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_nth_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)
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)
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)
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)
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)
def get_dividend(order_book_id, start_date):
# type: (str, Union[str, datetime.date, datetime.datetime, pd.Timestamp]) -> Optional[np.ndarray]
"""
获取某只股票到策略当前日期前一天的分红情况（包含起止日期）。
:param order_book_id: 股票代码
:param start_date: 开始日期，需要早于策略当前日期
========================= ===================================================
fields 字段名
========================= ===================================================
announcement_date 分红宣布日
book_closure_date 股权登记日
dividend_cash_before_tax 税前分红
ex_dividend_date 除权除息日
payable_date 分红到帐日
round_lot 分红最小单位
========================= ===================================================
:example:
获取平安银行2013-01-04 到策略当前日期前一天的分红数据:
.. code-block:: python3
:linenos:
get_dividend('000001.XSHE', start_date='20130104')
#[Out]
#array([(20130614, 20130619, 20130620, 20130620, 1.7 , 10),
# (20140606, 20140611, 20140612, 20140612, 1.6 , 10),
# (20150407, 20150410, 20150413, 20150413, 1.74, 10),
# (20160608, 20160615, 20160616, 20160616, 1.53, 10)],
# dtype=[('announcement_date', '<u4'), ('book_closure_date', '<u4'), ('ex_dividend_date', '<u4'), ('payable_date', '<u4'), ('dividend_cash_before_tax', '<f8'), ('round_lot', '<u4')])
"""
# adjusted 参数在不复权数据回测时不再提供
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(
_(
u"in get_dividend, start_date {} is later than the previous test day {}"
).format(start_date, dt)
)
order_book_id = assure_order_book_id(order_book_id)
array = env.data_proxy.get_dividend(order_book_id)
if array is None:
return None
sd = start_date.year * 10000 + start_date.month * 100 + start_date.day
ed = dt.year * 10000 + dt.month * 100 + dt.day
return array[
(array["announcement_date"] >= sd) & (array["announcement_date"] <= ed)
]
def is_suspended(order_book_id, count=1):
# type: (str, Optional[int]) -> Union[bool, pd.DataFrame]
"""
判断某只股票是否全天停牌。
:param order_book_id: 某只股票的代码或股票代码，可传入单只股票的order_book_id, symbol
: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
"""
dt = Environment.get_instance().calendar_dt.date()
order_book_id = assure_order_book_id(order_book_id)
return Environment.get_instance().data_proxy.is_suspended(order_book_id, dt, count)
def is_st_stock(order_book_id, count=1):
# type: (str, Optional[int]) -> Union[bool, pd.DataFrame]
"""
判断股票在一段时间内是否为ST股（包括ST与*ST）。
ST股是有退市风险因此风险比较大的股票，很多时候您也会希望判断自己使用的股票是否是'ST'股来避开这些风险大的股票。另外，我们目前的策略比赛也禁止了使用'ST'股。
:param order_book_id: 某只股票的代码，可传入单只股票的order_book_id, symbol
: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
"""
dt = Environment.get_instance().calendar_dt.date()
order_book_id = assure_order_book_id(order_book_id)
return Environment.get_instance().data_proxy.is_st_stock(order_book_id, dt, count)
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)
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)
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)
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)
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)
def order_target_portfolio(target_portfolio):
# type: (Dict[Union[str, Instrument], float]) -> List[Order]
"""
买入/卖出证券以批量调整证券的仓位，以期使其持仓市值占账户总权益的比重达到指定值。
:param target_portfolio: 下单标的物及其目标市值占比的字典
:example:
.. code-block:: python
# 调整仓位，以使平安银行和万科 A 的持仓占比分别达到 10% 和 15%
order_target_portfolio({
'000001.XSHE': 0.1
'000002.XSHE': 0.15
})
"""
if isinstance(target_portfolio, pd.Series):
# FIXME: kind of dirty
total_percent = sum(target_portfolio)
else:
total_percent = sum(target_portfolio.values())
if total_percent > 1 and not np.isclose(total_percent, 1):
raise RQInvalidArgument(
_(u"total percent should be lower than 1, current: {}").format(
total_percent))
env = Environment.get_instance()
account = env.portfolio.accounts[DEFAULT_ACCOUNT_TYPE.STOCK]
account_value = account.total_value
target_quantities = {}
for id_or_ins, target_percent in target_portfolio.items():
order_book_id = assure_order_book_id(id_or_ins)
if target_percent < 0:
raise RQInvalidArgument(
_(u"target percent of {} should between 0 and 1, current: {}").
format(order_book_id, target_percent))
price = env.data_proxy.get_last_price(order_book_id)
if not is_valid_price(price):
user_system_log.warn(
_(u"Order Creation Failed: [{order_book_id}] No market data").
format(order_book_id=order_book_id))
continue
target_quantities[order_book_id] = (account_value * target_percent /
price, price)
close_orders, open_orders = [], []
current_quantities = {
p.order_book_id: p.quantity
for p in account.get_positions()
if p.direction == POSITION_DIRECTION.LONG
}
for order_book_id, quantity in current_quantities.items():
if order_book_id not in target_portfolio:
close_orders.append(
Order.__from_create__(order_book_id, quantity, SIDE.SELL,
MarketOrder(), POSITION_EFFECT.CLOSE))
for order_book_id, (target_quantity, price) in target_quantities.items():
ins = assure_instrument(order_book_id)
round_lot = 100
if order_book_id in current_quantities:
delta_quantity = target_quantity - current_quantities[order_book_id]
else:
delta_quantity = target_quantity
if _is_ksh(ins):
# KSH can buy(sell) 201, 202 shares
delta_quantity = _get_ksh_amount(delta_quantity)
else:
delta_quantity = int(
Decimal(delta_quantity) / Decimal(round_lot)) * round_lot
if delta_quantity > 0:
open_order = Order.__from_create__(order_book_id, delta_quantity,
SIDE.BUY, MarketOrder(),
POSITION_EFFECT.OPEN)
open_order.set_frozen_price(price)
open_orders.append(open_order)
elif delta_quantity < -1:
close_order = Order.__from_create__(order_book_id,
abs(delta_quantity), SIDE.SELL,
MarketOrder(),
POSITION_EFFECT.CLOSE)
close_order.set_frozen_price(price)
close_orders.append(close_order)
submit_orders = []
for order in chain(close_orders, open_orders):
if env.can_submit_order(order):
submit_orders.append(order)
env.broker.submit_order(order)
return submit_orders
