OctoBot architecture
Philosophy
The goal behind OctoBot is to have a very fast and scalable trading robot.
To achieve this, OctoBot is entirely built around the asyncio (opens in a new tab) producer-consumer Async-Channel (opens in a new tab) framework which allows to very quickly and efficiently transmit data to different elements within the bot. The idea is all the time maintain fully up-to-date data without having to use update loops (that require have inefficient sleeping time) while also waking up the evaluation chain as quickly as possible when an update is available (without having to wait for any update cycle of any update loop).
Additionally, in order to save CPU time, as little threads as possible are use by OctoBot (usually less than 10 with a standard setup).
Overview
The OctoBot code is split into several repository.
Each module is handled as an independent python module and is available on the
official python package repository (opens in a new tab) (used in pip commands).
Modules are made available as python source modules as well as as compiled modules
which includes cython optimizations. Installing a module on a platform which as
not already been built and made available on pypi.org (opens in a new tab) will
take much more time as pip will cythonize and compile the given module, which
also requires a cpp compiler.
OctoBot
Simplified view of the OctoBot core components.
Inside the OctoBot part, each arrow is an async channel.
OctoBot tentacles
Tentacles are OctoBot's extensions, they are meant to be easily customizable, can be activated or not and do any specific action within OctoBot.
Evaluation chain tentacles
They are tools to analyze market data as well as any other type of data (reddit, telegram, etc). They implement abstract evaluators, strategies and trading modes.
Utility tentacles
These are OctoBot's interfaces (web, telegram), notification systems, social news feeds and backtesting data collectors. They implement abstract interfaces, services, service feeds, notifiers and data collectors
Evaluators, strategies and trading modes:
Evaluators
Simple python classes that will automatically be wake up when new data is available.
Their goal is to set self.eval_note and call await self.evaluation_completed
that will then be made available to the Strategy(ies). They should be dedicated to
a single simple task such as (for example) evaluate the RSI on the current data or
looks for a divergence in a trend.
Strategies
Strategies are more complex elements, they can read all the evaluators evaluations
on every time frame and are considering these evaluations to set their self.eval_note
and call await self.strategy_completed. As a comparison if evaluators are human
senses, strategies are the brain that will take these senses' signals and decide to
do something or not. Strategies can be generic like SimpleStrategyEvaluator that
will take any standard evaluator and time frame into account or using specific
evaluators only like MoveSignalsStrategyEvaluator.
Trading modes
Trading modes use the strategy(ies) evaluations to create, update or cancel orders. Using the strategies signals, they are responsible for the way to translate a signal into an order by looking at the available funds, open orders, considering stop loss or not and other trading related responsibilities.
Triggers
Evaluators, strategies and trading modes are automatically triggered when their channel has a new data. Trigger sources are:
For evaluators
- Technical evaluators: any new candle or refresh request (with updated candles data) from a strategy
- Real time evaluators: any new candle and any market price change
- Social evaluators: associated signal (ex: a post for a reddit social evaluator)
For strategies
-
After a technical evaluator cycle: when all TA have updated their
evaluation and called
await self.evaluation_completed -
After any real time evaluator evaluation and call of
await self.evaluation_completed -
After any social evaluator evaluation and call of
await self.evaluation_completed
For trading mode
-
After any strategy evaluation and call of
await self.strategy_completed
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