def get_price_change_rate(order_book_ids,
start_date=None,
end_date=None,
expect_df=False,
market="cn"):
"""获取价格变化信息
:param order_book_ids: 股票列表
:param start_date: 开始日期: 如'2013-01-04'
:param end_date: 结束日期: 如'2014-01-04'；在 start_date 和 end_date 都不指定的情况下，默认为最近3个月
:param expect_df: 返回 DataFrame (Default value = False)
:param market: 地区代码
:returns: 如果输入一只股票, 则返回pandas.Series, 否则返回pandas.DataFrame
"""
start_date, end_date = ensure_date_range(start_date, end_date)
order_book_ids = ensure_order_book_ids(order_book_ids)
data = get_client().execute("get_daily_returns",
order_book_ids,
start_date,
end_date,
market=market)
if not data:
return None
df = pd.DataFrame(data)
if len(order_book_ids) == 1 and not expect_df:
df = df.set_index("date")
series = df["daily_return"]
series.name = order_book_ids[0]
return series
df = df.pivot(index="date", columns="order_book_id", values="daily_return")
return df.sort_index()
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 get_descriptor_exposure(order_book_ids,
start_date,
end_date,
descriptors=None,
market="cn"):
"""获取个股细分因子暴露度
:param order_book_ids: 证券代码（例如：‘600705.XSHG’）
:param start_date: 开始日期（例如：‘2017-03-03’）
:param end_date: 结束日期（例如：‘2017-03-20’）
:param descriptors: 细分风格因子。默认调用全部因子的暴露度（'all'）。
具体细分因子名称见说明文档 (Default value = None)
:param market: (Default value = "cn")
:returns: MultiIndex DataFrame. index 第一个 level 为 order_book_id，第 二个 level 为 date，column 为细分风格因子字段名称。
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
if descriptors is not None:
if descriptors == "all":
descriptors = None
else:
descriptors = ensure_list_of_string(descriptors)
check_items_in_container(descriptors, _DESCRIPTORS, "descriptors")
df = get_client().execute("get_descriptor_exposure",
order_book_ids,
start_date,
end_date,
descriptors,
market=market)
if not df:
return
return pd.DataFrame(df).set_index(["order_book_id",
"date"]).sort_index(level=1)
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 get_block_trade(order_book_ids,
start_date=None,
end_date=None,
market='cn'):
"""获取大宗交易信息
:param order_book_ids: 股票代码
:param start_date: 起始日期，默认为前三个月
:param end_date: 截止日期，默认为今天
:param market: (default value = 'cn')
:return: pd.DataFrame or None
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
data = get_client().execute('get_block_trade',
order_book_ids,
start_date,
end_date,
market=market)
if not data:
return
df = pd.DataFrame(data)[[
'order_book_id', 'trade_date', 'price', 'volume', 'total_turnover',
'buyer', 'seller'
]]
df.set_index(["order_book_id", "trade_date"], inplace=True)
df.sort_index(inplace=True)
return df
def get_factor_return(
start_date,
end_date,
factors=None,
universe="whole_market",
method="implicit",
industry_mapping=True,
market="cn",
):
"""获取因子收益率数据
:param start_date: 开始日期（例如：‘2017-03-03’)
:param end_date: 结束日期（例如：‘2017-03-20’)
:param factors: 因子。默认获取全部因子的因子收益率
当 method 参数取值为'implicit' ，可返回全部因子（风格、行业、市场联动）的隐式因子收益率；
当 method 参数取值为'explicit' , 只返回风格因子的显式因子收益率。具体因子名称见说明文档 (Default value = None)
:param universe: 股票池。默认调用全市场收益率。可选沪深300（‘000300.XSHG’）、中证500（'000905.XSHG'）
、以及中证800（'000906.XSHG'） (Default value = "whole_market")
:param method: 计算方法。默认为'implicit'（隐式因子收益率），可选'explicit'（显式风格因子收益率) (Default value = "implicit")
:param market: 地区代码， 现在仅支持 'cn' (Default value = "cn")
:param industry_mapping(bool): 是否按 2014 年后的申万行业分类标 准计算行业收益率.默认为 True.
若取值为 False,则 2014 年前的行业 收益率按旧行业分类标准计算
:returns: pd.DataFrame. index 为日期，column 为因子字段名称。
Usage example::
# 获取介于2017-03-03 到 2017-03-20到隐式因子收益率数据
get_factor_return('2017-03-03', '2017-03-20')
"""
start_date, end_date = ensure_date_range(start_date, end_date)
if factors:
factors = ensure_list_of_string(factors)
method = ensure_string(method)
if method not in _METHOD_MAPPING:
raise ValueError(
"invalid method: {!r}, valid: explicit, implicit".format(method))
method = _METHOD_MAPPING[method]
if universe not in _UNIVERSE_MAPPING:
raise ValueError("invalid universe: {!r}, valid: {}".format(
universe, list(_UNIVERSE_MAPPING.keys())))
universe = _UNIVERSE_MAPPING[universe]
df = get_client().execute("get_factor_return",
start_date,
end_date,
factors,
universe,
method,
market=market,
industry_mapping=industry_mapping)
if not df:
return None
df = pd.DataFrame(df)
# convert to required format.
df = df.pivot(index="date", columns="factor")[universe]
df.sort_index(inplace=True)
return df
def get_capital_flow(order_book_ids,
start_date=None,
end_date=None,
frequency="1d",
market="cn"):
"""获取资金流入流出数据
:param order_book_ids: 股票代码or股票代码列表, 如'000001.XSHE'
:param start_date: 开始日期
:param end_date: 结束日期
:param frequency: 默认为日线。日线使用 '1d', 分钟线 '1m' 快照 'tick' (Default value = "1d"),
:param market: (Default value = "cn")
:returns: pandas.DataFrame or None
"""
ensure_string_in(frequency, ("1d", "1m", "tick"), "frequency")
if frequency == "tick":
return get_capital_flow_tickbar(order_book_ids, start_date, end_date,
TICKBAR_FIELDS, market)
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
if frequency == "1d":
return get_capital_flow_daybar(order_book_ids, start_date, end_date,
DAYBAR_FIELDS, 1, market)
return get_capital_flow_minbar(order_book_ids, start_date, end_date,
MINBAR_FIELDS, 1, market)
def get_style_factor_exposure(order_book_ids,
start_date,
end_date,
factors=None,
market="cn"):
"""获取个股风格因子暴露度
:param order_book_ids: 证券代码（例如：‘600705.XSHG’）
:param start_date: 开始日期（例如：‘2017-03-03’）
:param end_date: 结束日期（例如：‘2017-03-20’）
:param factors: 风格因子。默认调用全部因子的暴露度（'all'）。
具体因子名称见说明文档 (Default value = None)
:param market: (Default value = "cn")
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
if factors is not None:
factors = ensure_list_of_string(factors)
check_items_in_container(factors, _STYLE_FACTORS, "factors")
df = get_client().execute("get_style_factor_exposure",
order_book_ids,
start_date,
end_date,
factors,
market=market)
if not df:
return
return pd.DataFrame(df).set_index(["order_book_id",
"date"]).sort_index(level=1)
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 index_indicator(order_book_ids, start_date=None, end_date=None, fields=None, market="cn"):
"""获取指数指标
:param order_book_ids: 如'000016.XSHG'
:param start_date: 如'2016-01-01' (Default value = None)
:param end_date: 如'2017-01-01' (Default value = None)
:param fields: 如'pb', 默认返回全部 fields (Default value = None)
:param market: (Default value = "cn")
:returns: pd.DataFrame 或 None
"""
all_fields = ("pe_ttm", "pe_lyr", "pb_ttm", "pb_lyr", "pb_lf")
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
if fields is not None:
fields = ensure_list_of_string(fields)
for f in fields:
if f not in all_fields:
raise ValueError("invalid field: {}".format(f))
else:
fields = all_fields
df = get_client().execute(
"index_indicator", order_book_ids, start_date, end_date, fields, market=market
)
if not df:
return
df = pd.DataFrame(df)
df.set_index(["order_book_id", "trade_date"], inplace=True)
return df
def get_reserve_ratio(reserve_type="all",
start_date=None,
end_date=None,
market="cn"):
"""获取存款准备金率
:param reserve_type: 存款准备金详细类别，默认为'all'，目前仅支持'all'、'major'、'other'类别的查询
:param start_date: 开始查找时间，如'20180501'，默认为上一年的当天
:param end_date: 截止查找时间，如'20180501'，默认为当天
:param market: (Default value = "cn")
:return: pd.DataFrame
"""
check_items_in_container(reserve_type, ["all", "major", "other"],
"reserve_type")
start_date, end_date = ensure_date_range(start_date,
end_date,
delta=relativedelta(years=1))
ret = get_client().execute("econ.get_reserve_ratio", reserve_type,
start_date, end_date, market)
if not ret:
return
columns = [
"info_date", "effective_date", "reserve_type", "ratio_floor",
"ratio_ceiling"
]
df = pd.DataFrame(
ret, columns=columns).set_index("info_date").sort_index(ascending=True)
return df
def get_money_supply(start_date=None, end_date=None, market="cn"):
"""获取货币供应量信息
:param start_date: 开始日期，默认为一年前
:param end_date: 结束日期，默认为今天
:param market: (Default value = "cn")
"""
check_items_in_container("info_date", ["info_date", "effective_date"],
"date_type")
start_date, end_date = ensure_date_range(start_date,
end_date,
delta=relativedelta(years=1))
data = get_client().execute("econ.get_money_supply",
start_date,
end_date,
market=market)
if not data:
return
columns = [
"info_date",
"effective_date",
"m2",
"m1",
"m0",
"m2_growth_yoy",
"m1_growth_yoy",
"m0_growth_yoy",
]
df = pd.DataFrame(
data,
columns=columns).set_index("info_date").sort_index(ascending=True)
return df
def get_warehouse_stocks(underlying_symbols,
start_date=None,
end_date=None,
market="cn"):
"""获取时间区间内期货的注册仓单
:param underlying_symbols: 期货品种, 支持列表查询
:param start_date: 如'2015-01-01', 如果不填写则为去年的当日日期
:param end_date: 如'2015-01-01', 如果不填写则为当日日期
:param market: 市场, 默认为"cn"
:return: pd.DataFrame
"""
underlying_symbols = ensure_list_of_string(underlying_symbols,
name="underlying_symbols")
start_date, end_date = ensure_date_range(start_date,
end_date,
delta=relativedelta(years=1))
ret = get_client().execute("futures.get_warehouse_stocks",
underlying_symbols,
start_date,
end_date,
market=market)
if not ret:
return
columns = ["date", "underlying_symbol", "on_warrant", "exchange"]
df = pd.DataFrame(ret, columns=columns)
df.set_index(['date', 'underlying_symbol'], inplace=True)
return df.sort_index()
def get_ksh_tickbar(order_book_id, start_date, end_date, fields, market):
order_book_id = ensure_order_book_id(order_book_id)
start_date, end_date = ensure_date_range(start_date, end_date, datetime.timedelta(days=3))
data = get_client().execute(
"get_ksh_tickbar", order_book_id, start_date, end_date, fields, market
)
today = today_int()
if data:
data = [(obid, {k: np.frombuffer(*v) for k, v in d.items()}) for obid, d in data]
df_list = []
for obid, d in data:
df = pd.DataFrame(d)
df_list.append(df)
df = pd.concat(df_list) # type: pd.DataFrame
df["datetime"] = int17_to_datetime_v(df["datetime"].values)
history_latest_date = date_to_int8(df.iloc[-1]["datetime"])
df.set_index("datetime", inplace=True)
else:
df = None
history_latest_date = date_to_int8(get_previous_trading_date(today, market=market))
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
live_df = get_today_ksh_tick(order_book_id, today, fields, market=market)
if live_df is None:
return df
if df is None:
return live_df
return pd.concat([df, live_df])
def get_stock_connect(order_book_ids, start_date=None, end_date=None, fields=None, expect_df=False):
"""获取"陆股通"的持股、持股比例
:param order_book_ids: 股票列表
:param start_date: 开始日期: 如'2017-03-17' (Default value = None)
:param end_date: 结束日期: 如'2018-03-16' (Default value = None)
:param fields: 默认为所有字段，可输入shares_holding或者holding_ratio (Default value = None)
:param expect_df: 返回 MultiIndex DataFrame (Default value = False)
:returns: 返回pandas.DataFrame or pandas.Panel
"""
if order_book_ids not in ("shanghai_connect", "shenzhen_connect", "all_connect"):
order_book_ids = ensure_order_book_ids(order_book_ids, type="CS")
start_date, end_date = ensure_date_range(start_date, end_date)
if fields is not None:
fields = ensure_list_of_string(fields)
for f in fields:
if f not in ("shares_holding", "holding_ratio"):
raise ValueError("invalid field: {}".format(f))
else:
fields = ["shares_holding", "holding_ratio"]
data = get_client().execute("get_stock_connect", order_book_ids, start_date, end_date, fields)
if not data:
return None
df = pd.DataFrame(data, columns=["trading_date", "order_book_id"] + fields)
if not expect_df and not is_panel_removed:
df = df.set_index(["trading_date", "order_book_id"])
df = df.to_panel()
df.major_axis.name = None
df.minor_axis.name = None
if len(order_book_ids) == 1:
df = df.minor_xs(order_book_ids[0])
if len(fields) == 1:
df = df[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 df
else:
df.sort_values(["order_book_id", "trading_date"], inplace=True)
df.set_index(["order_book_id", "trading_date"], inplace=True)
if expect_df:
return df
if len(order_book_ids) != 1 and len(fields) != 1:
raise_for_no_panel()
if len(order_book_ids) == 1:
df.reset_index(level=0, drop=True, inplace=True)
if len(fields) == 1:
df = df[fields[0]]
return df
else:
df = df.unstack(0)[fields[0]]
df.index.name = None
df.columns.name = None
return df
def get_capital_flow_tickbar(order_book_id, start_date, end_date, fields,
market):
ensure_string(order_book_id, "order_book_id")
start_date, end_date = ensure_date_range(start_date, end_date,
datetime.timedelta(days=3))
history_permission_denied = realtime_permission_denied = False
try:
data = get_client().execute("get_capital_flow_tickbar",
order_book_id,
start_date,
end_date,
fields,
market=market)
except PermissionDenied:
data = []
history_permission_denied = True
today = today_int()
if data:
data = [(obid, {k: np.frombuffer(*v)
for k, v in d.items()}) for obid, d in data]
df_list = []
for obid, d in data:
df = pd.DataFrame(d)
df_list.append(df)
df = pd.concat(df_list) # type: pd.DataFrame
df["datetime"] = int17_to_datetime_v(df["datetime"].values)
history_latest_date = date_to_int8(df.iloc[-1]["datetime"])
df.set_index("datetime", inplace=True)
else:
df = None
history_latest_date = date_to_int8(
get_previous_trading_date(today, market=market))
if history_latest_date >= end_date or start_date > today or history_latest_date >= today:
return df
try:
live_df = get_today_capital_flow_tick(order_book_id,
today,
market=market)
except PermissionDenied:
live_df = None
realtime_permission_denied = True
except MarketNotSupportError:
live_df = None
if history_permission_denied and realtime_permission_denied:
raise PermissionDenied("get_capital_flow_tick")
if live_df is None:
return df
if df is None:
return live_df
return pd.concat([df, live_df])
def get_open_auction_info(order_book_ids, start_date=None, end_date=None, market="cn"):
"""获取盘前集合竞价数据
:param order_book_ids: 股票代码
:param start_date: 起始日期，默认为今天
:param end_date: 截止日期，默认为今天
:param market: (Default value = "cn")
:returns: pd.DataFrame or None
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date, datetime.timedelta(days=0))
history_permission_denied = realtime_permission_denied = False
try:
# obid add prefix 'OA_'
data = get_client().execute("get_open_auction_info_daybar", ["OA_" + obid for obid in order_book_ids],
start_date, end_date, OA_FIELDS + ["datetime", "date"], market=market)
except PermissionDenied:
data = []
history_permission_denied = True
today = today_int()
prev_trading_date = date_to_int8(get_previous_trading_date(today, market=market))
if data:
data = [(obid[3:], {k: np.frombuffer(*v) for k, v in d.items()}) for obid, d in data]
df = convert_bar_to_multi_df(data, 'datetime', OA_FIELDS + ["date"], int17_to_datetime_v, default=0.0)
if df is None:
history_latest_date = prev_trading_date
else:
history_latest_date = df["date"].max()
del df["date"]
else:
df = None
history_latest_date = prev_trading_date
if history_latest_date >= end_date or start_date > today or history_latest_date >= today or end_date < today:
return df
try:
live_df = get_today_open_auction(order_book_ids, today, market=market)
except PermissionDenied:
live_df = None
realtime_permission_denied = True
if history_permission_denied and realtime_permission_denied:
raise PermissionDenied("get_open_auction_info")
if live_df is None:
return df
if df is None:
return live_df
df = pd.concat([df, live_df])
df.sort_index(inplace=True)
return df
def get_private_placement(order_book_ids,
start_date=None,
end_date=None,
progress="complete",
market="cn"):
"""获取定增数据
:param order_book_ids: 合约代码
:param start_date: 开始日期，默认为None
:param end_date: 结束日期，默认为None
:param progress: 是否已完成定增，默认为complete。可选参数["complete", "incomplete", "all"]
:param market: (Default value = "cn")
:return:
"""
order_book_ids = ensure_order_book_ids(order_book_ids, market=market)
if start_date and end_date:
start_date, end_date = ensure_date_range(start_date, end_date)
elif start_date:
start_date = ensure_date_int(start_date)
elif end_date:
end_date = ensure_date_int(end_date)
ensure_string_in(progress, ["complete", "incomplete", "all"], "progress")
data = get_client().execute("get_private_placement",
order_book_ids,
start_date,
end_date,
progress,
market=market)
if not data:
return
progress_map = {
10: "董事会预案",
20: "股东大会通过",
21: "国资委通过",
22: "发审委通过",
23: "证监会通过",
29: "实施中",
30: "实施完成",
40: "国资委否决",
41: "股东大会否决",
42: "证监会否决",
43: "发审委否决",
50: "延期实施",
60: "停止实施",
70: "暂缓发行"
}
issue_type_map = {21: "非公开发行", 23: "非公开发行配套融资"}
df = pd.DataFrame(data)
df["progress"] = df["progress"].map(progress_map)
df["issue_type"] = df["issue_type"].map(issue_type_map)
df.set_index(["order_book_id", "initial_info_date"], inplace=True)
return df
def get_yield_curve(start_date=None, end_date=None, tenor=None, market="cn"):
"""获取国债收益率曲线
:param market: 地区代码, 如'cn', 'us' (Default value = "cn")
:param start_date: 开始日期 (Default value = "2013-01-04")
:param end_date: 结束日期 (Default value = "2014-01-04")
:param tenor: 类别, 如 OS, 1M, 3M, 1Y (Default value = None)
"""
start_date, end_date = ensure_date_range(start_date, end_date)
all_tenor = (
"0S",
"1M",
"2M",
"3M",
"6M",
"9M",
"1Y",
"2Y",
"3Y",
"4Y",
"5Y",
"6Y",
"7Y",
"8Y",
"9Y",
"10Y",
"15Y",
"20Y",
"30Y",
"40Y",
"50Y",
)
if tenor:
tenor = ensure_list_of_string(tenor, "tenor")
check_items_in_container(tenor, all_tenor, "tenor")
tenor = ensure_order(tenor, all_tenor)
df = get_client().execute("get_yield_curve",
start_date,
end_date,
tenor,
market=market)
if not df:
return
columns = ["trading_date"]
columns.extend(tenor or all_tenor)
df = pd.DataFrame(df, columns=columns)
df.set_index("trading_date", inplace=True)
return df.sort_index()
def index_components(order_book_id,
date=None,
start_date=None,
end_date=None,
market="cn"):
"""获取指数成分
:param order_book_id: 指数 id
:param date: 指定日期；如不指定，返回最近一个交易日的数据
:param start_date: 指定开始日期，不能和date同时指定
:param end_date: 指定结束日期, 需和start_date同时指定并且应当不小于开始日期
:param market: (Default value = "cn")
:returns list or dict
"""
order_book_id = ensure_order_book_id(order_book_id)
if date and (start_date or end_date):
raise ValueError(
"date cannot be input together with start_date or end_date")
elif (start_date and not end_date) or (end_date and not start_date):
raise ValueError("start_date and end_date need to be applied together")
if start_date:
start_date, end_date = ensure_date_range(start_date, end_date)
trading_dates = get_trading_dates_in_type(start_date,
end_date,
expect_type="int")
if not trading_dates:
return
data = get_client().execute("index_components_v2",
order_book_id,
trading_dates[0],
trading_dates[-1],
market=market)
if not data:
return
data = {d["trade_date"]: d["component_ids"] for d in data}
dates = sorted(data.keys())
for trading_date in trading_dates:
if trading_date not in data:
position = bisect.bisect_left(dates, trading_date) - 1
data[trading_date] = data[dates[position]]
return {int8_to_datetime(i): data[i] for i in trading_dates}
if date:
date = ensure_date_int(date)
return get_client().execute("index_components",
order_book_id,
date,
market=market)
def get_factors(factors, start_date, end_date, market="cn"):
start_date, end_date = ensure_date_range(start_date, end_date)
factors = ensure_list_of_string(factors, "factors")
data = get_client().execute("econ.get_factors",
factors,
start_date,
end_date,
market=market)
if not data:
return
df = pd.DataFrame(data)
df = df.reindex(
columns=["factor", "info_date", "start_date", "end_date", "value"])
df.set_index(["factor", "info_date"], inplace=True)
return df
def history(
order_book_ids,
start_date="2015-05-21",
end_date="2016-05-21",
frequency="1d",
fields=None,
market="cn",
):
"""获取雪球历史舆情数据
:param order_book_ids: 股票代码或代码列表
:param start_date: 如 '2015-05-21', 必须在2015年4月23日之后 (Default value = "2015-05-21")
:param end_date: 如 '2016-05-21' (Default value = "2016-05-21")
:param frequency: 如 '1d' (Default value = "1d")
:param fields: 如 'new_comments', 'total_comments', 'new_followers',
'total_followers', 'sell_actions', 'buy_actions' (Default value = None)
:param market: 地区代码, 如 'cn' (Default value = "cn")
:returns: 返回pd.Panel或pd.DataFrame或pd.Series
"""
order_book_ids = ensure_order_book_ids(order_book_ids, market=market)
if fields:
fields = ensure_list_of_string(fields, "fields")
check_items_in_container(fields, FIELDS, "fields")
else:
fields = FIELDS
frequency = ensure_string(frequency, "frequency")
check_items_in_container([frequency], {"1d"}, "frequency")
start_date, end_date = ensure_date_range(start_date, end_date)
if start_date < 20150423:
raise ValueError("date out of range, start_date "
"cannot be earlier than 2015-04-23")
data = get_client().execute("xueqiu.history", order_book_ids, start_date,
end_date, fields, market)
if not data:
return
df = pd.DataFrame(data)
df = df.set_index(["date", "order_book_id"])
df.sort_index(inplace=True)
pl = df.to_panel()
if len(pl.minor_axis) == 1:
pl = pl.minor_xs(pl.minor_axis[0])
if len(fields) == 1:
pl = pl[fields[0]]
return pl
def get_irr_sensitivity(order_book_ids, start_date=None, end_date=None, market="cn"):
"""获取基金利率风险敏感性分析数据
:param order_book_ids: 基金代码(str), 仅支持查询一个基金合约
:param start_date: 开始日期, 如'2013-01-04'
:param end_date: 结束日期, 如'2014-01-04'；在 start_date 和 end_date 都不指定的情况下，默认为最近6个月
:param market: (Default value = "cn")
:returns: DataFrame
"""
order_book_ids = ensure_string(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date,delta=relativedelta(months=6))
result = get_client().execute("fund.get_irr_sensitivity", order_book_ids, start_date, end_date, market=market)
if not result:
return
df = pd.DataFrame(result).set_index(keys=['date'])
df.sort_index(inplace=True)
return df
def _ensure_date(start_date, end_date, stocks, funds, indexes, futures, spots, options, convertibles, repos):
default_start_date, default_end_date = ensure_date_range(start_date, end_date)
only_futures = futures and (not stocks) and (not funds) and (not indexes) and (not spots) and (
not options) and (not convertibles) and (not repos)
if only_futures and len(futures) == 1:
# 如果只有一只期货, 则给 start_date 和 end_date 合适的默认值
# 连续合约的listed_date和de_listed_date都为0, 因此需要特殊处理
if futures[0].listed_date != "0000-00-00":
default_start_date = to_date_int(futures[0].listed_date)
if futures[0].de_listed_date != "0000-00-00":
default_end_date = to_date_int(futures[0].de_listed_date)
start_date = to_date_int(start_date) if start_date else default_start_date
end_date = to_date_int(end_date) if end_date else default_end_date
if start_date < 20000104:
warnings.warn("start_date is earlier than 2000-01-04, adjusted to 2000-01-04")
start_date = 20000104
return start_date, end_date
def get_cross_sectional_bias(start_date, end_date, type='factor'):
""" 获取横截面偏差系数
:param order_book_ids str or [list of str] 证券代码（例如：‘600705.XSHG’）
:param start_date str 开始日期（例如：‘2017-03-03’）
:param end_date str 结束日期（例如：‘2017-03-20’）
:param type str 默认为 'factor'，可选 'specific'
:return: pandas.DataFrame，其中 index 为date, column 包含 'daily'、'monthly' 和 'quarterly' 三个字段。
"""
start_date, end_date = ensure_date_range(start_date, end_date)
ensure_string_in(type, ['factor', 'specific'], 'horizon')
df = get_client().execute('get_cross_sectional_bias', start_date, end_date,
type)
if not df:
return
df = pd.DataFrame(df)
df = df.pivot(index='date', columns='horizon', values="bias").sort_index()
return df
def get_specific_return(order_book_ids, start_date, end_date):
""" 获取个股特异收益率
:param order_book_ids str or [list of str] 证券代码（例如：‘600705.XSHG’）
:param start_date str 开始日期（例如：‘2017-03-03’）
:param end_date str 结束日期（例如：‘2017-03-20’）
:return: pandas.DataFrame，其中 index 为date, column 为 order_book_ids。
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
df = get_client().execute('get_specific_return', order_book_ids,
start_date, end_date)
if not df:
return
df = pd.DataFrame(df)
df = df.pivot(index='date',
columns='order_book_id',
values="specific_return").sort_index()
return df
def get_specific_risk(order_book_ids, start_date, end_date, horizon='daily'):
""" 获取个股特异方差
:param order_book_ids str or [list of str] 证券代码（例如：‘600705.XSHG’）
:param start_date str 开始日期（例如：‘2017-03-03’）
:param end_date str 结束日期（例如：‘2017-03-20’）
:param horizon str 预测期限。默认为日度（'daily'），可选月度（‘monthly’）或季度（'quarterly'）
:return: pandas.DataFrame，其中 index 为date, column 为 order_book_ids。
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
ensure_string_in(horizon, HORIZON_CONTAINER, 'horizon')
df = get_client().execute('get_specific_risk', order_book_ids, start_date,
end_date, horizon)
if not df:
return
df = pd.DataFrame(df)
df = df.pivot(index="date",
columns="order_book_id",
values="specific_risk").sort_index()
return df
def get_main_shareholder(order_book_id,
start_date=None,
end_date=None,
is_total=False,
market="cn"):
"""获取十大股东信息
:param order_book_id: 股票代码
:param start_date: 开始日期，默认为一年前
:param end_date: 结束日期，默认为今天
:param is_total: 是否十大股东, True 和 False，默认为False
:param market: (Default value = "cn")
"""
check_items_in_container(is_total, [True, False], "is_total")
start_date, end_date = ensure_date_range(start_date,
end_date,
delta=relativedelta(years=1))
ret = get_client().execute("get_main_shareholder",
order_book_id,
start_date,
end_date,
is_total,
market=market)
if not ret:
return
columns = [
'info_date', 'end_date', 'rank', 'shareholder_name',
'shareholder_attr', 'shareholder_kind', 'shareholder_type',
'hold_percent_total', 'hold_percent_float', 'share_pledge',
'share_freeze'
]
df = pd.DataFrame(ret, columns=columns).sort_values(
by=['info_date', 'rank']).set_index('info_date')
return df
def get_stock_beta(order_book_ids,
start_date,
end_date,
benchmark="000300.XSHG",
market="cn"):
"""获取个股相对于基准的贝塔
:param order_book_ids: 证券代码（例如：‘600705.XSHG’）
:param start_date: 开始日期（例如：‘2017-03-03’)
:param end_date: 结束日期（例如：‘2017-03-20’）
:param benchmark: 基准指数。默认为沪深300（‘000300.XSHG’）
可选上证50（'000016.XSHG'）、中证500（'000905.XSHG'）、
中证800（'000906.XSHG'）以及中证全指（'000985.XSHG'） (Default value = "000300.XSHG")
:param market: (Default value = "cn")
:returns: pandas.DataFrame，index 为日期，column 为个股的 order_book_id
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
start_date, end_date = ensure_date_range(start_date, end_date)
all_benchmark = ("000300.XSHG", "000016.XSHG", "000905.XSHG",
"000906.XSHG", "000985.XSHG")
benchmark = ensure_string(benchmark, "benchmark")
check_items_in_container(benchmark, all_benchmark, "benchmark")
benchmark = benchmark.replace(".", "_")
df = get_client().execute("get_stock_beta",
order_book_ids,
start_date,
end_date,
benchmark,
market=market)
if not df:
return
df = pd.DataFrame(df)
df = df.pivot(index="date", columns="order_book_id",
values=benchmark).sort_index()
return df
def get_index_factor_exposure(order_book_ids,
start_date=None,
end_date=None,
factors=None,
market="cn"):
"""获取因子暴露度
:param order_book_ids: 股票代码或代码列表
:param start_date: 如'2013-01-04' (Default value = None)
:param end_date: 如'2014-01-04' (Default value = None)
:param factors: 如'yield', 'beta', 'volatility' (Default value = None)
:param market: 地区代码, 如'cn' (Default value = "cn")
"""
try:
order_book_ids = ensure_order_book_ids(order_book_ids, type="INDX")
except ValueError:
return
start_date, end_date = ensure_date_range(start_date, end_date)
if factors is not None:
factors = ensure_list_of_string(factors)
check_items_in_container(factors, exposure_factors, "factors")
results = get_client().execute("get_index_factor_exposure",
order_book_ids,
start_date,
end_date,
factors,
market=market)
if not results:
return None
df = pd.DataFrame.from_records(results, index=['date', 'order_book_id'])
df.sort_index(inplace=True)
return df