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Assignment Proposal
Title
Crypto & Stock Alerts Telegram Bot — A DevOps Pipeline for a Notification Service
Names and KTH ID
Deadline
11 October
Category
Project
Description
We will build and operate a small Telegram notification service for cryptocurrency and stock price alerts.
Users can subscribe to a symbol and define a target price or percentage-change threshold. The application periodically retrieves market prices from external APIs and sends a Telegram notification when an alert condition is reached. Alert subscriptions and state are stored persistently.
The main focus of the project is the DevOps workflow around the application, rather than the complexity of the bot itself.
The application will be written in Python and containerized with Docker. We will implement an automated CI/CD pipeline using GitHub Actions:
GitHub will be used as the development platform with issues, pull requests, branch protection, and required CI checks.
The repository will contain the application code, tests, Docker configuration, CI/CD workflows, infrastructure/deployment configuration, and documentation required to reproduce the complete system.
We will also produce a short report describing the architecture, CI/CD workflow, infrastructure setup, security and quality automation, tooling choices, and limitations of the system. Any use of AI-assisted development tools will be documented, including what they were used for and how the generated output was reviewed.
Relevance
The project demonstrates a complete DevOps workflow for a long-running service.
Each code change is automatically tested, analyzed, and packaged through CI, while accepted changes are automatically published and deployed through CD. The deployment environment is defined as code, making it reproducible rather than manually configured.
The application also introduces practical DevOps concerns such as external service dependencies, persistent state, secrets management, scheduled background work, containerization, automated security checks, and deployment reliability.
This keeps the application itself relatively small while allowing us to focus on designing, implementing, testing, and explaining an end-to-end DevOps pipeline.