adata

Agent-Native financial data infrastructure for A-shares.

adata is designed for AI agents, not humans. Agents say what data they need — adata handles where to get it, how to cache it, and when to refresh it. Zero configuration, zero data-source knowledge required.

from adata import DataClient

client = DataClient()
df = client.query_daily(["sh.600519", "sz.000001"], "2024-01-01")
# Auto: check cache → fetch missing from best available provider → cache → return DataFrame

Architecture

LLM Agent
    ↓ MCP tools (query_daily / query_benchmark / data_status ...)
adata MCP Server
    ↓
DataClient — smart routing layer
    ↓ cache-first → provider fallback chain
┌──────────┬──────────┬──────────┬──────────┬──────────┐
│ Parquet   │ PolarDB  │ rqdatac  │ baostock │ akshare  │
│ (cache)   │          │          │          │          │
└──────────┴──────────┴──────────┴──────────┴──────────┘

DataClient is the single entry point. It transparently:

• Checks local Parquet cache first
• Falls back through a configurable provider chain
• Caches fetched data for future queries
• Normalizes codes across formats (sh.600519 ↔ 600519.XSHG ↔ sh600519)

MCP Server exposes DataClient as MCP tools so any LLM agent can discover and query data through the standard MCP protocol.

Installation

The distribution name is quantgpt-adata; the import name stays adata. The adata package on PyPI is an unrelated project, so do not use pip install adata. Because both use the import name adata, do not install them in the same environment.

pip install -e .

# With specific data providers:
pip install -e ".[rqdatac]"    # 米筐
pip install -e ".[baostock]"   # baostock
pip install -e ".[akshare]"    # 东方财富
pip install -e ".[polardb]"    # Aliyun PolarDB
pip install -e ".[mcp]"        # MCP server

# Everything:
pip install -e ".[all]"

Usage

Python API (for agents and applications)

from adata import DataClient

client = DataClient()

# Daily OHLCV — auto-routed, auto-cached
df = client.query_daily(["sh.600519"], "2024-01-01")

# Benchmark index
bm = client.query_benchmark("hs300", "2024-01-01")

# Index constituents
codes = client.query_universe("hs300")

# Cache status
status = client.data_status()
# → {"data_dir": "~/.adata", "categories": {"stocks": {"files": 300, ...}}, "providers": [...]}

# Data freshness check
freshness = client.data_freshness(["sh.600519", "sz.000001"])
# → [{"code": "sh.600519", "last_date": "2026-05-14", "stale": false}, ...]

MCP Server (for LLM agents)

python -m adata mcp    # Start stdio MCP server

MCP tools available:

Tool	Description
query_daily	Query daily OHLCV data
query_benchmark	Query benchmark index (hs300, zz500, etc.)
query_universe	List index constituents
data_status	Cache overview: files, date ranges, providers
data_freshness	Check data staleness
update_data	Trigger data update by universe or codes

Claude Desktop / Claude Code config:

{
  "mcpServers": {
    "adata": {
      "command": "python",
      "args": ["-m", "adata", "mcp"]
    }
  }
}

CLI (for humans)

# Fetch data from a specific provider
python -m adata fetch --source rqdatac --universe hs300 --mode incremental

# Fetch specific codes
python -m adata fetch --source baostock --codes sh.600519,sz.000001

# Fetch benchmarks
python -m adata fetch --source rqdatac --benchmark hs300,zz500

# Full re-download
python -m adata fetch --source akshare --codes sh.600519 --mode full --start 2015-01-01

# Check cache status
python -m adata status

Data Providers

Provider	Asset Types	Auth Required	Notes
rqdatac	Stock, ETF, Index	Yes (RQDATAC_USERNAME/PASSWORD)	Fastest, most complete
tushare	Stock, Index	Yes (TUSHARE_TOKEN)	Tushare Pro, batch API
baostock	Stock	No	Free, sequential per-stock
akshare	Stock, ETF	No	Free, East Money data
polardb	Stock, Index	Yes (POLARDB_PASSWORD)	Private Aliyun DB

Provider priority is configurable:

client = DataClient(provider_priority=["rqdatac", "tushare", "baostock", "akshare"])

Default: ["polardb", "rqdatac", "tushare", "baostock", "akshare"]. Unavailable providers are automatically skipped.

Configuration

Environment variables (or .env file):

# Data directory (default: ~/.adata)
# ADATA_DIR=/path/to/data

# rqdatac
RQDATAC_USERNAME=
RQDATAC_PASSWORD=

# tushare pro (https://tushare.pro)
TUSHARE_TOKEN=

# PolarDB (private)
POLARDB_HOST=
POLARDB_PASSWORD=

Data Schema

Daily OHLCV (trade_date, stock_code, open, high, low, close, volume, amount, pct_change):

All providers normalize to this schema. Stored as per-instrument Parquet files under ~/.adata/{category}/.

Code format: {exchange}.{number} — e.g., sh.600519, sz.000001. Automatic normalization from rqdatac (600519.XSHG), PolarDB (sh600519), and raw (600519) formats.

Testing

pip install pytest
python -m pytest tests/ -v

94 tests covering all modules. All tests use in-memory fixtures, no network calls.

License

MIT