Why we’re publishing this
Most “algorithmic trading” content on the internet optimizes for excitement: backtests, screenshots, and the occasional “strategy reveal.” That’s not what we’re doing.
This journal is closer to an operations log for a production system: what breaks, how we detect it, how we decide what to change, and how we control risk when the system is making decisions continuously.
If you’re evaluating autonomous trading systems—or building any high-stakes autonomous system—these are the problems that actually decide whether the system is usable.
What we publish
We publish experience-based, non-actionable operating lessons, including:
1) Reliability and failure modes
- What can fail in real production (data feeds, brokers, execution, latency, model serving, time sync)
- How failures show up (symptoms) vs what caused them (root cause)
- Preventing repeat incidents: postmortems, action items, and follow-through
2) Monitoring that matters
- What we monitor (and what we don’t)
- Signal vs noise: alert thresholds, suppression, and escalation policies
- “Tripwires” that halt trading safely when the system goes out of spec
3) Incident response and runbooks
- How to respond when markets are moving and the system is misbehaving
- Runbooks: concrete steps, not vague principles
- Rollback plans, safe-mode operation, and decision logs
4) Change control in production
- How we propose changes, review them, test them, and ship them
- How we avoid “silent regressions”
- Rollout strategies: canarying, feature flags, staged enablement
5) Evaluation and governance
- What “good” means when you can’t trust a single metric
- Guardrails, constraints, and non-negotiables (risk limits, exposure rules, operational limits)
- Audit trails: what was decided, when, and why
6) Practical architecture notes
- High-level design patterns that reduce operational risk
- Observability patterns: structured logs, correlation IDs, reproducibility
- Data quality pipelines and “stop the line” checks
What we do not publish (by design)
To keep this journal non-actionable and professionally usable, we intentionally do not publish:
- Trade signals (buy/sell lists, entry/exit rules, “today’s picks”)
- Portfolio composition or live positions
- Real-time trade logs
- Strategy recipes intended to be copied
- Advice aimed at retail trading outcomes
- Anything that would enable “shadowing” the system
We may show examples of issues using redacted or abstracted data, but not in a way that becomes a trading instruction.
How to read the journal
Each entry aims to be:
- Specific enough to be useful (what happened, what we saw, what we changed)
- General enough to be safe (not a strategy or signal)
- Operational (how we reduce future risk)
A good entry should leave you with:
- a checklist,
- a design pattern,
- a failure mode to test for,
- or a governance practice to adopt.
Editorial policy: how we reduce bias
Autonomous systems invite storytelling bias: “we did X and performance improved.” We avoid that trap by focusing on:
- verifiable operational facts (incidents, deployments, monitoring changes)
- decision logs and hypotheses (what we believed at the time)
- and follow-up (did the change actually reduce incidents?)
Who this is for
This journal is written for:
- professionals evaluating systematic trading operations,
- engineers building high-stakes autonomous systems,
- and teams who care about reliability, governance, and long-run operability.
If you’re looking for signals, shortcuts, or a strategy to copy: you won’t find it here.
