Alphagbm Investment Thesis

Record and track the "why I bought" and "when I sell" for each position. Each thesis is attached to a company profile: buy reasons in prose, sell conditions as structured triggers (price drop, PE spike, thesis breach). The system monitors conditions automatically and flips the thesis to "triggered" when one fires. Use when: writing buy logic, setting exit triggers, reviewing active theses, seeing which triggered. Triggers on: "write a thesis for NVDA", "why did I buy AAPL", "set a stop loss logic on TSLA", "which theses are triggered", "update my thesis", "投资论据", "卖出条件", "买入理由", "论据被打破".

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

text

GET /api/research/theses?status=active

Query	Values	Description
status	active / triggered / closed	Optional filter

Response:

json

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

2. Get thesis by ticker

text

GET /api/research/theses/<TICKER>

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

3. Create thesis

text

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%" }
  ]
}

Parameter	Type	Required	Description
ticker	string	yes	Must match an existing profile
buy_thesis	string	yes	Free-form prose, recommend 2-4 sentences
sell_conditions	array	no	Structured triggers (see types below)

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)

text

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)

text

DELETE /api/research/theses/<THESIS_ID>

Hard-delete. Also uses numeric id.

Response schema — full thesis

text

{
  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

text

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

text

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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