rbot
is a python crypt trading bot framework written in Rust.
The feature is;
- Your Bot can be executed in three mode
backtest
,dry_run
andproduction
without modification. - TICK BASED backtesting.
- Written in Rust and Provided as Python plugin. So you can write your own bot in Python with super efficent Rust back end.
OHLCVのTimestampの型をi64からDateTimeへ変更(->Agentに影響あり)
約定ログの保存方式をSQLiteからparquetへ変更(ファイルサイズが5分の1へ縮小)
注意:過去にダウンロードした約定ログは使えません。DBディレクトリごと削除し、再度ダウンロードしてください。
DownloadとBackテストにProgressBarを導入
#244 立ち上げ直後に最初のTickでon_clockがよびだされていたのを修正
#255 Session.ohlcvで指定した本数よりも多く足が計算されていたのを修正。
バックテスト以外のDryRun,RealRunは修正後テストしていません。
一旦このβリリースでは動かないものと考えてください。
In the backtest mode, you can run with Jupyter notebook. So, you can make such analysis below. This example is provided in the github, which can be executed on the Google colab!! Please try.
sample1: basil bot sample2: breakout bot
The only you have to make is Agent
. The other class will support you Agent will work.
Session
will provide abstruction layer to the Market. Agent only interact withSession
for ordering, fetching market information and current order status.Market
is the API and DB wrapper for each trading pair in the exchange. At this versionBybit
andBiannce
exchange are supportd.Runner
makes session to runback_test
,dry_run
orreal_run
.Logger
stores the result of execution. That provide the result in polarsDataFrame
.
$ pip install rbot
from rbot import Bybit, BybitConfig
exchange = Bybit(production=True)
config = BybitConfig.BTCUSDT # use BTC/USDT pair
market = exchange.open_market(config)
you can find the locatin of data base.
market.file_name
In case you want to alter the location of db, you can specify the path by environment variable RBOT_DB_ROOT
.
Ordering is disabled by default. You can enable it by setting enable_order_with_my_own_risk
to True
.
exchange.enable_order_with_my_own_risk = True
market.download_archive(
ndays=1, # specify from past days
force=False, # if false, the cache data will be used.
verbose=True # verbose to print download progress.
)
ohlcv = market.ohlcv(
start_time=0, # start time in unix timestamp(microseconds)
end_time=0, # end time in unix timestamp(microseconds)
window_sec=60 # ohlc bar window size in seconds
)
The OHLCV data is a polars data frames with the following columns:
- timestamp (in asc order)
- open
- high
- low
- close
- volume (sum of volume in order size)
- count (number of ticks)
if you want to use it as pandas dataframe, you can convert it by the following code.
pandas_df = ohlcv.to_pandas(use_pyarrow_extension_array=True)
You can make Bot class with any kind of names. Only rules is you must implement on_init
, on_tick
, on_clock
, and on_update
, if you want to recieve such event(It is not necessary to implement all of them).
Each method receives session
object. You can use it to get market information, place order, cancel order, etc.
class SkeltonAgent: # you can use any names for trading bot agent / クラス名は任意です
def on_init(self, session):
"""
Bot initialization process. Bot initialization time. Called once at the start of the bot.
It is best place to seting up session.clock_interval_sec which is interval of on_clock call.
Args:
session: Session class
"""
session.clock_interval_sec = 60 * 60 * 1 # 1時間ごとにon_clockを呼び出す
def on_tick(self, session, side, price, size):
"""
If you implement this method, you can receive all tick data from exchange.
Args:
session: Session object (that can be used to order and get market information)
side: "Sell" or "Order"
price: executed price of tick
size: executed size of the tick
"""
pass
def on_clock(self, session, clock):
"""
If you implement this method and seting up session.clock_interval_sec,
you can receive clock event in specified interval.
Args:
session: Session object(that can be used to order or get market information)
clock: Unix time stamp in micro seconds.
"""
pass
def on_update(self, session, updated_order):
"""
If your order's status is changed, this method is called.
Args:
session: Session object
updated_order: Updated order
"""
pass
In your on_init
, on_tick
, on_clock
, and on_update
, you can use session
object to get market information, place order, cancel order, etc.
ohlcv = session.ohlcv(
interval=60, # ohlc bar window size in seconds
count=10, # number of bars to generate
)
The OHLCV data is a polars data frames with the following columns:
- timestamp (in asc order)
- open
- high
- low
- close
- volume (sum of volume in order size)
- count (number of ticks)
bid, ask = session.board
bid
, ask
are polars dataframes with the following columns:
- price: price of the order
- size: size of the order
- sum: cumulative size of the order
size = 0.001
market_order = session.market_order("BUY", size)
price = 50000.0
size = 0.001
sell_limit_order = session.limit_order("SELL", price, size)
cancelled_order =session.cancel_order(config, id_to_cancel)
You can get the order queue status(Limit orders that is not fullfied)
buy_orders = session.buy_orders
sell_orders = session.sell_orders
Expire the order in the order queue.
expire_time = 60 * 60 # expire older than 1H
session.expire_order(expire_time)
calculate psudo-position from the session starts.
position = session.position
You can run Agent without modification in three modes; backtest
, dry_run
, and production
.
from rbot import Runner
agent = SkeltonAgent()
runner = Runner()
session = runner.back_test(
exchange=exchange, # exchange object
market=market, # market object
agent=agent, # agent object
start_time=0, # start time in unix timestamp(microseconds)` 0 means from the beginig of DB
end_time=0, # end time in unix timestamp(microseconds) 0 means to the end of the DB
verbose=True # verbose to print progress.
)
from rbot import Runner
agent = SkeltonAgent()
runner = Runner()
session = runner.dry_run(
exchange=exchange, # exchange object
market=market, # market object
agent=agent, # agent object
execute_time = 60, # Time(Seconds) to execute
verbose=True # verbose to print progress.
)
from rbot import Runner
agent = SkeltonAgent()
runner = Runner()
session = runner.real_run(
exchange=exchange, # exchange object
market=market, # market object
agent=agent, # agent object
#execute_time = 60, # execute time, if not set, it runs forever
verbose=True,
log_file="skelton_bot.log"
)
you can get orders dataframe list by session.orders
.
- log_id
- symbol
- update_time
- create_time
- status
- order_id
- client_order_id
- order_side
- order_type
- order_price
- order_size
- remain_size
- transaction_id
- execute_price
- execute_size
- quote_vol
- commission
- commission_asset
- is_maker
- message
- commission_home
- commission_foreign
- home_change
- foreign_change
- free_home_change
- free_foreign_change
- lock_home_change
- lock_foreign_change
- open_position
- close_position
- position
- profit
- fee
- total_profit
- sum_profit
for real run mode, you can retreive log as below
from rbot import Logger
log = Logger()
log.restore("skelton_bot.log")
- Exchange and market is separated. So you must make Exchange(such as
Bybit
) first, thenopen_market
to build market object. Market#download
is separated into threeMarket#download_archive
,Market#download_latest
andMarket#donwload_gap
Runner#real_run
andRunner#dry_run
now haveno_download
optional flag.- Support
Bybit
exchange.
copyright(c) 2024 yasstake. All rights reserved. Distributed under LGPL license. NOTE: For some echange, it may have a such kind of an affliate link.