{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81215b75-f5f8-4c17-9cd4-08b1b0ed4234",
   "metadata": {},
   "source": [
    "# Backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f931497-c1f9-4cb4-969a-058676e42a24",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "import penaltyblog as pb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1b5c76-8f47-4f59-8351-d5add2f69309",
   "metadata": {},
   "source": [
    "## Get data from football-data.co.uk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "949b129d-e4e5-4975-8318-dd601d918e90",
   "metadata": {
    "tags": []
   },
   "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>datetime</th>\n",
       "      <th>season</th>\n",
       "      <th>competition</th>\n",
       "      <th>div</th>\n",
       "      <th>time</th>\n",
       "      <th>team_home</th>\n",
       "      <th>team_away</th>\n",
       "      <th>fthg</th>\n",
       "      <th>ftag</th>\n",
       "      <th>...</th>\n",
       "      <th>b365_cahh</th>\n",
       "      <th>b365_caha</th>\n",
       "      <th>pcahh</th>\n",
       "      <th>pcaha</th>\n",
       "      <th>max_cahh</th>\n",
       "      <th>max_caha</th>\n",
       "      <th>avg_cahh</th>\n",
       "      <th>avg_caha</th>\n",
       "      <th>goals_home</th>\n",
       "      <th>goals_away</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1565308800---liverpool---norwich</th>\n",
       "      <td>2019-08-09</td>\n",
       "      <td>2019-08-09 20:00:00</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>20:00</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Norwich</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.91</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.99</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565395200---bournemouth---sheffield_united</th>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>2019-08-10 15:00:00</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Bournemouth</td>\n",
       "      <td>Sheffield United</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565395200---burnley---southampton</th>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>2019-08-10 15:00:00</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.89</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565395200---crystal_palace---everton</th>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>2019-08-10 15:00:00</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Everton</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.96</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1565395200---tottenham---aston_villa</th>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>2019-08-10 17:30:00</td>\n",
       "      <td>2019-2020</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>17:30</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653177600---crystal_palace---man_united</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2022-05-22 16:00:00</td>\n",
       "      <td>2021-2022</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>Man United</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.23</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.74</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653177600---leicester---southampton</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2022-05-22 16:00:00</td>\n",
       "      <td>2021-2022</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.01</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653177600---liverpool---wolves</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2022-05-22 16:00:00</td>\n",
       "      <td>2021-2022</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Wolves</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.02</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653177600---man_city---aston_villa</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2022-05-22 16:00:00</td>\n",
       "      <td>2021-2022</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Man City</td>\n",
       "      <td>Aston Villa</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.84</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.09</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.87</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653177600---norwich---tottenham</th>\n",
       "      <td>2022-05-22</td>\n",
       "      <td>2022-05-22 16:00:00</td>\n",
       "      <td>2021-2022</td>\n",
       "      <td>ENG Premier League</td>\n",
       "      <td>E0</td>\n",
       "      <td>16:00</td>\n",
       "      <td>Norwich</td>\n",
       "      <td>Tottenham</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.16</td>\n",
       "      <td>1.98</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1140 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  date            datetime  \\\n",
       "id                                                                           \n",
       "1565308800---liverpool---norwich            2019-08-09 2019-08-09 20:00:00   \n",
       "1565395200---bournemouth---sheffield_united 2019-08-10 2019-08-10 15:00:00   \n",
       "1565395200---burnley---southampton          2019-08-10 2019-08-10 15:00:00   \n",
       "1565395200---crystal_palace---everton       2019-08-10 2019-08-10 15:00:00   \n",
       "1565395200---tottenham---aston_villa        2019-08-10 2019-08-10 17:30:00   \n",
       "...                                                ...                 ...   \n",
       "1653177600---crystal_palace---man_united    2022-05-22 2022-05-22 16:00:00   \n",
       "1653177600---leicester---southampton        2022-05-22 2022-05-22 16:00:00   \n",
       "1653177600---liverpool---wolves             2022-05-22 2022-05-22 16:00:00   \n",
       "1653177600---man_city---aston_villa         2022-05-22 2022-05-22 16:00:00   \n",
       "1653177600---norwich---tottenham            2022-05-22 2022-05-22 16:00:00   \n",
       "\n",
       "                                                season         competition  \\\n",
       "id                                                                           \n",
       "1565308800---liverpool---norwich             2019-2020  ENG Premier League   \n",
       "1565395200---bournemouth---sheffield_united  2019-2020  ENG Premier League   \n",
       "1565395200---burnley---southampton           2019-2020  ENG Premier League   \n",
       "1565395200---crystal_palace---everton        2019-2020  ENG Premier League   \n",
       "1565395200---tottenham---aston_villa         2019-2020  ENG Premier League   \n",
       "...                                                ...                 ...   \n",
       "1653177600---crystal_palace---man_united     2021-2022  ENG Premier League   \n",
       "1653177600---leicester---southampton         2021-2022  ENG Premier League   \n",
       "1653177600---liverpool---wolves              2021-2022  ENG Premier League   \n",
       "1653177600---man_city---aston_villa          2021-2022  ENG Premier League   \n",
       "1653177600---norwich---tottenham             2021-2022  ENG Premier League   \n",
       "\n",
       "                                            div   time       team_home  \\\n",
       "id                                                                       \n",
       "1565308800---liverpool---norwich             E0  20:00       Liverpool   \n",
       "1565395200---bournemouth---sheffield_united  E0  15:00     Bournemouth   \n",
       "1565395200---burnley---southampton           E0  15:00         Burnley   \n",
       "1565395200---crystal_palace---everton        E0  15:00  Crystal Palace   \n",
       "1565395200---tottenham---aston_villa         E0  17:30       Tottenham   \n",
       "...                                          ..    ...             ...   \n",
       "1653177600---crystal_palace---man_united     E0  16:00  Crystal Palace   \n",
       "1653177600---leicester---southampton         E0  16:00       Leicester   \n",
       "1653177600---liverpool---wolves              E0  16:00       Liverpool   \n",
       "1653177600---man_city---aston_villa          E0  16:00        Man City   \n",
       "1653177600---norwich---tottenham             E0  16:00         Norwich   \n",
       "\n",
       "                                                    team_away  fthg  ftag  \\\n",
       "id                                                                          \n",
       "1565308800---liverpool---norwich                      Norwich     4     1   \n",
       "1565395200---bournemouth---sheffield_united  Sheffield United     1     1   \n",
       "1565395200---burnley---southampton                Southampton     3     0   \n",
       "1565395200---crystal_palace---everton                 Everton     0     0   \n",
       "1565395200---tottenham---aston_villa              Aston Villa     3     1   \n",
       "...                                                       ...   ...   ...   \n",
       "1653177600---crystal_palace---man_united           Man United     1     0   \n",
       "1653177600---leicester---southampton              Southampton     4     1   \n",
       "1653177600---liverpool---wolves                        Wolves     3     1   \n",
       "1653177600---man_city---aston_villa               Aston Villa     3     2   \n",
       "1653177600---norwich---tottenham                    Tottenham     0     5   \n",
       "\n",
       "                                             ... b365_cahh  b365_caha  pcahh  \\\n",
       "id                                           ...                               \n",
       "1565308800---liverpool---norwich             ...      1.91       1.99   1.94   \n",
       "1565395200---bournemouth---sheffield_united  ...      1.95       1.95   1.98   \n",
       "1565395200---burnley---southampton           ...      1.87       2.03   1.89   \n",
       "1565395200---crystal_palace---everton        ...      1.82       2.08   1.97   \n",
       "1565395200---tottenham---aston_villa         ...      2.10       1.70   2.18   \n",
       "...                                          ...       ...        ...    ...   \n",
       "1653177600---crystal_palace---man_united     ...      1.68       2.15   1.74   \n",
       "1653177600---leicester---southampton         ...      1.83       2.07   1.88   \n",
       "1653177600---liverpool---wolves              ...      2.02       1.77   2.06   \n",
       "1653177600---man_city---aston_villa          ...      2.06       1.84   2.05   \n",
       "1653177600---norwich---tottenham             ...      1.95       1.95   1.96   \n",
       "\n",
       "                                            pcaha max_cahh  max_caha  \\\n",
       "id                                                                     \n",
       "1565308800---liverpool---norwich             1.98     1.99      2.07   \n",
       "1565395200---bournemouth---sheffield_united  1.95     2.00      1.96   \n",
       "1565395200---burnley---southampton           2.03     1.90      2.07   \n",
       "1565395200---crystal_palace---everton        1.96     2.03      2.08   \n",
       "1565395200---tottenham---aston_villa         1.77     2.21      1.87   \n",
       "...                                           ...      ...       ...   \n",
       "1653177600---crystal_palace---man_united     2.23     1.88      2.25   \n",
       "1653177600---leicester---southampton         2.03     1.94      2.26   \n",
       "1653177600---liverpool---wolves              1.83     2.19      1.99   \n",
       "1653177600---man_city---aston_villa          1.86     2.09      2.03   \n",
       "1653177600---norwich---tottenham             1.94     2.16      1.98   \n",
       "\n",
       "                                             avg_cahh  avg_caha  goals_home  \\\n",
       "id                                                                            \n",
       "1565308800---liverpool---norwich                 1.90      1.99           4   \n",
       "1565395200---bournemouth---sheffield_united      1.96      1.92           1   \n",
       "1565395200---burnley---southampton               1.86      2.02           3   \n",
       "1565395200---crystal_palace---everton            1.96      1.93           0   \n",
       "1565395200---tottenham---aston_villa             2.08      1.80           3   \n",
       "...                                               ...       ...         ...   \n",
       "1653177600---crystal_palace---man_united         1.74      2.16           1   \n",
       "1653177600---leicester---southampton             1.87      2.01           4   \n",
       "1653177600---liverpool---wolves                  2.07      1.80           3   \n",
       "1653177600---man_city---aston_villa              2.01      1.87           3   \n",
       "1653177600---norwich---tottenham                 1.99      1.89           0   \n",
       "\n",
       "                                             goals_away  \n",
       "id                                                       \n",
       "1565308800---liverpool---norwich                      1  \n",
       "1565395200---bournemouth---sheffield_united           1  \n",
       "1565395200---burnley---southampton                    0  \n",
       "1565395200---crystal_palace---everton                 0  \n",
       "1565395200---tottenham---aston_villa                  1  \n",
       "...                                                 ...  \n",
       "1653177600---crystal_palace---man_united              0  \n",
       "1653177600---leicester---southampton                  1  \n",
       "1653177600---liverpool---wolves                       1  \n",
       "1653177600---man_city---aston_villa                   2  \n",
       "1653177600---norwich---tottenham                      5  \n",
       "\n",
       "[1140 rows x 111 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(\n",
    "    [\n",
    "        pb.scrapers.FootballData(\"ENG Premier League\", \"2019-2020\").get_fixtures(),\n",
    "        pb.scrapers.FootballData(\"ENG Premier League\", \"2020-2021\").get_fixtures(),\n",
    "        pb.scrapers.FootballData(\"ENG Premier League\", \"2021-2022\").get_fixtures(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb84d3e-2e44-4de9-a1e1-221108b42cfe",
   "metadata": {},
   "source": [
    "## Create simple logic to make a bet where odds are between 2.5 and 4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8adf243e-e3e7-4c0c-b126-3643a0f1f5f4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2714d3421f4449738601b3922045bb42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/291 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Total Bets': 260,\n",
       " 'Successful Bets': 93,\n",
       " 'Successful Bet %': 35.76923076923077,\n",
       " 'Max Bankroll': np.float64(297.5066099204424),\n",
       " 'Min Bankroll': np.float64(99.0982388488196),\n",
       " 'Profit': 68.73465869630138,\n",
       " 'ROI': 68.73465869630138}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def logic(ctx):\n",
    "    fixture = ctx.fixture\n",
    "    account = ctx.account\n",
    "\n",
    "    if 2.5 <= fixture[\"b365_a\"] <= 4.0:\n",
    "        account.place_bet(\n",
    "            fixture[\"b365_a\"],\n",
    "            account.current_bankroll * 0.025,\n",
    "            1 if ctx.fixture[\"ftr\"] == \"A\" else 0,\n",
    "        )\n",
    "\n",
    "\n",
    "backtest = pb.backtest.Backtest(df, \"2020-04-01\", \"2022-06-01\", True)\n",
    "backtest.start(100, logic)\n",
    "backtest.results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14e8670b-1874-43ab-8650-6645a510232d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12da5a710>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(backtest.account.tracker)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc5d91c-f9b8-4572-a944-6a2ced05fd84",
   "metadata": {},
   "source": [
    "## Create logic to train a model and place a bet when kelly criterion is positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a13cc871-401a-4ce4-82c3-c612f9f981be",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def trainer(ctx):\n",
    "    weights = pb.models.dixon_coles_weights(ctx.lookback[\"date\"], 0.001)\n",
    "\n",
    "    model = pb.models.DixonColesGoalModel(\n",
    "        teams_home=ctx.lookback[\"team_home\"],\n",
    "        teams_away=ctx.lookback[\"team_away\"],\n",
    "        goals_home=ctx.lookback[\"goals_home\"],\n",
    "        goals_away=ctx.lookback[\"goals_away\"],\n",
    "        weights=weights,\n",
    "    )\n",
    "\n",
    "    model.fit()\n",
    "\n",
    "    return model\n",
    "\n",
    "\n",
    "def logic(ctx):\n",
    "    fixture = ctx.fixture\n",
    "    account = ctx.account\n",
    "    model = ctx.model\n",
    "\n",
    "    pred = model.predict(fixture[\"team_home\"], fixture[\"team_away\"])\n",
    "\n",
    "    kc = pb.kelly.criterion(fixture[\"b365_h\"], pred.home_win, 0.3)\n",
    "    if kc > 0:\n",
    "        account.place_bet(\n",
    "            fixture[\"b365_h\"],\n",
    "            account.current_bankroll * kc,\n",
    "            1 if ctx.fixture[\"ftr\"] == \"H\" else 0,\n",
    "        )\n",
    "\n",
    "    kc = pb.kelly.criterion(fixture[\"b365_a\"], pred.away_win, 0.3)\n",
    "    if kc > 0:\n",
    "        account.place_bet(\n",
    "            fixture[\"b365_a\"],\n",
    "            account.current_bankroll * kc,\n",
    "            1 if ctx.fixture[\"ftr\"] == \"A\" else 0,\n",
    "        )\n",
    "\n",
    "    kc = pb.kelly.criterion(fixture[\"b365_d\"], pred.draw, 0.3)\n",
    "    if kc > 0:\n",
    "        account.place_bet(\n",
    "            fixture[\"b365_d\"],\n",
    "            account.current_bankroll * kc,\n",
    "            1 if ctx.fixture[\"ftr\"] == \"D\" else 0,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c33889f4-0377-4cbc-afde-655ccc1b11c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f41f9ea575840b691a87ab04094cc27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/58 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'Total Bets': 155,\n",
       " 'Successful Bets': 54,\n",
       " 'Successful Bet %': 34.83870967741935,\n",
       " 'Max Bankroll': np.float64(129.67923829869412),\n",
       " 'Min Bankroll': np.float64(63.39116281234852),\n",
       " 'Profit': np.float64(-21.079029724162538),\n",
       " 'ROI': np.float64(-21.079029724162538)}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backtest = pb.backtest.Backtest(df, \"2022-01-01\", \"2022-05-01\", True)\n",
    "backtest.start(100, logic, trainer)\n",
    "backtest.results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c9accef-5b79-44cf-be61-4f8ef4fd1990",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12db90410>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(backtest.account.tracker)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}