LAMINAR: Measuring an Order-flow Coherence Label across Twelve Futures Instruments SSRN citation: September 9, 2026 · https://ssrn.com/abstract=7439319 Local manuscript: September 10, 2026 · https://www.davisgeometric.com/research/evidence/laminar/laminar.pdf HELICITY / LAMINAR: measuring an order-flow coherence label Bee Rosa Davis Research note · Version 0.3 · Updated 8 October 2026 HELICITY is the broader market-analysis project. Within it, LAMINAR measures an order-flow coherence label across twelve futures instruments: how often the label appears, how it responds to controlled perturbations, and whether it persists when the surrounding context changes. TL;DR The question: Do graph-based measures of how money and orders circulate describe market conditions in ways that survive controlled perturbations and out-of-sample checks? What the work does: A three-scale dashboard (macroeconomic series, futures bars, level-2 order book) and a measurement study of one order-flow label across 12 futures instruments, 194 sessions and surrogate controls. What is still open: Whether the label carries market-specific information beyond its own thresholds, and any trading use after costs, latency and drawdowns, is untested on a frozen chronological holdout. THE RESEARCH IN CONTEXT Market microstructure studies how individual trades and quotes form prices and liquidity. A recurring question is whether the organization of order flow (which side initiates trades, how trades follow one another, how much the book can absorb) changes before prices do. Measures of that organization are easy to compute and easy to over-read. A diagnostic that fires often may reflect real coordination among traders, or only the mechanics of a threshold set relative to its own recent history. Telling those apart requires controls in which chosen properties of the data are deliberately scrambled while others are kept. HELICITY consists of a FastAPI backend and a React dashboard working at three scales: macroeconomic FRED series, futures bars, and Databento MBP-10 level-2 book data, with each panel stating whether it runs on live, cached or stored data. An order-flow strategy treats the book as a network and reads its spectral gap, a measure of how quickly the network mixes, together with how self-similar the flow is over time. A blind trial generates twenty labeled synthetic sessions, seals the labels and fixes its scoring hypotheses in source before running. The theory notes' three ECON experiments currently run on generated rather than observed data. The later LAMINAR working paper measures one label rather than a strategy. It replays licensed CME Globex order-by-order data, 15 tapes covering 12 instruments, 36,533,928 trades and 194 trading-day sessions, through one fixed classifier configuration, recording a state per 120-second bin and keeping transitional readings distinct from turbulent ones. A separate yen experiment of 95,882 trades holds every event fixed and changes only the numerical price scale: in native units every reading is transitional, while reference scaling yields a 7.48 percent laminar rate. Price representation is therefore part of the measurement rather than a formatting detail. AVAILABLE RECORD LAMINAR working paper on SSRN; historical replays and controlled comparisons within HELICITY. The study concerns classifier measurement, not predictive returns or execution performance. TECHNICAL FIGURE source-informed technical atlas Source: HELICITY LAMINAR manuscript, original figures. The rate measures classifier incidence; seed ranges are descriptive and do not establish a trading edge. The classifier responds to surrogate inputs too Original manuscript figure Original figure: https://www.davisgeometric.com/research/evidence/market-structure-03_surrogates.png Original LAMINAR manuscript figure. Dashed lines give matched real-input incidence. Dots are seed results, diamonds their means, and segments their ranges—not confidence intervals. There are three surrogate seeds for 6J, ZT and GC, and two for ES. The denominator here includes terminal bins; the sample table below uses the manuscript’s different L + U + R denominator. 03_surrogates.png — Original paper figure; copied without alteration Session variation and two denominators Original manuscript figure Original figure: https://www.davisgeometric.com/research/evidence/market-structure-02_incidence.png Original manuscript figure comparing session incidence with tape-level summaries. This companion view exposes variation across sessions and how conditioning on an asserted regime changes the denominator. 02_incidence.png — Original paper figure; copied without alteration The measured sample, with both incidence denominators Manuscript table Tape | Trades | N = L + U + R | L / N (%) | L / (L + U) (%) | Sessions with L / all | 2026 period RTY | 421,982 | 3,370 | 9.67 | 16.47 | 5/5 | Jul 6E | 133,221 | 3,231 | 8.82 | 14.91 | 5/5 | Jul YM | 277,026 | 3,299 | 8.58 | 15.17 | 5/5 | Jul CL | 3,392,888 | 12,964 | 8.07 | 14.00 | 19/19 | Mar–Apr ZT | 128,267 | 2,976 | 7.90 | 11.60 | 5/5 | Jul NQ | 1,795,518 | 3,385 | 7.68 | 13.18 | 5/5 | Jul GC | 331,552 | 3,377 | 7.67 | 13.45 | 5/5 | Jul 6J | 95,882 | 3,221 | 7.48 | 12.31 | 5/5 | Jul ES | 10,298,409 | 13,303 | 7.35 | 12.50 | 20/20 | Mar–Apr ZB | 144,380 | 2,904 | 7.30 | 9.23 | 5/5 | Jul ZF | 263,750 | 3,060 | 7.06 | 8.90 | 5/5 | Jul ES-apr | 8,887,782 | 13,720 | 7.01 | 11.46 | 20/20 | Apr–May ES-jul | 2,088,165 | 3,385 | 7.00 | 11.62 | 5/5 | Jul ZN-summer | 5,647,076 | 42,144 | 6.73 | 9.20 | 65/65 | Apr–Jul ZN | 2,628,030 | 13,118 | 6.35 | 8.55 | 20/20 | Mar–Apr L = LAMINAR, U = TURBULENT, R = TRANSITIONAL. N excludes initializing and separately recorded terminal bins. Conditioning on an asserted regime drops R from the denominator. These are label-incidence measurements, not returns or trading performance. sample_table.tex — Table tab:sample; all 15 rows Risk overlay and order-flow measurement are different tasks The project README describes a risk overlay rather than a directional call. It acknowledges that its own backtests underperformed buy-and-hold on average and distinguishes defensive behavior in falling markets from general outperformance. The order-flow work examines spectral and temporal structure of the book. Its synthetic blind trial plants a signal in generated sessions; passing that test shows that the implementation responds to the planted structure, not that the same signal produces an edge in real markets. The later controls change the interpretation The reviewed LAMINAR manuscript separates label incidence, perturbation sensitivity, and context dependence. In its control grid, independently resampled session marginals produce mean incidence at least as high as the real inputs in three tested instruments. A label appearing often is therefore insufficient evidence of market-specific organization. The tested S&P session responds more strongly to price-path permutation. Grafted real segments have higher inside-than-outside incidence for some instruments and lower incidence for others. These mixed findings are substantive: the classifier responds to both selected structure and context, rather than identifying one universal flow state. THE NEXT RESEARCH STEP The measurement study should be the representative paper, with returns and execution performance treated as separate downstream questions. Price representation, initialization, withheld labels, and control construction must remain part of each reported incidence. TEACHING CONSTRUCTION (not project measurements) Declare the data level—macroeconomic series, bars, or order-book events—and its timestamp semantics. Construct the graph using only currently available information. Freeze the diagnostic and any decision rule before a chronological holdout. available observations → graph → diagnostic → separately evaluated decision Graph edge: A precisely defined relationship, not an unspecified notion of market similarity. Regime score: A descriptive quantity whose prospective interpretation requires testing. Availability time: The time at which data and any revisions become usable. A rolling statistic that includes tomorrow’s revised value is not a valid input for today’s decision. Likewise, a graph built from a full sample can encode future relationships even if the final prediction formula uses only a past timestamp. SOURCE RECORD HELICITY app: https://helicity.io LAMINAR working paper · SSRN: https://ssrn.com/abstract=7439319 LAMINAR · local manuscript (PDF): https://www.davisgeometric.com/research/evidence/laminar/laminar.pdf local-project/helicity/README.md — local source reviewed; not republished local-project/helicity/docs/paper/incidence_of_coherent_order_flow.tex — local source reviewed; not republished Source versions: https://www.davisgeometric.com/research/account-source-records.json No new experiments are reported in this research note. Illustrated study: https://www.davisgeometric.com/research/figures/draft-market-structure.html