The math stack hedge funds license for $25M/year, retail-priced by design.
Framler operationalises an academic factor stack across 1001 tickers into one Bayesian forward-return engine — composing z-score factor stack, conformal prediction intervals, BOCPD regime detection, copula tail-dependence, Kalman dynamic exposures, and Phase 3 trial failure prediction into a single coherent posterior per ticker.
Built by one engineer over four months, served free during the calibration window. First measured forward-return calibration landed 2026-05-17 (1d + 7d horizons across three regimes, IC 0.055-0.10). Paid tiers open Q3 2026 once 30d horizon calibration matures. The product is a quantitative research platform for independent investors and small funds — academic factor stack, public methodology, no AI guesswork in scoring.
The honest split
Track record — measured forward returns are live
Today — synthetic backtest
IC Sharpe ≈ 1.25 on a 5-year Fama-French composite-factor backtest (n=1,826 rolling windows, sector-neutral measurement). Walk-forward CV IC Sharpe ≈ 1.0 on the test fold. Backtest only — no live trading capital deployed. Discount accordingly.
Live — measured forward returns
First measured weights persisted 2026-05-17 on 1d and 7d horizons (3 regimes, in-sample IC 0.055-0.102, n=1091). 30-day horizon calibration matures ~2026-06-26; 90-day ~2026-09-15./track-record publishes measured per-horizon IC and early hit-rate diagnostics now. Portfolio-level metrics (Sharpe, Sortino, Calmar, max drawdown) remain gated until enough completed monthly rebalance periods exist — see /backtest.
Why this split matters to a fund: live measured numbers have an entirely different evidentiary weight from backtest. We refuse to blur the line because every retail-quant tool that has done so eventually got caught.
Today, on the wire
Live engine state
Tickers scored daily
1001
US equities universe — refreshed by daily cron with full multi-factor recompute
Confluence patterns firing
649
Currently matching the academic pattern library (squeeze, compounder, value-trap, …)
Phase 3 trials tracked
25
Pharma names with active Phase 3 readout + Bayesian failure-probability estimate
Verdict mix today: 46% bullish · 5% bearish · last refresh 19 May, 10:20. Verify live at /status.
Calendar-locked unlock
First verifiable forward returns publish in 0 days
Day 40 of 30 — every signal we generate is timestamped before any forward return is known, so the first measured Sharpe / IC / win rate landing on 9 May 2026 is auditable, not curated. This is what flips the engine from prior-mode to posterior-mode.
Market sizing
TAM · SAM · SOM
In plain words:TAM = the entire pie (everyone who could ever buy this kind of product). SAM = the slice we can actually sell to (right language, right price). SOM = the realistic bite in the next 3 years.
Multi-factor quantitative research is currently a B2B-priced product served via legacy terminals. Three concentric circles describe the addressable space:
TAM
Total Addressable Market
Global spend on multi-factor quant research today: Bloomberg Terminal (~$8B, ~350K seats × $25K), FactSet (~$1.7B), Refinitiv (~$6B), S&P Capital IQ + Compustat (~$2B), Bloomberg Quant Office add-ons (~$1B), institutional factor data feeds (~$3B), in-house factor pipelines at long-only / hedge funds (~$3B headcount-equivalent).
$25B / year
SAM
Serviceable Addressable Market
70M US retail investors + 5M financial advisors globally + 3M quant-curious analysts. At a $99/year median willingness-to-pay (between $0 free tier and $250-500 a Zacks Premium / Seeking Alpha Premium charges), the priceable layer is ~$8B/yr in English-speaking markets (US/UK/Canada/Australia).
$8B / year
SOM
Serviceable Obtainable Market
A specific 36-month ARR target is intentionally not published until the first Pro cohorts produce measured conversion, retention, and willingness-to-pay numbers. Pro tier opens after the 30d horizon calibration matures (~26 Jun 2026); the first defendable revenue model follows from that data, not from top-down forecasts.
Pending cohort data
Sources for TAM components: Bloomberg subscriber counts disclosed by parent (Bloomberg LP), FactSet 10-K, Refinitiv (LSEG) annual report, ICI Investment Company Fact Book on US retail investor count, BLS occupation data on US/UK/CA/AU financial advisors.
Competitive moat
Framler vs the incumbents
CapabilityFramlerBloombergRefinitivS&P CapIQ
Price per seat / yr$0–$348$25–30K$22K$30K+
Multi-factor composite scoreFramler 0–100No (raw data only)No (raw data only)No
Bayesian regime detectionBOCPD liveNoNoNo
Conformal prediction intervalsMondrianNoNoNo
Phase 3 trial failure predictionBayesian, every nameNoNoNo
Open methodology pagePublicProprietaryProprietaryProprietary
Factor citations to peer-reviewed papersEvery factorNoNoNo
The incumbents win on universe breadth, real-time data feeds, and qualitative content (transcripts, news, analytics packages). Framler wins on transparent factor methodology, Bayesian regime conditioning, conformal uncertainty quantification, Phase 3 prediction, and a price point that lets retail and small-fund users access the same factor literature institutional desks pay six figures for. Different product, overlapping shelf — the way Linux disrupted Solaris, not by being equivalent but by being structurally better suited to a different buyer.
The opportunity
Why retail quant is a $25B market
A Bloomberg Terminal costs $25,000-30,000 per seat per year. A FactSet seat is similar. A quant data feed (Compustat, Refinitiv, S&P CapIQ) starts at six figures before you write the first line of code that uses it. Multi-factor scoring engines built in-house at long-only funds typically take 3-5 quant-engineers two years to ship.
The 70 million US retail investors plus 5 million advisors and 3 million quant-curious analysts globally have access to none of this. They use analyst ratings (which underperform multi-factor scoring on every dimension we measure — see the full analysis), narrative trading, and P/E heuristics — a strictly inferior information set.
Framler is the same factor literature, same engineering rigour, 1/100th of the cost. Free during early access to drive citation and awareness, $29/mo Pro post-launch for active features (alerts, historical depth, REST API). Enterprise contracts start ~$2k/mo for funds who want bulk endpoints and custom universe.
Math symphony
Why engine v2 is the moat
Most retail screeners stop at sentiment classifiers around news headlines. Most institutional factor stacks add factor scores arithmetically (Naive-Bayes fallacy) or run them in isolation. engine v2 composes them coherently — every upstream layer flows into a single Bayesian posterior per ticker per horizon, not a stack of independent classifier outputs.
The engine fuses four families of evidence — a multi-factor composite signal, regime-aware conditioning, confluence-pattern recognition, and tail-dependence awareness — into one forward-return distribution. Variance is calibrated by conformal prediction so the published 90% intervals achieve their nominal coverage on out-of-sample data, and never shrink below the market's own implied uncertainty. Each layer reduces gracefully to a no-op when its evidence is absent, so missing data never poisons the output.
Every coefficient cites primary peer-reviewed literature. The result: a single concrete forward-return distribution per horizon (1d, 7d, 30d, 90d) with mean, 90% CI, and probability of positive return — fully audit-traceable to source papers, with all internal constants, functional forms, and blend coefficients held proprietary.
A high-level overview of the inputs and ideas (without functional forms or constants) lives at methodology. The full derivation, parameter values, and composition rules are intentionally unpublished — that’s the moat. Counsel-reviewed enterprise contracts include limited-distribution access for academic verification under NDA.
Built, shipped, free
Traction so far
Engine v2 deployed
Engine v1 → v2 in 14 weeks. An expanded academic factor stack (added Amihud 2002 microstructure, 8-K earnings press NLP, cross-asset macro). BOCPD + conformal + Kalman + copula + Shapley + confluence + pharma layers all live, with extensive mathematical invariants running on every request.
Public content layer
Hub pages (methodology / moat / patterns / track record / coherence / compare / about / blog) + long-form research posts + a per-pattern detail surface.
SEO surface
Sitemap covers ~290 pages. Schema.org FinancialProduct on every ticker. AI crawler allowlist explicit (ClaudeBot / GPTBot / PerplexityBot all citing).
Pipeline reliability
3-layer cron (daily factor refresh / hourly alerts / near-realtime prices). Public /status page shows freshness per layer.
First adaptive weights persisted 2026-05-17
Calibration window opened 2026-05-16; first measured factor weights landed the next day across 1d + 7d horizons × 3 regimes (n=1091, IC 0.055-0.102 — squarely in academic literature band Asness 2013). 30d horizon calibration matures ~2026-06-26, 90d ~2026-09-15. /track-record publishes measured per-horizon IC and early hit-rate diagnostics; portfolio-level metrics (Sharpe, Sortino, Calmar, max drawdown) remain gated until enough completed monthly rebalance periods exist.
Discipline
What we will not do
Custody assets. We are not a broker. No money flow, no fiduciary duty mismatch, no regulatory perimeter creep. Research tooling, period.
Sell a black box. Every score traces to a peer-reviewed paper. The methodology page is intentionally exhaustive. The moat is engineering, not opacity.
Inflate metrics before measurement. Track record page showed zero performance numbers until 2026-05-17, the day first measured forward returns calibrated. We'd rather under-promise and deliver than do the opposite.
Take dirty money. No payment-for-flow research arrangements. No paid tickers. No promoted picks. The engine is index-agnostic — it scores what the math says, including names that hate us.
Pre-seed allocation
Use of funds
Pre-seed thesis: $500K-$2M raise extends runway 18-24 months and funds three workstreams that structurally compound (engine breadth, data depth, paid-tier conversion). The mix below assumes a $1M raise; allocations scale proportionally.
50%
Engineering — factor R&D + infrastructure
Founder runway + 1 quant engineer hire post-seed. New factors (PEAD #14, options skew, microstructure depth, alternative data integrations). Migration to dedicated infra once Vercel free-tier ceiling binds. Calibration window automation.
20%
Data sources + paid feeds
Compustat fundamentals (depth on financials), Refinitiv I/B/E/S analyst estimates, intraday OHLCV, options volume real-time. Each data feed unlocks 1-2 new factors and tightens existing factor signal-to-noise.
12%
Marketing + community + content
Long-form research (academic-style posts), founder presence on quant Twitter / podcasts, conference attendance (NeurIPS / quant finance), backlink outreach for SEO compound, paid acquisition only after track-record validates.
10%
Legal + compliance + audits
Counsel-reviewed Terms + Privacy + risk disclosures, SEC research-firm posture review, SOC2 Type I prep, optional external penetration test, data-protection-officer engagement for EU.
5%
Founder runway + operations
Cost of being a full-time founder for 18-24 months. Health insurance, accounting, tooling subscriptions, office overhead.
3%
Reserve
Buffer for unexpected — server burst (track record viral), regulatory clarification, opportunistic data deal.
Detailed budget by line item available under NDA. Burn assumptions stress-tested at 2× and 0.5× — runway 18-24 months at base case, 9-12 at 2× burn (with paid-tier launching), 36+ at 0.5× burn (founder-only mode).
Next 12 months
2026 roadmap
Q2 (May 16): First OOS calibration. Mondrian conformal bins, Kalman DLM, adaptive factor weights all flip from prior to posterior. Track record page publishes measured Sharpe, IC per factor, max-drawdown with benchmarks.
Q3: Pro tier launches ($29/mo). Alerts, historical depth, REST API (1000 req/day), monthly research letter. Founder pricing for early Free users.
Q4: Universe expansion — beyond US equities to UK FTSE 350, EU STOXX 600, Japan TOPIX. Same math, broader market.
Q4: Quant API for funds — model-portfolio endpoints, factor exposure decomposition, regime-conditional rebalancing recommendations.
Talk to us
Investor enquiries
We are not currently raising publicly. Investor conversations happen privately, on the basis of the moat deck (separate from this public brief), and only with funds whose thesis aligns with the public-access- first model. Email founders@framler.com if you'd like the moat deck and a 30-minute conversation.
For research collaborations, academic verification access, or press: research@framler.com.