Lightweight, super fast (uses c libraries) pythonic wrapper for Betfair API-NG allowing all betting operations (including market and order streaming) and most account operations, see examples.
Currently tested on Python 2.7, 3.4, 3.5 and 3.6.
$ pip install betfairlightweight
Add your certificates to '/certs/' and app_key (optional) to environment variables with username as key before using.
.bash_profile
export username = "appkey"
The library can then be used as follows:
>>> import betfairlightweight
>>> trading = betfairlightweight.APIClient('username', 'password', app_key='app_key')
>>> trading.login()>>> event_types = trading.betting.list_event_types()
[<EventTypeResult>, <EventTypeResult>, ..]Following endpoints are available:
>>> trading.login
>>> trading.keep_alive
>>> trading.logout
>>> trading.betting
>>> trading.account
>>> trading.navigation
>>> trading.scores
>>> trading.streaming
>>> trading.in_play_service
>>> trading.race_cardCurrently two listeners available, below will run the base listener which prints anything it receives. Stream listener is able to hold an order stream or a market stream (one per listener). The listener can hold a cache and push market_books/order_books out via a queue.
In development so breaking changes likely.
from betfairlightweight.filters import (
streaming_market_filter,
streaming_market_data_filter,
)
betfair_socket = trading.streaming.create_stream(
unique_id=0,
description='Test Market Socket',
)
market_filter = streaming_market_filter(
event_type_ids=['7'],
country_codes=['IE'],
market_types=['WIN'],
)
market_data_filter = streaming_market_data_filter(
fields=['EX_ALL_OFFERS', 'EX_MARKET_DEF'],
ladder_levels=3
)
betfair_socket.subscribe_to_markets(
market_filter=market_filter,
market_data_filter=market_data_filter,
)
betfair_socket.start(async=False)Taking advantage of the streaming code lightweight can parse/output historical data in the same way it process streaming data allowing backtesting or with a custom listener, csv creation (see examples).
In development so breaking changes likely.
stream = trading.streaming.create_historical_stream(
directory='horse-racing-pro-sample',
)
stream.start(async=False)