def low_liquidity_filter(stocks,date,percentileThres):
"""
:param stocks: 股票列表
:param date: 日期
:param thres: 剔除换手率位于某百分位以下的股票，例如thres=5，则剔除换手率处于5分位以下的股票
:return:list
"""
liquidity = rqdatac.get_style_factor_exposure(stocks,date,date,factors="liquidity")['liquidity']
return liquidity[liquidity>np.nanpercentile(liquidity,q=percentileThres)].index.get_level_values(0).unique().tolist()
def get_style_exposure(stock_list, date):
style_exposure = rqdatac.get_style_factor_exposure(stock_list,
date,
date,
factors='all')
style_exposure.index = style_exposure.index.droplevel('date')
return style_exposure
def get_factor_exposure(stock_list,date):
industry_exposure = get_shenwan_industry_exposure(stock_list, date)
style_exposure = rqdatac.get_style_factor_exposure(stock_list, date, date, factors = 'all')
style_exposure.index = style_exposure.index.droplevel('date')
factor_exposure = pd.concat([style_exposure,industry_exposure],axis=1)
factor_exposure['comovement'] = 1
return factor_exposure
def get_explicit_factor_returns(date):
"""
:param date:日期
:return: pandas.Series
"""
previous_trading_date = rqdatac.get_previous_trading_date(date)
all_a_stocks = rqdatac.all_instruments(type="CS",date=previous_trading_date).order_book_id.tolist()
filtered_stocks = noisy_stocks_filter(all_a_stocks,previous_trading_date)
# print(all_a_stocks,previous_trading_date)
factor_exposures = rqdatac.get_style_factor_exposure(all_a_stocks, previous_trading_date, previous_trading_date, "all").sort_index()
factor_exposures.index=factor_exposures.index.droplevel(1)
closePrice = rqdatac.get_price(all_a_stocks, rqdatac.get_previous_trading_date(previous_trading_date),
previous_trading_date, fields="close")
priceChange = closePrice.pct_change().iloc[-1]
index_mapping = {"csi_300":'000300.XSHG',"csi_500":"000905.XSHG","csi_800":"000906.XSHG"}
all_stocks = {index:rqdatac.index_components(index_mapping.get(index),date=previous_trading_date) for index in index_mapping}
all_stocks['whole_market'] = filtered_stocks
def _calc_explicitReturns_with_stocksList(stocksList):
# 根据股票池计算收益率
_sizeBeta = factor_exposures[['size','beta']].loc[stocksList]
_quantileGroup = _sizeBeta.apply(lambda x:pd.cut(x,bins=3,labels=False)+1).reset_index()
_quantileStocks = _quantileGroup.groupby(['size','beta']).apply(lambda x:x.index.tolist())
market_neutralize_stocks = _quantileStocks.apply(
lambda x: pd.Series(stocksList).loc[x].values.tolist()).values.tolist()
return factor_exposures.loc[stocksList].apply(lambda x,y=market_neutralize_stocks:_calc_single_explicit_returns(x,y))
def _calc_single_explicit_returns(_factor_exposure,market_neutralize_stocks):
# 计算单一因子收益率
def _deuce(series):
median = series.median()
return [series[series<=median].index.tolist(),series[series>median].index.tolist()]
deuceResults = np.array([_deuce(_factor_exposure[neutralized_stks]) for neutralized_stks in market_neutralize_stocks]).flatten()
short_stocksList = list(reduce(lambda x,y:set(x)|set(y),np.array([s for i,s in enumerate(deuceResults) if i%2==0])))
long_stockList = list(reduce(lambda x,y:set(x)|set(y),np.array([s for i,s in enumerate(deuceResults) if i%2==1])))
return priceChange[long_stockList].mean() - priceChange[short_stocksList].mean()
results = {key: _calc_explicitReturns_with_stocksList(all_stocks.get(key)) for key in all_stocks}
return pd.DataFrame(results)[['whole_market','csi_300','csi_500','csi_800']]
def get_exposure(stock_list, date):
style_factors = [
'beta', 'momentum', 'size', 'earnings_yield', 'residual_volatility',
'growth', 'book_to_price', 'leverage', 'liquidity', 'non_linear_size'
]
non_missing_stock_list, industry_exposure = get_shenwan_industry_exposure(
stock_list, date)
style_exposure = rqdatac.get_style_factor_exposure(non_missing_stock_list,
date,
date,
factors=style_factors)
style_exposure.index = style_exposure.index.droplevel('date')
factor_exposure = pd.concat([style_exposure, industry_exposure], axis=1)
factor_exposure['comovement'] = 1
return factor_exposure
industry_exposure = get_shenwan_industry_exposure(stock_list, date)
style_exposure = rqdatac.get_style_factor_exposure(stock_list, date, date, factors = 'all')
style_exposure.index = style_exposure.index.droplevel('date')
factor_exposure = pd.concat([style_exposure,industry_exposure],axis=1)
factor_exposure['comovement'] = 1
return factor_exposure
date = '2018-07-12'
stock_list = rqdatac.all_instruments(type='CS',country='cn',date=date).order_book_id.tolist()
factor_exposure = get_factor_exposure(stock_list,date)
style_exposure = rqdatac.get_style_factor_exposure(stock_list, date, date, factors=['beta'])
style_exposure.index = style_exposure.index.droplevel('date')
liquidity_exposure = rqdatac.get_factor_exposure(stock_list,date,date,factors=['beta'])
liquidity_exposure.index = liquidity_exposure.index.droplevel('date')
data = pd.concat([factor_exposure['beta'],style_exposure,liquidity_exposure],axis=1)
data.columns=['源代码计算beta暴露度结果','get_style_factor_exposure API','get_factor_exposure API']
def to_constraint(self, order_book_ids, date):
factor_data = rqdatac.get_style_factor_exposure(
order_book_ids, date, date).xs(date, level=1)
return _to_constraint2(factor_data, self._rules)
def get_explicit_factor_returns(date, stock_list):
"""
:param date:日期
:return: pandas.Series
"""
previous_trading_date = rqdatac.get_previous_trading_date(date)
factor_exposures = rqdatac.get_style_factor_exposure(
stock_list, previous_trading_date, previous_trading_date,
"all").sort_index()
factor_exposures.index = factor_exposures.index.droplevel(1)
priceChange = rqdatac.get_price(
stock_list,
rqdatac.get_previous_trading_date(previous_trading_date),
previous_trading_date,
fields="close").pct_change().iloc[-1]
def _calc_explicitReturns_with_stocksList(stocksList):
# 根据股票池计算收益率
_sizeBeta = factor_exposures[['size', 'beta']].loc[stocksList]
_quantileGroup = _sizeBeta.apply(
lambda x: pd.cut(x, bins=3, labels=False) + 1).reset_index()
_quantileStocks = _quantileGroup.groupby(
['size', 'beta']).apply(lambda x: x.index.tolist())
market_neutralize_stocks = _quantileStocks.apply(lambda x: pd.Series(
stocksList).loc[x].values.tolist()).values.tolist()
return factor_exposures.loc[stocksList].apply(
lambda x, y=market_neutralize_stocks:
_calc_single_explicit_returns(x, y))
def _calc_single_explicit_returns(_factor_exposure,
market_neutralize_stocks):
# 计算单一因子收益率
def _deuce(series):
median = series.median()
return [
series[series <= median].index.tolist(),
series[series > median].index.tolist()
]
deuceResults = np.array([
_deuce(_factor_exposure[neutralized_stks])
for neutralized_stks in market_neutralize_stocks
]).flatten()
short_stocksList = list(
reduce(
lambda x, y: set(x) | set(y),
np.array([s for i, s in enumerate(deuceResults)
if i % 2 == 0])))
long_stockList = list(
reduce(
lambda x, y: set(x) | set(y),
np.array([s for i, s in enumerate(deuceResults)
if i % 2 == 1])))
return priceChange[long_stockList].mean(
) - priceChange[short_stocksList].mean()
results = _calc_explicitReturns_with_stocksList(stock_list)
return results