Compare financial data APIs for ChatGPT across market data, filings, macro coverage, freshness, licensing, and agent readiness.
从行情、监管文件、宏观覆盖、时效、许可和智能体适配度，对比适合 ChatGPT 的金融数据 API。
The API is only one layer. ChatGPT still needs a narrow tool schema, deterministic validation, timestamps, citations, and permission controls.API 只是其中一层。ChatGPT 还需要边界清晰的工具 Schema、确定性校验、时间戳、引用与权限控制。
This is a fit matrix, not a universal ranking. Coverage, entitlements, latency, and limits vary by plan and market; verify them in official documentation before launch.这是一张适配矩阵，不是通用排名。覆盖范围、授权、延迟与限额会因套餐和市场而异，上线前应以官方文档为准。
| Source来源 | Best fit最适合 | Useful data主要数据 | Agent advantageAgent 优势 | Verify before production上线前核查 |
|---|---|---|---|---|
| Alpha Vantage | Fast prototypes快速原型 | Equities, FX, crypto, indicators, fundamentals, economic series股票、外汇、加密资产、指标、基本面、经济序列 | Broad REST surface and official MCP/AI-agent entry pointREST 覆盖广，并提供官方 MCP/AI Agent 入口 | Rate limits, endpoint entitlements, data freshness速率限制、端点权限、数据时效 |
| Massive | U.S. market depth美国市场深度 | Stocks, options, futures, indices, FX, crypto, bulk files股票、期权、期货、指数、外汇、加密资产、批量文件 | REST, streaming, and bulk history support research and backtestsREST、流式与批量历史数据适合研究和回测 | Exchange entitlements, real-time status, redistribution rights交易所授权、实时状态、再分发权利 |
| Twelve Data | Multi-asset global workflows全球多资产工作流 | Stocks, FX, ETFs, funds, commodities, crypto, indicators股票、外汇、ETF、基金、商品、加密资产、指标 | Consistent REST and WebSocket patterns across asset classes跨资产类别保持较一致的 REST 与 WebSocket 模式 | Market-specific delay, null handling, credits and timezone rules不同市场延迟、空值、点数与时区规则 |
| Finnhub | Research with news and estimates新闻与预期研究 | Quotes, fundamentals, estimates, transcripts, news, alternative data报价、基本面、预期、电话会、新闻、另类数据 | One surface can support multi-step company research单一接口可支撑多步骤公司研究 | Dataset-specific plans, history depth, geographic coverage不同数据集套餐、历史深度、地域覆盖 |
| Financial Modeling Prep | Company fundamentals公司基本面 | Statements, ratios, profiles, estimates, transcripts, market endpoints财务报表、比率、公司资料、预期、电话会及行情端点 | Convenient normalized fields for structured company comparisons标准化字段便于进行结构化公司对比 | Restatement handling, source lineage, commercial usage terms重述处理、来源链路、商业使用条款 |
| Nasdaq Data Link | Specialist datasets专业数据集 | Curated free and premium datasets from multiple publishers来自多家发布方的精选免费与付费数据集 | Dataset catalog supports targeted research beyond common quotes数据集目录适合超出常规报价的专项研究 | Publisher methodology, update schedule, license per dataset各数据集发布方方法、更新周期与许可 |
| Alpaca Market Data | Data beside trading infrastructure与交易基础设施相邻 | Equities, options, crypto market data through historical and streaming interfaces通过历史与流式接口提供股票、期权和加密资产行情 | A practical fit when research and controlled execution share a stack适合研究与受控执行共用同一技术栈 | Feed selection, subscription limits, strict action approvals数据源选择、订阅限制、严格执行审批 |
| SEC EDGAR APIs | Authoritative U.S. filings权威美国监管文件 | Submissions and XBRL company facts from the regulator监管机构提供的申报记录与 XBRL 公司事实 | Primary-source evidence for cited filing research为带引用的监管文件研究提供一手证据 | Fair-access policy, taxonomy mapping, amended filings公平访问政策、分类映射、修订文件 |
Use Twelve Data or Alpha Vantage for approachable multi-asset prototypes. Evaluate Massive when U.S. ticks, options, streaming, or bulk history are central. A model should receive compact windows and calculated features—not an unbounded tick stream.多资产原型可先评估 Twelve Data 或 Alpha Vantage；若核心是美国逐笔、期权、流式或批量历史数据，则重点评估 Massive。不要把无界行情流直接塞给模型，应先聚合成有限窗口与确定性特征。
FMP and Finnhub offer normalized company data that is convenient for cross-company prompts. Pair normalized values with filing links or SEC facts when the conclusion depends on accounting definitions, restatements, or a specific reporting period.FMP 与 Finnhub 的标准化公司数据便于跨公司提问。若结论依赖会计口径、重述或特定报告期，应同时保留原始监管文件链接或 SEC 事实数据。
Use SEC EDGAR for U.S. filing evidence, then let ChatGPT summarize selected sections or explain deterministic calculations. Preserve accession, filing date, form type, period, units, and source URL with every extracted fact.美国监管文件应优先使用 SEC EDGAR，再让 ChatGPT 总结选定章节或解释确定性计算。每个抽取事实都应保留 accession、申报日期、表单类型、期间、单位与来源 URL。
Nasdaq Data Link is useful when the question requires a named specialist dataset. Alpha Vantage, Finnhub, and FMP also expose selected economic, news, or alternative fields. Judge each dataset independently; a provider brand does not guarantee identical methodology across feeds.当问题依赖特定专业数据集时，Nasdaq Data Link 较合适。Alpha Vantage、Finnhub 与 FMP 也覆盖部分经济、新闻或另类字段。每个数据集都应单独评估，服务商品牌并不代表所有数据源的方法一致。
OpenAI’s function-calling guidance supports structured tool definitions; MCP can expose tools to compatible clients. Neither replaces server-side validation or data licensing.OpenAI 的 Function Calling 支持结构化工具定义，MCP 可向兼容客户端暴露工具；但两者都不能替代服务端校验与数据许可。
Convert “analyze this stock” into a bounded job: symbols, market, metric definitions, reporting period, acceptable delay, currency, and output evidence.把“分析这只股票”改写成有边界的任务：标的、市场、指标定义、报告期、可接受延迟、币种与证据要求。
Prefer tools such as get_quote, get_financial_facts, and get_filing_section over a generic URL fetcher. Validate symbols, intervals, ranges, and response size on the server.优先提供 get_quote、get_financial_facts、get_filing_section 等窄工具，而不是通用 URL 抓取器；并在服务端校验标的、周期、区间和响应大小。
Return a compact envelope containing value, units, source, observed time, effective period, delay status, and warnings. Keep raw responses outside the prompt for audit and replay.返回紧凑数据包，包含数值、单位、来源、观测时间、生效期间、延迟状态与警告；原始响应保留在 Prompt 之外，用于审计与回放。
Use code for returns, growth rates, ratios, currency conversion, and date alignment. Let the model interpret validated outputs rather than perform long arithmetic from prose.收益率、增长率、比率、汇率换算与日期对齐应由代码完成；模型负责解释已验证输出，而不是在文本中做长计算。
Show the user which source and period support each conclusion. If feeds disagree or a field is missing, report the conflict instead of silently selecting a value.向用户展示每条结论对应的来源与期间。若数据源冲突或字段缺失，应明确报告，而不是静默挑选一个数值。
Illustrative schema only—the value is intentionally not real. See the official OpenAI function-calling guide and MCP documentation. If you want to discover finance tools without hard-coding every provider, review the QVeris financial data MCP pattern.以上仅为示意 Schema，其中数值刻意使用非真实占位。可参考官方 OpenAI Function Calling 指南与 MCP 文档。若希望在不硬编码每个服务商的前提下发现金融工具，可查看 QVeris 金融数据 MCP 模式。
A useful finance assistant usually needs one primary source, an optional corroborating source, and a deterministic calculation layer. These patterns show where each component earns its place.实用的金融助手通常需要一个主数据源、一个可选交叉验证源，以及一层确定性计算。下面按问题类型说明各组件为什么存在。
Use a market-data feed for the verified price window and a news or filing source for candidate events. The model may connect timing and narrative, but it should label causality as an inference unless an official disclosure supports it.用行情源确认价格窗口，再用新闻或监管文件寻找候选事件。模型可以整理时间关系与叙事，但除非官方披露明确支持，否则因果关系必须标为推断。
Use a normalized fundamentals API for speed, then preserve links to the underlying filing or company facts. Align fiscal periods, currencies, units, and accounting definitions before computing margins or growth.先用标准化基本面 API 提升效率，同时保留底层监管文件或公司事实链接。计算利润率或增长率前，必须对齐财政期间、币种、单位和会计口径。
Retrieve the exact form, accession, filing date, and relevant section. Compare passages or XBRL facts outside the model, then ask ChatGPT to explain material differences without inventing management intent.获取准确的表单、accession、申报日期与相关章节；在模型外比较段落或 XBRL 事实，再让 ChatGPT 解释重大变化，但不能虚构管理层意图。
Keep observation date, release date, revision status, frequency, and units. The first release and the latest revised series answer different questions; your tool contract must say which one it returns.保留观测日期、发布日期、修订状态、频率与单位。首次发布值和最新修订序列回答的是不同问题，工具契约必须说明返回哪一种。
Use WebSocket data only where continuous updates matter. A rules engine should deduplicate, aggregate, and threshold events before the model writes an alert, otherwise token use and false positives grow with every tick.只有持续更新确有价值时才使用 WebSocket。事件进入模型前，应由规则引擎完成去重、聚合与阈值判断，否则 Token 消耗和误报会随每个 Tick 增长。
Keep research and execution in different permission domains. The research agent may draft a proposed action with evidence; a policy layer and a human must approve parameters before a separate execution service receives them.研究与执行应处于不同权限域。研究 Agent 可以基于证据起草动作建议，但参数必须经过策略层与人工审批，之后才交给独立执行服务。
Documentation describes capabilities; a pilot reveals whether the feed works for your symbols, questions, controls, and traffic shape. Use the same evaluation set for every candidate.文档只能描述能力，试点才能验证数据源是否适合你的标的、问题、控制要求与流量形态。所有候选服务商应使用同一评测集。
Choose 20–30 representative questions across quotes, fundamentals, filings, and failure cases. Define required fields, acceptable freshness, evidence, and a correct or reviewable outcome.选择 20–30 个代表性问题，覆盖报价、基本面、监管文件与故障场景；定义必需字段、可接受时效、证据要求和可审核结果。
Test common and awkward symbols: multiple exchanges, share classes, delisted names, corporate actions, missing fields, non-USD values, and amended filings. Record every manual mapping.测试常规与复杂标的：多交易所、股类、退市公司、公司行动、字段缺失、非美元数值与修订文件，并记录所有人工映射。
Replay the query mix under expected concurrency. Exercise 429 responses, timeouts, nulls, schema changes, and provider outages. Verify caching does not hide stale data.按预期并发回放查询组合，测试 429、超时、空值、Schema 变化与服务中断，并确认缓存不会掩盖过期数据。
Review answer traceability, coverage gaps, latency distribution, projected request cost, licensing fit, support path, and migration effort. Approve, reject, or retain as a secondary source.审核答案可追溯性、覆盖缺口、延迟分布、预计请求成本、许可适配、支持渠道与迁移工作量，最终决定主用、淘汰或保留为备用源。
Store exchange time, provider retrieval time, delay class, and market-session state separately. Do not let the model infer “live” from a recent-looking price.应分别保存交易所时间、服务商获取时间、延迟类别与市场交易状态，不能让模型仅凭一个看起来较新的价格推断“实时”。
Fundamental providers may normalize fields differently, and filings can be amended. Pin fiscal period, form, units, and retrieval version; compare like with like before asking the model for a narrative.不同基本面服务商的字段标准化口径可能不同，监管文件也可能修订。应固定财政期间、表单、单位与获取版本，在让模型生成叙述前先确保可比。
Treat retrieved text as untrusted data. It must not change tool permissions, reveal credentials, or trigger trades. Separate read-only research from any execution tool and require explicit approval.检索文本必须视为不可信数据，不能改变工具权限、泄露凭据或触发交易。只读研究与任何执行工具应隔离，并要求明确审批。
An API key does not automatically grant the right to display, cache, redistribute, or train on every field. Review the contract for each feed and avoid sending unnecessary proprietary data into model context.拥有 API Key 并不等于自动拥有所有字段的展示、缓存、再分发或训练权利。应逐个数据源审查合同，并避免把无关的专有数据发送进模型上下文。
Yes, when it is connected to a licensed data source through an app, tool call, GPT Action, MCP server, or developer-built backend. The model alone is not a market-data feed.可以，但前提是通过 App、Tool Calling、GPT Action、MCP Server 或开发者后端接入已授权数据源。模型本身并不是行情数据源。
There is no universal best free option. Alpha Vantage is approachable for prototypes, SEC EDGAR is authoritative for U.S. filings, and vendor trial tiers can help validate coverage. Confirm current limits and licensing before production use.没有适合所有场景的免费冠军。Alpha Vantage 适合快速原型，SEC EDGAR 是美国监管文件的一手来源，各服务商试用套餐也可用于验证覆盖。生产使用前必须核实最新限额与许可。
Use REST for snapshots, filings, fundamentals, and scheduled research. Use WebSocket streams only when the workflow needs continuous low-latency updates, and aggregate events before passing them to the model.快照、监管文件、基本面与定时研究通常使用 REST。只有在工作流确实需要持续低延迟更新时才使用 WebSocket，并在传给模型前先聚合事件。
It can assist research, but financial data can be delayed, revised, incomplete, or licensed for limited uses. Keep provenance visible, validate calculations deterministically, and require human approval for consequential actions.它可以辅助研究，但金融数据可能延迟、修订、不完整，或仅许可用于有限场景。应保留来源、用确定性代码验证计算，并对高影响操作设置人工审批。
Start with one representative finance query, inspect matching capabilities and required parameters, then test with non-sensitive data before adding production credentials.从一个代表性金融问题开始，检查匹配能力与必需参数，再用非敏感数据测试，最后才接入生产凭据。
