OctoBot GitHub repositories
OctoBot code is split into multiple repositories, all hosted under the Drakkar-Software (opens in a new tab) organisation on GitHub.
- https://github.com/Drakkar-Software/OctoBot (opens in a new tab) (dev branch) for the main program initialization, backtesting and strategy optimizer setup as well as community data management.
- https://github.com/Drakkar-Software/OctoBot-Tentacles (opens in a new tab) (dev branch) tentacles: evaluators, strategies, trading modes, interfaces, notifiers, external data feeds (reddit, telegram etc), backtesting data formats management and exchange specific behaviors.
- https://github.com/Drakkar-Software/OctoBot-Trading (opens in a new tab) for everything trading and exchange related: exchange connections, exchange data fetch and update, orders, trades and portfolios management.
- https://github.com/Drakkar-Software/OctoBot-evaluators (opens in a new tab) for everything related to evaluators and strategies.
- https://github.com/Drakkar-Software/OctoBot-Services (opens in a new tab) for everything related to interfaces: graphic (web) and text(telegram), notifications push and social analysis data management: update engine to handle new data from an external feed (ex: reddit) when it gets available.
- https://github.com/Drakkar-Software/OctoBot-Backtesting (opens in a new tab) for the backtesting engine and scheduling as well as historical data collection unified storage management.
- https://github.com/Drakkar-Software/OctoBot-Tentacles-Manager (opens in a new tab) for tentacles installation, updates and interactions: get a tentacle documentation, configuration or it's dependencies.
- https://github.com/Drakkar-Software/OctoBot-Commons (opens in a new tab) for common tools and constants used across each above repository.
- https://github.com/Drakkar-Software/Async-Channel (opens in a new tab) which is used by OctoBot as a base framework for every data transfer within the bot. This allows a highly optimized and scalable architecture that adapts to any system while using a very low amount of CPU and RAM.