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_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_instrument_industry(order_book_ids,
source='sws',
level=1,
date=None,
market="cn"):
"""获取股票对应的行业
:param order_book_ids: 股票列表，如['000001.XSHE', '000002.XSHE']
:param source: 分类来源。sws: 申万, citics: 中信, gildata: 聚源
:param date: 如 '2015-01-07' (Default value = None)
:param level: (Default value = 1)
:param market: (Default value = "cn")
:returns: code, name
返回输入日期最近交易日的股票对应行业
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
source = ensure_string_in(source, ["sws", "citics", "gildata"], "source")
check_items_in_container(level, [0, 1, 2, 3], 'level')
date = ensure_date_or_today_int(date)
r = get_client().execute("get_instrument_industry",
order_book_ids,
source,
level,
date,
market=market)
if not r:
return
return pd.DataFrame(r).set_index("order_book_id")
def get_industry_mapping(source="citics", date=None, market="cn"):
"""获取行业分类列表
:param source: 分类来源，citics。citics: 中信, gildata: 聚源
:param market: (Default value = "cn")
:return: DataFrame
"""
source = ensure_string_in(source,
["sws", "citics", "gildata", "citics_2019"],
"source")
if date is None:
date = datetime.date.today()
date = ensure_date_int(date)
res = get_client().execute("get_industry_mapping_v2",
source,
date,
market=market)
if not res:
return
df = pd.DataFrame(res)
if source == "sws":
df.rename(columns=dict(zip(OTHER_COLUMNS, SHENWAN_COLUMNS)),
inplace=True)
columns = SHENWAN_COLUMNS
else:
columns = OTHER_COLUMNS
df = df.dropna().drop_duplicates()
df = df.sort_values(columns[::2]).reset_index(drop=True)
return df[columns]
def get_factor_covariance(date, horizon='daily'):
""" 获取因子协方差矩阵
:param date: str 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 和 column 均为因子名称。
"""
date = get_previous_trading_date(get_next_trading_date(date))
date = ensure_date_int(date)
ensure_string_in(horizon, HORIZON_CONTAINER, 'horizon')
df = get_client().execute('get_factor_covariance', date, horizon)
if not df:
return
df = pd.DataFrame(df)
df.drop("date", axis=1, inplace=True)
return df.reindex(columns=df.index)
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_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_instrument_industry(order_book_ids,
source='citics',
level=1,
date=None,
market="cn"):
"""获取股票对应的行业
:param order_book_ids: 股票列表，如['000001.XSHE', '000002.XSHE']
:param source: 分类来源。citics 以及 citics_2019: 中信, gildata: 聚源
:param date: 如 '2015-01-07' (Default value = None)
:param level: (Default value = 1)
:param market: (Default value = "cn")
:returns: code, name
返回输入日期最近交易日的股票对应行业
"""
order_book_ids = ensure_order_book_ids(order_book_ids)
source = ensure_string_in(source,
["sws", "citics", "gildata", "citics_2019"],
"source")
if source == "citics_2019":
check_items_in_container(level, [0, 1, 2, 3, "citics_sector"], 'level')
else:
check_items_in_container(level, [0, 1, 2, 3], 'level')
date = ensure_date_or_today_int(date)
r = get_client().execute("get_instrument_industry",
order_book_ids,
source,
level,
date,
market=market)
if not r:
return
res = [i['order_book_id'] for i in r]
if source == "citics_2019" and level == "citics_sector":
# is_special industry是否传入的是风格版块，产业板块和上下游产业版块
from rqdatac.services import basic
res_list = basic.instruments(res)
date = to_date_str(date)
for index, order_book in enumerate(res_list):
if order_book.de_listed_date == "0000-00-00" or order_book.de_listed_date is None:
order_book.de_listed_date = "2099-12-31"
if not order_book.listed_date <= date <= order_book.de_listed_date:
r.pop(index)
return pd.DataFrame(r).set_index("order_book_id")
def get_industry(industry, source='sws', date=None, market="cn"):
"""获取行业股票列表
:param industry: 行业名称或代码
:param source: 分类来源。sws: 申万, citics: 中信, gildata: 聚源
:param date: 查询日期，默认为当前最新日期
:param market: (Default value = "cn")
:return: 所属目标行业的order_book_id list or None
"""
industry = ensure_string(industry, "industry")
source = ensure_string_in(source, ["sws", "citics", "gildata"], "source")
date = ensure_date_or_today_int(date)
res = get_client().execute("get_industry",
industry,
source,
date,
market=market)
return sorted(res)
def get_industry_mapping(source="sws", market="cn"):
"""获取行业分类列表
:param source: 分类来源，默认为sws。sws: 申万, citics: 中信, gildata: 聚源
:param market: (Default value = "cn")
:return: DataFrame
"""
source = ensure_string_in(source, ["sws", "citics", "gildata"], "source")
columns = [
"index_code", "index_name", "second_index_code", "second_index_name",
"third_index_code", "third_index_name"
] if source == "sws" else [
"first_industry_code", "first_industry_name", "second_industry_code",
"second_industry_name", "third_industry_code", "third_industry_name"
]
res = get_client().execute("get_industry_mapping", source, market=market)
df = pd.DataFrame(res)
df = df.dropna().drop_duplicates()
df = df.sort_values(columns[::2]).reset_index(drop=True)
return df[columns]
def get_industry(industry, source='citics', date=None, market="cn"):
"""获取行业股票列表
:param industry: 行业名称或代码
:param source: 分类来源。citics 以及 citics_2019: 中信, gildata: 聚源
:param date: 查询日期，默认为当前最新日期
:param market: (Default value = "cn")
:return: 所属目标行业的order_book_id list or None
"""
industry = ensure_string(industry, "industry")
source = ensure_string_in(source,
["sws", "citics", "gildata", "citics_2019"],
"source")
date = ensure_date_or_today_int(date)
res = get_client().execute("get_industry",
industry,
source,
date,
market=market)
if not res:
return res
if res[-1] == "have_sector_name":
# have_sector_name 代表 industry传入的是风格版块，产业板块或者上下游产业版块
from rqdatac.services import basic
res_list = basic.instruments(res[:-1])
res = []
date = to_date_str(date)
for order_book in res_list:
if order_book.de_listed_date == "0000-00-00" or order_book.de_listed_date is None:
order_book.de_listed_date = "2099-12-31"
if order_book.listed_date <= date <= order_book.de_listed_date:
res.append(order_book.order_book_id)
return sorted(res)
def get_trading_hours(order_book_id,
date=None,
expected_fmt="str",
frequency="1m",
market="cn"):
"""获取合约指定日期交易时间
:param order_book_id: 合约代码
:param date: 日期，默认为今天
:param expected_fmt: 返回格式，默认为str, 也支持datetime.time和datetime.datetime格式
:param frequency: 频率，默认为1m, 对应米筐分钟线时间段的起始, tick和1m相比区别在于每个交易时间段开盘往前移一分钟
:param market: (Default value = "cn")
:return: trading_hours str or list of datetime.time/datetime.datetime list or None
"""
date = ensure_date_or_today_int(date)
if not is_trading_date(date, market):
warnings.warn(" %d is not a trading date" % date)
return
ensure_string(order_book_id, "order_book_id")
ins = instruments(order_book_id)
if ins is None:
return
ensure_string_in(expected_fmt, ("str", "time", "datetime"), "expected_fmt")
ensure_string_in(frequency, ("1m", "tick"), "frequency")
date_str = to_date_str(date)
if ins.listed_date > date_str:
return
if ins.type in (
"Future", "Option"
) and ins.de_listed_date < date_str and ins.de_listed_date != "0000-00-00":
return
if ins.type not in (
"Future", "Option"
) and ins.de_listed_date <= date_str and ins.de_listed_date != "0000-00-00":
return
if ins.type == "Repo":
trading_hours = "09:31-11:30,13:01-15:30"
elif ins.type == "Spot":
if has_night_trading(date, market):
trading_hours = "20:01-02:30,09:01-15:30"
else:
trading_hours = "09:01-15:30"
elif ins.type not in ("Future",
"Option") or (ins.type == "Option"
and ins.exchange in ("XSHG", "XSHE")):
trading_hours = "09:31-11:30,13:01-15:00"
else:
trading_hours = get_client().execute("get_trading_hours",
ins.underlying_symbol,
date,
market=market)
if trading_hours is None:
return
# 前一天放假或者该品种上市首日没有夜盘
no_night_trading = (not has_night_trading(date, market)
or get_underlying_listed_date(
ins.underlying_symbol, ins.type) == date_str)
if no_night_trading and not trading_hours.startswith("09"):
trading_hours = trading_hours.split(",", 1)[-1]
if frequency == "tick":
trading_hours = ",".join([
s[:4] + str(int(s[4]) - 1) + s[5:]
for s in trading_hours.split(",")
])
if expected_fmt != "str":
trading_hours = [t.split("-", 1) for t in trading_hours.split(",")]
for i, (start, end) in enumerate(trading_hours):
trading_hours[i][0] = str_to_dt_time(start)
trading_hours[i][1] = str_to_dt_time(end)
if expected_fmt == "datetime":
td = int8_to_date(date)
prev_td = get_previous_trading_date(date)
prev_td_next = prev_td + datetime.timedelta(days=1)
for i, (start, end) in enumerate(trading_hours):
if start.hour > 16:
start_dt = prev_td
end_dt = start_dt if end.hour > 16 else prev_td_next
else:
start_dt = end_dt = td
trading_hours[i][0] = datetime.datetime.combine(
start_dt, start)
trading_hours[i][1] = datetime.datetime.combine(end_dt, end)
return trading_hours
def get_contracts(
underlying,
option_type=None,
maturity=None,
strike=None,
trading_date=None
):
"""返回符合条件的期权
:param underlying: 标的合约, 可以填写'M'代表期货品种的字母；也可填写'M1901'这种具体 order_book_id
:param option_type: 期权类型, 'C'代表认购期权, 'P'代表认沽期权合约, 默认返回全部
:param maturity: 到期月份, 如'1811'代表期权18年11月到期, 默认返回全部到期月份
:param strike: 行权价, 向左靠档, 默认返回全部行权价
:param trading_date: 查询日期, 仅针对50ETF期权行权有效, 默认返回当前全部
:returns
返回order_book_id list；如果无符合条件期权则返回空list[]
"""
underlying = ensure_string(underlying, "underlying").upper()
instruments_df = all_instruments(type='Option')
underlying_symbols = instruments_df.underlying_symbol.unique()
underlying_order_book_ids = instruments_df.underlying_order_book_id.unique()
if underlying in underlying_symbols:
instruments_df = instruments_df[instruments_df.underlying_symbol == underlying]
elif underlying in underlying_order_book_ids:
instruments_df = instruments_df[instruments_df.underlying_order_book_id == underlying]
else:
raise ValueError("Unknown underlying")
if instruments_df.empty:
return []
if instruments_df.iloc[0].underlying_symbol != '510050.XSHG' and trading_date:
warnings.warn(
"Underlying symbol is not 510050.XSHG, trading_date ignored"
)
elif instruments_df.iloc[0].underlying_symbol == '510050.XSHG' and trading_date:
instruments_df = all_instruments(type='Option', date=trading_date)
instruments_df = instruments_df[instruments_df.underlying_symbol == '510050.XSHG']
if instruments_df.empty:
return []
if option_type is not None:
option_type = ensure_string(option_type, "option_type").upper()
ensure_string_in(option_type, {'P', 'C'}, "option_type")
instruments_df = instruments_df[instruments_df.option_type == option_type]
if maturity is not None:
maturity = int(maturity)
month = maturity % 100
if month not in range(1, 13):
raise ValueError("Unknown month")
year = maturity // 100 + 2000
str_month = str(month)
if len(str_month) == 1:
str_month = '0' + str_month
date_str = str(year) + '-' + str_month
# instruments_df.set_index(instruments_df.maturity_date, inplace=True)
# instruments_df = instruments_df.filter(like=date_str, axis=0)
instruments_df = instruments_df[instruments_df.maturity_date.str.startswith(date_str)]
if instruments_df.empty:
return []
if strike:
order_book_ids = instruments_df.order_book_id.tolist()
if instruments_df.iloc[0].underlying_symbol == '510050.XSHG' and trading_date:
strikes = get_price(order_book_ids, start_date=trading_date, end_date=trading_date, fields='strike_price')
if strikes.empty:
return []
instruments_df.set_index(instruments_df.order_book_id, inplace=True)
strikes = strikes.T
instruments_df['strike_price'] = strikes[strikes.columns[0]]
instruments_df = instruments_df[instruments_df.strike_price.notnull()]
if instruments_df.empty:
return []
l = []
for date in instruments_df.maturity_date.unique():
df = instruments_df[instruments_df.maturity_date == date]
df = df[df.strike_price <= strike]
if df.empty:
continue
df = df[df.strike_price.rank(method='min', ascending=False) == 1]
l += df.order_book_id.tolist()
return l
else:
return instruments_df.order_book_id.tolist()
