datahub_binary

使用示例

import os
from datetime import datetime

import polars as pl

os.environ["LOGURU_LEVEL"] = "TRACE"

from datahub import *


def main():
    starrocks_setting = StarRocksSetting(
        host="xxx",
        db_port=9030,
        http_port=8040,
        username="xxx",
        password="xxx",
        # sftp=sftp_setting  # 设置sftp后会启用大查询缓存
    )

    setting = Setting(
        starrocks=starrocks_setting,
    )
    datahub = DataHub(setting)
    start_time = datetime(2024, 12, 19)
    end_time = datetime(2024, 12, 29)
    # pl.Config.set_tbl_cols(-1)  # -1 表示显示所有列
    # pl.Config.set_fmt_str_lengths(100)  # 设置字符串显示长度
    # pl.Config.set_tbl_width_chars(200)  # 设置表格宽度字符数

    # 发送监控报警
    # mylogger = logger.Logger("test")
    # mylogger.monitor.to("alpha运维").info("test")
    # mylogger.monitor.to("alpha运维").at("all").error("test")

    print("获取交易日")
    result = datahub.get_trading_days(start_time, end_time)
    print(result)

    print("获取指定日期, 指定indicator 可用标的")
    result = datahub.get_instrument_list(
        trade_date=start_time.date(),
        indicators=["premiums_income"],
    )
    print(result)

    print("获取指定日期标的信息")
    result = datahub.get_instrument_info(
        trade_date=start_time.date(), market="XSHG", instrument_type="spot")
    print(result)

    print("获取指定日期标的池")
    result = datahub.get_universe(trade_date=datetime(
        2025, 2, 20), universe="basic_alpha")
    print(result)

    print("获取某日逐笔成交")
    result = datahub.get_md_transaction(
        start_date=datetime(2025, 1, 27).date(),
        instruments=["508086.XSHG"],
    )
    print(result)

    print("获取某日快照行情")
    result = datahub.get_md_snapshot(
        start_date=datetime(2025, 1, 27).date(),
        instruments=["508086.XSHG"],
    )
    print(result)

    print("获取某日快照行情")
    result = datahub.get_trading_days(
        start_date=start_time.date(),
        end_date=end_time.date(),
    )
    print(result)

    print("获取指标值")
    result = datahub.get_indicator_data(
        start_time=start_time,
        end_time=end_time,
        # indicators=["5min_stat_open"],
        instruments=["600519.XSHG"],
        types=["5min_stat"]
    )
    print(result)

    print("获取BarDataMatrix")
    bar_data = datahub.get_indicator_matrix(
        trade_time=datetime(2024, 12, 19, 10),
        indicators=["5min_stat_open", "5min_stat_low"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(bar_data)

    print("获取BarDataMatrix列表")
    bar_data = datahub.get_indicator_matrix_list(
        start_date=datetime(2024, 12, 19, 10),
        end_date=datetime(2024, 12, 20, 10),
        indicators=["5min_stat_open", "5min_stat_low"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(bar_data)

    print("获取因子值")
    result = datahub.get_factor_data(
        start_time=start_time,
        end_time=end_time,
        # factors=["5min_stat_open"],
        instruments=["600519.XSHG"],
        types=["5min_stat"]
    )
    print(result)

    print("获取因子值矩阵")
    result = datahub.get_factor_matrix(
        trade_time=datetime(2024, 12, 19, 10),
        factors=["5min_stat_open", "5min_stat_low"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(result)

    print("获取收益率数据")
    result = datahub.get_return_data(
        start_time=start_time,
        end_time=end_time,
        instruments=["600519.XSHG"],
    )
    print(result)

    print("获取收益率矩阵")
    result = datahub.get_return_matrix(
        trade_time=datetime(2024, 10, 31, 19, 15),
        factors=["forward_ret_raw_1d"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(result)

    print("获取K线")
    result = datahub.get_kline(
        "5min", instruments=["600519.XSHG", "603350.XSHG"],
        start_time=start_time, end_time=end_time, adj_method="backward"
    )
    print(result)

    print("获取指标信息")
    result = datahub.get_indicator_info()
    print(result)

    print("获取指标类型信息")
    result = datahub.get_indicator_type()
    print(result)

    print("获取因子类型信息")
    result = datahub.get_factor_type()
    print(result)

    print("获取行业分类信息")
    result = datahub.get_instrument_industry(datetime(2018, 1, 5).date())
    print(result)

    print("获取交易日")
    result = datahub.calendar.get_latest_trade_date(
        dt=datetime(2025, 1, 4).date())
    print(result)

    print("获取主力期货合约信息")
    result = datahub.get_future_domain_info("IC")
    print(result)

    print("获取主力期货快照行情")
    result = datahub.get_future_snapshot(
        "IC", start_date=start_time.date(), end_date=end_time.date())
    print(result)

    print("获取return_factor的数据")
    df = datahub.get_return_matrix(
        trade_time=datetime(2018, 1, 29, 9, 35),
        factors=["ret_raw_1d", "ret_raw_3d", "ret_raw_5d"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(df)

    print("获取risk_factor的数据")
    df = datahub.get_risk_factor_matrix(
        version="rq_v2_sws2021",
        trade_time=datetime(2018, 1, 11, 20),
        factors=["liquidity", "longterm_reversal", "mid_cap"],
        instrument_ids=["600519.XSHG", "000001.XSHE", "000002.XSHG"],
    )
    print(df)

    print("获取最新黑名单列表, 可以指定日期")
    df = datahub.get_blacklist(blacklist_ids=["cms_dma_blacklist", "XXX"], end_date=None)
    print(df)

    print("获取券池列表, 可以指定券池和日期")
    df = datahub.get_sbl_list(end_date=datetime(2025, 1, 1))
    print(df)

    print("获取seq y")
    df = datahub.get_seq_y_info(resample_type="time_interval_ms_500",)
    print(df)

    print("获取seq factor info")
    df = datahub.get_seq_factor_info(resample_type="time_interval_ms_500",)
    print(df)

    print("获取 seq factor")
    df = datahub.get_seq_factor_data(
        start_time=datetime(2025, 2, 10, 9, 30, 9, 920000),
        end_time=datetime(2025, 2, 10, 9, 30, 11, 920000),
        factors=["ActiveCashFlowFactors__lookback_tick_30s__time_interval_ms_500__buy_amount"],
        instruments=["000029.XSHE"]
    )
    print(df)

    print("获取get_seq_y_stat")
    df = datahub.get_seq_y_stat(start_time=datetime(2025, 1, 10, 23), end_time=datetime(2025, 1, 10, 23),
                                stat_type="std_1d")
    print(df)


if __name__ == "__main__":
    main()

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

datahub_binary-1.9.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded Jul 12, 2026 CPython 3.12manylinux: glibc 2.17+ x86-64

File details

Details for the file datahub_binary-1.9.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

Download URL: datahub_binary-1.9.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Upload date: Jul 12, 2026
Size: 1.1 MB
Tags: CPython 3.12, manylinux: glibc 2.17+ x86-64
Uploaded using Trusted Publishing? No
Uploaded via: uv/0.6.14

File hashes

Hashes for datahub_binary-1.9.27-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm	Hash digest
SHA256	`ce136d7af8fbad5bcebfebb27c4c07f18b7e3f8dbfb71e83cb1e1a10e582c695`
MD5	`448f17fb6e49de4b79294d05c3e69aa9`
BLAKE2b-256	`faa6580070172fac87c8e4abecdefc94af211b5c40757f17109290af8c7f66c2`