Mootdx China Stock Data

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.

Critical: Time & Timezone Considerations

Trading Hours (Beijing Time, UTC+8)

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

Stock Code Format

mootdx uses pure numeric codes (TDX format). Convert from standard format:

Conversion: Strip the .SH / .SZ / .BJ suffix.

Important: mootdx does NOT support Beijing Stock Exchange (.BJ) stocks. Filter them out before calling.

API Reference

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:

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

1. Empty results outside trading hours: Apply the time_frame patch (see above)
2. Beijing Exchange stocks: .BJ codes are NOT supported — always filter them out
3. Rate limiting: Default rate limit is 0.005s between calls; adjust if connection drops
4. Weekend/holiday queries: Always validate against trading calendar before querying
5. 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