Refactoring Signal-Trend Model in Trend Analysis: From Prototype to Production-Ready Code

When I started working on the Trend Analysis project, the signal prediction model looked like a pile of experimental code. Functions overlapped, logic was scattered across different files, and adding a new indicator meant rewriting half the pipeline. I had to tackle refactoring signal-trend-model — and it turned out to be much more interesting than it seemed at first glance.

The problem was obvious: the old architecture grew organically, like a weed. Every new feature was added wherever there was space, without an overall schema. Claude helped generate code quickly, but without proper structure this led to technical debt. We needed a clear architecture with proper separation of concerns.

I started with the trend card. Instead of a flat dictionary, we created a pydantic model that describes the signal: input parameters, trigger conditions, output metrics. This immediately provided input validation and self-documenting code. Python type hints became more than just decoration — they helped the IDE suggest fields and catch bugs at the editing stage.

Then I split the analysis logic into separate classes. There was one monolithic TrendAnalyzer — it became a set of specialized components: SignalDetector, TrendValidator, ConfidenceCalculator. Each handles one thing, can be tested separately, easily replaceable. The API between them is clear — pydantic models at the boundaries.

Integration with Claude API became simpler. Previously, the LLM was called haphazardly, results were parsed differently in different places. Now there’s a dedicated ClaudeEnricher — sends a structured prompt, gets JSON, parses it into a known schema. If Claude returned an error — we catch and log it without breaking the entire pipeline.

Made the migration to async/await more honest. There were places where async was mixed with sync calls — a classic footgun. Now all I/O operations (API requests, database work) go through asyncio, and we can run multiple analyses in parallel without blocking.

Curious fact about AI: models like Claude are great for refactoring if you give them the right context. I would send old code → desired architecture → get suggestions that I would refine. Not blind following, but a directed dialogue.

In the end, the code became: - Modular — six months later, colleagues added a new signal type in a day; - Testable — unit tests cover the core logic, integration tests verify the API; - Maintainable — new developers can understand the tasks in an hour, not a day.

Refactoring wasn’t magic. It was meticulous work: write tests first, then change the code, make sure nothing broke. But now, when I need to add a feature or fix a bug, I’m not afraid to change the code — it’s protected.

Why does Angular think it’s better than everyone else? Because Stack Overflow said so 😄