Using LLMs to review missed trades
How an AI review layer can help explain missing trades without hiding the account-level reason a broker order failed.
Why it matters
The best missed-trade review starts with boring facts: alert received, account selected, symbol resolved, broker called, response stored.
For Quantarya, this is not an abstract product lesson. Quantarya can use LLMs to turn routing facts into a readable explanation for traders.
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: Generate a short incident-style summary after every skipped account or failed order attempt.
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 percentage of no-trade alerts with a recorded account-level reason.
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
AI should not invent a cause when the system failed to store the broker response.
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.