data

name	data
description	Historical data access for the stock-analysis Backtesting pillar. Query the Rebyte Financial Data Service through the Relay Data API with read-only SQL, plus semantic search over historical news when needed for data exploration.

Backtesting Historical Data Access

Access to Rebyte Financial Data Service through the Relay Data API (/api/data/financial). Two modes:

• SQL (/sql) — read-only analytical SQL over every table (see the SQL patterns below for what works). Work in three steps: catalog → schema → query.
• Semantic search (/search) — vector search over historical datasets that carry content embeddings (currently news). Query by meaning, not keywords. This is data access, not a separate News subcommand.

This sub-skill belongs to the Backtesting pillar. Use it to discover tables, inspect schemas, pull bars, validate coverage, or explore historical context needed before a simulation. For analysis structures without API calls, use ../financial-templates/SKILL.md.

The lake is the ONLY data source: daily tables land the prior trading day and intraday is delayed. There is no realtime feed — answer "current" questions with the latest available bar and state its date; never invent fresher data.

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.

1. The catalog — every served table

IMPORTANT — metadata SQL is unavailable. SHOW TABLES, DESCRIBE, and any information_schema query are not supported by the service and fail. Do NOT use them, do NOT retry them. The table below is the catalog; discover columns with SELECT * FROM <table> LIMIT 1 (step 2).

US equities (identifier ticker; time column t unless noted):

Table	Range	What it is
us.eod	2021-06 → present	Daily price and volume history for US stocks (open/high/low/close/volume). Prices are as-traded — NOT adjusted for splits or dividends.
us.bars_1m	2021-06 → present	Minute-by-minute price and volume for US stocks. Very large — always filter by ticker and a time window.
us.fundamentals	filings → present	Company financial statements as filed with the SEC — income statement, balance sheet, cash flow — one row per company per reporting period (annual, quarterly, or trailing-12-months). 200+ metric columns.
us.news	2016-06 → present	Financial news articles: timestamp, headline, related tickers, full text. The only table you can search by meaning — use /search.
us.splits	1978 → future-dated	Every stock split: which ticker, the effective date, and the ratio (e.g. 2-for-1). Needed to adjust raw prices across a split.
us.dividends	2000 → future-dated	Every cash dividend: ex-dividend date, payment date, amount per share, payout frequency.
us.short_volume	2024-02 → present	For each stock and day, how much of that day's trading volume came from short selling, broken down by trading venue.
us.short_interest	2017-12 → present	Total shares currently sold short per stock, reported twice a month, with average daily volume and how many days of trading it would take to cover.
us.tickers	snapshot	Master list of every US ticker, listed or delisted, with exchange, security type, and identifiers. Use it to build universes and join other tables.
us.ipos	2008 → present	Companies going public: announcement and listing dates, offer price and size, and current status of each offering.

China A-shares (identifier ts_code e.g. 000001.SZ; time column trade_date/trade_time unless noted):

Table	Range	What it is
cn.bars_day	1990-12 → present	Daily price and volume history for China A-shares, including the cumulative adjustment factor needed to compute split/dividend-adjusted prices. Time column t.
cn.bars_1m	2021-06 → present	Minute-by-minute price and volume for China A-shares. Very large — filter by stock and time window.
cn.daily_basic	2021-06 → present	Per-stock daily valuation and trading snapshot: price-to-earnings, price-to-book, turnover rate, market cap, share counts.
cn.income	2013 → present	Quarterly/annual income statements of listed Chinese companies: revenue, costs, profit, earnings per share.
cn.balancesheet	2013 → present	Balance sheets of listed Chinese companies: assets, liabilities, shareholder equity.
cn.cashflow	2013 → present	Cash-flow statements of listed Chinese companies: operating, investing, and financing cash flows.
cn.fina_indicator	2013 → present	Precomputed financial ratios per company per period: profitability (ROE, margins), leverage, liquidity, growth rates.
cn.moneyflow	2010 → present	For each stock and day, the net value of buying versus selling — was money flowing into or out of the stock.
cn.top_list	2010 → present	The exchanges' daily disclosure of stocks with unusual price moves or turnover: which stocks triggered it, why, and the net amount the most active trading desks bought or sold.

Ranges are indicative (checked 2026-07); daily tables land the prior trading day, intraday is delayed.

The 1-minute bar tables are served through a heavier execution path — a query can take tens of seconds. Keep the window narrow (one ticker, days not months) and allow a generous client timeout (the CLI defaults to 180s).

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

DESCRIBE does not work (see above). Read the real columns from a single row:

python3 scripts/anyfinancial_cli.py query "SELECT * FROM cn.bars_1m LIMIT 1"

curl -fsS -X POST "$API_URL/api/data/financial/sql" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"sql":"SELECT * FROM cn.bars_1m LIMIT 1","parameters":[]}' | jq '.'

The keys of the returned row are the column names. Do not guess column names.

3. Query

python3 scripts/anyfinancial_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 '.'

Semantic search — find news by meaning (not keywords)

For news, prefer semantic search over ... WHERE content ILIKE '%...%'. You send a natural-language text; the service embeds it server-side and returns the most similar rows ranked by _score (higher = more relevant). No keys, no embedding on your side.

python3 scripts/anyfinancial_cli.py search "Fed rate cut expectations" --columns title,published_utc,tickers

curl -fsS -X POST "$API_URL/api/data/financial/search" \
  -H "Authorization: Bearer $AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"text":"Fed rate cut expectations","datasets":["us.news"],"limit":5,"additional_columns":["title","published_utc","tickers"]}' | jq '.'

Body fields: text (required, natural language) · datasets (default ["us.news"] — the only dataset with embeddings today) · limit (default 5) · additional_columns (optional extra columns returned in each result's data).

Each result: _score (similarity), matches.content (hit snippets), data (the columns you named in additional_columns), dataset.

Only us.news is searchable today. Other tables are SQL-only — use the three-step SQL flow above for them.

SQL patterns

Write SQL using these verified patterns:

Need	Use
Current time	now()
Truncate to period	date_trunc('day', trade_time)
Bucket into N-minute bars	date_bin(INTERVAL '5 minutes', trade_time, TIMESTAMP '1970-01-01')
Relative time filter	trade_time > now() - INTERVAL '7 days'
Parse a timestamp	to_timestamp('2024-01-01T00:00:00')
Part of a date	extract(year FROM trade_time)
Cast	CAST(x AS BIGINT) or arrow_cast(x, 'Int64')
String concat / match	`a
Paging	LIMIT 100 OFFSET 0

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.
• The catalog table (step 1) and a SELECT * … LIMIT 1 probe (step 2) are the source of truth for table and column names.

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.