Jupyter Notebook

gistfile1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### cargamos librerias necesarias\n",
    "%matplotlib inline\n",
    "import sys\n",
    "sys.path.insert(0, 'C:\\\\betting_2\\\\')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from betting import direcciones\n",
    "# sys.path.insert(0, '/home/andres/betting_2/')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>competicion</th>\n",
       "      <th>distancia</th>\n",
       "      <th>full_description</th>\n",
       "      <th>latest_taken</th>\n",
       "      <th>minuto</th>\n",
       "      <th>minuto_exacto</th>\n",
       "      <th>negocio</th>\n",
       "      <th>odds</th>\n",
       "      <th>operador</th>\n",
       "      <th>player</th>\n",
       "      <th>resultado</th>\n",
       "      <th>sentido</th>\n",
       "      <th>simbolo</th>\n",
       "      <th>superficie</th>\n",
       "      <th>unico</th>\n",
       "      <th>volume_matched</th>\n",
       "      <th>positivos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentinian Primera B Nacional</td>\n",
       "      <td>0</td>\n",
       "      <td>central cordoba v chacarita_20150801</td>\n",
       "      <td>1.438379e+09</td>\n",
       "      <td>1430</td>\n",
       "      <td>1430</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>11001</td>\n",
       "      <td>central cordoba</td>\n",
       "      <td>-20.00000</td>\n",
       "      <td>1</td>\n",
       "      <td>1_match odds_central cordoba v chacarita_20150...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1125369425</td>\n",
       "      <td>1602.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentinian Primera Division</td>\n",
       "      <td>0</td>\n",
       "      <td>ca temperley v velez_20150801</td>\n",
       "      <td>1.438380e+09</td>\n",
       "      <td>1430</td>\n",
       "      <td>1430</td>\n",
       "      <td>1</td>\n",
       "      <td>2.76</td>\n",
       "      <td>11001</td>\n",
       "      <td>ca temperley</td>\n",
       "      <td>15.45016</td>\n",
       "      <td>1</td>\n",
       "      <td>1_match odds_ca temperley v velez_20150801001000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1128486276</td>\n",
       "      <td>4856.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazilian Division 2</td>\n",
       "      <td>0</td>\n",
       "      <td>paysandu v mogi mirim_20150801</td>\n",
       "      <td>1.438381e+09</td>\n",
       "      <td>1430</td>\n",
       "      <td>1430</td>\n",
       "      <td>1</td>\n",
       "      <td>1.40</td>\n",
       "      <td>11001</td>\n",
       "      <td>paysandu</td>\n",
       "      <td>-17.85000</td>\n",
       "      <td>1</td>\n",
       "      <td>1_match odds_paysandu v mogi mirim_20150801003000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1119825143</td>\n",
       "      <td>21249.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazilian Division 2</td>\n",
       "      <td>0</td>\n",
       "      <td>abc v bahia_20150801</td>\n",
       "      <td>1.438381e+09</td>\n",
       "      <td>1430</td>\n",
       "      <td>1430</td>\n",
       "      <td>1</td>\n",
       "      <td>2.68</td>\n",
       "      <td>11001</td>\n",
       "      <td>abc</td>\n",
       "      <td>-9.35000</td>\n",
       "      <td>1</td>\n",
       "      <td>1_match odds_abc v bahia_20150801003000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1123429513</td>\n",
       "      <td>9585.03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mexican Primera Division</td>\n",
       "      <td>0</td>\n",
       "      <td>queretaro v pachuca_20150801</td>\n",
       "      <td>1.438381e+09</td>\n",
       "      <td>1430</td>\n",
       "      <td>1431</td>\n",
       "      <td>1</td>\n",
       "      <td>2.52</td>\n",
       "      <td>11001</td>\n",
       "      <td>queretaro</td>\n",
       "      <td>-9.90000</td>\n",
       "      <td>1</td>\n",
       "      <td>1_match odds_queretaro v pachuca_20150801003000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1119969182</td>\n",
       "      <td>2708.27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      competicion  distancia  \\\n",
       "0  Argentinian Primera B Nacional          0   \n",
       "1    Argentinian Primera Division          0   \n",
       "2            Brazilian Division 2          0   \n",
       "3            Brazilian Division 2          0   \n",
       "4        Mexican Primera Division          0   \n",
       "\n",
       "                       full_description  latest_taken  minuto  minuto_exacto  \\\n",
       "0  central cordoba v chacarita_20150801  1.438379e+09    1430           1430   \n",
       "1         ca temperley v velez_20150801  1.438380e+09    1430           1430   \n",
       "2        paysandu v mogi mirim_20150801  1.438381e+09    1430           1430   \n",
       "3                  abc v bahia_20150801  1.438381e+09    1430           1430   \n",
       "4          queretaro v pachuca_20150801  1.438381e+09    1430           1431   \n",
       "\n",
       "   negocio  odds  operador           player  resultado  sentido  \\\n",
       "0        1  2.50     11001  central cordoba  -20.00000        1   \n",
       "1        1  2.76     11001     ca temperley   15.45016        1   \n",
       "2        1  1.40     11001         paysandu  -17.85000        1   \n",
       "3        1  2.68     11001              abc   -9.35000        1   \n",
       "4        1  2.52     11001        queretaro   -9.90000        1   \n",
       "\n",
       "                                             simbolo  superficie       unico  \\\n",
       "0  1_match odds_central cordoba v chacarita_20150...         NaN  1125369425   \n",
       "1   1_match odds_ca temperley v velez_20150801001000         NaN  1128486276   \n",
       "2  1_match odds_paysandu v mogi mirim_20150801003000         NaN  1119825143   \n",
       "3            1_match odds_abc v bahia_20150801003000         NaN  1123429513   \n",
       "4    1_match odds_queretaro v pachuca_20150801003000         NaN  1119969182   \n",
       "\n",
       "   volume_matched  positivos  \n",
       "0         1602.77          0  \n",
       "1         4856.55          1  \n",
       "2        21249.30          0  \n",
       "3         9585.03          0  \n",
       "4         2708.27          0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### cargamos datos simulacion de excel\n",
    "file = os.path.join(direcciones.temporales,'futbol_inicio_local.xlsx')\n",
    "datos = pd.read_excel(file,sheetname='analisis')\n",
    "datos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "competicion\n",
      "UEFA Europa League               -963.979790\n",
      "Peruvian Primera Division        -877.488580\n",
      "Ligue 2 Orange                   -873.938775\n",
      "Colombian Primera A              -703.340270\n",
      "Ekstraklasa                      -628.498520\n",
      "Slovenian Prva Liga              -608.525270\n",
      "League Two                       -602.921530\n",
      "Spanish Cup                      -560.771250\n",
      "Regionalliga Nord                -558.019610\n",
      "J League                         -525.123140\n",
      "Brazilian Division 2             -505.845305\n",
      "The Championship                 -503.653435\n",
      "Erste Liga                       -492.458380\n",
      "Brazilian Cup                    -491.172380\n",
      "J2 League                        -461.705645\n",
      "Paraguayan Primera               -444.584580\n",
      "Ligue National                   -442.187660\n",
      "Belgian Second Division          -441.555175\n",
      "Scottish Premiership             -424.481040\n",
      "Segunda B                        -416.859580\n",
      "Swedish Cup                      -403.361375\n",
      "Primera Division                 -385.368765\n",
      "Friendlies                       -372.960595\n",
      "AFC Champions League             -369.986295\n",
      "Barclays Premier League          -355.253000\n",
      "Greek Super League               -349.106390\n",
      "Uruguayan Primera                -337.596390\n",
      "Dutch Eredivisie                 -328.929990\n",
      "Croatian Division 1              -310.522405\n",
      "Chilean Primera                  -295.914270\n",
      "Ukrainian U21 League             -292.731635\n",
      "International Friendly           -285.122650\n",
      "3 Liga                           -273.304220\n",
      "Turkish Cup                      -262.076740\n",
      "National League North            -250.823620\n",
      "Lega Pro                         -250.778985\n",
      "Austrian Bundesliga              -236.957800\n",
      "U19 Internationals               -235.574340\n",
      "Primeira Liga                    -235.469700\n",
      "U20 International                -235.199280\n",
      "Finnish Cup                      -228.736225\n",
      "UAE Premier League               -228.117495\n",
      "Belarusian Premier League        -226.836920\n",
      "National League South            -226.370115\n",
      "UEFA U19 Championship            -217.232470\n",
      "Brazilian Goiano                 -213.493570\n",
      "Capital One Cup                  -202.858190\n",
      "Saudi Premier                    -190.695890\n",
      "Argentinian Primera B Nacional   -187.414810\n",
      "Veikkausliiga                    -183.405300\n",
      "Name: resultado, dtype: float64\n",
      "competicion\n",
      "Liga 1                                 118.702860\n",
      "Slovakian Cup                          121.174260\n",
      "Israeli State Cup                      121.653105\n",
      "Brazilian Primeira Liga                124.123090\n",
      "Venezuelan Primera                     125.478000\n",
      "U19 Championship Qualifications        125.614460\n",
      "Icelandic U19 League                   125.810240\n",
      "Calcutta Premier Division              130.006120\n",
      "Brazilian Baiano                       131.829130\n",
      "EFL Trophy                             133.475490\n",
      "Copa Colombia                          134.130880\n",
      "Division 2                             141.069030\n",
      "Taca de Portugal                       147.264650\n",
      "Ukrainian Persha Liga                  151.496455\n",
      "Friendly                               156.263485\n",
      "Superettan                             156.514390\n",
      "Ykkonen                                158.622340\n",
      "Southern Premier                       159.931885\n",
      "Brazilian Paulista A1                  162.577205\n",
      "Swiss Super League                     167.247980\n",
      "Azerbaijan Premier                     170.558225\n",
      "Johnstones Paint Trophy                180.004080\n",
      "Romanian Liga II                       195.326755\n",
      "Airtricity Premier Division            198.734255\n",
      "FIFA World Cup 2018                    200.654230\n",
      "Singapore Cup                          206.614575\n",
      "Argentinian Primera B Metropolitana    207.255670\n",
      "U21 Euro Championships                 218.473120\n",
      "Sao Paulo Youth Cup                    236.392490\n",
      "Polish 1 Liga                          236.568495\n",
      "UEFA U19 Championship Qualifiers       245.910275\n",
      "Italian Cup                            255.861720\n",
      "French Ligue 2                         270.963030\n",
      "Mexican Liga de Ascenso                277.929890\n",
      "Colombian Primera B                    285.043360\n",
      "Argentinian Primera Division           303.792570\n",
      "Airtricity First Division              304.618860\n",
      "Russian Youth League                   314.695025\n",
      "Copa Libertadores                      321.470460\n",
      "SPFL Development League                322.874020\n",
      "Serie A                                326.606525\n",
      "Belgian Jupiler League                 386.244770\n",
      "Campeonato                             396.667635\n",
      "Chinese Super League                   406.751075\n",
      "Costa Rican Primera Division           418.986085\n",
      "Premier Division                       425.343700\n",
      "Norwegian 1 Division                   437.029060\n",
      "AFC Cup                                494.213420\n",
      "UEFA Champions League                  504.646070\n",
      "MLS                                    798.859025\n",
      "Name: resultado, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "### resultados por liga\n",
    "res_liga = datos.pivot_table(index='competicion',values='resultado',aggfunc=np.sum).sort_values()\n",
    "print(res_liga.head(50))\n",
    "print(res_liga.tail(50))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total : -3806.41357\n",
      "apuestas : 14450\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b4765e84a8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b472d56550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### equity por niveles de volumen\n",
    "condiciones = np.logical_and(datos['volume_matched'] >10000,datos['volume_matched'] <=5000000)\n",
    "df = datos[condiciones].copy()\n",
    "#condicion_operador=np.logical_or(df['operador'] ==11001,df['operador'] == 11002)\n",
    "#df = df[condicion_operador].copy()\n",
    "#df=df[df['operador']==11006]\n",
    "#df=df[df['odds']<2.3]\n",
    "#df=df[df['odds']>1.25]\n",
    "#df=df[df['volume_matched']<400000000]\n",
    "df = df.sort_values(by='odds')\n",
    "df.index = range(len(df))\n",
    "print('total : '+str(sum(df['resultado'])))\n",
    "print('apuestas : '+str(len(df['resultado'])))\n",
    "df['suma'] = df['resultado'].cumsum()\n",
    "df['suma'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b474e2d208>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W2qSZ2SJVbPnyhmVmsP768OyzYf+WWwp/n5tugg03DPsvvQT77dfwmB12gMmT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\nOVzfY8DG7t6bMFdsVOo5qpW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b474ecf748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.index = df['odds']\n",
    "df['suma'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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}