AlphaGBM Investment Thesis

Turn "I bought this because…" into a tracked, monitored record. Each thesis pairs a prose buy-reason with structured sell conditions so the system can auto-detect when the reasoning no longer holds.

When to use

• User wants to document why they bought a stock
• User wants to set exit triggers (price, PE, fundamental breach)
• User asks which theses are still valid vs triggered
• User asks to update / refine an existing thesis
• User mentions "论据" / "买入理由" / "卖出条件" / "thesis" / "exit trigger"

Prerequisites

• API Key: env ALPHAGBM_API_KEY (format agbm_xxxx…).
• Base URL: default https://alphagbm.zeabur.app. Override via ALPHAGBM_BASE_URL.
• Profile required: A thesis must attach to an existing company profile. If the user hasn't created a profile for the ticker, call POST /api/research/profiles first (see alphagbm-company-profile).

API Endpoints

All endpoints require Authorization: Bearer $ALPHAGBM_API_KEY.

1. List theses

GET /api/research/theses?status=active

Response:

{
  "success": true,
  "theses": [
    { "id": 12, "ticker": "NVDA", "buy_thesis": "...", "status": "active", ... }
  ]
}

2. Get thesis by ticker

GET /api/research/theses/<TICKER>

Returns the active thesis for a ticker. 404 if none exists.

3. Create thesis

POST /api/research/theses
Content-Type: application/json

{
  "ticker": "NVDA",
  "buy_thesis": "AI capex cycle; data-center GPU moat; FCF > $60B.",
  "sell_conditions": [
    { "type": "price_drop_pct",  "value": 20 },
    { "type": "pe_above",        "value": 60 },
    { "type": "growth_below",    "value": 15 },
    { "type": "thesis_breach",   "value": "cloud capex guidance cut > 20%" }
  ]
}

Common sell_conditions types:

• price_drop_pct — drop from purchase/peak %
• pe_above / pb_above — valuation ceiling
• growth_below — revenue/earnings growth threshold
• thesis_breach — free-text qualitative trigger (monitored manually)

4. Update thesis (by id)

PUT /api/research/theses/<THESIS_ID>
Content-Type: application/json

{"buy_thesis": "updated prose", "sell_conditions": [...], "status": "closed"}

Partial updates allowed. Note: uses thesis_id (int), not ticker — read the id from a prior list or get.

5. Delete thesis (by id)

DELETE /api/research/theses/<THESIS_ID>

Hard-delete. Also uses numeric id.

Response schema — full thesis

{
  id, ticker,
  buy_thesis,                     // prose
  sell_conditions,                // [{type, value}]
  status,                         // "active" | "triggered" | "closed"
  thesis_score,                   // AI confidence 0-100 (if scored)
  ai_feedback,                    // AI critique of the thesis (markdown)
  triggered_at, trigger_detail,   // populated when status flips
  created_at, updated_at
}

Status lifecycle

active ──(sell condition fires)──▶ triggered
   │                                   │
   └────────(user closes)──▶ closed ◀──┘

When status = "triggered", trigger_detail shows which condition fired. Surface this to the user — it's the whole point of the system.

Typical Workflow

1. User: "I'm buying NVDA because AI capex is still accelerating"
   → (ensure profile exists — see alphagbm-company-profile)
   → POST /api/research/theses with buy_thesis + sell_conditions
   → Confirm: "Saved. Monitoring: price drop > 20%, PE > 60, growth < 15%."

2. User: "What are my active theses?"
   → GET /api/research/theses?status=active
   → Table: ticker · one-line thesis · conditions · score

3. User: "Any theses triggered?"
   → GET /api/research/theses?status=triggered
   → Alert list with trigger_detail explaining why

4. User: "Update my NVDA thesis — exit if PE > 70 instead of 60"
   → GET /api/research/theses/NVDA to find id
   → PUT /api/research/theses/<id> with revised sell_conditions

Output Formatting Tips

When presenting a thesis to the user, highlight:

1. Ticker + status (with color/emoji: active=green, triggered=red, closed=gray)
2. Buy thesis — first 2 sentences verbatim
3. Sell conditions — bulleted, human-phrased ("Exit if price drops 20%")
4. If triggered — which trigger fired, lead with that
5. AI feedback / score — if present, show as a pull-quote
6. Age — "written 3 weeks ago, reviewed 2 days ago"

Related Skills

• alphagbm-company-profile — Prerequisite. A thesis attaches to a profile.
• alphagbm-health-check — Surfaces theses that may have drifted from their original premise
• alphagbm-stock-analysis — Run a fresh analysis to sanity-check a thesis

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