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Notes from the loop.

Short pieces on how I think about research-engineering for systematic trading. Practical, opinionated, sized for an evening's commute.

Read these three in order for a quick orientation to how this site thinks about research-engineering for systematic trading.

  1. 01 · intro · 3 min

    MLOps for quant research isn't MLOps for ML

    Why the MLOps vocabulary you know doesn't quite fit when the model trades.

  2. 02 · intro · 6 min

    Right-sizing the research data platform: five thresholds, in order

    How to pick the smallest research-data architecture that fits your team's current size — and when to upgrade.

  3. 03 · intro · 4 min

    The deploy contract isn't a YAML file

    Why the 3–4 promises a quant makes before going to production matter more than how the framework enforces them.

Forward edges from `cites:` frontmatter (validated at build time). Reverse edges are the same data, indexed by target — read across the row to follow a thesis backward.

NoteBuilds onCited by (writing)Cited by (outside)
AI research agents as platform citizens
An event-aware wrapper needs signal concentration, not just signal presence
Anti-correlation hedges, doesn't stack — the pairwise rule's 5th iteration
Audit isn't a feature you turn on
Composite strategies can interfere
Designing an event-aware strategy: a checklist
Disconfirmed: the transition-gate fix didn't recover the regime-filter
Don't pay for caution you can't justify
Drift vs reversal: the cleanest counterfactual for a post-event regime
Dual-signal at N=100, all 27 arms — the full picture
Dual-signal at N=100: the composite stacks (slightly)
Dual-signal data makes composites stack
Event gates cost Sharpe when the event has edge
Fifty seeds reveal the tie
Fifty seeds, twenty-four arms — the full leaderboard
Higher moments add noise faster than signal
How to use the comparison harness
Long-only buys asymmetric exposure, not just lower Sharpe
MLOps for quant research isn't MLOps for ML
Observability for alpha pipelines: three dashboards, one rule
One hundred seeds confirms and converges
Pairwise correlation predicts composition before you run it
Perfect correlation explains the interference
Property tests catch cross-strategy bugs that per-strategy tests miss
Redundant vs multiplicative composition — what +0.70 means
Refusal as planning hint
Right-sizing the research data platform: five thresholds, in order
Search isn't research
Sharpe is scale-invariant. Stop trying to make it not.
Six regression tests pin the findings
Strategy shape beats factor count: TSMom > 4-factor on the same data
Synthetic data shows what you were solving for
The baseline arm you forgot to include
The citation graph as substrate
The clustered-vol finding was also small-N — another tie at higher N
The deploy contract isn't a YAML file
The event clock isn't the panel clock
The first robust single-signal stack
The full pairwise matrix — 28 arms at N=100
The intervention-point rule, confirmed by a fourth experiment
The pairwise rule — final form (four iterations later)
The pairwise rule predicted this one
The promotion gate: why bad data should be unreachable
The sign-vs-rank composition conflict
The single-seed lead was a fluke
The strategy catalog — twenty-nine panel/event shapes, one ranking helper
Three vol-intervention experiments, zero wins on this synthetic
Three-clock momentum tops the harness
Vol ratio as cross-symbol regime gate
Vol-clustering breaks the regime filter — but not for the obvious reason
Vol-of-vol distinguishes regime from level
Vol-regime filter wins on mean, pays in variance
Walk-forward without leakage: a checklist that's saved me
What 19 arms told us about strategy composition
What 24 arms told us — the session's research log
What 27 arms told us — session research log v3
What 29 arms told us — session research log v4
Why ts_momentum leads at N=100
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