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orbyd

How it works · the process

From market noise to a defensible thesis, every trading day.

orbyd is a frontier language model reading the US tape on a fixed schedule. Five stages turn 30 days of news and four quarters of earnings into a per-ticker dossier you can audit — the full reasoning behind every call. Here's how a name becomes a thesis.

  1. 01 Ingest
  2. 02 Score
  3. 03 Assess
  4. 04 Compose
  5. 05 Learn

  1. Liquidity screen

    Rules-based

    Cut a ~400-name US universe to the tradable few before a model is ever called.

    A cheap, rules-only gate. No LLM touches a name that can't be traded cleanly — roughly three-quarters of the field is gone before any model runs.

    Inputs
    Spread · ADV · market cap · tradable shape
    Produces
    ~25% of the universe survives

  2. Momentum + narrative scoring

    Rules-based + Sonnet

    Rank survivors by structure × volume × news density × theme-cluster strength.

    Themes are treated as primary. The system reads basket behaviour — how a name moves with its cohort — not isolated ticker action.

    Inputs
    Price structure · volume · 30d news density · theme baskets
    Produces
    Ranked candidate shortlist

  3. Deep per-name assessment

    Claude Opus

    Read everything on each candidate and write the dossier you see on this site.

    Opus synthesises thesis, invalidation trigger, bull case, bear case, setup, catalysts, and correlation notes — and assigns a quality grade, strongest to weakest. This stage is the public product.

    Inputs
    30d news · 4Q earnings transcripts · filings
    Produces
    A dossier per ticker + a quality grade

  4. Portfolio composition

    Claude Opus · 1M context

    Reason across every candidate side-by-side in a single pass, under hard constraints.

    This is the stage a 1M-token window makes possible: the whole candidate set compared in one mind, not stitched from hundreds of isolated calls. The reasoning informs every dossier on the site.

    Inputs
    All dossiers at once · sizing caps · archetype + regime rules
    Produces
    A ranked, constraint-checked portfolio

  5. Postmortem + learning

    Claude Opus

    Grade the model's own calls and update how it weighs evidence.

    Recurring patterns promote to a playbook; the weightings update weekly from closed trades, and the lessons surface in the methodology.

    Inputs
    Closed-trade outcomes · 90-day rolling window
    Produces
    Playbook entries + updated weightings

Why a 1M-token window changes the work

The whole book reasoned in one mind — not stitched from hundreds of calls.

Most automated research scores names in isolation and bolts the results together. The composition stage doesn't. Claude Opus's million-token context lets the composition pass hold every candidate's full dossier at once — comparing theses, conflicts, correlations, and concentration across the entire set in a single reasoning pass. That's how a name gets sized against its basket and the regime, not judged on its own merits alone. The difference between a spreadsheet of scores and an analyst who has read every name in the field.

On the record

Open by default.

Every thesis, the names we hold and the ones we're watching, and every regime and macro call — published the day it's made, dated, and scored against the trigger that would prove it wrong. You can audit the reasoning behind every call and hold each one to the line it set for itself.

  • Thesis, bull & bear case, invalidation trigger
  • Setup, catalyst calendar, correlation notes
  • Archetype, conviction, theme & regime calls
  • The names we hold — and the ones we're circling
  • Every call dated, versioned, and scored in public

Common questions

How does an AI language model analyse stocks?
We run a five-stage process every trading day. A rules-based screen cuts a ~400-name universe to the tradable few; momentum and narrative scoring rank the survivors; then Claude Opus reads 30 days of news, four quarters of earnings transcripts and filings per candidate and writes a structured dossier — thesis, invalidation trigger, bull case and bear case. A 1M-token context lets it compare the whole candidate set in a single reasoning pass.
Which AI models does orbyd use?
Anthropic's Claude Opus and Claude Sonnet. Opus handles the deep per-name synthesis and the 1M-token portfolio-composition pass; Sonnet handles the faster scoring passes.
What does a 1M-token context window actually do for stock research?
It lets the composition stage hold every candidate's full dossier at once and compare theses, conflicts, correlations and concentration across the entire set in one reasoning pass — rather than scoring names in isolation and stitching the results together afterwards.
Does orbyd trade in real time?
No. The pipeline runs on a fixed New-York-time schedule (premarket scan, a pre-close decision window, midday and opportunistic checks, and a post-close reconcile). It publishes research and runs on a paper account.
Is orbyd's research automated or human-written?
Fully automated. The dossiers, regime calls and macro views are written by frontier language models; the methodology is open and every read is dated. Humans don't edit the model's output.

Today we track 433 names across 611 themes. Go deeper:

Anatomy of a dossier See the dossiers Full methodology For agents