| version | 2 |
| name | stock-analysis |
| description | The single financial skill: stock and company analysis plus strategy backtesting, on the Rebyte financial data lake (US equities + China A-shares). Covers price history, technical analysis, company research, news, fundamentals, dividends/splits, insider trading via SEC EDGAR, multi-stock comparison, and NautilusTrader backtesting with a first-class result bundle. Use when the user mentions stock tickers, stock prices, company analysis, investment research, financial data, or wants to backtest a strategy. Triggers: stock symbol (AAPL, TSLA, 000001.SZ), 'stock price', 'analyze stock', 'compare stocks', 'company financials', 'insider trading', 'SEC filing', 'is X a good buy', 'price history', 'backtest', '回测'. Do NOT use for full-blown multi-source research reports (use financial-deep-research instead). |
Stock Analysis
Stock and company analysis plus strategy backtesting on the Rebyte financial
data lake.
Requires Rebyte API auth — $AUTH_TOKEN and $API_URL are set up per the
agent's system prompt; use them as Bearer token and base URL.
Skill layout — load the pillar you need
| Pillar | When |
|---|
| this file | Analysis playbooks: price checks, company overviews, comparisons, fundamentals, technicals |
data/SKILL.md | Data access mechanics: full 19-table catalog (US + CN), SQL patterns, semantic news search, error rules. Read before writing SQL. |
backtesting/SKILL.md | Strategy simulation: 5-phase NautilusTrader workflow ending in a backtest result bundle |
financial-templates/SKILL.md | Analysis structures (DCF, comps, memo formats) with no data calls |
report-style/README.md | Kami design system for every HTML report this skill delivers |
references/sec-edgar.md | SEC filings, full 10-K/10-Q text, insider (Form 4) trades via edgartools |
Price/K-line charts: use the financial-charts skill (TradingView-style
Lightweight Charts).
Data sources
| Source | What it provides | Access |
|---|
| Rebyte financial data lake | US: daily + 1-minute bars, news (semantic-searchable), SEC-filing fundamentals, splits, dividends, short data, ticker universe, IPOs. CN A-shares: daily + 1-minute bars, valuation snapshots, financial statements, money flow, unusual-move disclosures. | Read-only SQL via POST $API_URL/api/data/financial/sql — see data/SKILL.md |
| SEC EDGAR | Full filing text (10-K, 10-Q, 8-K), filing sections, insider (Form 4) trades | edgartools Python library — see references/sec-edgar.md |
Freshness — the lake is historical (T+1). Daily tables land the prior
trading day; intraday is delayed. There is no realtime feed: answer "current
price" questions with the latest available bar and state its date. Never
invent fresher data.
Analysis Workflows
All data steps below are lake SQL (recipes in the next section).
1. Quick Stock Check
User: "What's AAPL doing?" / "AAPL price"
→ Latest daily bars (last 1 month) — quote the last close and its date
→ Ticker details
→ Present: last close + date, recent trend, basic company info
2. Company Overview
User: "Tell me about NVDA" / "What does Tesla do?"
→ Ticker details + latest fundamentals period (revenue, net income)
→ Daily bars (last 3 months)
→ Recent news (5 headlines) — semantic search for themes if needed
→ Present: business summary, market position, recent performance, news themes
3. Technical Analysis
User: "Is TSLA a good buy?" / "AAPL technical analysis"
→ Daily bars (last 6 months) — trend, support/resistance
→ 1-minute bars aggregated to hourly (last 5 trading days) — short-term momentum
→ Recent news (10 articles) — read and judge the tone yourself
→ Compute: moving averages, price range, volume trends
→ Present: trend direction, key levels, volume analysis, sentiment, outlook
4. Multi-Stock Comparison
User: "Compare AAPL vs MSFT vs GOOGL"
→ One SQL per dataset covering all tickers (WHERE ticker IN (...))
→ Compare: price performance, fundamentals, news flow
→ Present: side-by-side table, relative performance
5. Fundamental Deep Dive
User: "AAPL financials" / "NVDA revenue trend"
→ Fundamentals: annual (5 periods) + quarterly (4 periods)
→ Dividends (last 12) — history and implied yield vs latest close
→ Weekly-aggregated bars (last 2 years) — long-term price context
→ Compute: revenue growth, margin trends, EPS trend, payout ratio
→ Optional: SEC EDGAR for full 10-K text
6. Insider Activity
User: "Insider trading for TSLA" / "Are executives buying NVDA?"
→ SEC EDGAR: Form 4 filings (the lake does not carry insider trades)
→ Daily bars (last 3 months) — price context around the trades
→ Present: recent transactions, insider sentiment, correlation with price
7. Due Diligence Package
User: "Full analysis of MSFT" / "Due diligence on AMD"
→ Ticker details, 1y daily bars, annual + quarterly fundamentals,
dividends, splits, 20 recent news items
→ SEC EDGAR: latest 10-K, recent 8-Ks, Form 4 insider trades
→ Present: comprehensive report (Kami-styled HTML per report-style/)
8. Sector Research
User: "Compare cloud stocks" / "Best semiconductor stocks"
→ Identify tickers (us.tickers can filter by name/type/exchange)
→ Batch daily bars + fundamentals across the set
→ Semantic news search on the sector theme
→ Present: sector overview, leaders, relative performance
9. Strategy Backtest
User: "Backtest an SMA crossover on AAPL" / "验证我的策略"
→ Switch to backtesting/SKILL.md and run its 5 phases end-to-end
→ Deliverable is the backtest bundle at /code/backtests/<slug>/
Trigger Patterns
ALWAYS fetch data when the user mentions any of these. Do NOT answer from
memory — query the lake.
| User intent | Required actions |
|---|
| Stock symbol mentioned (AAPL, $TSLA, 000001.SZ) | Daily bars + ticker details |
| "price", "chart", "how is X doing" | Daily bars (aggregate 1-minute bars for intraday granularity) |
| "news", "what's happening with" | News query (10+ items) or semantic search |
| "analyze", "research", "tell me about" | Details + bars + news |
| "compare", "vs", "versus" | All datasets for each stock, side-by-side |
| "buy", "sell", "good investment" | Bars + news + fundamentals (annual + quarterly) |
| "financials", "revenue", "earnings" | Fundamentals (annual + quarterly) |
| "dividend", "yield", "payout" | us.dividends |
| "split", "stock split" | us.splits |
| "short interest", "shorts" | us.short_interest / us.short_volume |
| "insider", "who's buying/selling" | SEC EDGAR Form 4 filings |
| "10-K", "10-Q", "SEC filing" | SEC EDGAR filings |
| "backtest", "回测", "strategy performance" | backtesting/SKILL.md |
Lake SQL recipes
Auth + request format, DataFusion-style SQL patterns, the on-error rule, and
the full table catalog are in data/SKILL.md. The recipes below map the
common analysis needs; tickers are UPPERCASE for US, NNNNNN.SZ/NNNNNN.SH
for CN.
SELECT t, o, h, l, c, v FROM us.eod
WHERE ticker = 'AAPL' AND t >= to_timestamp('2026-01-01')
ORDER BY t
SELECT date_bin(INTERVAL '1 hour', t, TIMESTAMP '1970-01-01') AS bucket,
min(t) AS t_open, max(h) AS h, min(l) AS l, sum(v) AS v
FROM us.bars_1m
WHERE ticker = 'AAPL' AND t >= now() - INTERVAL '5 days'
GROUP BY bucket ORDER BY bucket
SELECT published_utc, title, tickers FROM us.news
WHERE array_has(tickers, 'AAPL')
ORDER BY published_utc DESC LIMIT 10
SELECT fiscal_year, fiscal_period, is_revenues, is_gross_profit,
is_operating_income_loss, is_net_income_loss,
is_diluted_earnings_per_share, bs_assets, bs_liabilities, bs_equity,
cf_net_cash_flow_from_operating_activities
FROM us.fundamentals
WHERE array_has(tickers, 'AAPL') AND timeframe = 'annual'
ORDER BY fiscal_year DESC LIMIT 5
SELECT ex_dividend_date, cash_amount, frequency FROM us.dividends
WHERE ticker = 'AAPL' ORDER BY ex_dividend_date DESC LIMIT 12;
SELECT execution_date, split_from, split_to FROM us.splits
WHERE ticker = 'AAPL' ORDER BY execution_date DESC
SELECT ticker, name, primary_exchange, type, active, cik FROM us.tickers
WHERE ticker = 'AAPL'
SELECT t, o, h, l, c, v, pct_chg, adj_factor FROM cn.bars_day
WHERE ts_code = '000001.SZ' AND t >= to_timestamp('2026-01-01')
ORDER BY t
SELECT trade_date, pe_ttm, pb, turnover_rate, total_mv FROM cn.daily_basic
WHERE ts_code = '000001.SZ' ORDER BY trade_date DESC LIMIT 20
The data/scripts/anyfinancial_cli.py helper wraps auth + the SQL endpoint:
python3 data/scripts/anyfinancial_cli.py catalog
python3 data/scripts/anyfinancial_cli.py schema us.eod
python3 data/scripts/anyfinancial_cli.py query "SELECT ... LIMIT 10"
python3 data/scripts/anyfinancial_cli.py search "Fed rate cut expectations" --columns title,published_utc
SEC EDGAR Reference
For full filing text, filing sections, and insider trading data, see
references/sec-edgar.md.
Quick start:
pip install edgartools
from edgar import Company, set_identity
set_identity("Rebyte Agent agent@rebyte.ai")
company = Company("AAPL")
filings = company.get_filings(form="10-K")
insider = company.get_filings(form="4")
Analysis Guidelines
Computing Technical Indicators from Price Bars
The lake returns raw OHLCV. Compute indicators yourself (or in SQL):
- Simple Moving Average (SMA): average of last N closes (20-day and 50-day)
- Price trend: current close vs 20-day and 50-day SMA
- Support/Resistance: recent lows/highs from daily bars
- Volume trend: recent volume vs 20-day average volume
- 52-week range: min low / max high over 1 year of daily bars
- Corporate actions:
us.eod is unadjusted — when the window spans a
split, adjust with us.splits; CN daily bars carry adj_factor directly
Reading News
Lake news carries no precomputed sentiment — read the headlines/content and
judge the tone yourself, noting publisher weight and recency. For thematic
questions, prefer the semantic search endpoint over keyword ILIKE.
Financial Statement Analysis
- Revenue growth: YoY change across periods
- Margin trends: gross/operating/net margin over time
- Cash position: cash & equivalents vs total debt
- Earnings quality: operating cash flow vs net income (should be close)
Presenting Results
- Lead with the answer (bullish/bearish/neutral, last close + its date, key metric)
- Use tables for multi-stock comparisons
- Include specific numbers with dates — never vague statements
- Distinguish facts (from data) from analysis (your interpretation)
- State the data date explicitly — the lake is T+1, not realtime
- Long-form deliverables: Kami-styled HTML per
report-style/
Important Notes
- US tickers UPPERCASE (
AAPL); CN codes suffixed (000001.SZ, 600519.SH)
- All timestamps UTC
- Data is T+1 — daily tables land the prior trading day; never present it as realtime
- Coverage: US from 2021-06 (bars) with reference data much deeper (splits 1978→, dividends 2000→); CN daily from 1990-12 — full catalog in
data/SKILL.md
- SEC EDGAR is free — no API key, but requires an identity string
When NOT to Use This Skill
- Full research reports with 10+ sources, citations, methodology → use
financial-deep-research
- Simple web lookup ("What's Apple's website?") → use web search
- Realtime quotes / live trading → not available; the lake is historical (T+1)
- Personal financial advice → not qualified
