XSignalsBot
XSignalsBot is a scalable ecosystem for automated trading, designed to receive, analyze, and execute trading signals in real time. The project combines a complex data aggregation system, AI analytics, and autonomous trade execution.
Architectural Concept
The system is built on the principles of Clean Architecture and an Event-Driven model. The use of a data bus (RabbitMQ) allows for complete separation of the data collection process from trade execution. This ensures high fault tolerance: if one component of the system is overloaded, others continue to operate independently.
Key Mechanisms
Intelligent aggregation and "honesty check": The system parses external Telegram channels, but does not simply broadcast signals; it conducts constant monitoring of them. Each signal undergoes an internal check — the system calculates the "honesty" and quality of the source, excluding dubious signals from further processing.
User Interface and Source Abstraction: The Telegram bot serves as the main interface, providing the user with clean analytics and trading results. At the same time, the system conceals the internal structure of the sources, ensuring confidentiality and protecting the logic of the algorithms.
Flexible Execution System (Live vs Paper Trading): The project supports seamless transitions between real trades (Real Trade Bot) and demo mode (Paper Trading). This allows testing trading strategies on real market data without financial risks, maintaining the full identity of the signal processing algorithms.
Signal Orchestration: The process from receiving a message to opening a trade is coordinated according to the Orchestrator pattern. This guarantees the consistency of stages: validation, AI enrichment, risk management, and final execution through the exchange API.
Technology Stack
The project is based on Python 3.11+ with a focus on asynchronous programming (asyncio / aio-pika). The backend is implemented on FastAPI, ensuring high event processing speed. PostgreSQL (via SQLAlchemy 2.0 Async) is used for storing trade and user states, while Redis is used for fast data access. Containerization in Docker provides easy deployment and scaling of the entire infrastructure.
Scalability
The architecture allows for horizontal scaling: as load increases, individual bot instances can be easily added for different trading groups or strategies. Each part of the system — from parsers to executors — is isolated, making the project suitable for handling large volumes of data in a 24/7 mode.
More details in the GitHub repository:
https://github.com/floyse-back/XSignalsBot-overview/blob/main/README.md
Architectural Concept
The system is built on the principles of Clean Architecture and an Event-Driven model. The use of a data bus (RabbitMQ) allows for complete separation of the data collection process from trade execution. This ensures high fault tolerance: if one component of the system is overloaded, others continue to operate independently.
Key Mechanisms
Intelligent aggregation and "honesty check": The system parses external Telegram channels, but does not simply broadcast signals; it conducts constant monitoring of them. Each signal undergoes an internal check — the system calculates the "honesty" and quality of the source, excluding dubious signals from further processing.
User Interface and Source Abstraction: The Telegram bot serves as the main interface, providing the user with clean analytics and trading results. At the same time, the system conceals the internal structure of the sources, ensuring confidentiality and protecting the logic of the algorithms.
Flexible Execution System (Live vs Paper Trading): The project supports seamless transitions between real trades (Real Trade Bot) and demo mode (Paper Trading). This allows testing trading strategies on real market data without financial risks, maintaining the full identity of the signal processing algorithms.
Signal Orchestration: The process from receiving a message to opening a trade is coordinated according to the Orchestrator pattern. This guarantees the consistency of stages: validation, AI enrichment, risk management, and final execution through the exchange API.
Technology Stack
The project is based on Python 3.11+ with a focus on asynchronous programming (asyncio / aio-pika). The backend is implemented on FastAPI, ensuring high event processing speed. PostgreSQL (via SQLAlchemy 2.0 Async) is used for storing trade and user states, while Redis is used for fast data access. Containerization in Docker provides easy deployment and scaling of the entire infrastructure.
Scalability
The architecture allows for horizontal scaling: as load increases, individual bot instances can be easily added for different trading groups or strategies. Each part of the system — from parsers to executors — is isolated, making the project suitable for handling large volumes of data in a 24/7 mode.
More details in the GitHub repository:
https://github.com/floyse-back/XSignalsBot-overview/blob/main/README.md
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