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()

Metadata

Release files for datahub_binary 2.0.1

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Built distribution (wheel)

Table of built distributions (wheels) for datahub_binary 2.0.1
File	Interpreter	ABI	Platform	Reset
datahub_binary-2.0.1-cp312-cp312-win_amd64.whl 773.5 kB Sep 21, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details

Release files / datahub_binary-2.0.1-cp312-cp312-win_amd64.whl

Download URL	datahub_binary-2.0.1-cp312-cp312-win_amd64.whl
Size	773.5 kB
Tags	CPython 3.12 Windows x86-64
SHA-256 checksum How to use checksums	`6ed29d5469238f5e21aa7f2b6c11073b88ecac7b83f9daec55dcb86356577b7a`
BLAKE2b-256 checksum How to use checksums	`59797e6e93cd17bded6f1e443379af329453102d05dab75571bc0ff2baadb37c`
Upload date	Sep 21, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`uv/0.11.4 {"installer":{"name":"uv","version":"0.11.4","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":null,"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}`