rqalpha.apis.api_rqdatac 源代码

# -*- coding: utf-8 -*-
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import datetime
from typing import Union, Optional, Iterable, List

import six
from dateutil.parser import parse
import pandas as pd

from rqalpha.const import EXECUTION_PHASE
from rqalpha.api import export_as_api
from rqalpha.apis.names import (
    VALID_HISTORY_FIELDS, VALID_MARGIN_FIELDS, VALID_SHARE_FIELDS, VALID_TURNOVER_FIELDS,
    VALID_CURRENT_PERFORMANCE_FIELDS, VALID_STOCK_CONNECT_FIELDS,
)
from rqalpha.execution_context import ExecutionContext
from rqalpha.environment import Environment
from rqalpha.utils.arg_checker import apply_rules, verify_that
from rqalpha.utils.exception import RQInvalidArgument
from rqalpha.apis.api_base import assure_order_book_id
from rqalpha.utils.i18n import gettext as _
from rqalpha.utils.logger import user_log

try:
    import rqdatac
except ImportError:
    class DummyRQDatac:
        __name__ = "rqdatac"

        def __getattr__(self, item):
            return self

        def __call__(self, *args, **kwargs):
            raise RuntimeError(_('rqdatac is not available, extension apis will not function properly'))


    rqdatac = DummyRQDatac()


def to_date(date):
    if isinstance(date, datetime.datetime):
        return date.date()
    if isinstance(date, datetime.date):
        return date

    if isinstance(date, str):
        return parse(date).date()

    raise RQInvalidArgument('unknown date value: {}'.format(date))


[文档]@export_as_api @apply_rules(verify_that('start_date').is_valid_date()) def get_split(order_book_ids, start_date=None): # type: (Union[str, List[str]], Optional[Union[str, datetime.date]]) -> pd.DataFrame """ 获取某只股票到策略当前日期前一天的拆分情况(包含起止日期)。 :param order_book_ids: 证券代码,证券的独特的标识符,例如:'000001.XSHE' :param start_date: 开始日期,用户必须指定,需要早于策略当前日期 :return: 查询时间段内的某个股票的拆分数据 * ex_dividend_date: 除权除息日,该天股票的价格会因为拆分而进行调整 * book_closure_date: 股权登记日 * split_coefficient_from: 拆分因子(拆分前) * split_coefficient_to: 拆分因子(拆分后) 例如:每10股转增2股,则split_coefficient_from = 10, split_coefficient_to = 12. :example: .. code-block:: python3 :linenos: get_split('000001.XSHE', start_date='2010-01-04') #[Out] # book_closure_date payable_date split_coefficient_from \ #ex_dividend_date #2013-06-20 2013-06-19 2013-06-20 10 # split_coefficient_to #ex_dividend_date #2013-06-20 16.0 """ # order_book_id 支持list类型 env = Environment.get_instance() dt = env.trading_dt.date() - datetime.timedelta(days=1) start_date = to_date(start_date) if start_date > dt: raise RQInvalidArgument(_('in get_split, start_date {} is no earlier than the previous test day {}').format( start_date, dt )) if isinstance(order_book_ids, six.string_types): order_book_ids = [order_book_ids] order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_split(order_book_ids, start_date, dt)
[文档]@export_as_api @apply_rules(verify_that('date').is_valid_date(ignore_none=True)) def index_components(order_book_id, date=None): # type: (str, Optional[Union[str, datetime.date]]) -> List[str] """ 获取某一指数的股票构成列表,也支持指数的历史构成查询。 :param order_book_id: 指数代码,可传入order_book_id :param date: 查询日期,默认为策略当前日期。如指定,则应保证该日期不晚于策略当前日期 :return: 构成该指数股票的 order_book_id :example: 得到上证指数在策略当前日期的构成股票的列表: .. code-block:: python3 :linenos: index_components('000001.XSHG') #[Out]['600000.XSHG', '600004.XSHG', ...] """ env = Environment.get_instance() dt = env.trading_dt.date() if date is None: date = dt else: date = to_date(date) if date > dt: raise RQInvalidArgument(_('in index_components, date {} is no earlier than test date {}').format( date, dt )) order_book_id = assure_order_book_id(order_book_id) return rqdatac.index_components(order_book_id, date=date)
[文档]@export_as_api @apply_rules(verify_that('date').is_valid_date(ignore_none=True)) def index_weights(order_book_id, date=None): # type: (str, Optional[Union[str, datetime.date]]) -> pd.Series """ 获取T-1日的指数权重 :param order_book_id: 指数 :param date: 可选,默认为T-1日 :return: 每只股票在指数中的构成权重 :example: 获取上证50指数上个交易日的指数构成 .. code-block:: python3 :linenos: index_weights('000016.XSHG') # [Out] # Order_book_id # 600000.XSHG 0.03750 # 600010.XSHG 0.00761 # 600016.XSHG 0.05981 # 600028.XSHG 0.01391 # 600029.XSHG 0.00822 # 600030.XSHG 0.03526 # 600036.XSHG 0.04889 # 600050.XSHG 0.00998 # 600104.XSHG 0.02122 """ env = Environment.get_instance() data_proxy = env.data_proxy dt = to_date(data_proxy.get_previous_trading_date(env.trading_dt.date())) if date is None: date = dt else: date = to_date(date) if date > dt: raise RQInvalidArgument(_('in index_weights, date {} is no earlier than previous test date {}').format( date, dt )) order_book_id = assure_order_book_id(order_book_id) return rqdatac.index_weights(order_book_id, date)
[文档]@export_as_api def concept(*concept_names): # type: (*str) -> List[str] """ 获取T日的概念股列表 :param concept_names: 概念名称。可以从概念列表中选择一个或多个概念填写, 可以通过 rqdatac.concept_list() 获取概念列表 :return: 属于该概念的股票 order_book_id :example: 得到一个概念的股票列表: .. code-block:: python3 :linenos: concept('民营医院') # [Out] # ['600105.XSHG', # '002550.XSHE', # '002004.XSHE', # '002424.XSHE', # ...] 得到某几个概念的股票列表: .. code-block:: :linenos: concept('民营医院', '国企改革') # [Out] # ['601607.XSHG', # '600748.XSHG', # '600630.XSHG', # ...] """ env = Environment.get_instance() dt = env.trading_dt.date() return rqdatac.concept(*concept_names, date=dt)
[文档]@export_as_api @ExecutionContext.enforce_phase( EXECUTION_PHASE.ON_INIT, EXECUTION_PHASE.BEFORE_TRADING, EXECUTION_PHASE.OPEN_AUCTION, EXECUTION_PHASE.AFTER_TRADING, EXECUTION_PHASE.SCHEDULED ) @apply_rules(verify_that('order_book_ids').are_valid_instruments(), verify_that('start_date').is_valid_date(ignore_none=False), verify_that('end_date').is_valid_date(ignore_none=True), verify_that('frequency').is_valid_frequency(), verify_that('fields').are_valid_fields(VALID_HISTORY_FIELDS, ignore_none=True), verify_that('adjust_type').is_in(['pre', 'post', 'none', 'internal']), verify_that('skip_suspended').is_instance_of(bool)) def get_price( order_book_ids, # type: Union[str, Iterable[str]] start_date, # type: Union[datetime.date, str] end_date=None, # type: Optional[Union[datetime.date, datetime.datetime, str]] frequency='1d', # type: Optional[str] fields=None, # type: Optional[Iterable[str]] adjust_type='pre', # type: Optional[str] skip_suspended=False, # type: Optional[bool] expect_df=False # type: Optional[bool] ): # type: (...) -> Union[pd.DataFrame, pd.Panel, pd.Series] """ 获取指定合约或合约列表的历史行情(包含起止日期,日线或分钟线),不能在'handle_bar'函数中进行调用。 注意,这一函数主要是为满足在研究平台编写策略习惯而引入。在编写策略中,使用history_bars进行数据获取会更方便。 :param order_book_ids: 合约代码,合约代码,可传入order_book_id, order_book_id list, symbol, symbol list :param start_date: 开始日期,用户必须指定 :param end_date: 结束日期,默认为策略当前日期前一天 :param frequency: 历史数据的频率。 现在支持日/分钟级别的历史数据,默认为'1d'。使用者可自由选取不同频率,例如'5m'代表5分钟线 :param fields: 期望返回的字段名称,如 open,close 等 :param adjust_type: 权息修复方案。前复权 - pre,后复权 - post,不复权 - none :param skip_suspended: 是否跳过停牌数据。默认为False,不跳过,用停牌前数据进行补齐。True则为跳过停牌期。注意,当设置为True时,函数order_book_id只支持单个合约传入 :param expect_df: 是否期望始终返回 DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 当 expect_df 为 False 时,返回值的类型如下 * 传入一个order_book_id,多个fields,函数会返回一个pandas DataFrame * 传入一个order_book_id,一个field,函数会返回pandas Series * 传入多个order_book_id,一个field,函数会返回一个pandas DataFrame * 传入多个order_book_id,函数会返回一个pandas Panel ========================= ========================= ============================================================================== 参数 类型 说明 ========================= ========================= ============================================================================== open float 开盘价 close float 收盘价 high float 最高价 low float 最低价 limit_up float 涨停价 limit_down float 跌停价 total_turnover float 总成交额 volume float 总成交量 acc_net_value float 累计净值(仅限基金日线数据) unit_net_value float 单位净值(仅限基金日线数据) discount_rate float 折价率(仅限基金日线数据) settlement float 结算价 (仅限期货日线数据) prev_settlement float 昨日结算价(仅限期货日线数据) open_interest float 累计持仓量(期货专用) basis_spread float 基差点数(股指期货专用,股指期货收盘价-标的指数收盘价) trading_date pandas.TimeStamp 交易日期(仅限期货分钟线数据),对应期货夜盘的情况 ========================= ========================= ============================================================================== :example: 获取单一股票历史日线行情: .. code-block:: python3 :linenos: get_price('000001.XSHE', start_date='2015-04-01', end_date='2015-04-12') #[Out] #open close high low total_turnover volume limit_up limit_down #2015-04-01 10.7300 10.8249 10.9470 10.5469 2.608977e+09 236637563.0 11.7542 9.6177 #2015-04-02 10.9131 10.7164 10.9470 10.5943 2.222671e+09 202440588.0 11.9102 9.7397 #2015-04-03 10.6486 10.7503 10.8114 10.5876 2.262844e+09 206631550.0 11.7881 9.6448 #2015-04-07 10.9538 11.4015 11.5032 10.9538 4.898119e+09 426308008.0 11.8288 9.6787 #2015-04-08 11.4829 12.1543 12.2628 11.2929 5.784459e+09 485517069.0 12.5409 10.2620 #2015-04-09 12.1747 12.2086 12.9208 12.0255 5.794632e+09 456921108.0 13.3684 10.9403 #2015-04-10 12.2086 13.4294 13.4294 12.1069 6.339649e+09 480990210.0 13.4294 10.9877 #... """ env = Environment.get_instance() yesterday = env.trading_dt.date() - datetime.timedelta(days=1) if end_date is not None: end_date = to_date(end_date) if end_date > yesterday: raise RQInvalidArgument( _('in get_price, end_date {} is no earlier than the previous test day {}').format( end_date, yesterday )) else: end_date = yesterday start_date = to_date(start_date) if start_date > yesterday: raise RQInvalidArgument(_('in get_price, start_date {} is no earlier than the previous test day {}').format( start_date, yesterday )) if end_date < start_date: raise RQInvalidArgument(_('in get_price, start_date {} > end_date {}').format( start_date, end_date )) if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_price(order_book_ids, start_date, end_date, frequency=frequency, fields=fields, adjust_type=adjust_type, skip_suspended=skip_suspended, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('count').is_instance_of(int).is_greater_than(0), verify_that('fields').are_valid_fields(VALID_MARGIN_FIELDS, ignore_none=True)) def get_securities_margin( order_book_ids, # type: Union[str, Iterable[str]] count=1, # type: Optional[int] fields=None, # type: Optional[str] expect_df=False # type: Optional[bool] ): # type: (...) -> Union[pd.Series, pd.DataFrame, pd.Panel] """ 获取融资融券信息。包括 `深证融资融券数据 <http://www.szse.cn/main/disclosure/rzrqxx/rzrqjy/>`_ 以及 `上证融资融券数据 <http://www.sse.com.cn/market/othersdata/margin/detail/>`_ 情况。既包括个股数据,也包括市场整体数据。需要注意,融资融券的开始日期为2010年3月31日。 :param order_book_ids: 可输入order_book_id, order_book_id list, symbol, symbol list。另外,输入'XSHG'或'sh'代表整个上证整体情况;'XSHE'或'sz'代表深证整体情况 :param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据 :param fields: 期望返回的字段,默认为所有字段。见下方列表 :param expect_df: 是否期望始终返回 DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 ========================= =================================================== fields 字段名 ========================= =================================================== margin_balance 融资余额 buy_on_margin_value 融资买入额 margin_repayment 融资偿还额 short_balance 融券余额 short_balance_quantity 融券余量 short_sell_value 融券卖出额 short_sell_quantity 融券卖出量 short_repayment_quantity 融券偿还量 total_balance 融资融券余额 ========================= =================================================== 当 expect_df 为 False 时,返回值的类型如下: * 多个order_book_id,单个field的时候返回DataFrame,index为date,column为order_book_id * 单个order_book_id,多个fields的时候返回DataFrame,index为date,column为fields * 单个order_book_id,单个field返回Series * 多个order_book_id,多个fields的时候返回DataPanel Items axis为fields Major_axis axis为时间戳 Minor_axis axis为order_book_id :example: * 获取沪深两个市场一段时间内的融资余额: .. code-block:: python3 :linenos: logger.info(get_securities_margin('510050.XSHG', count=5)) #[Out] #margin_balance buy_on_margin_value short_sell_quantity margin_repayment short_balance_quantity short_repayment_quantity short_balance total_balance #2016-08-01 7.811396e+09 50012306.0 3597600.0 41652042.0 15020600.0 1645576.0 NaN NaN #2016-08-02 7.826381e+09 34518238.0 2375700.0 19532586.0 14154000.0 3242300.0 NaN NaN #2016-08-03 7.733306e+09 17967333.0 4719700.0 111043009.0 16235600.0 2638100.0 NaN NaN #2016-08-04 7.741497e+09 30259359.0 6488600.0 22068637.0 17499000.0 5225200.0 NaN NaN #2016-08-05 7.726343e+09 25270756.0 2865863.0 40423859.0 14252363.0 6112500.0 NaN NaN * 获取沪深两个市场一段时间内的融资余额: .. code-block:: python3 :linenos: logger.info(get_securities_margin(['XSHE', 'XSHG'], count=5, fields='margin_balance')) #[Out] # XSHE XSHG #2016-08-01 3.837627e+11 4.763557e+11 #2016-08-02 3.828923e+11 4.763931e+11 #2016-08-03 3.823545e+11 4.769321e+11 #2016-08-04 3.833260e+11 4.776380e+11 #2016-08-05 3.812751e+11 4.766928e+11 * 获取上证个股以及整个上证市场融资融券情况: .. code-block:: python3 :linenos: logger.info(get_securities_margin(['XSHG', '601988.XSHG', '510050.XSHG'], count=5)) #[Out] #<class 'pandas.core.panel.Panel'> #Dimensions: 8 (items) x 5 (major_axis) x 3 (minor_axis) #Items axis: margin_balance to total_balance #Major_axis axis: 2016-08-01 00:00:00 to 2016-08-05 00:00:00 #Minor_axis axis: XSHG to 510050.XSHG * 获取50ETF融资偿还额情况 .. code-block:: python3 :linenos: logger.info(get_securities_margin('510050.XSHG', count=5, fields='margin_repayment')) #[Out] #2016-08-01 41652042.0 #2016-08-02 19532586.0 #2016-08-03 111043009.0 #2016-08-04 22068637.0 #2016-08-05 40423859.0 #Name: margin_repayment, dtype: float64 """ env = Environment.get_instance() data_proxy = env.data_proxy dt = data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_dt = dt else: start_dt = data_proxy.get_previous_trading_date(dt, count - 1) if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_securities_margin(order_book_ids, start_dt, dt, fields=fields, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('count').is_instance_of(int).is_greater_than(0), verify_that('fields').are_valid_fields(VALID_SHARE_FIELDS, ignore_none=True)) def get_shares( order_book_ids, # type: Union[str, List[str]] count=1, # type: Optional[int] fields=None, # type: Optional[str] expect_df=False # type: Optional[bool] ): # type: (...) -> Union[pd.DataFrame, pd.Series] """ :param order_book_ids: 可输入 order_book_id, order_book_id list, symbol, symbol list :param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据 :param fields: 期望返回的字段,默认为所有字段。见下方列表 :param expect_df: 是否期望始终返回 DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 ========================= =================================================== fields 字段名 ========================= =================================================== total 总股本 circulation_a 流通A股 management_circulation 已流通高管持股 non_circulation_a 非流通A股合计 total_a A股总股本 ========================= =================================================== :return: 查询时间段内某个股票的流通情况,当 expect_df 为 False 且 fields 指定为单一字段的情况时返回 pandas.Series :example: 获取平安银行总股本数据: .. code-block:: python3 :linenos: logger.info(get_shares('000001.XSHE', count=5, fields='total')) #[Out] #2016-08-01 1.717041e+10 #2016-08-02 1.717041e+10 #2016-08-03 1.717041e+10 #2016-08-04 1.717041e+10 #2016-08-05 1.717041e+10 #Name: total, dtype: float64 """ env = Environment.get_instance() dt = env.trading_dt if count == 1: start_dt = dt else: start_dt = env.data_proxy.get_previous_trading_date(dt, count - 1) if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_shares(order_book_ids, start_dt, dt, fields=fields, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('count').is_instance_of(int).is_greater_than(0), verify_that('fields').are_valid_fields(VALID_TURNOVER_FIELDS, ignore_none=True)) def get_turnover_rate( order_book_ids, # type: Union[str, List[str]] count=1, # type: Optional[int] fields=None, # type: Optional[set] expect_df=False # type: Optional[bool] ): # type: (...) -> Union[pd.Series, pd.DataFrame, pd.Panel] """ 获取截止T-1交易日的换手率数据 :param order_book_ids: 可输入 order_book_id, order_book_id list, symbol, symbol list :param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据 :param fields: 期望返回的字段,默认为所有字段。见下方列表 :param expect_df: 是否期望始终返回 DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 ========================= =================================================== fields 字段名 ========================= =================================================== today 当天换手率 week 过去一周平均换手率 month 过去一个月平均换手率 three_month 过去三个月平均换手率 six_month 过去六个月平均换手率 year 过去一年平均换手率 current_year 当年平均换手率 total 上市以来平均换手率 ========================= =================================================== 当 expect_df 为 False 时,返回值的类型如下: * 如果只传入一个order_book_id,多个fields,返回 `pandas.DataFrame` * 如果传入order_book_id list,并指定单个field,函数会返回一个 `pandas.DataFrame` * 如果传入order_book_id list,并指定多个fields,函数会返回一个 `pandas.Panel` :example: 获取平安银行历史换手率情况: .. code-block:: python3 :linenos: logger.info(get_turnover_rate('000001.XSHE', count=5)) #[Out] # today week month three_month six_month year \ #2016-08-01 0.5190 0.4478 0.3213 0.2877 0.3442 0.5027 #2016-08-02 0.3070 0.4134 0.3112 0.2843 0.3427 0.5019 #2016-08-03 0.2902 0.3460 0.3102 0.2823 0.3432 0.4982 #2016-08-04 0.9189 0.4938 0.3331 0.2914 0.3482 0.4992 #2016-08-05 0.4962 0.5031 0.3426 0.2960 0.3504 0.4994 # current_year total #2016-08-01 0.3585 1.1341 #2016-08-02 0.3570 1.1341 #2016-08-03 0.3565 1.1339 #2016-08-04 0.3604 1.1339 #2016-08-05 0.3613 1.1338 """ env = Environment.get_instance() data_proxy = env.data_proxy dt = data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_dt = dt else: start_dt = data_proxy.get_previous_trading_date(dt, count - 1) if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_turnover_rate(order_book_ids, start_dt, dt, fields=fields, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('count').is_instance_of(int).is_greater_than(0)) def get_price_change_rate( order_book_ids, # type: Union[str, List[str]] count=1, # type: Optional[int] expect_df=False # type: Optional[bool] ): # type: (...) -> Union[pd.DataFrame, pd.Series] """ 获取股票/指数截止T-1日的日涨幅 :param order_book_ids: 可输入 order_book_id, order_book_id list, symbol, symbol list :param count: 回溯获取的数据个数。默认为当前能够获取到的最近的数据 :param expect_df: 是否期望始终返回 DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 当 expect_df 为 False 时,返回值的类型如下: * 传入多个order_book_id,函数会返回pandas DataFrame * 传入一个order_book_id,函数会返回pandas Series :example: 获取平安银行以及沪深300指数一段时间的涨跌幅情况: .. code-block:: python3 :linenos: get_price_change_rate(['000001.XSHE', '510050.XSHG'], 1) # [Out] # 2016-06-01 15:30:00.00 INFO order_book_id 000001.XSHE 510050.XSHG # date # 2016-05-31 0.026265 0.033964 # 2016-06-02 15:30:00.00 INFO order_book_id 000001.XSHE 510050.XSHG # date # 2016-06-01 -0.006635 -0.008308 """ env = Environment.get_instance() data_proxy = env.data_proxy if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] end_date = data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_date = end_date else: start_date = data_proxy.get_previous_trading_date(end_date, count - 1) return rqdatac.get_price_change_rate(order_book_ids, start_date, end_date, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('universe').are_valid_instruments(ignore_none=True)) def get_factor( order_book_ids, # type: Union[str, List[str]] factors, # type: Union[str, List[str]] count=1, # type: Optional[int] universe=None, # type: Optional[Union[str, List[Union]]] expect_df=False # type: Optional[bool] ): # type: (...) -> pd.DataFrame """ 获取股票截止T-1日的因子数据 :param order_book_ids: 合约代码,可传入order_book_id, order_book_id list :param factors: 因子名称,可查询 rqdatac.get_all_factor_names() 得到所有有效因子字段 :param count: 获取多少个交易日的数据 :param universe: 当获取横截面因子时,universe指定了因子计算时的股票池 :param expect_df: 默认为False。当设置为True时,总是返回 multi-index DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 """ env = Environment.get_instance() data_proxy = env.data_proxy end_date = data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_date = end_date else: start_date = data_proxy.get_previous_trading_date(end_date, count - 1) if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] return rqdatac.get_factor(order_book_ids, factors, start_date=start_date, end_date=end_date, universe=universe, expect_df=expect_df)
[文档]@export_as_api def get_industry(industry, source='citics'): # type: (str, Optional[str]) -> List[str] """ 通过传入行业名称、行业指数代码或者行业代号,拿到 T 日指定行业的股票列表 :param industry: 行业名字 :param source: 默认为中信(citics),可选聚源(gildata) :return: list of order_book_id """ env = Environment.get_instance() return rqdatac.get_industry(industry, source, env.calendar_dt)
[文档]@export_as_api def get_instrument_industry(order_book_ids, source='citics', level=1): # type: (Union[str, List[str]], Optional[str], Optional[int]) -> pd.DataFrame """ 获取T日时股票行业分类 :param order_book_ids: 合约代码,可传入order_book_id, order_book_id list :param source: 默认为中信(citics),可选聚源(gildata) :param level: 默认为1,可选 0 1 2 3,0表示返回所有级别 """ if isinstance(order_book_ids, six.string_types): order_book_ids = assure_order_book_id(order_book_ids) else: order_book_ids = [assure_order_book_id(i) for i in order_book_ids] env = Environment.get_instance() return rqdatac.get_instrument_industry(order_book_ids, source, level, env.calendar_dt)
[文档]@export_as_api @apply_rules(verify_that('count').is_instance_of(int).is_greater_than(0), verify_that('fields').are_valid_fields(VALID_STOCK_CONNECT_FIELDS, ignore_none=True)) def get_stock_connect(order_book_ids, count=1, fields=None, expect_df=False): # type: (Union[str, List[str]], Optional[int], Optional[str], Optional[bool]) -> pd.DataFrame """ 获取截止T-1日A股股票在香港上市交易的持股情况 :param order_book_ids: 合约代码,可传入order_book_id, order_book_id list,这里输入的是A股编码 :param count: 向前获取几个交易日 :param fields: 持股量(shares_holding),持股比例(holding_ratio),默认为所有字段 :param expect_df: 默认为False。当设置为True时,总是返回 multi-index DataFrame。pandas 0.25.0 以上该参数应设为 True,以避免因试图构建 Panel 产生异常 当 expect_df 为 False 时,返回值的类型如下: * 多个order_book_id,多个fields的时候返回pandas Panel * 单个order_book_id,多个fields的时候返回pandas DataFrame * 单个order_book_id,单个field返回pandas Series :return: """ env = Environment.get_instance() end_date = env.data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_date = end_date else: start_date = env.data_proxy.get_previous_trading_date(end_date, count - 1) return rqdatac.get_stock_connect(order_book_ids, start_date, end_date, fields=fields, expect_df=expect_df)
[文档]@export_as_api @apply_rules(verify_that('order_book_id').is_valid_instrument(), verify_that('quarter').is_valid_quarter(), verify_that('fields').are_valid_fields(VALID_CURRENT_PERFORMANCE_FIELDS, ignore_none=True)) def current_performance(order_book_id, info_date=None, quarter=None, interval='1q', fields=None): # type: (str, Optional[str], Optional[str], Optional[str], Optional[str, List[str]]) -> pd.DataFrame """ 默认返回给定的 order_book_id 当前最近一期的快报数据 :param order_book_id: 合约代码 :param info_date: yyyymmdd 或者 yyyy-mm-dd。如果不填(info_date和quarter都为空),则返回策略运行当前日期的最新发布的快报。如果填写,则从info_date当天或者之前最新的报告开始抓取。 :param quarter: info_date参数优先级高于quarter。如果info_date填写了日期,则不查看quarter这个字段。 如果info_date没有填写而quarter 有填写,则财报回溯查询的起始报告期,例如'2015q2', '2015q4'分别代表2015年半年报以及年报。默认只获取当前报告期财务信息 :param interval: 查询财务数据的间隔。例如,填写'5y',则代表从报告期开始回溯5年,每年为相同报告期数据;'3q'则代表从报告期开始向前回溯3个季度。不填写默认抓取一期。 :param fields: 抓取对应有效字段返回。默认返回所有字段。具体快报字段可参看 Ricequant 官网财务数据文档。 """ env = Environment.get_instance() dt = env.trading_dt if info_date is None and quarter is None: info_date = dt return rqdatac.current_performance(order_book_id, info_date, quarter, interval, fields)
@export_as_api @apply_rules(verify_that('underlying_symbol').is_instance_of(str)) def get_dominant_future(underlying_symbol, rule=0): # type: (str, Optional[int]) -> Optional[str] """ 获取某一期货品种策略当前日期的主力合约代码。 合约首次上市时,以当日收盘同品种持仓量最大者作为从第二个交易日开始的主力合约。当同品种其他合约持仓量在收盘后超过当前主力合约1.1倍时,从第二个交易日开始进行主力合约的切换。日内不会进行主力合约的切换。 :param underlying_symbol: 期货合约品种,例如沪深300股指期货为'IF' :param rule: 默认是rule=0,采用最大昨仓为当日主力合约,每个合约只能做一次主力合约,不会重复出现。针对股指期货,只在当月和次月选择主力合约。 当rule=1时,主力合约的选取只考虑最大昨仓这个条件。 :example: 获取某一天的主力合约代码(策略当前日期是20160801): .. code-block:: python3 :linenos: get_dominant_future('IF') #[Out] #'IF1608' """ dt = Environment.get_instance().trading_dt.date() ret = rqdatac.get_dominant_future(underlying_symbol, dt, dt, rule) if isinstance(ret, pd.Series) and ret.size == 1: return ret.item() else: user_log.warn(_("\'{0}\' future does not exist").format(underlying_symbol)) return None @export_as_api class econ: pass @apply_rules(verify_that('reserve_type').is_in(['all', 'major', 'other']), verify_that('n').is_instance_of(int).is_greater_than(0)) def _econ_get_reserve_ratio(reserve_type='all', n=1): # type: (str, int) -> Optional[pd.DataFrame] """ 获取截止T日的存款准备金率 :param reserve_type: 目前有大型金融机构('major') 和 其他金融机构('other')两种分类。默认为all,即所有类别都会返回。 :param n: 返回最近 n 个生效日期的存款准备金率数据 """ df = rqdatac.econ.get_reserve_ratio(reserve_type) if df is None or df.empty: return df.sort_values(by=['effective_date', 'reserve_type'], ascending=[False, True], inplace=True) effective_date_unique = df['effective_date'].unique() if len(effective_date_unique) <= n: return df df = df[df['effective_date'] >= effective_date_unique[n - 1]] return df @apply_rules(verify_that('n').is_instance_of(int).is_greater_than(0)) def _econ_get_money_supply(n=1): # type: (int) -> Optional[pd.DataFrame] """ 获取截止T日的货币供应量指标 :param n: 返回前 n 个消息公布日期的货币供应量指标数据 """ dt = Environment.get_instance().calendar_dt.date() start_date = 19780101 end_date = 10000 * dt.year + 100 * dt.month + dt.day df = rqdatac.econ.get_money_supply(start_date, end_date) if df is None or df.empty: return df.sort_index(ascending=False, inplace=True) return df.head(n) econ.get_reserve_ratio = staticmethod(_econ_get_reserve_ratio) econ.get_money_supply = staticmethod(_econ_get_money_supply) @export_as_api class futures: pass @apply_rules(verify_that('underlying_symbol').is_instance_of(str)) def _futures_get_dominant(underlying_symbol, rule=0): # type: (str, Optional[int]) -> Optional[str] """ 获取某一期货品种策略当前日期的主力合约代码。 合约首次上市时,以当日收盘同品种持仓量最大者作为从第二个交易日开始的主力合约。当同品种其他合约持仓量在收盘后超过当前主力合约1.1倍时,从第二个交易日开始进行主力合约的切换。日内不会进行主力合约的切换。 :param underlying_symbol: 期货合约品种,例如沪深300股指期货为'IF' :param rule: 默认是rule=0,采用最大昨仓为当日主力合约,每个合约只能做一次主力合约,不会重复出现。针对股指期货,只在当月和次月选择主力合约。 当rule=1时,主力合约的选取只考虑最大昨仓这个条件。 :example: 获取某一天的主力合约代码(策略当前日期是20160801): .. code-block:: python3 :linenos: get_dominant_future('IF') #[Out] #'IF1608' """ dt = Environment.get_instance().trading_dt.date() ret = rqdatac.futures.get_dominant(underlying_symbol, dt, dt, rule) if ret is None or ret.empty: return None return ret.item() @apply_rules(verify_that('which').is_instance_of(str), verify_that('rank_by').is_in(['short', 'long'])) def _futures_get_member_rank(which, count=1, rank_by='short'): # type: (str, int, str) -> pd.DataFrame """ 获取截止T-1日的期货或品种的会员排名情况 :param which: 期货合约或品种 :param count: 获取多少个交易日的数据,默认为1 :param rank_by: short/long """ env = Environment.get_instance() end_date = env.data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_date = end_date else: start_date = env.data_proxy.get_previous_trading_date(end_date, count - 1) return rqdatac.futures.get_member_rank(which, start_date=start_date, end_date=end_date, rank_by=rank_by) def _futures_get_warehouse_stocks(underlying_symbols, count=1): # type: (Union[str, List[str]], int) -> pd.DataFrame """ 获取截止T-1日的期货仓单数据 :param underlying_symbols: 期货品种,可以为str或列表 :param count: 获取多少个交易日的数据,默认为1 """ env = Environment.get_instance() end_date = env.data_proxy.get_previous_trading_date(env.trading_dt) if count == 1: start_date = end_date else: start_date = env.data_proxy.get_previous_trading_date(end_date, count - 1) return rqdatac.futures.get_warehouse_stocks(underlying_symbols, start_date=start_date, end_date=end_date) futures.get_dominant = staticmethod(_futures_get_dominant) futures.get_member_rank = staticmethod(_futures_get_member_rank) futures.get_warehouse_stocks = staticmethod(_futures_get_warehouse_stocks) # ======================= 以下 API 不建议使用 ================================= _get_fundamentals_warning_fired = False @export_as_api @apply_rules(verify_that('entry_date').is_valid_date(ignore_none=True), verify_that('interval').is_valid_interval(), verify_that('report_quarter').is_instance_of(bool)) def get_fundamentals(query, entry_date=None, interval='1d', report_quarter=False, expect_df=False, **kwargs): global _get_fundamentals_warning_fired if not _get_fundamentals_warning_fired: user_log.warn('get_fundamentals is deprecated, use get_factor instead') _get_fundamentals_warning_fired = True env = Environment.get_instance() dt = env.calendar_dt.date() if entry_date is None and 'date' in kwargs: entry_date = kwargs.pop('date') if kwargs: raise RQInvalidArgument('unknown arguments: {}'.format(kwargs)) latest_query_day = dt - datetime.timedelta(days=1) if entry_date: entry_date = to_date(entry_date) if entry_date <= latest_query_day: query_date = entry_date else: raise RQInvalidArgument( _('in get_fundamentals entry_date {} is no earlier than test date {}').format(entry_date, dt)) else: query_date = latest_query_day result = rqdatac.get_fundamentals(query, query_date, interval, report_quarter=report_quarter, expect_df=expect_df) if result is None: return pd.DataFrame() if expect_df: return result if len(result.major_axis) == 1: frame = result.major_xs(result.major_axis[0]) # research 与回测返回的Frame维度相反 return frame.T return result @export_as_api @apply_rules(verify_that('interval').is_valid_interval()) def get_financials(query, quarter=None, interval='4q', expect_df=False): if quarter is None: valid = True else: valid = isinstance(quarter, six.string_types) and quarter[-2] == 'q' if valid: try: valid = 1990 <= int(quarter[:-2]) <= 2050 and 1 <= int(quarter[-1]) <= 4 except ValueError: valid = False if not valid: raise RQInvalidArgument( _(u"function {}: invalid {} argument, quarter should be in form of '2012q3', " u"got {} (type: {})").format( 'get_financials', 'quarter', quarter, type(quarter) )) env = Environment.get_instance() dt = env.calendar_dt.date() - datetime.timedelta(days=1) # Take yesterday's data as default year = dt.year mon = dt.month day = dt.day int_date = year * 10000 + mon * 100 + day q = (mon - 4) // 3 + 1 y = year if q <= 0: y -= 1 q = 4 default_quarter = str(y) + 'q' + str(q) if quarter is None or quarter > default_quarter: quarter = default_quarter include_date = False for d in query.column_descriptions: if d['name'] == 'announce_date': include_date = True if not include_date: query = query.add_column(rqdatac.fundamentals.announce_date) result = rqdatac.get_financials(query, quarter, interval, expect_df=expect_df) if result is None: return pd.DataFrame() if isinstance(result, pd.Series): return result elif isinstance(result, pd.DataFrame): result = result[(result['announce_date'] <= int_date) | pd.isnull(result['announce_date'])] if not include_date: del result['announce_date'] else: d = dict() for order_book_id in result.minor_axis: df = result.minor_xs(order_book_id) df = df[(df.announce_date < int_date) | (pd.isnull(df.announce_date))] d[order_book_id] = df pl = pd.Panel.from_dict(d, orient='minor') if not include_date: pl.drop('announce_date', axis=0, inplace=True) if len(pl.items) == 1: pl = pl[pl.items[0]] return pl return result @export_as_api @apply_rules(verify_that('if_adjusted').is_in([0, 1, '0', '1', 'all', 'ignore'], ignore_none=True)) def get_pit_financials(fields, quarter=None, interval=None, order_book_ids=None, if_adjusted='all'): if quarter is None: valid = True else: valid = isinstance(quarter, six.string_types) and quarter[-2] == 'q' if valid: try: valid = 1990 <= int(quarter[:-2]) <= 2050 and 1 <= int(quarter[-1]) <= 4 except ValueError: valid = False if not valid: raise RQInvalidArgument( _(u"function {}: invalid {} argument, quarter should be in form of '2012q3', " u"got {} (type: {})").format( 'get_pit_financials', 'quarter', quarter, type(quarter) )) env = Environment.get_instance() dt = env.calendar_dt.date() year = dt.year mon = dt.month day = dt.day int_date = year * 10000 + mon * 100 + day q = (mon - 4) // 3 + 1 y = year if q <= 0: y -= 1 q = 4 default_quarter = str(y) + 'q' + str(q) if quarter is None or quarter > default_quarter: quarter = default_quarter result = rqdatac.get_pit_financials(fields, quarter, interval, order_book_ids, if_adjusted, max_info_date=int_date, market='cn') if result is None: return pd.DataFrame() if if_adjusted == 'ignore': result = result.reset_index().sort_values('info_date') result = result.groupby(['order_book_id', 'end_date'], as_index=False).fillna(method='ffill') result = result.drop(['info_date', 'if_adjusted'], axis=1) result = result.drop_duplicates(['order_book_id', 'end_date'], keep='last') result = result.set_index(['order_book_id', 'end_date']).sort_index() return result @export_as_api @apply_rules(verify_that('entities').are_valid_query_entities()) def query(*entities): return rqdatac.query(*entities) export_as_api(rqdatac.financials, name='financials') export_as_api(rqdatac.financials, name='Financials') export_as_api(rqdatac.fundamentals, name='fundamentals') export_as_api(rqdatac.Fundamentals, name='Fundamentals')