Simple architecture beats microservices in trading bots
Why Quantarya should keep its trading automation architecture simple until boundaries truly demand more services.
AI signal review without black boxes
AI-assisted trade journaling
AI-generated strategy notes need evidence
Using LLMs to review missed trades
AI risk summaries for trading teams
AI execution anti-patterns
When AI should not place the trade
Backtesting prompts are not proof
Why human review still matters for bots
Why it matters
Distributed architecture is expensive when the domain boundaries are still changing.
For Quantarya, this is not an abstract product lesson. Quantarya benefits from clear Lambda boundaries, shared libraries, and explicit persistence before adding more moving parts.
The platform becomes more useful when it explains trade behavior in a way that survives real broker routing, manual review, and messy market conditions.
The Quantarya shape
The practical shape is straightforward: Split services only when ownership, scaling, deployment, or failure isolation clearly requires it.
That means the product should keep strategy metadata, trade records, lifecycle rows, account outcomes, and chart context close together.
A trader should be able to open one page and understand what the signal intended, what the system did, what the broker accepted, and what the market did afterward.
What to measure
The metric I would watch here is operational issues caused by boundaries versus solved by boundaries.
That metric should not stand alone. It belongs beside trade count, average profit, average loss, RRR, drawdown, drawup, session, symbol, and final lifecycle outcome.
Numbers become useful when they help explain the trade, not when they decorate the dashboard.
The common trap
Microservices can make a missing trade harder to explain if the event trail is incomplete.
The trap is usually a product shortcut: hiding an exception, flattening lifecycle state, or treating a partial milestone as the final result.
Quantarya should make those shortcuts uncomfortable because the journal is supposed to protect the trader from false clarity.
Practical takeaway
The useful direction is simple: keep automation powerful, but keep the evidence readable.
If a trade wins, the product should explain why it counted as a win. If it loses, the product should show whether the plan, broker, symbol, session, or lifecycle path caused the pain.
That is the kind of trading automation I want Quantarya to become: not only fast, but inspectable after the market has moved.
Christian Weiss
Christian has worked in software engineering, data platforms, and cloud infrastructure for over a decade. He currently works on large-scale AWS-based data platforms and writes about software engineering, trading systems, automation, and the lessons learned while building Quantarya. He is also a hobby quant and the founder of Quantarya.