Alphagbm Stock Analysis

AI-powered stock analysis using AlphaGBM's Five Pillars framework (Fundamental, Technical, Sentiment, Flow, Valuation) with real market data. Returns a 1-10 composite score with actionable signals. Use when: analyzing any stock ticker, evaluating buy/sell decisions, comparing stock fundamentals, assessing risk levels. Triggers on: "analyze AAPL", "what do you think about NVDA", "should I buy TSLA", "stock analysis for META", "is SPY overvalued", "risk assessment for GOOGL".

Analyze stocks via the AlphaGBM API — a G = B + M (Gain = Basics + Momentum) model combining fundamental analysis, market sentiment, EV expectation, ATR stop-loss, sector rotation, and AI reports.

When to use

• User asks to analyze a stock ticker (US / HK / A-share)
• User asks for a stock quote, target price, risk score, or EV recommendation
• User mentions AlphaGBM or wants a comprehensive stock analysis

Prerequisites

• API Key: stored in env ALPHAGBM_API_KEY (format agbm_xxxx…).
• Base URL: default https://alphagbm.zeabur.app. Override with env ALPHAGBM_BASE_URL.
• If the user has neither, tell them to register at https://alphagbm.com and create a key at /api-keys.

API Endpoints

All endpoints require Authorization: Bearer $ALPHAGBM_API_KEY.

1. Quick Quote (instant, no quota cost)

text

GET /api/stock/quick-quote/<TICKER>

Returns: price, change%, PE, forward PE, 52-week range, sector, market cap.

Example:

bash

curl -H "Authorization: Bearer $ALPHAGBM_API_KEY" \
  https://alphagbm.zeabur.app/api/stock/quick-quote/AAPL

2. Full Stock Analysis — Synchronous (blocks 10-30s)

text

POST /api/stock/analyze-sync
Content-Type: application/json

{"ticker": "AAPL", "style": "balanced"}

Parameter	Type	Required	Description
ticker	string	yes	Stock ticker (e.g. AAPL, 0700.HK, 600519.SS)
style	string	no	quality (default), value, growth, momentum, balanced

Add ?compact=true for a condensed agent-friendly response (~500 tokens).

Response contains:

• data — price, PE, PEG, growth, margin, target_price, stop_loss_price, market_sentiment (0-10), ev_model, sector_analysis, capital_analysis
• risk — score (0-10), level, suggested_position%, risk flags
• report — AI-generated narrative report (markdown, ~2000 chars)

3. Full Stock Analysis — Async (for web frontend)

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POST /api/stock/analyze-async
Content-Type: application/json

{"ticker": "TSLA", "style": "growth"}

Returns {"task_id": "uuid"}. Poll task:

text

GET /api/tasks/<task_id>

4. Stock Search (no auth required)

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GET /api/stock/search?q=AAPL&limit=8

Fuzzy search — supports US (AAPL), HK (700, 0700.HK), A-share (600519).

5. Analysis History

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GET /api/stock/history?page=1&per_page=10&ticker=AAPL

6. Stock Summary (for options page linkage)

text

GET /api/stock/summary/<TICKER>

Returns condensed analysis. First-time analysis per ticker is free.

Analysis Model Summary

G = B + M

Dimension	Components	Weight
B (Basics)	PE/PEG, growth rate, profit margin, ROE, FCF	Fundamental valuation
M (Momentum)	VIX, technical indicators, fund flow, macro	Market sentiment 0-10

Risk Score (0-10, additive)

Factor	Trigger	Points
Valuation	PE > 60	+2.0
Growth	Growth < -10%	+2.0
Liquidity	Volume below threshold	+2.0
Market	VIX > 30	+1.5
Technical	Price < MA200	+1.0

Risk 0-2 → Max position 20% · Risk 8-10 → Don't buy.

EV Expectation Model

text

EV = (upside_prob x upside_range) + (downside_prob x downside_range)
Weighted = 50% x 1-week + 30% x 1-month + 20% x 3-month

EV	Recommendation
> +8%	STRONG_BUY
+3% ~ +8%	BUY
-3% ~ +3%	HOLD
< -8%	STRONG_AVOID

Target Price — 5 methods, industry-weighted

PE valuation · PEG valuation · Growth discount · DCF · Technical analysis. Risk adjustment: high risk → -15%, medium risk → -8%.

ATR Stop-Loss

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stop = price - ATR(14) x multiplier(1.5-4.0)

Multiplier adjusts for Beta and VIX. Hard floor: -15%.

Typical Workflow

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1. Quick check → GET /api/stock/quick-quote/NVDA
2. If interesting → POST /api/stock/analyze-sync {"ticker":"NVDA","style":"growth"}
3. Present: recommendation, target price, risk score, EV, AI report

Quota

• Free users: 2 stock analyses/day
• Plus: 1000/month · Pro: 5000/month
• Quick quote costs nothing

Output Formatting Tips

When presenting results to the user, highlight:

1. Recommendation (STRONG_BUY / BUY / HOLD / AVOID / STRONG_AVOID) + confidence
2. Target price vs current price → upside %
3. Risk score + level + top risk flags
4. Stop-loss price + method
5. EV score + weighted EV%
6. Key excerpt from AI report (first 2-3 paragraphs)

Mock Data

When no API key is configured, this skill uses built-in market data snapshots from mock-data/. Supported demo tickers: AAPL, NVDA, SPY, TSLA, META.

Related Skills

• alphagbm-options-score — After stock analysis, evaluate options opportunities
• alphagbm-compare — Compare multiple stocks side-by-side
• alphagbm-market-sentiment — Broader market context for the analysis

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