Data

Financial Data Access

Access financial data through the Relay Data API. Four modes:

• Direct recent prices — price-only OHLCV bars across the current and previous exchange-local calendar dates. Use /stocks/bars for US equities, /cn-stocks/bars for CN daily bars, and /cn-stocks/bars_1min for CN minute bars. The server owns the date range; callers cannot widen it.

• SQL (financial/sql) — read-only analytical SQL over every table (see the SQL patterns below for what works). Work in three steps: catalog → schema → query.

• News search (research/news) — search US equity news coverage by meaning, not keywords. This is data access, not a separate News subcommand.

• Research search (research/search) — search the investment research library. Use it for theses and mechanisms; use news for events.

Use this sub-skill for both analysis and the Backtesting pillar: fetch recent prices, discover lake tables, inspect schemas, pull historical bars, validate coverage, or explore historical context. For analysis structures without API calls, use ../financial-templates/SKILL.md.

Route by freshness

• For "current", "today", "latest price", and recent intraday requests, call the direct price API first. During market hours, use US stocks/bars with interval: "1min" and CN cn-stocks/bars_1min.
• For ranges older than two exchange-local calendar dates and for every non-price fact, use the data lake.
• For analysis needing both current price and historical context, use both sources. Keep their provenance separate; if combining bars, de-duplicate by ticker and timestamp.
• Direct APIs are not streaming quote or execution feeds. State the returned marketDateRange, latest bar timestamp, and Polygon upstreamStatus when present. Never call delayed OHLCV tick-level realtime.
• Weekends and holidays can leave one or both direct dates empty. Do not widen the direct window; use the lake for older context.

Authentication

AUTH_TOKEN="$(rebyte-auth 2>/dev/null || jq -r '.sandbox.token' /home/user/.rebyte.ai/auth.json)"
API_URL="$(jq -r '.sandbox.relay_url // empty' /home/user/.rebyte.ai/auth.json 2>/dev/null || true)"
API_URL="${API_URL:-https://api.rebyte.ai}"

If AUTH_TOKEN is empty or null, report that authentication is unavailable and stop. Do not invent credentials.

Direct recent-price APIs

These endpoints accept identifiers and intervals only. Do not send from, to, start_date, end_date, or trade_date; the Relay derives the two-date window in the exchange's time zone.

US equities

curl -fsS -X POST "$API_URL/api/data/stocks/bars" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"ticker":"AAPL","interval":"5min"}' | jq '.'

Intervals: 1min, 5min, 15min, 30min, 1hour, 4hour, 1day. Response: { ticker, marketDateRange: { from, to }, upstreamStatus, count, bars: [{ t, o, h, l, c, v, vw, n }] }. Treat upstreamStatus: "DELAYED" as delayed data and say so. For an intraday/current-price check before the US close, use interval: "1min"; the current date's 1day bar is not a completed closing bar until the market closes.

China A-shares

Daily bars for one stock (omit ts_code only when the whole market is truly needed):

curl -fsS -X POST "$API_URL/api/data/cn-stocks/bars" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"ts_code":"000001.SZ"}' | jq '.'

One-minute bars for one stock:

curl -fsS -X POST "$API_URL/api/data/cn-stocks/bars_1min" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"ts_code":"000001.SZ"}' | jq '.'

Both return { success, marketDateRange: { from, to }, rows, rowCount, elapsedMs }. For an intraday/current-price check before the CN close, use bars_1min; the current date's daily bars row is available only after market close. This is the same minute-during-session rule as US. The direct providers expose prices only: use the lake for news, fundamentals, company details, valuation, dividends, splits, and screening.

Historical data lake

Use the three-step flow below for historical and non-price data.

1. The catalog — what tables exist

financial/catalog returns every table and a one-line description of what it is. Filter to a market with market = us (US equities) or cn (China A-shares); omit it for both. Call this first, then pull columns for the tables you actually need (step 2).

python3 scripts/financial_cli.py catalog            # both markets
python3 scripts/financial_cli.py catalog --market cn

curl -fsS -X POST "$API_URL/api/data/financial/catalog" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"market":"cn"}' | jq '.'

Returns { tables: [{ table, market, description }, …] }. US descriptions are English, CN descriptions are Chinese.

IMPORTANT — do not use metadata SQL. SHOW TABLES, DESCRIBE, and information_schema queries are not a reliable discovery path here. Use catalog for tables and schema (below) for columns — never guess.

2. Get a table's columns — before querying it

financial/schema returns the live column list for one or more tables — each column's name, type, and a human doc (US-English / CN-Chinese). This is the source of truth for column names and meanings; it is always current, so never guess column names and never hardcode them.

python3 scripts/financial_cli.py schema cn.daily_basic us.eod

curl -fsS -X POST "$API_URL/api/data/financial/schema" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"tables":["cn.daily_basic","us.eod"]}' | jq '.'

Returns { schemas: { "cn.daily_basic": [{ name, type, doc }, …], … } }. Pass several tables at once when a query joins across them.

Notes that still hold: us.eod is raw (unadjusted) — join us.splits to adjust; cn.bars_day carries an adjustment factor. Daily tables land the prior trading day (T+1); intraday is delayed. The 1-minute bar tables (us.bars_1m, cn.bars_1m) run through a heavier path — a query can take tens of seconds, so keep the window narrow (one ticker, days not months) and allow a generous client timeout.

3. Query

python3 scripts/financial_cli.py query "SELECT trade_time, o, h, l, c, v FROM cn.bars_1m WHERE ts_code = '000001.SZ' ORDER BY trade_time DESC LIMIT 10"

curl -fsS -X POST "$API_URL/api/data/financial/sql" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"sql":"SELECT trade_time, c FROM cn.bars_1m WHERE ts_code = '''000001.SZ''' ORDER BY trade_time DESC LIMIT 10","parameters":[]}' | jq '.'

Search — find writing by meaning (not keywords)

Never hunt text with ... WHERE content ILIKE '%...%'. Send a natural-language text; the service embeds it server-side and ranks by meaning and exact terms, so tickers and paraphrases both work. No keys, no embedding on your side.

News — what happened

US equity news coverage going back to 2016.

python3 scripts/financial_cli.py news "Fed rate cut expectations" --ticker NVDA --recent

curl -fsS -X POST "$API_URL/api/data/research/news" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"text":"Fed rate cut expectations","ticker":"NVDA","limit":5}' | jq '.'

Body: text (required) · ticker · since/until (ISO dates) · sort (relevance default, or recent) · limit (default 5, max 25). Each result: title, published_at, tickers, score, snippet.

Research — what it means

The investment research library: ~4,300 long-form articles from 13 publications, 2020 to today, including SemiAnalysis and SemiVision (semiconductors, datacenter buildout), MacroCharts and Capital Wars (global liquidity, cycles), Citrini (thematic trades), Doomberg (energy and commodities), and Michael J Burry. This is primary analysis by named practitioners — reach for it on any thesis, debate, or mechanism question, before falling back on general knowledge. The agent's system prompt carries the full channel table with each publication's beat.

python3 scripts/financial_cli.py research "global liquidity and central bank balance sheets"

curl -fsS -X POST "$API_URL/api/data/research/search" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"text":"global liquidity and central bank balance sheets","limit":5}' | jq '.'

Body: text (required) · channel · since/until · limit (default 5, max 25). Each result: channel, publication, slug, chunk_index, title, published_at, score, snippet. channel is the filter value (a slug); publication is how the source is written in a report. Filter one publication with channel: semianalysis, semivision, macrocharts, capitalwars, citrini, doomberg, michaeljburry, fundaai, photoncap, jamesbulltard, viksnewsletter, asymmetricalbets, damnang. Search unfiltered first — you usually want the best argument, not one author's.

A hit is one section of an article. Before quoting it as anyone's position, read around it — a section often states a view the author goes on to demolish:

python3 scripts/financial_cli.py context capitalwars <slug> 2 --radius 1   # neighbouring sections
python3 scripts/financial_cli.py article capitalwars <slug>                # the whole piece

Citing what you find

Reports are internal, and so is every source reachable here — our data lake and our research library. Never put a URL, slug, chunk_index, or any other response identifier into a report. None of them resolve for a reader; a dead link reads worse than no link. Nothing in these responses is a public address, and none of the indices even store a URL.

Identify the source in prose instead, richly enough that a reader could find the piece:

Err toward more attribution, not less. When several publications support one point, name them all — for an internal reader, four sources agreeing is itself a finding.

Search covers news and research articles. Every other table is SQL-only — use the three-step SQL flow above for them.

SQL patterns

Write SQL using these verified patterns:

Do not use (they error — switch syntax, do not retry as-is): DATEADD / DATEDIFF / GETDATE() (use now(), date_trunc, INTERVAL math), TOP n (use LIMIT n), SELECT INTO, stored procedures, or vendor-specific functions.

Rules

• Read-only, one statement per request. Start with SELECT or WITH (SHOW/DESCRIBE/EXPLAIN are not supported by the service — don't use them).
• No mutating statements (INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, TRUNCATE, …).
• Project only needed columns and add LIMIT while exploring.
• financial/catalog (step 1) and financial/schema (step 2) are the source of truth for table and column names — always current. Never guess or hardcode.

On error — do not loop

If a query fails, do not resubmit the same or a near-identical statement. Instead:

1. Read the error message. Invalid function / No field named … means unsupported SQL syntax or wrong column → fix it using the pattern table above and the table schema from step 2.
2. Change exactly one thing and retry.
3. After 2–3 failed attempts, stop and report the exact failing SQL and the exact error. Do not keep retrying.