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- OpenClaw
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Free A Share Real Time Data
Fetch China A-share stock market data (bars, realtime quotes, tick-by-tick transactions) via mootdx/TDX protocol. Use when working with Chinese stock data, mootdx library, TDX quotes, intraday minute bars, transaction history, or real-time A-share market data.
skill openclawclawhub Free
Fetch China A-share stock market data (bars, realtime quotes, tick-by-tick transactions) via mootdx/TDX protocol. Use when working with Chinese stock data, mootdx library, TDX quotes, intraday minute bars, transaction history, or real-time A-share market data.
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- Tencent SkillHub
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- SKILL.md, api-reference.md, scripts/setup_and_verify.py
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- Tencent SkillHub
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- Version
- 1.0.1
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- wangdinglu
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Documentation
Mootdx China A-Share Stock Data Client
A wrapper around the mootdx library (TDX protocol) for fetching China A-share market data including K-line bars, real-time quotes, and tick-by-tick transaction records.
Installation
pip install mootdx mootdx depends on tdxpy internally. Both are installed together.
Verify & Demo
python scripts/setup_and_verify.py # Install + verify + connectivity test python scripts/setup_and_verify.py --check # Verify only (skip install) python scripts/setup_and_verify.py --demo # Full API demo with real output The --demo mode exercises every major API and prints real data — useful as a runnable reference for correct calling patterns.
Trading Hours (Beijing Time, UTC+8)
SessionTimeMorning09:30 - 11:30 (120 min)Lunch break11:30 - 13:00Afternoon13:00 - 15:00 (120 min)Total240 trading minutes/day
Trading Time Bypass Patch
Problem: mootdx / tdxpy has a built-in time_frame() check that blocks API calls outside trading hours. On servers with non-Beijing timezone, this breaks even during valid trading hours. Solution: Monkey-patch tdxpy.hq.time_frame to always return True: import tdxpy.hq tdxpy.hq.time_frame = lambda: True This patch is applied automatically during MootdxClient.__init__(). Without it, transactions() and transaction() calls will silently return empty results outside detected trading hours.
Trading Calendar
When querying historical data, always check if a date is a trading day. Non-trading days (weekends, holidays) have no data. The client uses TradingCalendarStrategy.is_trading_day(date_str) for this — you must have a trading calendar service available.
Date/Time Parameter Formats
ParameterFormatExampledateYYYYMMDD"20250210"timeHH:MM:SS or HH:MM"10:30:00" or "10:30"
Stock Code Format
mootdx uses pure numeric codes (TDX format). Convert from standard format: Standard FormatTDX FormatMarket000001.SZ000001Shenzhen600300.SH600300Shanghai300750.SZ300750Shenzhen (ChiNext)688001.SH688001Shanghai (STAR) Conversion: Strip the .SH / .SZ / .BJ suffix. Important: mootdx does NOT support Beijing Stock Exchange (.BJ) stocks. Filter them out before calling.
1. Initialize Client
from mootdx.quotes import Quotes client = Quotes.factory(market='std')
2. get_bars() — K-Line / Candlestick Data
Fetch historical or real-time K-line bars. await client.get_bars( stock_code="000001.SZ", # Standard format (auto-converted) frequency=7, # K-line period (see table below) offset=240, # Number of bars to fetch date="20250210", # Optional: specific date (YYYYMMDD) time="10:30:00", # Optional: specific time (HH:MM:SS) filter_by_time=True # Filter to closest bar matching time ) Frequency codes: CodePeriod71-minute bars81-minute bars (alternative)4Daily bars9Daily bars (alternative) Return format (list of dicts): { "stock_code": "000001.SZ", "datetime": "2025-02-10 10:30:00", "open": 12.50, "high": 12.65, "low": 12.45, "close": 12.60, "vol": 150000.0, "amount": 1890000.0 } Start position calculation: For historical dates, the start parameter is calculated as the number of trading minutes (for 1-min bars) or trading days (for daily bars) between now and the target datetime. This accounts for: Whether today is a trading day Current trading session status (pre-market / in-session / post-market) Lunch break gap (11:30-13:00)
3. get_realtime_quote() — Single Stock Real-Time Quote
await client.get_realtime_quote(stock_code="000001.SZ") Returns (dict): Price, OHLC, volume, amount, and full Level-2 order book (5-level bid/ask): { "stock_code": "000001.SZ", "price": 12.60, "last_close": 12.50, "open": 12.45, "high": 12.65, "low": 12.40, "volume": 5000000, "amount": 63000000, "bid1": 12.59, "bid2": 12.58, ..., "bid5": 12.55, "ask1": 12.60, "ask2": 12.61, ..., "ask5": 12.65, "bid_vol1": 500, ..., "ask_vol5": 300, "pct_chg": 0.8 }
4. get_realtime_quotes() — Batch Real-Time Quotes
Native batch interface — much faster than looping get_realtime_quote(). await client.get_realtime_quotes(["000001.SZ", "600300.SH", "300750.SZ"]) Returns (list of dicts): { "stock_code": "000001.SZ", "trade_date": "2025-02-10", "open": 12.45, "high": 12.65, "low": 12.40, "close": 12.60, "pre_close": 12.50, "change": 0.15, "pct_chg": 1.2048, "vol": 5000000.0, "amount": 63000000.0, "is_realtime": true } pct_chg is calculated from today's open price, not previous close.
5. get_batch_bars() — Batch K-Line Data
Parallel fetch K-line bars for multiple stocks with concurrency control. await client.get_batch_bars( stock_codes=["000001.SZ", "600300.SH"], date="20250210", time="10:30:00", max_concurrent=10 ) Returns: Dict[str, List[Dict]] — {stock_code: [bar_data, ...]}
6. get_transactions_history() — Historical Tick Data
Tick-by-tick transaction records for a specific historical date. await client.get_transactions_history( stock_code="000001.SZ", date="20250210", # Required: YYYYMMDD start=0, offset=1000 ) Returns (list of dicts): { "stock_code": "000001.SZ", "time": "09:30:05", "price": 12.50, "vol": 100, "buyorsell": 0, # 0=buy, 1=sell, 2=neutral "num": 5, # Number of trades in this tick "volume": 100 }
7. get_transactions_realtime() — Real-Time Tick Data
Today's live tick-by-tick transaction stream. await client.get_transactions_realtime( stock_code="000001.SZ", start=0, offset=1000 ) Same return format as get_transactions_history().
8. get_transactions_with_fallback() — Tick Data with Fallback
Tries real-time first, falls back to today's historical data if empty. await client.get_transactions_with_fallback( stock_code="000001.SZ", start=0, offset=1000, use_history_fallback=True )
Raw mootdx API (Low-Level)
If using mootdx directly without the wrapper: from mootdx.quotes import Quotes client = Quotes.factory(market='std') # K-line bars df = client.bars(symbol="000001", frequency=7, start=0, offset=240) # Real-time quotes (supports list of symbols for batch) df = client.quotes(symbol="000001") df = client.quotes(symbol=["000001", "600300"]) # Historical transactions df = client.transactions(symbol="000001", start=0, offset=1000, date="20250210") # Real-time transactions df = client.transaction(symbol="000001", start=0, offset=1000) All raw APIs return pandas DataFrames.
Common Pitfalls
Empty results outside trading hours: Apply the time_frame patch (see above) Beijing Exchange stocks: .BJ codes are NOT supported — always filter them out Rate limiting: Default rate limit is 0.005s between calls; adjust if connection drops Weekend/holiday queries: Always validate against trading calendar before querying 1-min bar offset calculation: Must account for 240 trading minutes/day with lunch gap
Additional Resources
For detailed method signatures and time calculation logic, see api-reference.md
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Scripts
- SKILL.md
- api-reference.md
- scripts/setup_and_verify.py