def is_suspended(order_book_ids, start_date=None, end_date=None):
"""获取停牌信息
:param order_book_ids: 可转债ID
:param start_date: 开始日期, 如'2013-01-04' (Default value = None)
:param end_date: 结束日期，如'2014-01-04' (Default value = None)
:returns: DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market="cn")
start_date, end_date = ensure_dates_base_on_listed_date(
instrument, start_date, end_date, "cn")
if start_date is None:
return
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(
get_trading_dates(start_date, end_date, market="cn"))
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
data = get_client().execute("convertible.is_suspended",
order_book_ids,
start_date,
end_date,
market="cn")
for (order_book_id, date) in data:
date = to_datetime(date)
df.at[date, order_book_id] = True
return df
def is_st_stock(order_book_ids, start_date=None, end_date=None, market="cn"):
"""判断股票在给定的时间段是否是ST股, 返回值为一个DataFrame
:param order_book_ids: 股票 id
:param start_date: (Default value = None)
:param end_date: (Default value = None)
:param market: (Default value = "cn")
"""
order_book_ids = ensure_order_book_ids(order_book_ids,
type="CS",
market=market)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market=market)
start_date, end_date = ensure_dates_base_on_listed_date(
instrument, start_date, end_date, market)
if start_date is None:
return
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(
get_trading_dates(start_date, end_date, market=market))
data = get_client().execute("get_st_days",
order_book_ids,
start_date=start_date,
end_date=end_date)
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
for idx, dates in data.items():
for date in dates:
date = to_datetime(date)
df.at[date, idx] = True
return df
def is_suspended(order_book_ids, start_date=None, end_date=None, market="cn"):
"""获取股票停牌信息
:param order_book_ids: 股票名称
:param start_date: 开始日期, 如'2013-01-04' (Default value = None)
:param end_date: 结束日期，如'2014-01-04' (Default value = None)
:param market: 地区代码, 如 'cn' (Default value = "cn")
:returns: DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids, type="CS", market=market)
if len(order_book_ids) == 1:
instrument = instruments(order_book_ids[0], market=market)
start_date, end_date = ensure_dates_base_on_listed_date(instrument, start_date, end_date, market)
if start_date is None:
return
if end_date is None:
end_date = datetime.date.today()
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = pd.to_datetime(get_trading_dates(start_date, end_date, market=market))
df = pd.DataFrame(data=False, columns=order_book_ids, index=trading_dates)
data = get_client().execute("get_suspended_days", order_book_ids, start_date, end_date, market=market)
for idx, dates in data.items():
for date in dates:
date = to_datetime(int(date))
df.at[date, idx] = True
df.sort_index(inplace=True)
return df
def ensure_trading_date(date):
from rqdatac.services.calendar import get_trading_dates, get_previous_trading_date, get_next_trading_date
trading_dates = get_trading_dates(get_previous_trading_date(date),
get_next_trading_date(date))
if date not in trading_dates:
raise ValueError(
"expect a trading date, got {}, for reference {}".format(
date.strftime("%Y%m%d"), trading_dates))
return date
def get_factor(order_book_ids,
factor,
start_date=None,
end_date=None,
universe=None,
expect_df=False,
**kwargs):
"""获取因子
:param order_book_ids: 股票代码或代码列表
:param factor: 如 'total_income'
:param date: 如 date='2015-01-05', 默认为前一交易日
:param start_date: 开始日期'2015-01-05', 默认为前一交易日, 最小起始日期为'2000-01-04'
:param end_date: 结束日期
:param universe: 股票池，默认为全A股
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:returns: pd.DataFrame
"""
order_book_ids = ensure_order_book_ids(order_book_ids, type="CS")
order_book_ids = list(set(order_book_ids))
factor = ensure_list_of_string(factor)
factor = list(OrderedDict.fromkeys(factor))
if start_date and end_date:
start_date, end_date = ensure_date_range(start_date, end_date,
datetime.timedelta(days=15))
if start_date < 20000104:
warnings.warn(
"start_date is earlier than 2000-01-04, adjusted to 2000-01-04"
)
start_date = 20000104
elif start_date:
raise ValueError("Expect end_date")
elif end_date:
raise ValueError("Expect start_date")
else:
date = kwargs.pop("date", None)
date = ensure_date_int(
date or get_previous_trading_date(datetime.date.today()))
start_date = end_date = date
if kwargs:
raise ValueError('unknown kwargs: {}'.format(kwargs))
if universe is not None:
universe = ensure_string(universe, "universe")
if universe != "all":
universe = ensure_order_book_id(universe, type="INDX")
from rqdatac import index_components
allowed_order_book_ids = set(
index_components(universe, date=end_date) or [])
not_permit_order_book_ids = [
order_book_id for order_book_id in order_book_ids
if order_book_id not in allowed_order_book_ids
]
if not_permit_order_book_ids:
warnings.warn(
"%s not in universe pool, value of those order_book_ids will always be NaN"
% not_permit_order_book_ids)
data = get_client().execute("get_factor_from_store",
order_book_ids,
factor,
start_date,
end_date,
universe=universe)
if not data:
return
factor_value_length = len(data[0][2])
if factor_value_length == 0:
return
dates = pd.to_datetime(get_trading_dates(start_date, end_date))
if len(dates) > factor_value_length:
_get_factor_warning_msg(dates[factor_value_length], dates[-1])
dates = dates[0:factor_value_length]
if expect_df or len(factor) > 1:
order_book_id_index_map = {o: i for i, o in enumerate(order_book_ids)}
factor_index_map = {f: i for i, f in enumerate(factor)}
arr = np.full((len(order_book_ids) * len(dates), len(factor)), np.nan)
for order_book_id, factor_name, values in data:
order_book_id_index = order_book_id_index_map[order_book_id]
factor_index = factor_index_map[factor_name]
slice_ = slice(order_book_id_index * len(dates),
(order_book_id_index + 1) * len(dates), None)
arr[slice_, factor_index] = values
multi_index = pd.MultiIndex.from_product(
[order_book_ids, dates], names=["order_book_id", "date"])
df = pd.DataFrame(index=multi_index, columns=factor, data=arr)
return df
order_book_id_index_map = {o: i for i, o in enumerate(order_book_ids)}
arr = np.full((len(dates), len(order_book_ids)), np.nan)
for order_book_id, _, values in data:
arr[:, order_book_id_index_map[order_book_id]] = values
df = pd.DataFrame(index=dates, columns=order_book_ids, data=arr)
if len(df.index) == 1:
return df.iloc[0]
if len(df.columns) == 1:
return df[df.columns[0]]
return df
def get_securities_margin(order_book_ids,
start_date=None,
end_date=None,
fields=None,
expect_df=False,
market="cn"):
"""获取股票融资融券数据
:param order_book_ids: 股票代码或代码列表
:param start_date: 开始时间，支持 str, date, datetime, pandasTimestamp
默认为 end_date 之前一个月 (Default value = None)
:param end_date: 结束时间 默认为当前日期前一天 (Default value = None)
:param fields: str 或 list 类型. 默认为 None, 返回所有字段。可选字段包括：
today, week, month, three_month, six_month, year, current_year, total
(Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:param market: 地区代码, 如: 'cn' (Default value = "cn")
:returns: 如果传入多个股票代码，且 fields 为多个或者 None，返回 pandas.Panel
如果传入一只股票或者 fields 为单个字段，则返回 pandas.DataFrame
如果传入的股票代码和字段数都是1，则返回 pandas.Series
"""
order_book_ids = ensure_list_of_string(order_book_ids, "order_book_ids")
all_list = []
for order_book_id in order_book_ids:
if order_book_id.upper() in MARGIN_SUMMARY_MAP:
all_list.append(MARGIN_SUMMARY_MAP[order_book_id.upper()])
else:
inst = instruments(order_book_id, market)
if inst.type in ["CS", "ETF", "LOF"]:
all_list.append(inst.order_book_id)
else:
warnings.warn(
"{} is not stock, ETF, or LOF.".format(order_book_id))
order_book_ids = all_list
if not order_book_ids:
raise ValueError("no valid securities in {}".format(order_book_ids))
if fields is None:
fields = list(MARGIN_FIELDS)
else:
fields = ensure_list_of_string(fields, "fields")
check_items_in_container(fields, MARGIN_FIELDS, "fields")
fields = ensure_order(fields, MARGIN_FIELDS)
start_date, end_date = ensure_date_range(start_date, end_date)
if end_date > ensure_date_or_today_int(None):
end_date = ensure_date_or_today_int(
get_previous_trading_date(datetime.date.today()))
trading_dates = pd.to_datetime(
get_trading_dates(start_date, end_date, market=market))
data = get_client().execute("get_securities_margin",
order_book_ids,
start_date,
end_date,
market=market)
if not data:
return
if expect_df:
df = pd.DataFrame(data)
df.sort_values(["order_book_id", "date"], inplace=True)
df.set_index(["order_book_id", "date"], inplace=True)
df = df.reindex(columns=fields)
return df
pl = pd.Panel(items=fields,
major_axis=trading_dates,
minor_axis=order_book_ids)
for r in data:
for field in fields:
value = r.get(field)
pl.at[field, r["date"], r["order_book_id"]] = value
if len(order_book_ids) == 1:
pl = pl.minor_xs(order_book_ids[0])
if len(fields) == 1:
pl = pl[fields[0]]
if len(order_book_ids) != 1 and len(fields) != 1:
warnings.warn(
"Panel is removed after pandas version 0.25.0."
" the default value of 'expect_df' will change to True in the future."
)
return pl