Backtest

[1]:

import sys

sys.path.append("../../")

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': 297.5066099204424,
 'Min Bankroll': 99.0982388488196,
 'Profit': 68.73465869630138,
 'ROI': 68.73465869630138}

[4]:

plt.plot(backtest.account.tracker)

[4]:

[<matplotlib.lines.Line2D at 0x2841636a0>]

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': 129.66675196285382,
 'Min Bankroll': 63.38512933669685,
 'Profit': -21.086198588073273,
 'ROI': -21.086198588073273}

[7]:

plt.plot(backtest.account.tracker)

[7]:

[<matplotlib.lines.Line2D at 0x2857ed2d0>]