About DeepVane

Institutional-grade quantitative equity research, built on public data, available without a $25,000/year Bloomberg terminal.

Why this exists

The math behind professional quant investing isn't secret. Fama-French 1992 is public. Sloan 1996 is public. Asquith-Pathak-Ritter 2005 is public. The papers that describe the edges institutional funds trade on are literally citable from this page. What's not public is the engineering — the pipeline that turns thirty years of academic findings into a daily score you can actually use.

DeepVane is that pipeline, built once and exposed for free. Twelve factor families, fifteen confluence patterns, Bayesian regime detection, Bayesian Phase 3 failure prediction, conformal prediction intervals. The code implementations exist because we wrote them from the primary literature, not because we licensed them from anyone.

The product thesis: if retail investors had the same math institutional funds use, they'd size positions more honestly and lose less money to overtrading, consensus-chasing, and narrative investing. The best way to test that thesis is to put the math in front of them and see what happens.

Who built it

DeepVane is the work of a single independent engineer, self-funded through the calibration phase. The math stack and pipeline were written over two intense months — every factor scorer implemented from the original paper, every integration layer written and reviewed against published benchmarks.

No VC capital has been accepted. No founder-friend-led institutional money. No acquisition talks. This is deliberate — the only way to maintain the honest pricing and honest timeline described on the pricing page is to not have external runway pressure. Post-16 May 2026 with verified track record, that posture may change — but only on terms that keep the public-access layer free.

Founder bio TBD. Full public attribution — name, LinkedIn, prior work, academic background — will be added here with the Pro launch on 16 May 2026. Before we put a face on the project, we want the track record to speak for itself. In the meantime, reach out at the contact below for any investor or partnership questions.

Timeline

Feb 2026
Project start
APEX v1-v6 factor scoring and database infrastructure. Initial factor implementations from the primary literature.
Mar 2026
v7-v8 consolidation
PEAD, accruals, spillover, options, NLP, short-interest factors brought to production. 234-ticker universe stabilised.
Apr 2026
v9 mathematical maturity
Copula tail-dependence (v9.2), regime-blended Markowitz (v9.3), BOCPD regime detection (v9.4), Mondrian conformal intervals (v9.5), Kalman DLM dynamic exposures (v9.6), Shapley attribution. Pharma-APEX integration (v9.8).
Apr 2026
Public content layer
Methodology, moat, track-record, pattern library, compare tickers, blog posts, homepage SEO. Everything on the public site available without login.
16 May 2026
First forward returns live
Calibration window closes — Mondrian bins, Kalman DLM, and adaptive factor weights begin tracking out-of-sample performance. Track-record page switches from pipeline diagnostics to measured IC / Sharpe / max-drawdown.
Q3 2026
Pro tier + API
Alerts, historical depth, Shapley per-ticker, REST API access. Enterprise tier available for funds and quant teams.

Principles

Honest before impressive
If a number isn't measured, we say so. The interval width is published alongside the score. The calibration state is displayed on every ticker. We'd rather under-promise on track record and deliver than the opposite.
Composition, not opacity
Every factor traces to a peer-reviewed paper. The citations are on the methodology page, on every pattern detail page, on every blog post. The moat is how the layers compose, not that the ingredients are secret.
Free as a weight on the industry
Good quantitative research gated behind institutional paywalls is a market inefficiency. Making it free — and well-engineered enough to actually use — is the bet.
Small team by design
One engineer until revenue justifies more. Building infrastructure with one hand tied makes you ruthless about what doesn't matter. The pipeline runs on public APIs, Vercel hobby tier, Supabase free, and free/low-cost paid data feeds — and it works.

Contact

Research & methodology
research@deepvane.com
Academic collaborators, curious quants, press
Enterprise sales
enterprise@deepvane.com
Funds, quant teams, API licensing
Founders
founders@deepvane.com
Investors, partnerships, personal reach
Support
support@deepvane.com
Bugs, account issues, feature requests

Related: methodology · moat · track record · pricing