TipAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports and Data Munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import sys\n",
    "import pyodbc\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import math\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from collections import OrderedDict\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#make the layout full width\n",
    "from IPython.display import clear_output\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "\n",
    "#prefer locally cached copy of SQL Server data\n",
    "runSql = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the tip data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>RACE</th>\n",
       "      <th>NO.</th>\n",
       "      <th>SELECTION</th>\n",
       "      <th>UNITS INVESTED</th>\n",
       "      <th>MARKET TYPE</th>\n",
       "      <th>W.E.I. ODDS</th>\n",
       "      <th>RESULT</th>\n",
       "      <th>UNITS COLLECTED</th>\n",
       "      <th>P/L</th>\n",
       "      <th>EMAIL ODDS</th>\n",
       "      <th>AVAILABLE ODDS</th>\n",
       "      <th>BEST FIXED EMAIL SENT</th>\n",
       "      <th>BEST FIXED CLOSING</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Place</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Win</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Place</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>HASTINGS</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>GINGERNUTS</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE      EVENT  RACE  NO.     SELECTION  UNITS INVESTED MARKET TYPE  \\\n",
       "0 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.4         Win   \n",
       "1 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.8       Place   \n",
       "2 2017-09-01      WYONG     5    3  WIDE ACCLAIM             0.5         Win   \n",
       "3 2017-09-01      WYONG     5    3  WIDE ACCLAIM             1.0       Place   \n",
       "4 2017-09-02   HASTINGS     7    7    GINGERNUTS             0.4         Win   \n",
       "\n",
       "   W.E.I. ODDS RESULT  UNITS COLLECTED  P/L  EMAIL ODDS  AVAILABLE ODDS  \\\n",
       "0         15.0      0              0.0 -0.4        15.0           16.00   \n",
       "1          5.5      2              4.4  3.6         5.5            6.00   \n",
       "2          8.6      0              0.0 -0.5         8.0            8.50   \n",
       "3          2.7      0              0.0 -1.0         2.7            2.88   \n",
       "4         14.0      0              0.0 -0.4        14.0           17.00   \n",
       "\n",
       "   BEST FIXED EMAIL SENT  BEST FIXED CLOSING  \n",
       "0                   15.0                6.50  \n",
       "1                    5.5                3.10  \n",
       "2                    8.0                8.50  \n",
       "3                    2.7                2.62  \n",
       "4                   14.0                6.00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips= pd.read_csv('JL.csv')\n",
    "tips['DATE'] = pd.to_datetime(tips['DATE'], format=\"%d/%m/%Y\")\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate tip delta and tip delta rounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>RACE</th>\n",
       "      <th>NO.</th>\n",
       "      <th>SELECTION</th>\n",
       "      <th>UNITS INVESTED</th>\n",
       "      <th>MARKET TYPE</th>\n",
       "      <th>W.E.I. ODDS</th>\n",
       "      <th>RESULT</th>\n",
       "      <th>UNITS COLLECTED</th>\n",
       "      <th>P/L</th>\n",
       "      <th>EMAIL ODDS</th>\n",
       "      <th>AVAILABLE ODDS</th>\n",
       "      <th>BEST FIXED EMAIL SENT</th>\n",
       "      <th>BEST FIXED CLOSING</th>\n",
       "      <th>DELTA</th>\n",
       "      <th>DELTA ROUNDED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Place</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.436364</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Win</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Place</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.62</td>\n",
       "      <td>0.029630</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>HASTINGS</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>GINGERNUTS</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE      EVENT  RACE  NO.     SELECTION  UNITS INVESTED MARKET TYPE  \\\n",
       "0 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.4         Win   \n",
       "1 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.8       Place   \n",
       "2 2017-09-01      WYONG     5    3  WIDE ACCLAIM             0.5         Win   \n",
       "3 2017-09-01      WYONG     5    3  WIDE ACCLAIM             1.0       Place   \n",
       "4 2017-09-02   HASTINGS     7    7    GINGERNUTS             0.4         Win   \n",
       "\n",
       "   W.E.I. ODDS RESULT  UNITS COLLECTED  P/L  EMAIL ODDS  AVAILABLE ODDS  \\\n",
       "0         15.0      0              0.0 -0.4        15.0           16.00   \n",
       "1          5.5      2              4.4  3.6         5.5            6.00   \n",
       "2          8.6      0              0.0 -0.5         8.0            8.50   \n",
       "3          2.7      0              0.0 -1.0         2.7            2.88   \n",
       "4         14.0      0              0.0 -0.4        14.0           17.00   \n",
       "\n",
       "   BEST FIXED EMAIL SENT  BEST FIXED CLOSING     DELTA  DELTA ROUNDED  \n",
       "0                   15.0                6.50  0.566667            0.6  \n",
       "1                    5.5                3.10  0.436364            0.4  \n",
       "2                    8.0                8.50 -0.062500           -0.1  \n",
       "3                    2.7                2.62  0.029630            0.0  \n",
       "4                   14.0                6.00  0.571429            0.6  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips['DELTA'] = (tips['BEST FIXED EMAIL SENT'] - tips['BEST FIXED CLOSING']) / tips['BEST FIXED EMAIL SENT']\n",
    "tips['DELTA ROUNDED'] = tips['DELTA'].apply(lambda x: round(x, 1))\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping and display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2257362dbc8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipsGrouped = tips.groupby(['DELTA ROUNDED']).agg({'P/L':'sum','UNITS INVESTED':'sum' }).reset_index()\n",
    "tipsGrouped['POT'] = tipsGrouped['P/L'] / tipsGrouped['UNITS INVESTED']\n",
    "tipsGrouped[(tipsGrouped['DELTA ROUNDED'] > -0.4) & (tipsGrouped['DELTA ROUNDED'] < 0.9)].plot.bar(x='DELTA ROUNDED', y='POT', rot=0, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DELTA ROUNDED</th>\n",
       "      <th>P/L</th>\n",
       "      <th>UNITS INVESTED</th>\n",
       "      <th>POT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>-1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>4.55</td>\n",
       "      <td>-0.410989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>5.10</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-7.48</td>\n",
       "      <td>9.80</td>\n",
       "      <td>-0.763265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>5.26</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.374377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>4.57</td>\n",
       "      <td>22.60</td>\n",
       "      <td>0.202212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-21.77</td>\n",
       "      <td>40.00</td>\n",
       "      <td>-0.544250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-23.63</td>\n",
       "      <td>70.60</td>\n",
       "      <td>-0.334703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-27.23</td>\n",
       "      <td>133.05</td>\n",
       "      <td>-0.204660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-75.50</td>\n",
       "      <td>289.35</td>\n",
       "      <td>-0.260930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-54.22</td>\n",
       "      <td>476.65</td>\n",
       "      <td>-0.113752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.1</td>\n",
       "      <td>28.63</td>\n",
       "      <td>596.90</td>\n",
       "      <td>0.047964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.2</td>\n",
       "      <td>132.05</td>\n",
       "      <td>649.90</td>\n",
       "      <td>0.203185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.3</td>\n",
       "      <td>141.21</td>\n",
       "      <td>478.55</td>\n",
       "      <td>0.295079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.4</td>\n",
       "      <td>159.72</td>\n",
       "      <td>305.70</td>\n",
       "      <td>0.522473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.5</td>\n",
       "      <td>116.17</td>\n",
       "      <td>152.90</td>\n",
       "      <td>0.759778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.6</td>\n",
       "      <td>62.12</td>\n",
       "      <td>58.60</td>\n",
       "      <td>1.060068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.7</td>\n",
       "      <td>20.10</td>\n",
       "      <td>15.50</td>\n",
       "      <td>1.296774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.55</td>\n",
       "      <td>1.064516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DELTA ROUNDED     P/L  UNITS INVESTED       POT\n",
       "0            -2.1   -0.30            0.30 -1.000000\n",
       "1            -1.5   -0.50            0.50 -1.000000\n",
       "2            -1.4   -1.00            1.00 -1.000000\n",
       "3            -1.3   -1.30            1.30 -1.000000\n",
       "4            -1.2   -1.00            1.00 -1.000000\n",
       "5            -1.1   -0.50            0.50 -1.000000\n",
       "6            -1.0    8.20            2.00  4.100000\n",
       "7            -0.9   -1.87            4.55 -0.410989\n",
       "8            -0.8   -5.10            5.10 -1.000000\n",
       "9            -0.7   -7.48            9.80 -0.763265\n",
       "10           -0.6    5.26           14.05  0.374377\n",
       "11           -0.5    4.57           22.60  0.202212\n",
       "12           -0.4  -21.77           40.00 -0.544250\n",
       "13           -0.3  -23.63           70.60 -0.334703\n",
       "14           -0.2  -27.23          133.05 -0.204660\n",
       "15           -0.1  -75.50          289.35 -0.260930\n",
       "16            0.0  -54.22          476.65 -0.113752\n",
       "17            0.1   28.63          596.90  0.047964\n",
       "18            0.2  132.05          649.90  0.203185\n",
       "19            0.3  141.21          478.55  0.295079\n",
       "20            0.4  159.72          305.70  0.522473\n",
       "21            0.5  116.17          152.90  0.759778\n",
       "22            0.6   62.12           58.60  1.060068\n",
       "23            0.7   20.10           15.50  1.296774\n",
       "24            0.8    1.65            1.55  1.064516\n",
       "25            0.9   -0.20            0.20 -1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipsGrouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repeat for under $6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22573c69b08>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipsGrouped = tips[tips['BEST FIXED EMAIL SENT']<= 6].copy().groupby(['DELTA ROUNDED']).agg({'P/L':'sum','UNITS INVESTED':'sum' }).reset_index()\n",
    "tipsGrouped['POT'] = tipsGrouped['P/L'] / tipsGrouped['UNITS INVESTED']\n",
    "\n",
    "tipsGrouped\n",
    "\n",
    "tipsGrouped[(tipsGrouped['DELTA ROUNDED'] > -0.4) & (tipsGrouped['DELTA ROUNDED'] < 0.9)].plot.bar(x='DELTA ROUNDED', y='POT', rot=0, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tip Profit By Implied Prod of Winning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>EVENT</th>\n",
       "      <th>RACE</th>\n",
       "      <th>NO.</th>\n",
       "      <th>SELECTION</th>\n",
       "      <th>UNITS INVESTED</th>\n",
       "      <th>MARKET TYPE</th>\n",
       "      <th>W.E.I. ODDS</th>\n",
       "      <th>RESULT</th>\n",
       "      <th>UNITS COLLECTED</th>\n",
       "      <th>P/L</th>\n",
       "      <th>EMAIL ODDS</th>\n",
       "      <th>AVAILABLE ODDS</th>\n",
       "      <th>BEST FIXED EMAIL SENT</th>\n",
       "      <th>BEST FIXED CLOSING</th>\n",
       "      <th>DELTA</th>\n",
       "      <th>DELTA ROUNDED</th>\n",
       "      <th>ImpliedProd</th>\n",
       "      <th>ImpliedProdRounded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>ASHBURTON</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>MISS SPECIAL</td>\n",
       "      <td>0.8</td>\n",
       "      <td>Place</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.436364</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Win</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>WYONG</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>WIDE ACCLAIM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Place</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.88</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.62</td>\n",
       "      <td>0.029630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2017-09-02</td>\n",
       "      <td>HASTINGS</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>GINGERNUTS</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Win</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE      EVENT  RACE  NO.     SELECTION  UNITS INVESTED MARKET TYPE  \\\n",
       "0 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.4         Win   \n",
       "1 2017-09-01  ASHBURTON     3    6  MISS SPECIAL             0.8       Place   \n",
       "2 2017-09-01      WYONG     5    3  WIDE ACCLAIM             0.5         Win   \n",
       "3 2017-09-01      WYONG     5    3  WIDE ACCLAIM             1.0       Place   \n",
       "4 2017-09-02   HASTINGS     7    7    GINGERNUTS             0.4         Win   \n",
       "\n",
       "   W.E.I. ODDS RESULT  UNITS COLLECTED  P/L  EMAIL ODDS  AVAILABLE ODDS  \\\n",
       "0         15.0      0              0.0 -0.4        15.0           16.00   \n",
       "1          5.5      2              4.4  3.6         5.5            6.00   \n",
       "2          8.6      0              0.0 -0.5         8.0            8.50   \n",
       "3          2.7      0              0.0 -1.0         2.7            2.88   \n",
       "4         14.0      0              0.0 -0.4        14.0           17.00   \n",
       "\n",
       "   BEST FIXED EMAIL SENT  BEST FIXED CLOSING     DELTA  DELTA ROUNDED  \\\n",
       "0                   15.0                6.50  0.566667            0.6   \n",
       "1                    5.5                3.10  0.436364            0.4   \n",
       "2                    8.0                8.50 -0.062500           -0.1   \n",
       "3                    2.7                2.62  0.029630            0.0   \n",
       "4                   14.0                6.00  0.571429            0.6   \n",
       "\n",
       "   ImpliedProd  ImpliedProdRounded  \n",
       "0     0.066667                 0.1  \n",
       "1     0.181818                 0.2  \n",
       "2     0.125000                 0.1  \n",
       "3     0.370370                 0.4  \n",
       "4     0.071429                 0.1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['ImpliedProd'] = 1 / tips['BEST FIXED EMAIL SENT']\n",
    "\n",
    "#0.05 unit increments\n",
    "tips['ImpliedProdDoubled'] = tips['ImpliedProd']* 2\n",
    "tips['ImpliedProdDoubleRounded'] = tips['ImpliedProdDoubled'].apply(lambda x: round(x, 1))\n",
    "tips['ImpliedProdRounded'] = tips['ImpliedProdDoubleRounded'] / 2\n",
    "\n",
    "tipsGroupedByImpliedProb = tips.groupby(['ImpliedProdRounded']).agg({'P/L':'sum','UNITS INVESTED':'sum' }).reset_index()\n",
    "tipsGroupedByImpliedProb['POT'] = tipsGroupedByImpliedProb['P/L'] / tipsGroupedByImpliedProb['UNITS INVESTED']\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(15,8)) # Create matplotlib figure\n",
    "ax = fig.add_subplot(111)\n",
    "ax1 = ax.twinx()\n",
    "\n",
    "pot = tipsGroupedByImpliedProb.plot.bar(x='ImpliedProdRounded', y='POT', ax = ax, rot=0, position=1, label = 'POT', width = 0.3)\n",
    "ui = tipsGroupedByImpliedProb.plot.bar(x='ImpliedProdRounded', y='UNITS INVESTED', ax = ax1, rot=0, color='r', label = 'Total Units', position=0, width = 0.3)\n",
    "\n",
    "ax.get_legend().remove()\n",
    "ax1.get_legend().remove()\n",
    "\n",
    "ax.set_xlabel(\"Implied Prob (Win)\")\n",
    "ax.set_ylim(0,0.6)\n",
    "ax.set_ylabel(\"POT\")\n",
    "ax1.set_ylabel(\"Total Units\")\n",
    "fig.legend()\n",
    "\n",
    "ax.axhline(y=0)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}