What I have done
Over this session, I focused on making Egis more observable, calmer, and more explainable, without refactoring core logic.
On the Signal Hooks and Orders side, I:
- Standardized datetime formatting (short format, GMT+7) and price formatting across Admin pages, aligning everything with the User > Bot Detail > Positions rules.
- Simplified Admin tables by removing low-signal columns (Exchange, User, TP/SL where unnecessary) and replacing verbose text with icon-based representations.
- Added Binance Order ID visibility and search on Admin > Orders, then extended it with an explicit admin-only repair flow to fetch missing orders from Binance when a searched ID isn’t found locally.
- Tightened the semantics: orders remain immutable facts, repairs are explicit inserts, never silent syncs.
On the logging and audit side, I:
- Generated multiple evidence-based log reports from
laravel.log, including:- Signal Hook activity and outcomes
- INFO / DEBUG / ERROR aggregation
- A deep, order-specific forensic analysis for a Binance algo order
- Introduced daily log rotation and separate testing logs, keeping production logs readable and tests isolated.
- Removed high-volume, low-value DEBUG spam (Binance param dumps, precision checks, throttling noise).
- Suppressed expected-but-noisy AuthenticationException logs to keep errors meaningful.
On the testing and safety side, I:
- Formalized test visibility by generating a complete test inventory report.
- Enforced a process to run Feature tests one by one, fix failures incrementally, and document outcomes directly into a Markdown report.
- Maintained strict invariants: SQLite-only tests, no Binance calls, no accidental prod/dev DB access.
Finally, I performed a deep forensic analysis of a “failed” stop-loss order by:
- Proving, via logs, that the order did not fail to place.
- Separating placement success from trigger execution, which are often conflated.
- Showing that repeated log entries were audit polling, not retries or failures.
What I have learned
- Most “failures” are actually observability failures.
The stop-loss order didn’t fail — the system simply didn’t have enough structured visibility to explain why it didn’t trigger. Logs told the truth once they were read correctly. - Audit noise is more dangerous than missing logs.
Excessive DEBUG logging hid real signals. Removing noise made real anomalies easier to see without adding new code paths. - Explicit repair beats silent correction.
Allowing an admin to explicitly fetch a missing Binance order preserves immutability, auditability, and trust in the data model. - Consistency creates calm UX.
Unified price and datetime formatting across Admin and User views removed cognitive friction. The system “felt” more correct without adding features. - Logs are part of the domain model.
Once logs are treated as first-class evidence (with rotation, structure, and intent), debugging becomes reasoning, not guessing. - Testing discipline is architectural, not procedural.
SQLite-only enforcement, per-test execution, and written test inventories turn “tests” into a safety system, not a checkbox.
Closing thought
This session wasn’t about adding features.
It was about reducing ambiguity.
Egis became:
- more explainable,
- more auditable,
- quieter under normal operation,
- and more trustworthy when something goes wrong.
That’s the kind of progress that compounds.