n",
    "\n",
    "FY06 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2006.TXT\" \n",
    "FY07 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2007.TXT\" \n",
    "FY08 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2008.TXT\" \n",
    "FY09 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2009.TXT\" \n",
    "FY10 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2010.TXT\" \n",
    "FY11 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2011.TXT\" \n",
    "FY12 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2012.TXT\" \n",
    "FY13 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2013.TXT\" \n",
    "FY14 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2014.TXT\" \n",
    "FY15 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2015.TXT\" \n",
    "FY16 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2016.TXT\" \n",
    "FY17 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2017.TXT\" \n",
    "FY18 = \"/Users/pedrotodescan/projects data/Cotacoes_Hist/COTAHIST_A2018.TXT\" \n",
    "\n",
    " \n",
    "dataFY06 = parse_dataset(FY06)\n",
    "dataFY07 = parse_dataset(FY07)\n",
    "dataFY08 = parse_dataset(FY08)\n",
    "dataFY09 = parse_dataset(FY09)\n",
    "dataFY10 = parse_dataset(FY10)\n",
    "dataFY11 = parse_dataset(FY11)\n",
    "dataFY12 = parse_dataset(FY12)\n",
    "dataFY13 = parse_dataset(FY13)\n",
    "dataFY14 = parse_dataset(FY14)\n",
    "dataFY15 = parse_dataset(FY15)\n",
    "dataFY16 = parse_dataset(FY16)\n",
    "dataFY17 = parse_dataset(FY17)\n",
    "dataFY18 = parse_dataset(FY18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = dataFY13.append(dataFY14).append(dataFY15).append(dataFY16).append(dataFY17.append(dataFY18))\n",
    "data2 = dataFY06.append(dataFY07).append(dataFY08).append(dataFY09).append(dataFY10).append(dataFY11).append(dataFY12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data1.append(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = data.loc[data['Ticker'] == 'PETR4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = series.Date.astype('O')\n",
    "series.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "x=series.plot(y = 'OpenPrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120442e10>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x\n"
   ]
  }
 ],
 "metadata": {
  "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",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}