Deliberately uncorrelated — names with no relationship, traded as one book.
Blurred is a member of the Blaque Baux family. The core repo is the engine and blueprint — a governed, systematic platform (Julia) with a venue-agnostic execution controller and a Layer-3 live-money safety gate. Blurred points that engine in its own direction and inherits the governance wholesale.
Not investment advice. Educational/research software. Nothing here is validated. See LICENSE.
git clone --recursive https://github.com/blaquebaux/blurred.git
julia --project=engine -e 'using Pkg; Pkg.instantiate()' # one-time engine setupWhere pairs trading seeks cointegrated (correlated) names, Blurred seeks the opposite: a basket of genuinely UNCORRELATED names for maximum diversification, or spreads between names that shouldn't move together. The base correlation study is the caution — truly uncorrelated equities are rare (most share market beta), so the real work is finding and verifying low-correlation structure.
Full detail in research/README.md. The scorecard:
| # | Question | Verdict |
|---|---|---|
| 1 | Can you build an uncorrelated book from equities? | ❌ no — 40 names → 7.5 bets (avg corr +0.30); best-10 still +0.17 |
| 1 | Does the low correlation hold out-of-sample / in crises? | ❌ unstable — pairs +0.02→+0.17; crash corr +0.30→**+0.66** |
| 1 | Where does uncorrelation actually live? | ✅ across asset classes — 8 classes avg corr −0.00, 60% efficient |
| 2 | Trade uncorrelated names against each other? | ❌ no anchor — the spread is a random walk (−0.33) |
The synthesis: you cannot build an uncorrelated book out of equities — they share market beta, so 40 broad names collapse to ~7.5 effective bets (avg corr +0.30) and even the lowest-correlation 10 still average +0.17 (a floor, not zero). Worse, that thin uncorrelation is unstable: the lowest pairs drift +0.02→+0.17 out-of-sample and the crash-window average jumps +0.30→**+0.66** — it converges exactly when you need it (Bore's caution). Genuine uncorrelation lives across asset classes (8 classes at ~0.00 avg corr, 60% efficient — 3× the efficiency), which is what the spine already harvests. And "trade uncorrelated names against each other" has no anchor: a spread needs a cointegrated tie to mean-revert (the opposite of uncorrelated), so the most-uncorrelated pair fades to −0.33 while only a correlated pair tilts positive (+0.15, and weak — dead pairs, per Bore).
Research: first pass complete — a null / re-derivation (research/). Uncorrelation is the right
ingredient for diversification, the wrong one for a spread, and within equities you get neither. The
honest conclusion points back to the spine: diversify across asset classes, not across names.
No live driver. Nothing validated to the spine's bar.
Blaque Baux is a quantitative research initiative and a subsidiary of Carter Warrens. BlaqueBaux.com is the home for the work; the code lives here on GitHub — open to study, test, and build bespoke strategies on top of.
Anyone can point an AI at a market. The edge is understanding what the data actually says — and turning it into something you can act on. We test relentlessly and put most of it on the record as rejected, with the reason; what survives is built, governed, and validated before it is ever called real. That combination — honest research, reproducible evidence, and execution you can trust — is why Carter Warrens leads on strategy and implementation, not merely uses the tools everyone now has.
This repo is one sleeve of the Blaque Baux family — a single governed engine steered in many directions. The core repo is the base/blueprint and holds the full family roster.
engine/ the Blaque Baux platform (git submodule -> blaquebaux/base)
research/ two Path-A sketches (diversification floor + instability, no-spread) + scorecard
live/ governed live drivers (once a sleeve graduates to paper A/B)
MIT. (c) 2026 Carter Warrens.