Free Stock Data
for Backtesting用于回测的
免费股票数据
Compare free APIs, CSV downloads, and hosted platforms, then validate adjustments, delisted stocks, timestamps, gaps, and licenses before trusting a result.
比较免费 API、CSV 下载和托管平台，并在相信回测结果前核对复权、退市股票、时间戳、缺口与许可。
TL;DR核心摘要
Daily OHLCV from a documented API or CSV archive is enough for many first backtests. Intraday strategies need finer bars and stricter timestamp checks.
Expect history limits, request caps, fewer exchanges, delayed updates, or usage restrictions. Free access does not mean complete coverage.
A clean price chart can still contain survivorship bias, bad adjustments, missing sessions, or look-ahead information.
Keep the raw snapshot, normalize once, validate automatically, and version the exact dataset used for every result.
对于许多初次回测，文档清晰的日线 OHLCV API 或 CSV 已经足够；分钟级策略则需要更细粒度数据和严格时间检查。
通常伴随历史深度、请求次数、交易所覆盖、更新时效或用途限制，并不代表数据完整。
看似平滑的价格图仍可能包含幸存者偏差、错误复权、交易日缺口或未来信息。
保留原始快照，只标准化一次，自动执行校验，并为每次结果记录准确的数据版本。
Choose free stock data that fits the backtest选择适合回测的免费股票数据
Start with the test specification: market, universe, bar interval, history depth, adjustment method, and whether you need delisted securities. The right source is the one that meets those requirements reproducibly.
先写清回测规格：市场、股票池、K 线周期、历史深度、复权方式，以及是否需要退市证券。能够可重复满足这些要求的数据源才是合适的数据源。
Best for repeatable symbol and date-range queries. Compare bar intervals, maximum rows, adjusted versus raw prices, corporate actions, and rate limits.
Best for large portfolio sweeps because files can be versioned and queried locally without repeated API calls. Check schema, compression, and update cadence.
Use the symbols that were eligible on each historical date, including later delistings. A present-day constituent list creates survivorship bias before the strategy runs.
Execution simulations generally need tradable raw prices plus explicit corporate actions. Total-return research may use adjusted series, but the adjustment method must be consistent across OHLC fields.
Fundamentals and classifications need the date and time they became knowable. Fiscal period end or revised values alone can leak future information.
适合按股票代码与日期区间重复查询。应比较 K 线周期、单次最大行数、复权与原始价格、公司行动和调用限额。
适合大规模组合扫描，因为文件可版本化并在本地查询，无需反复调用 API。需检查字段、压缩方式和更新频率。
每个历史日期都应使用当时符合条件的股票代码，并包含后来退市的标的。直接套用今天的成分列表，会在策略运行前就引入幸存者偏差。
模拟成交通常需要当时可交易的原始价格和明确的公司行动；研究总回报可以使用复权序列，但 OHLC 各字段必须采用一致方法。
基本面和分类数据要保留市场真正能够获知它们的日期与时间。只使用报告期末或后续修订值，会泄露未来信息。
Python example: prepare bars for a backtestPython 示例：为回测准备行情数据
Normalize every source into the same OHLCV schema, sort in event-time order, and reject duplicate timestamps before calculating a signal. Replace the example endpoint with a documented provider.
在计算信号前，把每个来源标准化为相同的 OHLCV 字段，按事件时间排序，并拒绝重复时间戳。请将示例端点替换为文档化的数据供应商。
import os
import requests
import pandas as pd
url = "https://api.example.com/v1/daily"
params = {"symbol": "AAPL", "apikey": os.environ["STOCK_API_KEY"]}
response = requests.get(url, params=params, timeout=20)
response.raise_for_status()
rows = response.json()["data"]
df = pd.DataFrame(rows)
df["date"] = pd.to_datetime(df["date"], utc=True)
df = df.sort_values("date").drop_duplicates("date")
print(df[["date", "open", "high", "low", "close", "volume"]].tail())| Step步骤 | Why it matters为什么重要 | Minimum check最低检查项 |
|---|---|---|
| Request请求 | Timeouts and HTTP errors prevent silent failures.超时和 HTTP 错误处理可避免静默失败。 | Status code and provider error body.状态码与供应商错误信息。 |
| Normalize标准化 | Stable columns isolate analysis from provider schemas.稳定列名可隔离分析逻辑与供应商格式。 | Types, UTC dates, numeric columns.类型、UTC 日期、数值列。 |
| Validate验证 | Bad rows can produce believable but wrong charts.错误记录可能生成看似可信的错误图表。 | Order, duplicates, nulls, price bounds.顺序、重复、空值、价格边界。 |
Build a signal only from values available at that decision time, then apply the fill at a later tradable event. A same-bar close signal filled at that close is usually look-ahead.
Generate expected sessions from the relevant venue, not weekdays. Handle holidays, early closes, halts, and daylight-saving transitions explicitly.
Include delistings, symbol changes, liquidity filters, spreads, fees, and realistic order size. Missing failed companies can dominate an apparently strong result.
Split by time before tuning, fit transformations only on the training window, and keep a final untouched period. Random row splits are unsafe for serial market data.
信号只能使用决策时点已经可见的数据，成交则应落在之后可交易的事件上。用本根 K 线收盘价生成信号，又按同一收盘价成交，通常属于未来数据偏差。
应根据对应场所生成预期交易日，而不是简单排除周末；节假日、提前收盘、停牌和夏令时切换都需要显式处理。
纳入退市、代码变化、流动性筛选、点差、费用和合理订单规模。遗漏失败公司，可能成为回测收益虚高的主要原因。
调参前先按时间切分，只在训练窗口拟合转换规则，并保留最终未触碰区间。对具有时间顺序的市场数据随机拆行并不安全。
Use QVeris for provider-flexible stock data access用 QVeris 灵活接入股票数据
There is no single verified QVeris result that supplies every point-in-time field a backtest may require. Use the QVeris documentation for the execution model and the Playground to inspect candidate historical capabilities before freezing a dataset.
目前没有一个经过核实的 QVeris 结果，可以提供回测所需的全部时点字段。先通过 QVeris 文档了解调用方式，再到 Playground 检查候选历史数据能力，确认后再冻结数据版本。
- Discover stock-data capabilities and inspect their inputs before calling them.
- Keep application code focused on symbols, dates, and normalized outputs.
- Use auditable capability calls when agents need current financial data.
- 发现股票数据能力，并在调用前检查输入参数。
- 让应用代码只关注股票代码、日期与标准化输出。
- 当 Agent 需要当前金融数据时，使用可审计的能力调用。
FAQ常见问题
Start with a documented historical-data API, downloadable archive, or platform dataset. Match market, interval, depth, adjustments, and license to your test.
It can support prototypes and daily-price research, but independently verify adjustments, gaps, symbol history, and terms before relying on it.
Yes when testing a historical universe. Excluding securities that disappeared can materially overstate performance and understate risk.
It depends on the strategy. Total-return studies may use documented adjustments; execution simulations need raw tradable prices and separate corporate actions.
可从文档化的历史数据 API、可下载归档或回测平台数据开始，并按市场、周期、深度、复权和许可筛选。
可用于原型和日线研究，但在正式使用前仍需独立核对复权、缺口、代码历史与使用条款。
只要测试历史股票池，就需要包含。排除后来消失的证券，可能显著高估收益并低估风险。
取决于策略。总回报研究可以使用规则明确的复权数据；成交模拟则需要原始可交易价格，并单独处理公司行动。
References and next steps参考资料与下一步
Massive stocks flat-file documentation
Alpha Vantage API documentation
Twelve Data API documentation
Free stock data with Python
QVeris Playground
Massive 股票平面文件文档
Alpha Vantage API 文档
Twelve Data API 文档
使用 Python 获取免费股票数据
QVeris Playground