Backtest#

[1]:

import matplotlib.pyplot as plt
import pandas as pd

import penaltyblog as pb

Get data from football-data.co.uk#

[2]:

df = pd.concat(
    [
        pb.scrapers.FootballData("ENG Premier League", "2019-2020").get_fixtures(),
        pb.scrapers.FootballData("ENG Premier League", "2020-2021").get_fixtures(),
        pb.scrapers.FootballData("ENG Premier League", "2021-2022").get_fixtures(),
    ]
)

df

[2]:
date datetime season competition div time team_home team_away fthg ftag ... b365_cahh b365_caha pcahh pcaha max_cahh max_caha avg_cahh avg_caha goals_home goals_away
id
1565308800---liverpool---norwich 2019-08-09 2019-08-09 20:00:00 2019-2020 ENG Premier League E0 20:00 Liverpool Norwich 4 1 ... 1.91 1.99 1.94 1.98 1.99 2.07 1.90 1.99 4 1
1565395200---bournemouth---sheffield_united 2019-08-10 2019-08-10 15:00:00 2019-2020 ENG Premier League E0 15:00 Bournemouth Sheffield United 1 1 ... 1.95 1.95 1.98 1.95 2.00 1.96 1.96 1.92 1 1
1565395200---burnley---southampton 2019-08-10 2019-08-10 15:00:00 2019-2020 ENG Premier League E0 15:00 Burnley Southampton 3 0 ... 1.87 2.03 1.89 2.03 1.90 2.07 1.86 2.02 3 0
1565395200---crystal_palace---everton 2019-08-10 2019-08-10 15:00:00 2019-2020 ENG Premier League E0 15:00 Crystal Palace Everton 0 0 ... 1.82 2.08 1.97 1.96 2.03 2.08 1.96 1.93 0 0
1565395200---tottenham---aston_villa 2019-08-10 2019-08-10 17:30:00 2019-2020 ENG Premier League E0 17:30 Tottenham Aston Villa 3 1 ... 2.10 1.70 2.18 1.77 2.21 1.87 2.08 1.80 3 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1653177600---crystal_palace---man_united 2022-05-22 2022-05-22 16:00:00 2021-2022 ENG Premier League E0 16:00 Crystal Palace Man United 1 0 ... 1.68 2.15 1.74 2.23 1.88 2.25 1.74 2.16 1 0
1653177600---leicester---southampton 2022-05-22 2022-05-22 16:00:00 2021-2022 ENG Premier League E0 16:00 Leicester Southampton 4 1 ... 1.83 2.07 1.88 2.03 1.94 2.26 1.87 2.01 4 1
1653177600---liverpool---wolves 2022-05-22 2022-05-22 16:00:00 2021-2022 ENG Premier League E0 16:00 Liverpool Wolves 3 1 ... 2.02 1.77 2.06 1.83 2.19 1.99 2.07 1.80 3 1
1653177600---man_city---aston_villa 2022-05-22 2022-05-22 16:00:00 2021-2022 ENG Premier League E0 16:00 Man City Aston Villa 3 2 ... 2.06 1.84 2.05 1.86 2.09 2.03 2.01 1.87 3 2
1653177600---norwich---tottenham 2022-05-22 2022-05-22 16:00:00 2021-2022 ENG Premier League E0 16:00 Norwich Tottenham 0 5 ... 1.95 1.95 1.96 1.94 2.16 1.98 1.99 1.89 0 5

1140 rows × 111 columns

Create simple logic to make a bet where odds are between 2.5 and 4.0#

[3]:

def logic(ctx):
    fixture = ctx.fixture
    account = ctx.account

    if 2.5 <= fixture["b365_a"] <= 4.0:
        account.place_bet(
            fixture["b365_a"],
            account.current_bankroll * 0.025,
            1 if ctx.fixture["ftr"] == "A" else 0,
        )


backtest = pb.backtest.Backtest(df, "2020-04-01", "2022-06-01", True)
backtest.start(100, logic)
backtest.results()

[3]:

{'Total Bets': 260,
 'Successful Bets': 93,
 'Successful Bet %': 35.76923076923077,
 'Max Bankroll': np.float64(297.5066099204424),
 'Min Bankroll': np.float64(99.0982388488196),
 'Profit': 68.73465869630138,
 'ROI': 68.73465869630138}

[4]:

plt.plot(backtest.account.tracker)

[4]:

[<matplotlib.lines.Line2D at 0x12da5a710>]
../_images/backtest_backtest_6_1.png

Create logic to train a model and place a bet when kelly criterion is positive#

[5]:

def trainer(ctx):
    weights = pb.models.dixon_coles_weights(ctx.lookback["date"], 0.001)

    model = pb.models.DixonColesGoalModel(
        teams_home=ctx.lookback["team_home"],
        teams_away=ctx.lookback["team_away"],
        goals_home=ctx.lookback["goals_home"],
        goals_away=ctx.lookback["goals_away"],
        weights=weights,
    )

    model.fit()

    return model


def logic(ctx):
    fixture = ctx.fixture
    account = ctx.account
    model = ctx.model

    pred = model.predict(fixture["team_home"], fixture["team_away"])

    kc = pb.kelly.criterion(fixture["b365_h"], pred.home_win, 0.3)
    if kc > 0:
        account.place_bet(
            fixture["b365_h"],
            account.current_bankroll * kc,
            1 if ctx.fixture["ftr"] == "H" else 0,
        )

    kc = pb.kelly.criterion(fixture["b365_a"], pred.away_win, 0.3)
    if kc > 0:
        account.place_bet(
            fixture["b365_a"],
            account.current_bankroll * kc,
            1 if ctx.fixture["ftr"] == "A" else 0,
        )

    kc = pb.kelly.criterion(fixture["b365_d"], pred.draw, 0.3)
    if kc > 0:
        account.place_bet(
            fixture["b365_d"],
            account.current_bankroll * kc,
            1 if ctx.fixture["ftr"] == "D" else 0,
        )

[6]:

backtest = pb.backtest.Backtest(df, "2022-01-01", "2022-05-01", True)
backtest.start(100, logic, trainer)
backtest.results()

[6]:

{'Total Bets': 155,
 'Successful Bets': 54,
 'Successful Bet %': 34.83870967741935,
 'Max Bankroll': np.float64(129.67923829869412),
 'Min Bankroll': np.float64(63.39116281234852),
 'Profit': np.float64(-21.079029724162538),
 'ROI': np.float64(-21.079029724162538)}

[7]:

plt.plot(backtest.account.tracker)

[7]:

[<matplotlib.lines.Line2D at 0x12db90410>]
../_images/backtest_backtest_10_1.png