菜单
Paid capability marketplace for global multi-asset data; use it when free Vibe-Trading sources lack coverage, depth, or provider quality, and keep free sources as the default for routine OHLCV.
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基于 SOC 职业分类
| name | qveris |
| category | data-source |
| description | Paid capability marketplace for global multi-asset data; use it when free Vibe-Trading sources lack coverage, depth, or provider quality, and keep free sources as the default for routine OHLCV. |
QVeris is a paid capability marketplace for global market, fundamental, macro,
derivatives, crypto, China/HK, news, filings, and alternative-data calls. Use it
when the built-in free sources cannot cover the requested dataset, when the user
explicitly asks for QVeris, or when a premium provider is needed for depth such
as options Greeks, analyst/calendar feeds, broad provider comparison, or paid
China/HK/global coverage. For ordinary OHLCV, keep source: "auto" on the free
loader chain unless the user explicitly selects source: "qveris".
Signup link: QVeris via Vibe-Trading.
Invite code fallback: Vyjjo5G_1cAHJA.
qveris_searchCall qveris_search first. Search is free and returns candidate capabilities
with tool_id, provider, parameters, examples, expected_cost, billing rule,
categories, and stats such as success_rate and average execution time.
Good queries are concrete:
US listed options chain implied volatility Greeks AAPLHong Kong market daily OHLCV financial statementsFRED CPI Treasury yield curve macro seriesChina A share northbound fund flow dailyqveris_inspectCall qveris_inspect on one or more tool_id values before any paid call.
Inspect is free and returns the fuller parameter schema and sample parameters.
Use it to verify required parameters, enum values, date formats, and output
shape before execution.
qveris_executeCall qveris_execute only after the user has enabled QVeris and the tool has
been inspected. Pass the inspected tool_id, the exact parameters, and a
reasonable max_response_size. Use session_id and search_id when available
so usage can be reconciled later.
Prefer the candidate with the best combination of:
stats.success_rate.expected_cost for equivalent data.Do not assume tools with similar names are interchangeable. The measured catalog
has very large quality variance inside the same category; design notes recorded
FMP tools ranging from near-zero to 100% success and some tradefeeds tools at
0%. Surface success_rate and expected_cost in the answer whenever the user is
choosing among paid options.
mode="free".qveris_search) and inspect (qveris_inspect) are free only after
the user explicitly enables the paid QVeris route.qveris_execute) is paid only when the provider call is billable.success=false,
cost=0.0, status=not_charged, and unchanged balance.mode="free" means the original free public-data path stays active and
QVeris is hidden from agent/tool/backtest routing.mode="paid" permits QVeris discovery, inspect, and execute calls, bounded by
budget_credits_per_session.cost and
remaining_credits for the user.Configuration can come from QVERIS_API_KEY / QVERIS_BASE_URL, or from
Settings -> QVeris / vibe-trading data mode paid. If QVeris is disabled, in
free mode, or no API key is configured, the tools should be treated as
unavailable.
qveris_execute accepts max_response_size (20480 default, -1 means do not
ask QVeris to truncate). When a response is too large, QVeris returns a result
object containing a message, truncated_content, and full_content_file_url.
If the full JSON is required, fetch that signed URL and use the full payload.
If a summary is enough, cite that the response was truncated and work only from
truncated_content.
Check balance and recent usage through:
vibe-trading data status or vibe-trading data usage.For reconciliation, prefer execution_id, tool_id, charge_outcome, cost,
and remaining_credits over text summaries.
Professional finance research toolkit — backtesting (8 engines + benchmark comparison panel), factor analysis, Alpha Zoo (460 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 87 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 20 market-data sources (tushare, yfinance, okx, akshare, baostock, tencent, mootdx, ccxt, futu, local, eastmoney, sina, stooq, yahoo, india_broker, qveris, plus optional-key finnhub/alphavantage/tiingo/fmp).
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Create, modify, and optimize quantitative trading strategies, then backtest and evaluate them.
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Strategy Development Manager: convert academic papers and research reports into validated factors and strategies with automated backtesting, persistent storage, and decay monitoring.
The single ROUTER for every data need. Load this skill BEFORE any backtest, data-fetch, or research task to pick the best available source/tool, honour auth (env) requirements, and avoid ban-risk providers.