Starting every morning at 9 AM by manually sifting through a flood of numbers is an exhausting ritual. Developers working in finance or investment know all too well how much repetitive data collection eats into their resources. Macro-Pulse was born to strip away this inefficiency. It goes well beyond simply scraping numbers – the attention paid to stable system operations is evident throughout. The overall architecture is focused on practicality. Built on Python, it fetches a wide range of financial data in real time – not just domestic and international market indices, but also interest rates and commodity price volatility. Since plain text hurts readability, the system also captures heatmap screenshots that visualize market conditions at a glance. A single Telegram message delivers the morning market pulse in one shot. The choice of uv as the package manager is noteworthy. Adopting a tool that has been gaining traction for its speed, it reduces the hassle of environment setup. Test coverage is equally thorough – logic verification and external communication are rigorously separated to boost operational stability. The approach of adjusting test scope based on environment variables reflects real-world operational thinking. To minimize infrastructure costs, it uses GitHub Actions as the scheduler. Workflows run at set times, and the results are published to GitHub Pages. This demonstrates that a stable dashboard can be maintained without any paid servers. Execution history is retained for rapid incident response, and notification features further reduce the operational burden. The ability to flexibly adjust report format through a single configuration file is another strength. Output items and section ordering can be modified without touching code. Tailoring the report layout to market conditions or selectively including specific data is straightforward. Sensitive information is managed through secure variables, and detailed guidelines are well documented. Running it locally, browser configuration turned out to be the trickiest point. Differences between server and local environments frequently cause screenshot capture to malfunction. The decision to pre-configure the browser inside a container is an excellent choice for preventing this. The internal logic is also cleanly separated between data acquisition and processing, making it easy to read. An option to test functionality without actually sending messages significantly improves developer convenience. The project provides a container-based runtime infrastructure to ensure it can be executed anywhere without constraints. Mounting configuration files and sharing output follows standard development workflows. Running the full test suite and confirming how robustly the code is written reveals the structural completeness. Automating repetitive tasks frees up time to focus on what truly matters: designing core logic and conducting analysis. Deciding what to do next with the collected data is now the human’s job. The hours once spent filling spreadsheets can now be devoted to writing more valuable code. Next, we plan to explore architectures that extract deeper insights from the collected metrics. Key Takeaways Combining GitHub Actions and Cron enables building automated data pipelines with zero server costs. uv package manager and Docker ensure consistency across development and deployment environments. JSON-based configuration files allow flexible management of report layouts and collection items without any code changes. Source: https://github.com/yeseoLee/Macro-Pulse