def get_future_daily(self, date: DateUtils.DateType):
renaming_dict = self._factor_param['行情数据']
tickers = self.future_tickers.ticker(date)
tickers = [self.windcode2jqcode(it) for it in tickers]
data = jq.get_price(tickers,
start_date=date,
end_date=date,
frequency='daily',
fq=None,
fill_paused=True,
fields=[
'open', 'high', 'low', 'close', 'volume',
'money', 'open_interest'
])
settle_data = jq.get_extras('futures_sett_price',
tickers,
start_date=date,
end_date=date)
settle = settle_data.stack().reset_index()
settle.columns = ['time', 'code', 'settle']
combined_data = pd.merge(data, settle)
db_data = self._standardize_df(combined_data,
renaming_dict).sort_index()
self.db_interface.insert_df(db_data, '期货日行情')
def get_stock_list(cur_date='2018-06-26',
begin_date='2018-01-01',
MARKET_MIN_CAP=100,
MARKET_MAX_CAP=500):
"""
获取从指定日期开始的，市值在指定区间的，非st，股票列表, 以及详细信息（code，circulating_cap，circulating_market_cap）
"""
# 总市值在100-500亿
q = jq.query(jq.valuation.code, jq.valuation.circulating_cap,
jq.valuation.circulating_market_cap).filter(
jq.valuation.code.notin_(['002473.XSHE',
'000407.XSHE']), # why?
jq.valuation.circulating_market_cap < MARKET_MAX_CAP,
jq.valuation.circulating_market_cap >= MARKET_MIN_CAP)
df = jq.get_fundamentals(q, date=begin_date)
df.index = list(df['code'])
# 去除st
st = jq.get_extras('is_st',
list(df['code']),
start_date=cur_date,
end_date=cur_date,
df=True)
st = st.iloc[0]
stock_list = list(st[st == False].index)
return stock_list, df
def remove_st(stocks, start, end):
"""
去掉ST股票之后的股票列表
"""
# 去除st
st = jq.get_extras(
'is_st', stocks, start_date=start, end_date=end, df=True)
st = st.iloc[0]
stock_list = list(st[st == False].index)
return stock_list
def get_jj_data():
clear('./bob')
today = '2019-06-27'
data = jq.get_price(security='150019.XSHE', start_date='2015-01-01', end_date=today)
jz = jq.get_extras(info='unit_net_value', security_list='150019.XSHE', start_date='2015-01-01', end_date=today, df=True,count=None)
result = []
for indexs in data.index:
w_time = indexs
w_close = data.loc[indexs].values[1]
w_jz = jz.loc[indexs].values[0]
diff = float(w_close)-float(w_jz)
diffrea = diff/float(w_close)
write('{0}--{1}--{2}--{3}--{4}\n'.format(w_time,w_close,w_jz,diff,diffrea),'./bob')
def _get_future_settle_info(self, date):
# table_name = '期货结算参数'
tickers = self.future_tickers.ticker(date)
tickers = [self.windcode2jqcode(it) for it in tickers]
data = jq.get_extras('futures_sett_price',
tickers,
start_date=date,
end_date=date)
data.columns = [self.jqcode2windcode(it) for it in data.columns]
df = data.stack()
df.name = '结算价'
df.index.names = ['DateTime', 'ID']
return df
def get_day_st(security, date):
data_df = jq.get_extras("is_st",
security,
start_date=date,
end_date=date,
df=True)
if data_df.empty:
return pd.DataFrame({col: [] for col in ["security", "is_st", "date"]})
data_df.index = [0]
data_df.columns = ["is_st"]
data_df["security"] = security
data_df["date"] = date
result_df = data_df[["security", "is_st", "date"]]
return result_df
def get_securities_day_st(security_list, date):
data_df = jq.get_extras("is_st",
security_list,
start_date=date,
end_date=date,
df=True)
if data_df.empty:
return pd.DataFrame({col: [] for col in ["security", "is_st", "date"]})
data_df = data_df.reset_index(drop=True).T
data_df.columns = ["is_st"]
data_df["security"] = data_df.index
data_df["date"] = date
data_df = data_df.reset_index(drop=True)
result_df = data_df[["security", "is_st", "date"]]
return result_df
def fetch_magic_candidators(engine, t_day):
# 中证价值回报量化策略指数的样本空间由满足以下条件的沪深 A 股构成：
# （1）非 ST、*ST 股票，非暂停上市股票； （2）非金融类股票。
all_stocks = list(jq.get_all_securities(types=['stock'], date=t_day).index)
#all_stocks = all_stocks[:100]
# 排除金融类的股票
banks = jq.get_industry_stocks('J66', date=t_day)
brokers = jq.get_industry_stocks('J67', date=t_day)
insurances = jq.get_industry_stocks('J68', date=t_day)
others = jq.get_industry_stocks('J69', date=t_day)
exclude = banks + brokers + insurances + others
filtered_1 = []
for code in all_stocks:
if (code in exclude):
print " ... %s 是金融类, 排除..." % code
continue
filtered_1.append(code)
# 排除 ST
st = jq.get_extras('is_st',
filtered_1,
start_date=t_day,
end_date=t_day,
df=False)
filtered_2 = []
for code in filtered_1:
if st[code][0]:
print " ... %s 是ST，排除" % code
continue
filtered_2.append(code)
filtered_3 = []
# 排除停牌
for code in filtered_2:
if data_fetcher.is_paused(engine, code, t_day):
print " ... %s 停牌，排除" % code
continue
filtered_3.append(code)
return filtered_3
def get_stock(stockPool,begin_date):
if stockPool=='HS300':
stockList=jq.get_index_stocks('000300.XSHG',begin_date)
elif stockPool=='ZZ500':
stockList=jq.get_index_stocks('399905.XSHE',begin_date)
elif stockPool=='ZZ800':
stockList=jq.get_index_stocks('399906.XSHE',begin_date)
elif stockPool=='CYBZ':
stockList=jq.get_index_stocks('399006.XSHE',begin_date)
elif stockPool=='ZXBZ':
stockList=jq.get_index_stocks('399005.XSHE',begin_date)
elif stockPool=='A':
stockList=jq.get_index_stocks('000002.XSHG',begin_date)+jq.get_index_stocks('399107.XSHE',begin_date)
#剔除ST股
st_data=jq.get_extras('is_st',stockList, count = 1,end_date=begin_date)
stockList = [stock for stock in stockList if not st_data[stock][0]]
#剔除停牌、新股及退市股票
stockList=delect_stop(stockList,begin_date)
#print(stockList)
return stockList
def del_st(date):
stocks_sql = "select code from security"
stock_codes = my.select_all(stocks_sql, ())
jq.login()
st_list = []
for stock_code in stock_codes:
code = stock_code['code']
st_data = sdk.get_extras('is_st', [code],
start_date=date,
end_date=date)
result = st_data.iloc[0][code]
if result:
print('%s 结果为：%s', code, result)
st = code
st_list.append(st)
print(st_list)
del_sql = "delete from security where code in (%s)"
my.delete_many(del_sql, st_list)
def get_extras(*args, **kwargs):
return jqdatasdk.get_extras(*args, **kwargs)