Financial Agent Data Sources
for Verifiable AI
面向可验证 AI 的
金融 Agent 数据源
Why Financial Agent Data Sources Matter 为什么金融 Agent 数据源很重要
Finance is unforgiving when an AI agent guesses. A model can write a polished explanation about revenue, valuation, or market sentiment even when it has not checked the source. For investment research, compliance, and market monitoring, the workflow needs data provenance before language. 金融场景无法容忍 AI Agent 猜测。模型即使没有检查来源，也能写出看起来专业的收入、估值或市场情绪解释。对于投资研究、合规和市场监控，工作流必须先有数据来源，再有语言表达。
The related QVeris blog makes the same point: stop letting AI make up financial analysis. A financial agent should locate useful data sources first, then use those sources to support or reject a claim. QVeris 相关博客也强调同一个观点：不要让 AI 编造金融分析。金融 Agent 应先定位可用数据源，再用这些来源支持或否定某个结论。
Core Data Sources for Financial Agents 金融 Agent 的核心数据源
A source-backed finance agent usually needs several classes of data. The exact mix depends on the task, but the map should be explicit before the agent writes a conclusion. 一个有来源支撑的金融 Agent 通常需要多类数据。具体组合取决于任务，但在 Agent 写结论前，数据源地图必须明确。
Prices, volume, movers, historical ranges, and liquidity context. 价格、成交量、异动、历史区间和流动性背景。
10-K, 10-Q, 8-K, risk factors, management discussion, and source URLs. 10-K、10-Q、8-K、风险因素、管理层讨论和来源 URL。
Revenue, margins, cash flow, debt, ROE, valuation, and share data. 收入、利润率、现金流、债务、ROE、估值和股本数据。
Company news, market-moving events, analyst reactions, and sentiment signals. 公司新闻、市场事件、分析师反应和情绪信号。
Rates, inflation, economic calendars, central-bank events, and currency context. 利率、通胀、经济日历、央行事件和汇率背景。
Crypto, web, supply-chain, social, or product-level signals when relevant. 在相关场景下使用加密、网页、供应链、社媒或产品级信号。
The Source-First Rule for AI Finance AI 金融分析的来源优先规则
Financial Agent Data Sources vs Generic AI Answers 金融 Agent 数据源与普通 AI 回答的区别
| Dimension 维度 | Generic AI answer 普通 AI 回答 | Source-first financial agent 来源优先的金融 Agent |
|---|---|---|
| Starting point 起点 | Prompt and model memory. 提示词和模型记忆。 | Specific data source and retrieval path. 具体数据源和检索路径。 |
| Trust 可信度 | Depends on generated language quality. 依赖生成语言的质量。 | Depends on evidence, timestamp, and source coverage. 依赖证据、时间戳和来源覆盖。 |
| Output 输出 | A polished narrative. 看起来流畅的叙述。 | A claim with linked data, caveats, and next checks. 带链接数据、限制说明和下一步检查的结论。 |
How QVeris Helps Agents Find the Right Data Source QVeris 如何帮助 Agent 找到合适数据源
QVeris gives agents a capability routing layer for financial data. Instead of hardcoding one provider per task, an agent can discover available capabilities, inspect inputs and metadata, then call the best matching source for market data, filings, fundamentals, news, macro, crypto, or workflow actions. QVeris 为金融数据提供 Agent 能力路由层。Agent 不必为每个任务硬编码一个供应商，而是可以发现可用能力、检查输入与元数据，再调用最匹配的市场数据、文件、基本面、新闻、宏观、加密或工作流动作。
This makes QVeris especially useful for teams that want verifiable financial AI: the agent can cite what it used, reveal what it could not verify, and route to another capability when the first source is incomplete. 这让 QVeris 特别适合想构建可验证金融 AI 的团队：Agent 可以引用使用过的数据，说明无法验证的部分，并在第一个来源不完整时路由到其他能力。
Build a Source Hierarchy and Financial Data Contract建立来源层级与金融数据契约
A financial agent should not treat every source as interchangeable. Before retrieval begins, define which source is authoritative for each claim type, how freshness is measured, and what metadata must travel with a value into the final answer.
金融 Agent 不应把所有来源视为可以互换。开始检索前，应明确每类说法以什么来源为权威、如何判断新鲜度，以及一个数值进入最终答案时必须携带哪些元数据。
For filings, financial statements, guidance, corporate actions, and official policy data, prefer the issuer, regulator, exchange, central bank, or statistics agency. Aggregators improve access, but important claims should preserve a path back to the originating record.
对于公告、财务报表、业绩指引、公司行动和官方政策数据，应优先使用发行人、监管机构、交易所、央行或统计机构。聚合商可以提升访问效率，但重大说法应保留回到原始记录的路径。
A price is incomplete without asset identity, venue, currency, timestamp, session, and adjustment method. Document whether data is real time, delayed, end of day, consolidated, or venue-specific so the agent cannot compare incompatible observations.
价格如果缺少资产身份、交易场所、币种、时间戳、交易时段和复权方式，就不完整。应说明数据是实时、延迟、日终、合并还是特定场所数据，避免 Agent 比较不兼容的观测值。
Consensus estimates, factors, ratios, and scores depend on methodology. Store the formula, fiscal-period mapping, constituent set, update time, restatement policy, and provider so a plausible number does not hide a different definition.
一致预期、因子、比率和评分都依赖计算方法。应保存公式、财务期间映射、样本范围、更新时间、重述政策和供应商，避免看似合理的数字隐藏不同定义。
Attach source URL or identifier, provider, retrieval time, effective period, units, transformations, confidence, and licensing constraints to each material field. When two sources conflict, surface the disagreement instead of silently averaging or overwriting it.
为每个重要字段附上来源链接或标识符、供应商、获取时间、生效期间、单位、转换过程、置信度和许可限制。两个来源冲突时，应展示分歧，而不是静默求平均或覆盖。
Financial Agent Source Selection Matrix金融 Agent 数据源选择矩阵
Choose sources by the decision being made, not by a generic provider ranking. The same agent may need an official filing for a disclosed number, a low-latency feed for an alert, and cross-source confirmation for a material conclusion.
应根据要做的决策选择来源，而不是套用一个通用供应商排名。同一个 Agent 可能需要用官方公告确认披露数字、用低延迟行情触发预警，并用多个来源交叉确认重大结论。
| Research need研究需求 | Preferred source profile优先来源特征 | Validation questions验证问题 |
|---|---|---|
| Reported fundamentals已披露基本面 | Issuer or regulator record, plus normalized structured data for scale发行人或监管记录，并辅以便于规模化处理的规范化结构化数据 | Is the value filed or adjusted? Which fiscal period, currency, taxonomy, and restatement version?数值是原始披露还是调整值？对应哪个财务期间、币种、分类体系和重述版本？ |
| Market monitoring市场监控 | Timestamped venue or consolidated feed with explicit latency and coverage带明确延迟与覆盖范围的交易场所或合并行情源 | Is it real time or delayed? Which session, venue, corporate-action treatment, and outage policy?是实时还是延迟？对应哪个时段与场所？如何处理公司行动与服务中断？ |
| News and events新闻与事件 | Original announcement plus reputable reporting and event metadata原始公告，加上可信报道和事件元数据 | When did the event occur versus publish? Is the claim confirmed, corrected, duplicated, or still developing?事件发生时间与发布时间是否不同？说法已确认、已更正、重复出现，还是仍在发展？ |
| Investment conclusion投资结论 | Multiple independent source types with a documented evidence chain多种相互独立的来源，并记录完整证据链 | Which facts are observed, which are estimates, which are model outputs, and what evidence could disprove the thesis?哪些是观测事实、哪些是估计值、哪些是模型输出，以及什么证据可能推翻结论？ |
Financial Agent Data Sources FAQ金融 Agent 数据源常见问题
Most research workflows combine filings and fundamentals, market prices and reference data, earnings materials, news and corporate events, macroeconomic series, and sometimes alternative or workflow data. The right mix depends on the claim and required freshness.
It may be enough for a narrow prototype, but production research often needs primary-source verification, specialized coverage, and a fallback for gaps or outages. Adding providers only helps when identities, units, periods, and provenance are normalized.
Do not silently choose or average. Compare definitions, timestamps, reporting periods, adjustment policies, and source authority. Keep both observations, explain the likely reason for the conflict, and escalate when the difference could change the decision.
QVeris lets an agent discover capabilities by intent, inspect schemas and provider signals, and call the best match through a consistent workflow. The application should still define its own authority hierarchy, compliance rules, and evidence thresholds.
多数研究工作流会组合公告与基本面、行情与参考数据、财报材料、新闻与公司事件、宏观经济序列，有时还包括另类数据或工作流数据。正确组合取决于所要证明的说法和时效要求。
对狭窄的原型可能够用，但生产级研究通常还需要第一方来源验证、专门领域覆盖，以及面对缺失或中断的降级来源。只有当实体、单位、期间和来源信息被规范化时，增加供应商才真正有价值。
不要静默选择或求平均。应比较定义、时间戳、报告期间、调整政策和来源权威性，保留两个观测值，解释冲突的可能原因，并在差异可能改变决策时升级人工处理。
QVeris 让 Agent 按意图发现能力、检查 Schema 和供应商信号，并通过一致流程调用最佳匹配项。应用仍需定义自己的来源权威层级、合规规则和证据门槛。