Factor-First AI Investment Platform Narrated by a Six-Persona Committee

The Problem

Standard brokerage tools expose raw financial metrics but do not synthesize them into a conviction-level judgment. Investors still do their own mental weighting, often inconsistently and without a documented framework.

The core gap is not data access; financial APIs are commodity. The real problem is a missing, repeatable scoring architecture that combines quantitative fundamentals with qualitative judgment, and stays trustworthy enough to run with personal capital.

A one-off grade is not enough on its own: the same engine has to keep portfolio grades fresh over time, point itself at new opportunities, and prove its grades against real outcomes instead of asserting them.

The Approach

Converged on a four-factor composite (ADR-013): Quality, Valuation, Momentum, and Health each score independently and aggregate into a single weighted 0-100 number, with quality carrying the heaviest emphasis. Growth was folded into Quality once it stopped earning a standalone weight.

Built a six-persona investment committee (Graham, Buffett, Munger, Lynch, Dalio, Bogle) that narrates the factor scores in each investor's voice rather than recomputing them in parallel, and surfaces which factor and which persona drove conviction.

Added Piotroski F-Score and Altman Z-Score as distress gates that hard-cap the composite instead of being averaged into it, so a weak balance sheet can't be masked by a strong factor.

Made the engine market-aware: live SPY drawdown plus a VIX cooling-off counter gate SELL/TRIM recommendations, so the model doesn't tell you to sell into a panic.

Pointed the same scorer at a ~400-name large-cap universe every night and surfaced it three ways: a public Market's Best leaderboard at caskeycoding.com/market-best (split domestic vs. foreign), a private watchlist Discovery scanner for A/A+ opportunities, and a chat-driven universe screener.

Built a quant-grade backtester to prove the grade scale rather than assert it: a daily engine self-test computes the information coefficient of the composite against forward market-relative returns at 1m/3m/6m/12m horizons, and an offline panel backtester re-scores a point-in-time S&P 500 universe (strictly pre-filing fundamentals, decile-spread Sharpe net of costs, per-factor IC). Any change to a weight, factor, or formula has to clear a measurable decision gate on that panel before it merges.

Wrapped everything in production guardrails: a Response Validator (ticker closure, confidence floor, mandatory disclaimer, no forecasting) on every LLM output, immutable hashed audit records for every review, and async execution to clear the API timeout.

The Impact

• Returns a repeatable 0-100 composite and A+ to F grade for any US-listed equity, with six-persona narration underneath, one consistent framework across every ticker instead of ad-hoc mental weighting
• Grew from a single-ticker grader into a platform: portfolio review by Fidelity CSV or phone-snapshot upload, a net-worth tracker with trend, a daily auto-review that keeps grades fresh, and a conversational advisor scoped to your own review data
• A nightly scan scores ~400 large caps and powers the live, public, no-login Market's Best leaderboard at caskeycoding.com/market-best plus a private Discovery watchlist scanner: the engine shown working, at zero per-view cost
• Distress gates and market-state cooling-off keep the model honest: a weak balance sheet hard-caps the grade, and SELL/TRIM signals are suppressed during drawdowns and VIX spikes
• A backtester validates the grade scale against historical outcomes (a daily information-coefficient self-test plus a point-in-time panel backtest with a merge gate factor changes must clear), and every review is written as an immutable, hashed audit record
• Engineered for safe public exposure: per-IP and per-session rate caps, a hard daily cost ceiling, and a default-off kill switch