Free Stock Data API
with Python用 Python 调用
免费股票数据 API
Fetch quotes and historical OHLCV with Python, turn JSON into pandas data, and design around free-tier limits.
用 Python 获取股票报价与历史 OHLCV，
将 JSON 转为 pandas 数据，并妥善处理免费额度限制。
TL;DR核心摘要
Use Python requests for a transparent REST integration, then normalize the response into a pandas DataFrame.
Start with daily OHLCV or delayed quotes. Intraday, tick, and real-time feeds usually have tighter free limits.
Check calls per minute, daily quotas, history depth, exchange coverage, delay, and redistribution rights.
Validate schemas, cache repeat queries, honor 429 responses, and record source and retrieval time.
用 Python requests 完成清晰可见的 REST 调用，再把响应标准化为 pandas DataFrame。
先从日线 OHLCV 或延迟报价开始；分钟线、逐笔和实时流通常有更严格的免费限制。
核对每分钟调用数、日配额、历史深度、交易所覆盖、延迟和再分发许可。
校验结构、缓存重复查询、正确处理 429，并记录数据来源与采集时间。
How to choose a free stock data API for Python如何为 Python 选择免费股票数据 API
Match the provider to your actual job: notebook research, a portfolio dashboard, backtesting, or live execution. A generous daily quota is not useful if the feed lacks the exchange, interval, corporate-action adjustments, or license your project needs.
先明确任务是交互式笔记本研究、投资组合看板、回测还是实时执行。即使日配额很高，若缺少所需交易所、周期、复权规则或使用许可，也不适合你的项目。
Quotes describe a current snapshot. OHLCV bars provide open, high, low, close, and volume for charting, indicators, and backtests.
Free plans often provide delayed or end-of-day data. Confirm timestamps and market-session rules before calling a feed real time.
REST is simplest for snapshots and history. WebSockets fit streaming use cases but require reconnect, heartbeat, and subscription logic.
Check whether history uses cursors, date windows, or page numbers and whether a multi-symbol request consumes one quota unit or one per symbol. This determines the shape of your Python loader.
Decide whether you need raw or adjusted prices, then preserve split and dividend metadata. Use stable instrument identifiers when tickers can be reused or changed.
报价是当前快照；OHLCV 提供开、高、低、收、成交量，适合图表、指标和回测。
免费套餐通常提供延迟或日终数据。宣称实时前，应核对时间戳和交易时段规则。
REST 适合快照和历史查询；WebSocket 适合流式数据，但必须处理重连、心跳和订阅状态。
确认历史数据采用游标、日期窗口还是页码分页，同时核对多代码请求按一次还是按每个代码消耗额度。这会直接决定 Python 加载器的结构。
先决定需要原始价格还是复权价格，并保留拆股、分红元数据。遇到代码更名或重复使用时，应使用稳定的标的标识符。
Python example: fetch OHLCV into pandasPython 示例：把 OHLCV 读入 pandas
This provider-neutral pattern keeps the API key outside source code, makes a bounded request, checks the HTTP response, validates required fields, and creates a time-indexed DataFrame.
下面的供应商中立模式把 API Key 留在环境变量中，设置请求超时，检查 HTTP 响应，校验必要字段，并创建按时间索引的 DataFrame。
import os
import requests
import pandas as pd
url = "https://api.example.com/v1/stocks/AAPL/bars"
params = {"interval": "1day", "limit": 100}
headers = {"Authorization": f"Bearer {os.environ['STOCK_API_KEY']}"}
response = requests.get(url, params=params, headers=headers, timeout=15)
response.raise_for_status()
rows = response.json()["data"]
required = {"timestamp", "open", "high", "low", "close", "volume"}
if not rows or not required.issubset(rows[0]):
raise ValueError("Unexpected stock API response")
df = pd.DataFrame(rows)
df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True)
df = df.set_index("timestamp").sort_index()
print(df.tail())Replace the sample URL and response path with the current documentation for your provider. Never commit a real key, and never assume all APIs use the same field names or adjustment rules.
请按照所选供应商的当前文档替换示例 URL 与响应路径。不要提交真实密钥，也不要假设所有 API 的字段名和复权规则一致。
Convert price columns with numeric coercion, keep volume in a sufficiently wide integer type, map provider null markers, and reject rows whose high is below low.
Parse provider timestamps with their declared zone, store them in UTC, and retain the exchange calendar separately. A date-only daily bar should not be guessed into a UTC instant.
Save raw responses or immutable partitions with provider, request parameters, retrieval time, and schema version. Research results should be reproducible after an upstream correction.
Mock 401, 429, 5xx, truncated JSON, empty pages, and duplicate bars. Retry only transient errors with bounded exponential backoff and jitter.
对价格列执行可控的数值转换，为成交量选择足够宽的整数类型，统一供应商的空值标记，并拦截最高价低于最低价等异常记录。
按供应商声明的时区解析时间戳，统一以 UTC 存储，并单独保留交易所日历。只有日期的日 K 线不应被随意猜成某个 UTC 时刻。
保存原始响应或不可变分区，并记录供应商、请求参数、采集时间和字段版本。即使上游后来修正数据，研究结果仍应能够复现。
模拟 401、429、5xx、JSON 截断、空分页和重复 K 线。只有临时性错误才重试，并设置次数上限、指数退避和随机抖动。
Use QVeris to discover stock data capabilities用 QVeris 发现股票数据能力
There is no single verified QVeris result for every Python stock-data workflow. Use the QVeris documentation to understand authentication and execution, then inspect candidate capabilities in the Playground before binding them to a Python adapter.
目前没有一个经过核实的 QVeris 结果，可以覆盖所有 Python 股票数据工作流。先通过 QVeris 文档了解鉴权和调用方式，再到 Playground 检查候选能力，最后再接入 Python 适配层。
- Search by capability and inspect schemas before choosing an integration.
- Keep secrets server-side and validate every external response.
- Record provider and tool metadata so analysis remains traceable.
- 按能力搜索，并在选择集成前检查工具结构。
- 把密钥保留在服务端，并校验每一个外部响应。
- 记录供应商和工具元数据，确保分析结果可追溯。
FAQ常见问题
Yes. Many providers offer limited free historical bars or delayed quotes. Coverage, latency, quotas, and rights vary by plan.
It is a convenient Python library, but it is not a contracted exchange data feed. Check its documentation and data terms before production use.
Requests keeps transport and schemas visible; an SDK can reduce boilerplate. Either way, isolate the provider behind your own interface and pin tested dependency versions.
Read quota headers, centralize scheduling, cache repeat queries, and retry 429 only after the instructed delay. Parallel workers should share one quota budget.
可以。很多供应商提供有限的免费历史 K 线或延迟报价，但覆盖、延迟、配额和权利因套餐而异。
它是便捷的 Python 库，但不是签约的交易所数据源。用于生产前应核对其文档与数据条款。
requests 能让传输过程和字段结构保持透明，SDK 则可减少样板代码。无论选择哪种方式，都应在自有接口后隔离供应商，并锁定测试过的依赖版本。
读取额度响应头，统一调度请求，缓存重复查询，并按服务端提示的等待时间重试 429。多个并行任务必须共享同一份额度预算。
References and next steps参考资料与下一步
Alpaca market data documentation
Alpha Vantage API documentation
Twelve Data API documentation
QVeris documentation
QVeris Playground
Alpaca 市场数据文档
Alpha Vantage API 文档
Twelve Data API 文档
QVeris 文档
QVeris Playground