The individual building blocks in DeepVane's stack are all public. Every factor family traces to a peer-reviewed paper. BOCPD is an open algorithm; copula tail-dependence is standard risk-management textbook material; Shapley attribution is 70 years old.
The moat is composition. Getting all these layers to feed each other coherently — so one Bayesian posterior drives weights, covariance, intervals, and dynamic exposures simultaneously, and then interpreting the output with a literature-effect-size pattern engine — is months of engineering, not weeks of library integration.
Five specific integrations
01
One regime posterior feeds four downstream decisions
Published BOCPD implementations are changepoint detectors — they output a posterior over run-length. DeepVane extends the same posterior into four coherent places at once: (a) factor-weight blending across regimes, (b) tail-dependence weighting in the covariance matrix, (c) prediction- interval width scaling, (d) dynamic-exposure process noise in the Kalman filter. Typical quant stacks wire a regime signal into one of these; we wire it into all four, so a regime flip propagates through the entire pipeline in one step rather than via four inconsistent recalibrations.
02
Tail-dependence integrated into three separate layers
Non-parametric tail-dependence (Schmidt-Stadtmüller 2006) is a one-page estimator. Applying it in isolation gives you a pair of numbers per factor pair. DeepVane uses the same estimator output to (a) adjust pattern confidence through the Confluence Engine, (b) blend into the Markowitz covariance matrix per regime, (c) inflate the marginal prediction interval when Mondrian bins lack samples. Three uses of one physical quantity — with the blend coefficients themselves regime-conditioned by item (1).
03
Kalman process noise driven by the changepoint posterior
Published dynamic-linear-model implementations use static process noise Q or an exogenous schedule (e.g. "Q is 5x higher on earnings days"). DeepVane drives Qt directly from the BOCPD change-probability posterior at time t, so factor exposures update fastest on precisely the days the regime detector signals a change-point. Synthetic validation showed a 25% MSE reduction versus the flat-Q baseline. The reason this is rare in production stacks is engineering, not theory — keeping both filters numerically stable while one feeds the other requires per-step covariance projection and diag-floor safeguards.
04
Mondrian bins partition on a proprietary taxonomy
Bin-conditional conformal prediction (Vovk 2003) is standard. The non-obvious choice is what to condition on. Conditioning on sector is noisy (sectors differ but ticker-level residuals within a sector don't). Conditioning on quality decile misses regime effects. DeepVane conditions on a 2D taxonomy — tail-alignment bucket × dominant regime — because residuals actually cluster by these axes. This is an empirical design choice derived from our residual histogram, not an off-the-shelf technique.
05
Pattern engine with literature-derived effect sizes
Most quant tools output a composite score and stop. DeepVane adds a second layer: fifteen multi-factor patterns, each with a published effect size from a named paper, each with a priority rank so the highest-conviction pattern wins when several fire, each tail-alignment adjusted so pattern confidence tightens when factor co-movement actually supports the setup. This turns an abstract composite into something a user can point to and say "it's a Piotroski deep-value setup at 72% conviction" — with a citation.
What we don't claim
Credibility comes from being honest about scope. We are not claiming:
Novel academic methods. Every individual layer is published. Our work is at the composition layer, not the theoretical one.
Proprietary data. All inputs are public: SEC EDGAR, Yahoo price history, Finnhub options flow, FMP fundamentals. No alternative-data edge from satellite, credit-card, geo-location, etc.
Backtested performance before 2026-05-16. Deployment to production predates the forward-return window; out-of-sample results arrive after calibration completes. See Track Record.
Retail democratisation as the primary moat. Being cheaper than Bloomberg isn't a moat; that's a positioning choice. The moat is the engineering.
Who this is built for
Retail investors
People who want institutional-grade math without Bloomberg-level spend. The confluence engine translates factor output into named patterns anyone can read.
Quant researchers
Small funds and independent quants who don't have the headcount to implement five integrated math layers from scratch. Our admin APIs expose per-ticker Shapley decomposition, per-regime covariance, and per-bin conformal calibration for review.
Fintech teams
Product teams integrating a factor signal into their own UI. The scoring API returns a point estimate, an interval, and a pattern label — reducing the "how do I explain this number to a user" problem.