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_score(self, helper):
order_book_ids = helper.order_book_ids
weight = np.repeat(1.0 / len(order_book_ids), len(order_book_ids))
risk = weight.dot(helper.get_cov_matrix())
rtn = self.expected_returns
if rtn is None:
start_date = rqdatac.trading_date_offset(helper.prev_trading_date,
-self.window)
rtn = rqdatac.get_price_change_rate(
order_book_ids, start_date,
helper.prev_trading_date).mean() * 252
rtn = rtn.reindex(order_book_ids, fill_value=0.0)
return rtn / risk
def get_score(self, helper):
order_book_ids = helper.order_book_ids
weight = np.repeat(1.0 / len(order_book_ids), len(order_book_ids))
not_in_pool = len(helper.union_components) - len(helper.order_book_ids)
if not_in_pool > 0:
active_weight = np.hstack([weight,
np.repeat(0.0, not_in_pool)
]) - helper.benchmark_weight.values
else:
active_weight = weight - helper.benchmark_weight.values
active_risk = active_weight.dot(helper.get_union_cov_matrix())
rtn = self._expected_active_returns
if rtn is None:
start_date = rqdatac.trading_date_offset(helper.prev_trading_date,
-self.window)
rtn = rqdatac.get_price_change_rate(
order_book_ids + [helper.benchmark], start_date,
helper.prev_trading_date).mean() * 252
benchmark_returns = rtn[helper.benchmark]
rtn -= benchmark_returns
rtn = rtn.reindex(order_book_ids, fill_value=0.0)
return rtn / active_risk[:len(order_book_ids)]
holding = pd.read_sql(sql, conn)
holding['stock_code'] = holding['stock_code'].apply(rq.id_convert)
sql = f'''
SELECT * FROM jarvis.lgt_inst_north_holding_em
WHERE inst_code in {str(inst_code_all)}
AND as_of_date BETWEEN '{date_start}' AND '{date_end}'
'''
holding_all = pd.read_sql(sql, conn)
holding_all['stock_code'] = holding_all['stock_code'].apply(rq.id_convert)
#所有股票代码
instruments = holding['stock_code'].drop_duplicates().sort_values().to_list()
#历史涨跌幅
pct_change = rq.get_price_change_rate(instruments,
start_date=date_start,
end_date=date_end)
#获取历史价格
md = rq.get_price(instruments,
start_date=date_start,
end_date=date_end,
fields=['close'])
ind_data = w.wsd(
"CI005003.WI,CI005005.WI,CI005006.WI,CI005001.WI,CI005002.WI,CI005012.WI,CI005013.WI,CI005010.WI,CI005011.WI,CI005016.WI,CI005018.WI,CI005009.WI,CI005017.WI,CI005014.WI,CI005015.WI,CI005019.WI,CI005020.WI,CI005027.WI,CI005025.WI,CI005026.WI,CI005028.WI,CI005022.WI,CI005030.WI,CI005021.WI,CI005007.WI,CI005023.WI,CI005008.WI,CI005024.WI,CI005004.WI,CI005029.WI",
"pct_chg", date, date, "")
citics_group = {
'交通运输': '基地',
'传媒': '科技',
'农林牧渔': '消费',
'医药': '医药',