Real-Time Stock Price APIs for AI Agents AI Agent 实时股票价格 API 对比

Which real-time stock API should you choose?实时股票 API 到底该怎么选？

This guide deliberately avoids invented latency leaderboards. Network distance, plan entitlements, feed type, symbol activity, batching, and client code can move a benchmark more than the provider name. Instead, it gives you a repeatable way to test each candidate in your own region and workload.

本文不会用缺少可复现实验条件的“延迟排行榜”制造确定性。网络距离、套餐权限、数据源类型、标的活跃度、消息批处理方式和客户端代码，都可能比供应商品牌更影响测试结果。更可靠的方法，是在你的部署地区和真实负载下，用统一标准测试候选服务。

What does “real-time stock price API” actually mean?“实时股票价格 API”究竟意味着什么？

A real-time stock price API is a programmatic market-data service that returns or streams current trades, quotes, or bars. But “real-time” describes freshness, not completeness. A price can be live from one exchange and still differ from the consolidated US market.

实时股票价格 API 是通过程序返回或推送最新成交、买卖报价或 K 线的市场数据服务。但“实时”只说明新鲜度，并不自动代表完整性。一条价格可以来自某一家交易所并且完全实时，却仍然不等同于美国全市场的综合行情。

IEX is one US exchange. SIP data is the consolidated stream to which US exchanges report trades and quotes. Alpaca’s documentation makes the difference concrete: its free live stock stream uses IEX, while recent SIP data requires the appropriate subscription. Therefore, “free and real-time” can be true while “complete consolidated US coverage” is false.

IEX 是美国的一家交易所；SIP 则汇总美国各交易所上报的成交与报价。Alpaca 官方文档明确说明：免费实时股票流使用 IEX，而访问近期 SIP 数据需要相应订阅。因此，“免费且实时”可能是真的，但“覆盖完整的美国综合市场”可能是假的。

6 real-time stock market data APIs compared6 家实时股票市场数据 API 对比

The table prioritizes decision-useful facts over a single score. Plan limits and exchange entitlements change frequently; values below were checked against official pages on August 3, 2026 and should be verified again before purchase.

下表不做看似精确却容易误导的总分，而是呈现真正影响选型的事实。套餐额度和交易所权限变化频繁，以下内容于 2026 年 8 月 3 日依据官方页面核对，购买前仍应再次确认。

For a live US watchlist: shortlist Massive and Alpaca, then compare the exact plan’s trade/quote feed and license. For a cross-asset prototype: Twelve Data reduces the number of API families you must learn. For a classroom script or daily research notebook: Alpha Vantage may be sufficient. For a research agent that also needs company context: Finnhub deserves evaluation. For simple end-of-day REST ingestion: Marketstack is easy to understand, but its free tier should not be described as a free real-time stock API.

实时美国股票自选列表：优先测试 Massive 与 Alpaca，并逐项核对具体套餐的成交/报价数据源和授权。跨资产原型：Twelve Data 可减少需要学习的 API 家族数量。教学脚本或每日研究 Notebook：Alpha Vantage 可能已经足够。还需要公司信息的研究 Agent：可重点评估 Finnhub。简单的日线 REST 摄取：Marketstack 易于理解，但其免费层不应被描述为“免费实时股票 API”。

What each stock API is actually good at六家股票 API 各自真正擅长什么？

A comparison table is useful for scanning, but it cannot explain why two APIs with similar feature lists behave very differently in a real product. These profiles focus on the workload, the integration advantage, and the limitation most likely to surprise a team after launch.

对比表适合快速浏览，却无法解释为什么功能列表相似的两种 API 在真实产品中表现完全不同。下面从适用工作负载、集成优势，以及最容易在上线后让团队意外的限制三个角度逐家分析。

Massive is the natural shortlist candidate when the product needs more than a “current price.” Its stock WebSocket family covers trade events, quote events, per-second and per-minute aggregates, and specialized market-status events according to plan. That breadth is valuable for live charts, market monitors, research infrastructure, and alert systems that must distinguish a trade from a quote or bar. The REST, WebSocket, and bulk-file options also let a team use one vendor for recent snapshots, continuous events, and historical backfills.

当产品需要的不只是一个“当前价格”时，Massive 很自然会进入候选名单。它的股票 WebSocket 系列按套餐提供逐笔成交、买卖报价、秒级与分钟级聚合数据，以及部分市场状态事件。这种粒度适合实时图表、市场监控、研究基础设施和需要区分成交、报价与 K 线的告警系统。REST、WebSocket 与批量文件并存，也方便团队用同一家供应商完成近期快照、持续事件和历史回补。

The important caveat is plan semantics. “WebSocket available,” “all US tickers,” and “real-time quotes” are not interchangeable claims. A lower tier may expose delayed aggregates or a subset of event types while real-time trades and NBBO-style quote access sit higher. Teams should map every required channel to the exact plan and license, especially if data will be displayed to customers rather than used only for personal analysis.

关键限制在于套餐语义。“提供 WebSocket”“覆盖全部美国股票代码”和“提供实时报价”并不是同一件事。较低套餐可能只开放延迟聚合数据或部分事件类型，而实时成交和类似 NBBO 的报价权限位于更高层。尤其当数据要展示给客户、而不只是个人分析时，团队必须把每一个所需频道对应到准确的套餐和授权。

Alpaca becomes compelling when quotes are only one part of a trading-adjacent workflow. A developer can combine market-data snapshots or streams with paper trading, account state, positions, and order APIs in a consistent ecosystem. That reduces integration friction for portfolio dashboards, educational trading applications, rebalancing prototypes, and agents that monitor a position before preparing—but not autonomously submitting—an action.

当报价只是交易相关工作流的一部分时，Alpaca 的优势会更明显。开发者可以在同一生态中把市场数据快照或流，与模拟交易、账户状态、持仓和订单 API 组合起来。这能降低投资组合仪表盘、交易教学应用、再平衡原型，以及“先监控持仓、再准备动作但不自动下单”的 Agent 的集成成本。

Its free live stock stream is also a good development entry point, but the IEX-versus-SIP distinction must remain visible in the product. IEX is one venue; SIP represents consolidated activity across participating US exchanges. A live IEX trade can be fresh and valid while volume, last trade, and price differ from a broker screen using consolidated data. Use the free feed for appropriate prototypes, then test the paid entitlement that matches the production definition of “market price.”

它的免费实时股票流也非常适合开发入门，但产品中必须明确保留 IEX 与 SIP 的差异。IEX 是一个交易场所，SIP 代表参与的美国交易所综合活动。实时 IEX 成交可以既新鲜又有效，但成交量、最新成交价仍可能与使用综合数据的券商界面不同。免费数据适合边界明确的原型，生产环境则应测试与产品“市场价格”定义相匹配的付费权限。

Twelve Data is attractive when a team wants a consistent developer experience across equities, ETFs, foreign exchange, and crypto rather than the deepest possible US equity microstructure. Its straightforward endpoints and broad instrument model work well for prototypes, international watchlists, technical-analysis tools, and products whose symbols span several asset classes. Separate API and WebSocket credit models make it possible to choose snapshots for some workflows and streams for others.

如果团队希望用一致的开发体验覆盖股票、ETF、外汇和加密资产，而不是追求最深的美国股票微观结构，Twelve Data 很有吸引力。其端点和金融工具模型适合原型、国际自选列表、技术分析工具，以及标的跨越多个资产类别的产品。API 与 WebSocket 采用分开的 credits 模型，也便于部分工作流使用快照、另一部分使用流式数据。

The trap is assuming that “global coverage” means identical freshness and entitlement everywhere. Exchange availability, market count, technical-indicator access, symbol subscriptions, and real-time status differ by plan and venue. Before committing, build a representative symbol list from every target country and asset class, then verify exchange, currency, session, timestamp, and update frequency for each group.

容易踩坑的地方，是把“全球覆盖”理解为所有地区都具有相同的新鲜度和权限。交易所可用性、市场数量、技术指标、标的订阅数量和实时状态会随套餐与场所变化。正式选型前，应从每个目标国家和资产类别中建立代表性标的列表，并逐组验证交易所、币种、市场时段、时间戳和更新频率。

Alpha Vantage remains useful for tutorials, notebooks, scheduled research, indicators, and applications that need a handful of queries rather than a live event stream. The API is familiar to many Python users, responses are easy to inspect, and the surrounding documentation makes it practical for learning data ingestion and technical-analysis workflows. For daily screeners or a small internal research task, simplicity can matter more than streaming. You can inspect the Alpha Vantage provider in QVeris before comparing its current access model.

Alpha Vantage 仍然适合教程、Notebook、定时研究、技术指标，以及只需要少量查询而非实时事件流的应用。许多 Python 用户熟悉它的调用方式，响应容易检查，配套文档也适合学习数据摄取和技术分析流程。对于每日筛选器或小型内部研究任务，简洁性可能比流式能力更重要。选型前可先在 QVeris 中查看 Alpha Vantage Provider，再核对它当前的接入方式。

The free allowance is not a realistic continuous-monitoring budget. Twenty-five daily requests disappear quickly once symbols, retries, multiple endpoints, and development environments are counted. Teams must also distinguish standard API access from exchange-licensed real-time or delayed US-market products. Alpha Vantage should be selected because the workload is intentionally low frequency—not because a marketing list called it a free real-time monitoring solution. For the cost details, read the Alpha Vantage pricing and alternatives guide.

免费额度并不适合持续监控。一旦把多个标的、重试、多个端点和开发环境都计算在内，每日 25 次请求会很快用完。团队还应区分常规 API 权限与交易所授权的实时或延迟美国市场产品。选择 Alpha Vantage 的理由应是工作负载本来就是低频，而不是因为某篇榜单把它称为“免费实时监控方案”。如需进一步核算成本，可阅读 Alpha Vantage 定价与替代方案指南。

Finnhub deserves consideration when the application must explain a move, not merely observe it. Quotes can sit beside company profiles, financial statements, filings, earnings data, news, analyst information, or alternative datasets, depending on the endpoint and plan. That combination is useful for research assistants and event-monitoring agents that need to answer “what changed, what evidence supports it, and what should a human review next?”

当应用不仅要观察价格，还要解释价格变化时，Finnhub 值得重点评估。根据端点和套餐，报价可以与公司资料、财务报表、监管文件、财报、新闻、分析师信息或另类数据结合。这种组合适合研究助手和事件监控 Agent，用于回答“发生了什么、证据是什么、人工下一步应检查什么”。

Because the catalog is broad, teams should not reduce Finnhub to one headline request limit. Dataset availability, commercial rights, history depth, and rate limits can differ by endpoint. Create an endpoint inventory for the exact research workflow, identify which calls are interactive versus scheduled, and test how the system behaves when one supporting dataset is unavailable even though the price endpoint still works.

由于数据目录很广，团队不应只用一个总限流数字概括 Finnhub。不同端点的数据集可用性、商业权利、历史深度和限流可能不同。应为实际研究流程建立端点清单，区分交互调用与定时调用，并测试当某个辅助数据集不可用、但价格端点仍正常时系统如何降级。

Marketstack fits teams that want a conventional HTTP integration for end-of-day ingestion, historical lookups, symbol reference, or basic intraday features. It can be easier to operate than a persistent stream when the product refreshes on a schedule and does not need every market event. This makes it reasonable for reporting pipelines, educational dashboards, and low-frequency international data exploration.

Marketstack 适合希望用常规 HTTP 集成完成日线摄取、历史查询、代码参考或基础日内功能的团队。当产品按计划刷新、不需要每一个市场事件时，它可能比持久流更容易运维，因此适合报告管道、教学仪表盘和低频国际数据探索。

The free plan is primarily an end-of-day learning tier, not proof of free real-time coverage. Product language such as “real-time updates” must be tied to the plan, interval, exchange source, and commercial terms. If the roadmap may evolve from daily reporting to live alerts, test the upgrade path and total request volume early; otherwise the system may need a provider migration just as usage begins to grow.

其免费层主要用于日线学习，并不能证明存在免费的实时覆盖。“实时更新”一类产品用语必须绑定具体套餐、时间间隔、交易所来源和商业条款。如果路线图可能从每日报告演进到实时告警，应尽早测试升级路径与总请求量，否则产品刚开始增长时就可能被迫迁移供应商。

Can you get a free real-time stock API?真的有免费的实时股票 API 吗？

Yes—but a free plan usually constrains one or more of five dimensions: venue coverage, freshness, number of symbols, call or subscription quota, and usage rights. “Free” is a prototyping attribute, not a data-quality guarantee.

有，但免费套餐通常会在五个维度中的至少一项进行限制：交易场所覆盖、新鲜度、标的数量、请求/订阅额度、使用与再分发权利。“免费”只是原型阶段的成本属性，不是数据质量保证。

REST vs WebSocket for live stock prices实时股票价格应使用 REST 还是 WebSocket？

REST answers a request with a snapshot. WebSocket keeps a connection open and pushes events as they arrive. The choice should follow the product’s event rate and failure model, not the desire to sound “real-time.”

REST 对一次请求返回一次快照；WebSocket 保持连接并在事件到达时持续推送。选择应由产品需要的事件频率和故障模型决定，而不是为了让产品听起来更“实时”。

Use REST to bootstrap a snapshot and recover state; use WebSocket for incremental events; periodically reconcile the stream against a fresh snapshot. This prevents a silent disconnect or dropped message from leaving the application permanently wrong. For an AI agent, the stream should update a deterministic state store—the language model should not ingest every tick directly.

实践中可用 REST 初始化快照和恢复状态，用 WebSocket 接收增量事件，再定期通过新快照校准数据流。这样可以避免静默断线或丢消息让应用长期处于错误状态。对于 AI Agent，行情流应先更新确定性的状态存储，而不是把每一笔 tick 直接塞进语言模型。

Data quality matters more than a fast response数据质量比快速响应更重要

An API can return in 80 milliseconds and still give the application the wrong answer. Market data has identity rules, correction events, session boundaries, corporate actions, and trade conditions that must be preserved. Production quality comes from handling those semantics consistently—not from choosing the provider with the smallest number on a marketing benchmark.

API 可以在 80 毫秒内返回，却仍然让应用得到错误答案。市场数据包含身份规则、更正事件、市场时段边界、公司行动和成交条件，这些语义必须被保留。生产质量来自对这些语义的一致处理，而不是选择营销基准中数字最小的供应商。

A ticker is not a permanent global identifier. Symbols can be reused, companies can rename, share classes can differ, and the same letters may represent different instruments on different exchanges. Store the provider symbol together with exchange, asset class, currency, and a durable identifier such as FIGI, ISIN, CUSIP, or the provider’s instrument ID where licensing permits. A multi-provider layer should map from an internal instrument identity to each provider’s symbol—not map strings opportunistically at request time.

股票代码不是永久的全球唯一标识。代码可能被重复使用，公司可能更名，不同股份类别也可能使用相近代码；相同字母在不同交易所甚至可能代表不同证券。应把供应商代码与交易所、资产类别、币种，以及在授权允许时可用的 FIGI、ISIN、CUSIP 或供应商 instrument ID 一起存储。多供应商层应从内部统一的证券身份映射到各家代码，而不是请求时临时猜测字符串。

One timestamp is not enough. Event time tells you when the market event occurred; receive time tells you when your gateway observed it; processing time tells you when downstream state changed. These fields let operators distinguish a delayed feed from network latency, queue backlog, or a slow consumer. They also prevent an AI agent from treating an old last trade as current simply because the API response itself arrived quickly.

一个时间戳并不够。事件时间表示市场事件实际发生的时刻；接收时间表示网关观察到事件的时刻；处理时间表示下游状态完成更新的时刻。三者可以帮助运维人员区分数据源延迟、网络延迟、队列积压或消费者处理缓慢，也能防止 AI Agent 因为 API 响应刚刚到达，就把一笔很久以前的最新成交误判为当前行情。

Not every trade updates the official open, high, low, close, or volume in the same way. Odd lots, out-of-sequence trades, prior-reference-price transactions, extended-hours trades, corrections, and cancellations can affect charts and alerts differently. If the provider supplies condition codes, preserve them through normalization. If you create your own bars, document the eligibility rules; otherwise your OHLCV values may disagree with the provider’s aggregates even when both systems received the same raw events.

并不是每一笔成交都会以相同方式更新官方开盘价、最高价、最低价、收盘价或成交量。零股、乱序成交、前参考价成交、盘前盘后成交、更正与撤销事件，对图表和告警的影响可能不同。如果供应商提供 condition code，应在规范化过程中保留。如果自行生成 K 线，则必须记录哪些成交有资格参与聚合，否则即使双方接收到相同原始事件，你的 OHLCV 仍可能与供应商聚合结果不同。

Historical research often needs split- and dividend-adjusted prices; current execution context needs raw tradable prices. Never overwrite one with the other. Store adjustment status, factor, corporate-action source, and calculation version beside every derived series. When an agent compares today’s price with a historical threshold, it should know whether both values are on the same adjustment basis. Otherwise a stock split can look like a catastrophic overnight decline.

历史研究通常需要拆股与分红复权价格，而当前交易上下文需要原始可交易价格，两者不能互相覆盖。每条衍生序列都应保存复权状态、复权因子、公司行动来源和计算版本。当 Agent 把今日价格与历史阈值比较时，应确认两者采用同一复权口径，否则一次拆股可能看起来像灾难性的隔夜暴跌。

A universal “30-second stale” rule is too crude. A liquid US mega-cap during regular hours may be stale after a few seconds; an illiquid instrument may trade rarely; a closed market may correctly show yesterday’s last trade. Staleness should consider expected event cadence, market calendar, session, symbol liquidity, price type, and the action being considered. Displaying a watchlist can tolerate more age than triggering a risk escalation.

统一的“30 秒即过期”规则过于粗糙。正常交易时段的高流动性美国大盘股，几秒没有更新就可能异常；低流动性证券本来就很少成交；休市时显示昨日最新成交可能完全正确。过期判断应同时考虑预期事件频率、市场日历、市场时段、标的流动性、价格类型和准备执行的动作。展示自选列表可以容忍更旧的数据，而触发风险升级则应更严格。

Real-time stock price API in Python用 Python 调用实时股票价格 API

A useful Python example should preserve provenance and freshness, not merely print a number. The REST sample below uses Alpaca’s documented latest-trade endpoint with the IEX feed; the validation pattern applies to other providers.

高质量的 Python 示例不应只打印一个数字，还应保留数据来源与新鲜度。下面的 REST 示例使用 Alpaca 官方文档中的最新成交端点和 IEX 数据源；其中的验证模式同样适用于其他供应商。

import os
from datetime import datetime, timezone
import requests

symbol = "AAPL"
url = f"https://data.alpaca.markets/v2/stocks/{symbol}/trades/latest"
headers = {
    "APCA-API-KEY-ID": os.environ["APCA_API_KEY_ID"],
    "APCA-API-SECRET-KEY": os.environ["APCA_API_SECRET_KEY"],
}

response = requests.get(url, headers=headers, params={"feed": "iex"}, timeout=8)
response.raise_for_status()
trade = response.json()["trade"]

event_time = datetime.fromisoformat(trade["t"].replace("Z", "+00:00"))
age_seconds = (datetime.now(timezone.utc) - event_time).total_seconds()
if age_seconds > 30:
    raise RuntimeError(f"Stale quote: {age_seconds:.1f}s old")

result = {
    "symbol": symbol,
    "price": trade["p"],
    "size": trade["s"],
    "venue": trade["x"],
    "event_time": trade["t"],
    "feed": "iex",
    "price_type": "last_trade",
}
print(result)

Keep secrets in environment variables, set a network timeout, reject stale data, and return the feed plus price type with the value. If the consumer needs an executable price rather than the latest trade, query the latest bid/ask instead.

密钥应保存在环境变量中，请求必须设置超时，过期数据应被拒绝，并且返回值中要同时包含数据源和价格类型。如果调用方需要的是可成交价格而非最新成交，应改为查询最新买卖报价。

How should an AI agent use real-time stock data?AI Agent 应如何使用实时股票数据？

The safest architecture separates deterministic market-data handling from probabilistic language-model reasoning. The agent requests a bounded capability; a data layer validates and normalizes the response; only a compact evidence object reaches the model.

更安全的架构会把确定性的市场数据处理与概率性的语言模型推理解耦。Agent 请求一个边界清晰的能力，数据层负责验证与规范化，最后只有紧凑的证据对象进入模型。

{
  "symbol": "AAPL",
  "price": 213.42,
  "currency": "USD",
  "price_type": "last_trade",
  "feed": "iex",
  "venue": "IEX",
  "market_session": "regular",
  "event_time": "2026-08-03T14:32:18.418Z",
  "received_time": "2026-08-03T14:32:18.503Z",
  "is_stale": false,
  "provider": "primary_market_data"
}

The cheapest API plan is not always the lowest-cost system最便宜的 API 套餐不一定带来最低的系统成本

Market-data cost includes the provider invoice, exchange entitlements, redistribution rights, infrastructure, storage, observability, and the engineering time required to keep data correct. A plan that is inexpensive for one developer may be unsuitable for a customer-facing product even if the endpoint technically returns the desired field.

市场数据成本不仅是供应商账单，还包括交易所权限、再分发权、基础设施、存储、可观测性，以及长期保持数据正确所需的工程时间。某个套餐可能对个人开发者很便宜，却不适合面向客户的产品，即使端点技术上能够返回所需字段。

For polling, estimate active symbols × polls per minute × active hours × environments. A watchlist of 100 symbols polled once per minute during a 6.5-hour regular session already implies 39,000 symbol observations per day before pre-market, after-hours, retries, staging, and user-specific lists. Batch endpoints can reduce HTTP calls, but the provider may still charge credits per symbol. Cache shared snapshots so ten users watching AAPL do not create ten identical upstream requests.

轮询场景可按活跃标的数 × 每分钟轮询次数 × 活跃小时数 × 环境数估算。100 个标的在 6.5 小时正常交易时段内每分钟轮询一次，每天已经产生 39,000 次标的观察，还没有计算盘前盘后、重试、预发布环境和用户自定义列表。批量端点可以减少 HTTP 请求数，但供应商仍可能按标的扣 credits。应缓存共享快照，避免 10 个用户关注 AAPL 就产生 10 次完全相同的上游请求。

Streaming avoids polling, but it does not make high-volume data free. Estimate subscribed symbols, event rate during market peaks, concurrent connections, message size, normalization cost, queue throughput, database writes, and client fan-out. A popular stock can generate far more events than an illiquid one. Most products should aggregate or coalesce updates before writing to storage or pushing to browsers; otherwise downstream infrastructure becomes the cost center even when the provider allows unlimited API calls.

流式接口避免轮询，但不意味着高吞吐数据没有成本。应估算订阅标的数量、市场高峰事件率、并发连接、消息大小、规范化成本、队列吞吐、数据库写入和客户端扇出。热门股票产生的事件可能远多于低流动性证券。大多数产品都应在写入存储或推送浏览器前聚合或合并更新，否则即使供应商宣称 API 调用不限量，下游基础设施仍会成为主要成本中心。

A developer subscription can permit personal analysis without permitting public display, client delivery, raw-data resale, or redistribution inside another product. Derived metrics may have different rules from raw trades and quotes. Ask the provider in writing whether the intended application may display values to authenticated users, cache them, retain history, export CSV files, train models, create derived indicators, and serve customers in each target country. Do not rely on a pricing-page feature list as a license interpretation.

开发者订阅可能允许个人分析，却不允许公开展示、交付客户、转售原始数据，或在另一款产品中再分发。衍生指标与原始成交、报价还可能适用不同规则。应书面确认目标应用是否可以向登录用户展示数值、缓存数据、保留历史、导出 CSV、训练模型、生成衍生指标，以及服务各目标国家的客户。不能把价格页的功能列表当作授权解释。

A second provider is not automatically a usable fallback. If the primary returns consolidated NBBO quotes and the backup returns only a single-venue last trade, automatic failover changes the meaning of the field. Define an equivalence matrix for feed, price type, currency, session coverage, timestamp precision, corporate-action treatment, and license. When equivalence is impossible, fail closed or clearly downgrade the product instead of silently substituting a different data object.

第二家供应商并不自动等于可用的备用源。如果主源返回综合 NBBO 报价，而备用源只返回单一场所最新成交，自动切换会改变字段含义。应针对数据源、价格类型、币种、市场时段、时间戳精度、公司行动处理和授权建立等价矩阵。无法等价时，应停止关键动作或明确降级产品，而不是悄悄替换成不同的数据对象。

How to benchmark a real-time stock API without misleading yourself如何测试实时股票 API，避免得出误导性结论？

A credible benchmark measures the complete workload under controlled conditions. It does not compare one provider’s WebSocket event timestamp with another provider’s HTTP round-trip time and call both numbers “latency.” Use the same data object, feed class, region, clock discipline, symbols, session, and sample window.

可信的基准测试应在受控条件下测量完整工作负载，不能把一家供应商的 WebSocket 事件时间差与另一家的 HTTP 往返时间放在一起，并统称为“延迟”。应使用相同的数据对象、数据源类别、部署地区、时钟标准、标的、市场时段和采样窗口。

Publish the plan, feed, date, region, client version, sample size, and limitations beside any result. If those details cannot be shared, describe the result as an internal observation rather than an objective provider ranking. This page removed the old fixed millisecond leaderboard for exactly that reason.

任何结果旁都应公布套餐、数据源、日期、地区、客户端版本、样本量和限制。如果这些细节无法公开，就应把结果描述为内部观察，而不是客观的服务商排名。本页正是因为这一原因，删除了旧版固定毫秒数排行榜。

How to evaluate a stock market API before you buy购买股票市场 API 前应如何评估？

Why is my “real-time” stock price delayed, missing, or wrong?为什么“实时”股票价格会延迟、缺失或看起来不对？

Real-time stock price API FAQ实时股票价格 API 常见问题

How this comparison was built本次对比是如何完成的？

We reviewed official documentation and pricing pages for delivery method, feed definitions, free access and plan caveats. We removed the previous page’s unrepeatable provider latency claims and avoided treating advertised response time as measured end-to-end market-data latency. QVeris provides capability routing, so this page has a commercial interest in multi-provider access; provider selection guidance is therefore tied to explicit workload criteria rather than a QVeris-first ranking.

我们依据各服务商官方文档与价格页核对交付方式、数据源定义、免费权限和套餐限制；删除了旧页面中无法复现实验过程的供应商延迟数字，也没有把宣传的响应时间当作已测量的端到端市场数据延迟。QVeris 提供能力路由服务，因此本文对多供应商接入存在商业利益；为降低偏差，所有推荐都绑定明确工作负载，而不是把 QVeris 或某家服务商固定排在第一。

• Massive Stocks WebSocket overview — feed types and streaming behavior数据流类型与推送行为
• Massive stock pricing — current plan coverage and timing当前套餐覆盖与时效
• Alpaca Market Data FAQ — IEX versus SIP and entitlement behaviorIEX 与 SIP 及权限行为
• Alpaca streaming market data — live endpoint model实时端点模型
• Twelve Data pricing — API and WebSocket creditsAPI 与 WebSocket credits
• Twelve Data WebSocket FAQ — connections, subscriptions and plan access连接、订阅与套餐权限
• Alpha Vantage support — free request allowance and licensed real-time note免费请求额度与授权实时数据说明
• Finnhub API documentation — available market and company datasets市场与公司数据集
• Marketstack pricing — free, intraday and real-time plan distinctions免费、日内与实时套餐差异

Last reviewed: August 3, 2026. Provider features, exchange entitlements, limits and prices can change without notice.最后核对：2026 年 8 月 3 日。服务商功能、交易所权限、额度和价格可能随时变化。