alphagbm-stock-analysis
GitHub基于AlphaGBM五支柱框架的AI股票分析技能，整合基本面、技术面等数据，提供1-10综合评分及买卖建议。适用于美股/港股/A股的分析、风险评估及交易决策支持。
Trigger Scenarios
Install
npx skills add AlphaGBM/skills --skill alphagbm-stock-analysis -g -y
SKILL.md
Frontmatter
{
"name": "alphagbm-stock-analysis",
"globs": [
"mock-data\/*.json"
],
"description": "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\".\n"
}
AlphaGBM Stock Analysis
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(formatagbm_xxxx…). - Base URL: default
https://alphagbm.zeabur.app. Override with envALPHAGBM_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)
GET /api/stock/quick-quote/<TICKER>
Returns: price, change%, PE, forward PE, 52-week range, sector, market cap.
Example:
curl -H "Authorization: Bearer $ALPHAGBM_API_KEY" \
https://alphagbm.zeabur.app/api/stock/quick-quote/AAPL
2. Full Stock Analysis — Synchronous (blocks 10-30s)
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_analysisrisk— score (0-10), level, suggested_position%, risk flagsreport— AI-generated narrative report (markdown, ~2000 chars)
3. Full Stock Analysis — Async (for web frontend)
POST /api/stock/analyze-async
Content-Type: application/json
{"ticker": "TSLA", "style": "growth"}
Returns {"task_id": "uuid"}. Poll task:
GET /api/tasks/<task_id>
4. Stock Search (no auth required)
GET /api/stock/search?q=AAPL&limit=8
Fuzzy search — supports US (AAPL), HK (700, 0700.HK), A-share (600519).
5. Analysis History
GET /api/stock/history?page=1&per_page=10&ticker=AAPL
6. Stock Summary (for options page linkage)
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
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
stop = price - ATR(14) x multiplier(1.5-4.0)
Multiplier adjusts for Beta and VIX. Hard floor: -15%.
Typical Workflow
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:
- Recommendation (STRONG_BUY / BUY / HOLD / AVOID / STRONG_AVOID) + confidence
- Target price vs current price → upside %
- Risk score + level + top risk flags
- Stop-loss price + method
- EV score + weighted EV%
- 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
Powered by AlphaGBM — Real-data options & research intelligence for traders and AI agents. 10K+ users.
Version History
- c69fa1b Current 2026-07-05 20:18
