Securities

Initialization

All examples on this page assume the following initialization has been completed:

from tigeropen.tiger_open_config import TigerOpenClientConfig
from tigeropen.quote.quote_client import QuoteClient

client_config = TigerOpenClientConfig(props_path='your_config_directory_path')
quote_client = QuoteClient(client_config)

For details, see Prerequisites.

Get Market Status

QuoteClient.get_market_status(market=Market.ALL, lang=None)

Description

Retrieves the trading status of specified markets (such as intraday, pre-market, after-hours, etc.) and retrieve the latest opening time for that market.

Request Frequency

For rate limits, see API Request Limits

Parameters

Return

list

Elements are MarketStatus objects. MarketStatus structure is as follows:

Example

market_status = quote_client.get_market_status(Market.US)

# View attributes
print(market_status)

Example Response

[MarketStatus({'market': 'US', 'status': 'Pre-market Trading', 'open_time': datetime.datetime(2019, 1, 7, 9, 30, tzinfo=<DstTzInfo 'US/Eastern' EST-1 day, 19:00:00 STD>), 'trading_status': 'PRE_HOUR_TRADING'})]

Get Market Trading Calendar

QuoteClient.get_trading_calendar(market, begin_date=None, end_date=None)

Description

Provides market trading calendars from 2015 to the end of the current year (excludes weekends and legal holidays for that market, but does not exclude temporary market closures). If the selected time exceeds the available range, start and end times will be automatically adjusted to the available data range.

Request Frequency

For rate limits, see API Request Limits

Parameters

begin_time and end_time parameter handling:

Return

list

Each element is a dict, with the following key meanings:

Example

calendar = quote_client.get_trading_calendar(Market.US, begin_date='2022-11-01', end_date='2022-12-01')
print(calendar)

Example Response

[
    {
        "date": "2025-07-02",
        "type": "TRADING",
        "open_time": "09:30:00",
        "close_time": "16:00:00"
    },
    {
        "date": "2025-07-03",
        "type": "EARLY_CLOSE",
        "open_time": "09:30:00",
        "close_time": "13:00:00"
    },
    {
        "date": "2025-07-07",
        "type": "TRADING",
        "open_time": "09:30:00",
        "close_time": "16:00:00"
    }
]

Note: Non-trading days (weekends and market holidays) are excluded from the results. In the example above, July 4 (Independence Day) and July 5-6 (weekend) are all excluded.

Get All Symbols

QuoteClient.get_symbols(market=Market.ALL, include_otc=False)

Description

Retrieves a list of all security symbols for the specified market, including delisted or temporarily non-tradable securities, as well as index codes.

Request Frequency

For rate limits, see API Request Limits

Parameters

Return

Type

list

Elements are symbols of all securities in the market, including delisted and non-tradable symbols. Codes starting with . are indices, e.g., .DJI represents the Dow Jones Index.

Example

symbols = quote_client.get_symbols(Market.US)
print(symbols)

Example Response

['.DJI', '.IXIC', '.SPX', 'A', 'AA', 'AAA', 'AAAU', 'AAC', 'AAC.U', 'AAC.WS', 'AACG', 'AACI', 'AACIU', 'AACIW', 'AACQW', 'AADI', 'AADR', 'AAIC', 'AAIN', 'AAL', 'AAMC', 'AAME', 'AAN', 'AAOI', 'AAON', 'AAP', 'AAPL',....,'ZYME', 'ZYNE', 'ZYXI']

Get Symbols and Names

QuoteClient.get_symbol_names(market=Market.ALL, lang=None, include_otc=False)

Description

Retrieves all security symbols and names for a specified market.

Request Frequency

For rate limits, see API Request Limits

Parameters

Return

list

Each element in the list is a tuple, where the first element is the symbol and the second element is the name.

Example

symbol_names = quote_client.get_symbol_names(market=Market.ALL)
print(symbol_names)

Example Response

[('AAAP', 'Advanced Accelerator Applications SA'), ('AAAU', 'Perth Mint Physical Gold ETF'), ('AABA', 'Altaba'), ('AAC', 'AAC Holdings Inc')]

Get Real-time Quotes

QuoteClient.get_stock_briefs(symbols, include_hour_trading=False, lang=None)

Description

Retrieves real-time stock quotes. The required market data access must be purchased before calling this API. Each request supports up to 50 stocks.

Request Frequency

For rate limits, see API Request Limits

Parameters

Return

pandas.DataFrame

Structure:

status (Trading Status) values:

• "UNKNOWN": Unknown
• "NORMAL": Normal
• "HALTED": Halted
• "DELIST": Delisted
• "NEW": New stock
• "ALTER": Change
• "CIRCUIT_BREAKER": Circuit breaker
• "ST": Special treatment

Example

import pandas as pd


briefs = quote_client.get_stock_briefs(['00700'])
print(briefs)

# Convert latest_time to a datetime in the market's time zone
briefs['cn_date'] = pd.to_datetime(briefs['latest_time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
briefs['us_date'] = pd.to_datetime(briefs['latest_time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

Example Response

  symbol     open   high       low   close  pre_close  latest_price    latest_time  \
    AAPL  241.225  246.3  240.2106  245.50     237.88        245.50  1758312000000   

   ask_price  ask_size  bid_price  bid_size     volume  status  adj_pre_close  \
         0.0         0        0.0         0  163796191  NORMAL         237.88   

  hour_trading_tag  hour_trading_latest_price  hour_trading_pre_close  \
          Post-Mkt                     245.69                  245.50   

  hour_trading_latest_time  hour_trading_volume  hour_trading_timestamp  
                 19:59 EDT             23831923           1758326399684  

Get Depth Quotes

QuoteClient.get_depth_quote(symbols, market)

Description

Retrieves N-level bid/ask order data for specified securities, including order price, quantity, and number of orders. Each request supports up to 50 securities.

Request Frequency

For rate limits, see API Request Limits

Parameters

Return

dict

Data example:

If returning a single symbol:

{'symbol': '03833', 
 'asks': [(1.81, 139000, 7), (1.82, 211000, 9), (1.83, 245000, 9), (1.84, 59000, 6), (1.85, 83000, 7), (1.86, 30000, 2), (1.87, 54000, 4), (1.88, 141000, 6), (1.89, 180000, 4), (1.9, 117000, 7)], 
 'bids': [(1.8, 355000, 6), (1.79, 242000, 5), (1.78, 71000, 5), (1.77, 142000, 8), (1.76, 616000, 10), (1.75, 120000, 7), (1.74, 90000, 4), (1.73, 80000, 2), (1.72, 210000, 7), (1.71, 258000, 6)]}

If returning multiple symbols:

{'03833': 
  {'symbol': '03833', 
   'asks': [(1.81, 131000, 7), (1.82, 164000, 8), (1.83, 245000, 9), (1.84, 59000, 6), (1.85, 83000, 7), (1.86, 30000, 2), (1.87, 54000, 4), (1.88, 141000, 6), (1.89, 180000, 4), (1.9, 117000, 7)], 
   'bids': [(1.8, 356000, 7), (1.79, 242000, 5), (1.78, 71000, 5), (1.77, 142000, 8), (1.76, 616000, 10), (1.75, 120000, 7), (1.74, 90000, 4), (1.73, 80000, 2), (1.72, 210000, 7), (1.71, 258000, 6)]}, 

'01810': 
 {'symbol': '01810', 
  'asks': [(55.8, 306800, 134), (55.85, 242400, 78), (55.9, 394000, 87), (55.95, 421000, 69), (56.0, 526000, 158), (56.05, 320600, 42), (56.1, 327000, 71), (56.15, 277400, 46), (56.2, 116800, 74), (56.25, 163200, 42)], 
  'bids': [(55.75, 71200, 46), (55.7, 1013000, 211), (55.65, 574200, 202), (55.6, 1297800, 749), (55.55, 1136200, 485), (55.5, 2075400, 1030), (55.45, 386800, 149), (55.4, 484800, 216), (55.35, 267000, 107), (55.3, 328200, 230)]
}
}

The data meaning of list items corresponding to asks and bids is (order price, order quantity, number of orders):

[(ask_price1, ask_volume1, order_count), (ask_price2, ask_volume2, order_count), ...]
[(bid_price1, bid_volume2, order_count), (bid_price2, bid_volume2, order_count), ...]

Example

depth_quote = quote_client.get_depth_quote(['02833'], Market.HK)

Example Response

{'symbol': '02833',
'asks': [(27.4, 300, 2), (27.45, 500, 1), (27.5, 4400, 1), (27.55, 0, 0), (27.6, 5700, 3), (27.65, 0, 0),
        (27.7, 500, 1), (27.75, 0, 0), (27.8, 0, 0), (27.85, 0, 0)],
'bids': [(27, 4000, 3), (26.95, 200, 1), (26.9, 0, 0), (26.85, 400, 1), (26.8, 0, 0), (26.75, 0, 0),
        (26.7, 0, 0), (26.65, 0, 0), (26.6, 0, 0), (26.55, 0, 0)]
}

Get Tick-by-Tick Trade Data

QuoteClient.get_trade_ticks(symbols, trade_session=None, begin_index=None, end_index=None, limit=None, lang=None)

Description

Retrieves trade tick data. This API supports both querying full tick records for the current trading day after market close and obtaining the latest real-time tick data during trading hours.

Request Frequency

For rate limits, see API Request Limits

Parameters

Usage Instructions for begin_index and end_index Parameters

Return

pandas.DataFrame

Structure:

Example

from tigeropen.common.consts import TradingSession


ticks = quote_client.get_trade_ticks(['00700'])

# Query latest tick data
ticks = quote_client.get_trade_ticks(['AAPL'], begin_index=-1, end_index=-1)

# Request pre-market tick data
ticks = quote_client.get_trade_ticks(['AAPL'], trade_session=TradingSession.PreMarket)
# Request after-hours tick data
ticks = quote_client.get_trade_ticks(['00700'], trade_session=TradingSession.AfterHours)

Example Response

    symbol           time   volume   price direction   index
0     AAPL  1712347199527      291  169.53         -  521645
1     AAPL  1712347199564      200  169.53         -  521646
2     AAPL  1712347199564      400  169.53         -  521647
3     AAPL  1712347199564      500  169.53         -  521648
4     AAPL  1712347199566      900  169.53         -  521649
...    ...            ...      ...     ...       ...    ...
395    AMD  1712347200234       40  170.40         *  556800
396    AMD  1712347200273       55  170.39         *  556801
397    AMD  1712347200274        5  170.39         *  556802
398    AMD  1712347200488  1577655  170.42         *  556803
399    AMD  1712347200489       33  170.42         *  556804

Get K-Line Data

QuoteClient.get_bars(symbols, period=BarPeriod.DAY, begin_time=-1, end_time=-1, right=QuoteRight.BR, limit=251, lang=None, page_token=None, trade_session=None)

Description

Retrieves candlestick bars (K-line data) for Hong Kong and US stocks at daily, weekly, monthly, yearly, 1-minute, 5-minute, 15-minute, 30-minute, 60-minute, and other intervals. Each request returns at most 1,200 records. Use multiple requests for longer ranges. The API accepts either a date range or a specific date.

• Minute-level bars (1/5/15/30/60 minutes): Supports nearly 10 years of historical data
• Daily bars and above (daily/weekly/monthly/yearly): Provides complete historical data

US pre-market and after-hours only support 60-minute and below candlestick bars after April 2024

Request Frequency

For rate limits, see API Request Limits

Parameters

When trade_session parameter is set to PreMarket, AfterHours, or OverNight, the period parameter must be one of the following:
BarPeriod.ONE_MINUTE, BarPeriod.THREE_MINUTES, BarPeriod.FIVE_MINUTES, BarPeriod.TEN_MINUTES, BarPeriod.FIFTEEN_MINUTES, BarPeriod.HALF_HOUR, BarPeriod.ONE_HOUR, BarPeriod.TWO_HOURS, or BarPeriod.FOUR_HOURS, otherwise candlestick bars cannot be displayed correctly.

Return

pandas.DataFrame

Structure:

Example

import pandas as pd


bars = quote_client.get_bars(['AAPL'])
#trade_session optional parameter values
# bars = quote_client.get_bars(['AAPL'],period = BarPeriod.ONE_MINUTE, trade_session=TradingSession.PreMarket)
# bars = quote_client.get_bars(['AAPL'],period = BarPeriod.HALF_HOUR, trade_session=TradingSession.AfterHours)
# bars = quote_client.get_bars(['AAPL'],period = BarPeriod.ONE_MINUTE,trade_session=TradingSession.OverNight)

print(bars.head())
# Minute bars
bars = quote_client.get_bars(symbols, period=BarPeriod.ONE_MINUTE,
                             begin_time='2023-06-05 00:30:00',
                             end_time='2023-06-10 23:31:00'
                             )
# Convert time format
bars['cn_date'] = pd.to_datetime(bars['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
bars['us_date'] = pd.to_datetime(bars['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

Example Response

     symbol           time      open      high       low   close    volume   amount
0     00700  1515474000000  174.5500  175.0600  173.4100  174.33  21583997   1000
1     00700  1515560400000  173.1600  174.3000  173.0000  174.29  23959895   1000
2     00700  1515646800000  174.5900  175.4886  174.4900  175.28  18667729   1000
3     00700  1515733200000  176.1800  177.3600  175.6500  177.09  25418080   1000
4     00700  1516078800000  177.9000  179.3900  176.1400  176.19  29565947   1000

Get K-Line Data by Page

QuoteClient.get_bars_by_page(symbol, period=BarPeriod.DAY, begin_time=-1, end_time=-1, total=10000, page_size=1000, right=QuoteRight.BR, time_interval=2, lang=None, trade_session=None)

Description

Retrieves candlestick bars for specified stocks by page.

Request Frequency

For rate limits, see API Request Limits

Parameters

Returns

pandas.DataFrame

Structure:

Example

import pandas as pd


bars = quote_client.get_bars_by_page(
            'AAPL',
            period=BarPeriod.DAY,
            begin_time='2024-06-25 21:30:00',
            end_time='2025-02-02 23:31:00',
            total=1000,
            page_size=10,
            time_interval=0.01,
        )
print(bars.head().to_string())

Example Response

  symbol           time    open      high     low   close    volume        amount       \                                       
0   AAPL  1719374400000  211.50  214.8600  210.64  213.25  66213186  1.411181e+10       \
1   AAPL  1719460800000  214.69  215.7395  212.35  214.10  49772707  1.064720e+10       \

next_page_token
xxxxxx

Get Intraday Data for the Latest Trading Day

QuoteClient.get_timeline(symbols, include_hour_trading=False, begin_time=-1, lang=None, trade_session=None)

Description

Retrieves one record per minute for the latest trading day. Historical trading days are not supported.

Request Frequency

For rate limits, see API Request Limits

Parameters

Returns

pandas.DataFrame

Structure:

Example

import pandas as pd


timeline = quote_client.get_timeline(['01810'], include_hour_trading=False)

# Convert time to a datetime in the market's time zone
timeline['cn_date'] = pd.to_datetime(timeline['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
timeline['us_date'] = pd.to_datetime(timeline['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

# View the latest data
print(timeline.iloc[-1].price)
print(timeline.iloc[-1].volume)
# Filter by symbol
df = timeline.loc[timeline['symbol'] == 'AAPL']

Example Response

     symbol           time    price  avg_price  pre_close  volume trade_session
0     01810  1547217000000  23.4700  23.211563       23.4  233000       regular
1     01810  1547217060000  23.6700  23.408620       23.4  339296       regular
2     01810  1547217120000  23.5900  23.423038       23.4   46337       regular
3     01810  1547217180000  23.5000  23.428830       23.4   66697       regular
4     01810  1547217240000  23.5108  23.433360       23.4   46762       regular

Get Historical Intraday Data

QuoteClient.get_timeline_history(symbols, date, right=QuoteRight.BR, trade_session=None)

Description

Retrieves historical intraday data for a specified date.

Request Frequency

For rate limits, see API Request Limits

Parameters

Returns

pandas.DataFrame

Structure:

Example

import pandas as pd


timeline = quote_client.get_timeline_history(['AAPL', 'BABA'], '2021-04-11')

# Convert time to a datetime in the market's time zone
timeline['cn_date'] = pd.to_datetime(timeline['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
timeline['us_date'] = pd.to_datetime(timeline['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

# Filter by symbol
df = timeline.loc[timeline['symbol'] == 'AAPL']

print(timeline)

Example Response

    symbol           time   volume     price   avg_price 
0     AAPL  1649683800000  1569372  168.5000  168.749760
1     AAPL  1649683860000   323022  168.5800  168.739030 

Get Delayed Stock Quotes

QuoteClient.get_stock_delay_briefs(symbols, lang=None)

Description

This API provides free delayed quotes without requiring market data access. You can use it directly after opening a developer account. Currently supports only US stock delayed quotes, which are delayed by approximately 15 minutes compared to real-time quotes.

Request Frequency

For rate limits, see API Request Limits

Parameters

Returns

pandas.DataFrame

Structure:

Example

import pandas as pd


briefs = quote_client.get_stock_delay_briefs(['AAPL'])
print(briefs)

# Convert time to a datetime in the market's time zone
briefs['cn_date'] = pd.to_datetime(briefs['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('Asia/Shanghai')
briefs['us_date'] = pd.to_datetime(briefs['time'], unit='ms').dt.tz_localize('UTC').dt.tz_convert('US/Eastern')

Example Response

  symbol  pre_close  halted           time    open   high       low  close  \
0   AAPL     174.33     0.0  1639602000000  175.11  179.5  172.3108  179.3   
  

      volume                   cn_date                   us_date  
0  131063257 2021-12-16 05:00:00+08:00 2021-12-15 16:00:00-05:00 

Get Stock Trading Information

QuoteClient.get_trade_metas(symbols)

Description

Retrieves essential information for stock trading, such as shares per lot.

Request Frequency

For rate limits, see API Request Limits

Parameters

Returns

pandas.DataFrame

Structure:

Example

trade_metas = quote_client.get_trade_metas(symbols=['00700', '00336'])

Example Response

  symbol  lot_size  min_tick  spread_scale
0  00700       100      0.20             0
1  00336      1000      0.01             0

Get Stock Capital Flow Data

QuoteClient.get_capital_flow

Description

Retrieves stock net capital inflow data, including real-time minute data for the latest trading day and historical net inflow data for different periods. Supports daily, weekly, monthly, quarterly, semi-annual, and annual data. Maximum 1200 records per request, default returns 200 records.

Parameters

Returns

pandas.DataFrame

Example

from tigeropen.common.consts import Market, CapitalPeriod


result = quote_client.get_capital_flow('AAPL', market=Market.US, period=CapitalPeriod.INTRADAY)
print(result)

# Get net_inflow from the first row
result.iloc[0]['net_inflow']
# or result['net_inflow'].iloc[0]

Example Response

           time      timestamp    net_inflow symbol period
0    2022-02-24  1645678800000 -5.889058e+08   AAPL    day
1    2022-02-25  1645765200000 -1.229127e+08   AAPL    day
2    2022-02-28  1646024400000  1.763644e+08   AAPL    day

Get Stock Capital Distribution

QuoteClient.get_capital_distribution

Description

Retrieves stock capital distribution.

Parameters

Returns

tigeropen.quote.domain.capital_distribution.CapitalDistribution

Example

result = quote_client.get_capital_distribution('JD', market=Market.US)
print(result)

Example Response

CapitalDistribution({'symbol': 'JD', 'net_inflow': -14178801.76, 'in_all': 157357147.5,
'in_big': 25577130.842900004, 'in_mid': 13664116.789999994, 'in_small': 118115899.86410056,
'out_all': 171535949.25, 'out_big': 22642951.677099995, 'out_mid': 12733553.691200001,
'out_small': 136159443.88620025})

Get HK Broker Trading Seats

QuoteClient.get_stock_broker

Description

Retrieves Hong Kong broker trading seats.

Parameters

Returns

tigeropen.quote.domain.stock_broker.StockBroker

LevelBroker attributes:

Broker attributes:

Example

result = quote_client.get_stock_broker('01810', limit=5)
print(result)

Example Response

StockBroker({'symbol': '01810',
    'bid_broker': [
        LevelBroker({'level': 1, 'price': 11.46, 'broker_count': 5,
            'broker': [Broker({'id': '5999', 'name': 'China Chuangying'}), Broker({'id': '4374', 'name': 'Barclays Asia'}),
                    Broker({'id': '1438', 'name': 'Susquehanna'}), Broker({'id': '4821', 'name': 'Upsmart'}),
                     Broker({'id': '6998', 'name': 'China Investment'})]})],
    'ask_broker': [
        LevelBroker({'level': 1, 'price': 11.48, 'broker_count': 5,
            'broker': [Broker({'id': '4374', 'name': 'Barclays Asia'}), Broker({'id': '9056', 'name': 'UBS'}),
                    Broker({'id': '2027', 'name': 'Bank of East Asia'}), Broker({'id': '4821', 'name': 'Upsmart'}),
                    Broker({'id': '4374', 'name': 'Barclays Asia'})]})]})

Get Market Value of HK Broker Holdings

QuoteClient.get_broker_hold

Description

Retrieves Hong Kong stock broker holdings market value.

Parameters

Returns

pd.DataFrame

Example

result = quote_client.get_broker_hold(market=Market.HK, limit=5, order_by='marketValue', direction=SortDirection.ASC, lang=Language.zh_CN)
print(result)

Example Response

   org_id  org_name        date   shares_hold  market_value  buy_amount  buy_amount5 \
0  C00019  The Hongkong and Shanghai Banking Corporation  2025-04-10  697552502881  8.943928e+12 -1405700331  -3893473916 \  
1  A00003    Shanghai-Hong Kong Stock Connect  2025-04-10  293590702492  2.493193e+12   843074553   4263428837   \
2  C00010      Citibank  2025-04-10  206768714970  1.926757e+12   386287281    483932517   \  

buy_amount20  buy_amount60 market  page  total_page  total_count
 -7103861935  -29489710005     HK     0         135          672
 10882949005   17126490078     HK     0         135          672
   -22419868   -5182619457     HK     0         135          672

Popular Trading Rankings

QuoteClient.get_trade_rank(market, lang=Language.en_US)

Description

Retrieves stock popular trading rankings, data is updated approximately every 20 seconds.

Parameters

Returns

pandas.DataFrame

US stocks return 30 records, Hong Kong and Singapore stocks return 10 records, structure as follows:

Example

result = quote_client.get_trade_rank(market=Market.US, lang=Lang.ZH_CN)
print(result)

Example Response

   symbol market                name           sec_type  change_rate  sell_order_rate   \
0    TSLA     US        Tesla Motors                STK     0.022118         0.490115   \     
1    NVDA     US              NVIDIA                STK     0.002440         0.418413   \     
2    INTC     US               Intel                STK    -0.032385         0.401211   \     

buy_order_rate  hour_trading_trading_status hour_trading_trade_session  hour_trading_change_rate
      0.509885                            3                 AfterHours                 -0.001338
      0.581587                            3                 AfterHours                 -0.003792
      0.598789                            3                 AfterHours                 -0.003381