OHLCVault

OHLCV = Open/High/Low/Close/Volume, the universal bar format. Vault = immutable, checksummed snapshots.

Reproducible daily OHLCV data for A-share, Hong Kong and US markets — served as static files from a CDN. No API keys, no rate limits, no per-request billing.

import ohlcvault as ov

ov.connect()
df = ov.daily("600519.SH").to_pandas()      # 跨月自动拼接
ov.cross_section("cn", 20260918, limit=50)  # 当日全市场截面，按成交额排序

Why this exists

Most free market-data endpoints are APIs: stateful, rate-limited, silently revised, and impossible to reproduce. A backtest that ran last month can't be rerun today with the same inputs.

OHLCVault publishes immutable monthly shards with checksums instead. Each read is anchored to a snapshot id you can write down and reproduce later, on any machine.

Install

pip install ohlcvault            # core, zero runtime dependencies
pip install "ohlcvault[pandas]"  # + DataFrame helpers

The core has no third-party dependencies — only the standard library. That's deliberate: a data client shouldn't drag a dependency tree into your project, and it matters even more for agent/tooling contexts.

Usage

Everything (the five things you actually need)

import ohlcvault as ov
ov.connect()

# 1. Trading calendar
ov.calendar("cn", start=20260101)

# 2. Stock daily bars (cross-month stitching is handled for you)
ov.daily("600519.SH", start=20260101, end=20260918)

# 3. Index daily bars — a separate namespace, never mixed with stocks
ov.index_daily("000300.SH")

# 4. Symbol list — includes delisted stocks
ov.symbols("cn", type="stock")
ov.symbols("cn", type="stock", status="delisted")

# 5. Daily cross-section — sorted by turnover, no extra data files
ov.cross_section("cn", 20260918, sort_by="amount", limit=50)

Batch backtests

Month shards hold every symbol in the market for that month. Loading 200 symbols one-by-one would decompress the same file 200 times:

bars = ov.daily_many(["600519.SH", "000001.SZ", "300750.SZ"], start=20260101)
bars["600519.SH"].to_pandas()

Adjustment is a view, not a stored field

The dataset stores unadjusted prices only, plus the official cumulative back-adjustment factor. Forward/backward adjusted prices are computed client-side:

b = ov.daily("600519.SH")
ov.adjust(b, to="hfq")   # 后复权
ov.adjust(b, to="qfq")   # 前复权

This is not a limitation — it's the reason historical files never change. If forward-adjusted prices were stored, every dividend would rewrite all of history, and immutable caching would be impossible.

Reproducibility

st = ov.connect()
sid = st.snapshot                       # e.g. "6b197df3723871c5"
ov.connect(snapshot=sid)                # later, anywhere: exact same inputs

Offline / self-hosted mirrors

A mirror can be an HTTP(S) URL or a local directory:

ov.connect(mirrors=["/path/to/data"])

Mirrors are tried in order; whichever one succeeds is promoted to first place. Every file is checked against the sha256 in the snapshot manifest, and anything that fails is discarded and the next mirror is tried — bad bytes are never handed to the caller.

Data integrity

Delisted stocks matter. If your backtest universe only contains companies that are still listed today, your historical returns are systematically overstated. ov.symbols("cn", status="delisted") returns them, and their daily bars are complete over ipo … out.

Coverage and known gaps

Coverage is declared explicitly in meta/symbols/{market}.json under coverage, and ov.connect() prints it on startup. Current state:

hk / us symbol lists carry survivorship bias (the upstream source only returns currently-listed securities) and have no IPO dates. Their daily bars are pending.

An honest data project states its gaps. A dataset that quietly omits them is worse than one that is merely incomplete.

API

Date parameters (start / end / date) accept an int YYYYMMDD (preferred) or common string forms — "2026-09-18", "20260918", "2026/09/18". Anything unparseable raises DateError instead of failing deep inside the library.

The frozen data contract lives in SPEC.md — the client and the pipeline share nothing but this document and the files it describes. schema/example-*.json are machine-generated from real data, so the examples cannot drift from the contract.

License

MIT. See LICENSE.

Data is gathered from public sources. Verify before relying on it for anything consequential.