def run(self):
# 抓取fund列表
df = finance.run_query(query(finance.FUND_MAIN_INFO))
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df.main_code.apply(lambda x: normalize_code(x))
df['entity_id'] = df['entity_id'].apply(
lambda x: to_entity_id(entity_type='fund', jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = 'fund'
df['exchange'] = df['entity_id'].apply(
lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['category'] = 'fund'
# df['choice_code'] = df.apply(lambda x:x.main_code+'.'+x.exchange.upper(),axis=1)
# loginResult = c.start("ForceLogin=1", '')
# df['underlying_index_code'] = df.apply(lambda x:c.css(x.choice_code, "BMINDEXCODE", "Rank=1").Data if x.operate_mode == 'ETF' else None,axis=1)
# df['underlying_index_code'] = df['underlying_index_code'].apply(lambda x:[i for i in x.values()][0][0].lower().replace(".","_") if x else None)
# c.stop()
df_to_db(df,
data_schema=FundDetail,
provider=self.provider,
force_update=self.force_update)
# self.logger.info(df_index)
self.logger.info("persist etf list success")
logout()
def run(self):
# 抓取fund列表
df = finance.run_query(query(finance.FUND_MAIN_INFO))
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df.main_code.apply(lambda x: normalize_code(x))
df['entity_id'] = df['entity_id'].apply(
lambda x: to_entity_id(entity_type='fund', jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = 'fund'
df['exchange'] = df['entity_id'].apply(
lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['category'] = 'fund'
df_to_db(df,
data_schema=FundDetail,
provider=self.provider,
force_update=self.force_update)
# self.logger.info(df_index)
self.logger.info("persist etf list success")
logout()
def to_zvt_entity(self, df, entity_type, category=None):
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df['entity_id'].apply(lambda x: to_entity_id(entity_type=entity_type, jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = entity_type
df['exchange'] = df['entity_id'].apply(lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['name'] = df['display_name']
if entity_type == 'etf':
# ETF 查询标的指数
df['choice_code'] = df.apply(lambda x: x.code + '.' + x.exchange.upper(), axis=1)
loginResult = c.start("ForceLogin=1", '')
df['underlying_index_code'] = df.apply(lambda x: c.css(x.choice_code, "BMINDEXCODE", "Rank=1").Data, axis=1)
df['index_codes'] = df['underlying_index_code'].apply(lambda x: [i for i in x.values()][0][0])
df['index_exchange'] = df['index_codes'].apply(lambda x: str(x).split('.'))
df['index_code'] = df['index_codes'].apply(lambda x: str(x).split('.')[0])
df['index_exchange'] = df['index_exchange'].apply(
lambda x: x[1] if isinstance(x, list) and len(x) > 1 else None)
df['underlying_index_code'] = df.apply(
lambda x: 'index_' + x.index_exchange.lower() + '_' + x.index_code if x.index_exchange else None,
axis=1)
loginResult = c.stop()
if category:
df['category'] = category
return df
def to_zvt_entity(self, df, entity_type, category=None):
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df['entity_id'].apply(lambda x: x.lower())
df['entity_id'] = df['entity_id'].apply(lambda x: to_entity_id(entity_type=entity_type, jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = entity_type
df['exchange'] = df['entity_id'].apply(lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['end_date'].fillna(pd.to_datetime("22000101"),inplace=True)
if category:
df['category'] = category
return df
def get_economy_data(entity_id, indicators=None, date=None):
country = get_entity_code(entity_id=entity_id)
if not indicators:
indicators = _economy_indicator_map.keys()
dfs = []
for indicator in indicators:
data = get_indicator_data(
indicator=_economy_indicator_map.get(indicator),
indicator_name=indicator,
country=country,
date=date)
dfs.append(data)
df = pd.concat(dfs, axis=1)
df = df.reset_index(drop=False)
df["entity_id"] = entity_id
df["id"] = df[["entity_id",
"timestamp"]].apply(lambda x: "_".join(x.astype(str)),
axis=1)
df = normal_index_df(df, drop=False)
return df
def to_zvt_entity(self, df, entity_type: EntityType, category=None):
df.index.name = 'entity_id'
df = df.reset_index()
# 上市日期
df.rename(columns={'start_date': 'timestamp'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['list_date'] = df['timestamp']
df['end_date'] = pd.to_datetime(df['end_date'])
df['entity_id'] = df['entity_id'].apply(lambda x: to_entity_id(entity_type=entity_type, jq_code=x))
df['id'] = df['entity_id']
df['entity_type'] = entity_type.value
df['exchange'] = df['entity_id'].apply(lambda x: get_entity_exchange(x))
df['code'] = df['entity_id'].apply(lambda x: get_entity_code(x))
df['name'] = df['display_name']
if category:
df['category'] = category
return df
def to_zvt_entity(self, df, entity_type, category=None):
df = df.set_index("code")
df.index.name = "entity_id"
df = df.reset_index()
# 上市日期
df.rename(columns={"start_date": "timestamp"}, inplace=True)
df["timestamp"] = pd.to_datetime(df["timestamp"])
df["list_date"] = df["timestamp"]
df["end_date"] = pd.to_datetime(df["end_date"])
df["entity_id"] = df["entity_id"].apply(
lambda x: to_entity_id(entity_type=entity_type, jq_code=x))
df["id"] = df["entity_id"]
df["entity_type"] = entity_type
df["exchange"] = df["entity_id"].apply(
lambda x: get_entity_exchange(x))
df["code"] = df["entity_id"].apply(lambda x: get_entity_code(x))
df["name"] = df["display_name"]
if category:
df["category"] = category
return df
def report_state(region):
while True:
error_count = 0
email_action = EmailInformer(ssl=True)
try:
latest_day: Stock1dKdata = Stock1dKdata.query_data(
region=region,
order=Stock1dKdata.timestamp.desc(),
limit=1,
return_type='domain')
target_date = latest_day[0].timestamp
# target_date = to_pd_timestamp('2020-01-02')
# 计算均线
my_selector = TargetSelector(region=region,
start_timestamp='2018-01-01',
end_timestamp=target_date)
# add the factors
factor1 = ImprovedMaFactor(region=region,
start_timestamp='2018-01-01',
end_timestamp=target_date)
my_selector.add_filter_factor(factor1)
my_selector.run()
long_stocks = my_selector.get_open_long_targets(
timestamp=target_date)
stock_map_slope = {}
logger.info(long_stocks)
msg = 'no targets'
if long_stocks:
pre_date = target_date - datetime.timedelta(2 * 365)
ma_state = MaStateStatsFactor(region=region,
entity_ids=long_stocks,
start_timestamp=pre_date,
end_timestamp=target_date,
need_persist=False)
ma_state.factor_df['slope'] = 100 * ma_state.factor_df[
'current_pct'] / ma_state.factor_df['current_count']
high_stocks = []
for entity_id, df in ma_state.factor_df.groupby(level=0):
if df['current_pct'].max() >= 0.7:
high_stocks.append(entity_id)
stock_map_slope[entity_id] = round(df['slope'].iat[-1], 2)
if high_stocks:
stocks = get_entities(region=region,
provider=Provider.JoinQuant,
entity_schema=Stock,
entity_ids=high_stocks,
return_type='domain')
info = [
f'{stock.name}({stock.code})[{stock_map_slope.get(stock.entity_id)}]'
for stock in stocks
]
msg = msg + '2年内高潮过:' + ' '.join(info) + '\n'
# 过滤风险股
if long_stocks:
risky_codes = risky_company(region=region,
the_date=target_date,
entity_ids=long_stocks,
income_yoy=-0.8,
profit_yoy=-0.8)
if risky_codes:
long_stocks = [
entity_id for entity_id in long_stocks
if get_entity_code(entity_id) not in risky_codes
]
stocks = get_entities(region=region,
provider=Provider.JoinQuant,
entity_schema=Stock,
codes=risky_codes,
return_type='domain')
info = [
f'{stock.name}({stock.code})[{stock_map_slope.get(stock.entity_id)}]'
for stock in stocks
]
msg = msg + '风险股:' + ' '.join(info) + '\n'
if long_stocks:
stocks = get_entities(region=region,
provider=Provider.JoinQuant,
entity_schema=Stock,
entity_ids=long_stocks,
return_type='domain')
# add them to eastmoney
try:
try:
eastmoneypy.del_group('real')
except:
pass
eastmoneypy.create_group('real')
for stock in stocks:
eastmoneypy.add_to_group(stock.code, group_name='real')
except Exception as e:
email_action.send_message(
"*****@*****.**", f'report state error',
'report state error:{}'.format(e))
info = [
f'{stock.name}({stock.code})[{stock_map_slope.get(stock.entity_id)}]'
for stock in stocks
]
msg = msg + '选中:' + ' '.join(info) + '\n'
logger.info(msg)
email_action.send_message('*****@*****.**',
f'{target_date} 放量突破年线state选股结果', msg)
break
except Exception as e:
logger.exception('report state error:{}'.format(e))
time.sleep(60 * 3)
error_count = error_count + 1
if error_count == 10:
email_action.send_message("*****@*****.**",
f'report state error',
'report state error:{}'.format(e))