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.
Framler is that pipeline, built once and exposed for free. A multi-factor stack, a confluence pattern library, 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
Framler is the work of a single independent engineer - Illya Kuznetsov - self-funded through the calibration phase. Built solo Feb–May 2026: every factor scorer implemented from the primary paper, every cron, every page, every test. The moat is the engineering, not pedigree.
No VC capital has been accepted. 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.
Independent does not mean unreviewed. Every commit runs through 740+ unit tests, the engine is covered by behavioural invariants checked live against production data (visible at /coherence), every factor traces to a peer-reviewed paper on the methodology page, and external math review is planned before the paid Pro tier launches. Until then we publish what works and what doesn't - including the live calibration progress and the module-by-module status page.
accumulate-signals starts writing daily snapshots — the substrate for future forward-return back-fills and calibration. Day-zero of the calibration clock.
Amihud 2002 illiquidity premium as the final factor. NLP scoring (Loughran-McDonald), short-interest factor, Fama-French regression in production. Engine v6-v8 series.
Two-round audit closed 4 CRITICAL + 13 HIGH + 24 MEDIUM findings. CSP nonce with strict-dynamic, Kelly sizing moved server-side, RLS hardening. 688 invariant tests.
Full domain cutover from deepvane.com. Brand v5 wordmark (FRAMLER with violet l-and-r accents). Counter-AI repositioning across hero, methodology, story.
14 May 2026
Forecaster v2.1 / v2.2 + version consolidation
Piecewise alpha (Moskowitz 2012), VIX-conditional variance, pattern-conditional dampener, width-aware mean. Internal v11 → public v2 consolidation. 696 invariant tests. SEO push (3,394 new URLs).
isPriorMode flips at 00:00 UTC. Conformal calibration window opens — 30-day forward returns from 16 Apr snapshots become measurable. UX-marathon: ModeSelectorHero, sparkline saga, terminology unification (Conviction/Confidence → "Agreement").
●17 May 2026
First adaptive weights persisted
1d and 7d horizons calibrate on 1091 samples across 3 regimes (all / risk_on / transition). Measured ICs 0.055–0.10 replace literature priors via inverse-covariance Markowitz with copula-blend covariance. Conformal halfwidth = 43.96 pts on 485 samples (7 days). Engine LIVE.
Projected
~26 Jun 2026
30d horizon calibration
Sample threshold (≥30 weekday filled forward_return_30d) reached. Adaptive weights extend to medium horizon — dashboard cards re-score against measured 30-day IC rather than literature priors.
~15 Sep 2026
Full 90d calibration
All four horizons (1d / 7d / 30d / 90d) on measured ICs across all regimes. Engine fully on out-of-sample alpha measurement. First honest 90-day walk-forward Sharpe ratio publishable.
Q3 2026
Pro tier + API
Alerts, historical depth, Shapley per-ticker, REST API access. Enterprise tier 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.