betting-env 0.1.0

Description:

bettingenv 0.1.0

betting_env

from IPython.display import HTML
import pandas as pd

from betting_env.config.localconfig import CONFIG,DB_HOSTS
from betting_env.utils.data_extractor import data_aggregator
from betting_env.betting_env import BettingEnv

Install
pip install betting_env

Config
In order to connect to the mongo database we require some connection
parameters defined in toml format and should be read when the library
is loaded. The package will look first under /secrets/config.toml or
in the environment variable BETTING_ENV_CONFIG. An example of config
file is provided with the package and will be used by default. It is the
user’s responsibility to make sure this file is saved at the right
location if you want to use your own.
Simplified betting environment
The punter starts with $N (N>0) in his Bank account and can use them
to place bets on several football games.
He is offered the option to bet on the 2 main markets: 1X2
(home/draw/away) and Asian handicap (we focus on the even line) and is
only allowed to place a small, medium, or big stake on one and
only one of the 5 possible selections home team win, away team win,
or draw (1X2 case) or home or away (Asian handicap) or skip
the betting opportunity. At each step, the punter is presented with some
information about a game and the associated betting opportunities. If he
decides to bet, he receives a reward that could be positive (profit)
or negative (loss of his stake). His balance is then updated
accordingly and he moves to the next step i.e next game. An episode ends
when the punter goes bankrupt (Balance <= 0) or if no more betting
opportunities are available.
Load games
fixtures = data_aggregator(
db_hosts=DB_HOSTS, config=CONFIG, db_host="public_atlas", limit=None
)

Init environment
env = BettingEnv(fixtures)
max_steps_limit = fixtures.shape[0]

Playing random choices
# Init RL env.
env.reset()

# Init done Flag to False.
done = False
# Init loop counter.
i = 0
# Stops when it is done or when we have bet on all provided games.
while not done and i < max_steps_limit:
# Make a step.
obs, reward, done, info = env.step(env.action_space.sample())
# Increment counter.
i = i + 1

HTML('<img src="./images/img_1.gif">')


Playing Medium Stake on Home Team Win (1X2)
# Init RL env.
env.reset()
# Init done Flag to False.
done = False
# Init loop counter.
i = 0
# Stops when it is done or when we have bet on all provided games.
while not done and i < max_steps_limit:
# Make a step.
obs, reward, done, info = env.step(2)
# Increment counter.
i = i + 1

HTML('<img src="./images/img_2.gif">')