def show_industry_composition(entity_ids, timestamp):
block_df = Block.query_data(provider='eastmoney', filters=[Block.category == 'industry'], index='entity_id')
block_ids = block_df.index.tolist()
block_df = BlockStock.query_data(entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)])
s = block_df['name'].value_counts()
cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()])
cycle_df['entity_id'] = 'stock_cn_industry'
cycle_df['timestamp'] = timestamp
drawer = Drawer(main_df=cycle_df)
drawer.draw_pie(show=True)
def show_industry_composition(entity_ids, timestamp):
block_df = Block.query_data(provider="eastmoney",
filters=[Block.category == "industry"],
index="entity_id")
block_ids = block_df.index.tolist()
block_df = BlockStock.query_data(
entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)])
s = block_df["name"].value_counts()
cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()])
cycle_df["entity_id"] = "stock_cn_industry"
cycle_df["timestamp"] = timestamp
drawer = Drawer(main_df=cycle_df)
drawer.draw_pie(show=True)
def tag(self, timestamp):
stock_df = Stock.query_data(filters=[Stock.list_date <= timestamp],
index="entity_id")
block_df = Block.query_data(provider="eastmoney",
filters=[Block.category == "industry"],
index="entity_id")
block_ids = block_df.index.tolist()
block_stock_df = BlockStock.query_data(
provider="eastmoney",
entity_ids=block_ids,
filters=[BlockStock.stock_id.in_(stock_df.index.tolist())],
index="stock_id",
)
block_stock_df["cycle_tag"] = block_stock_df["name"].apply(
lambda name: get_cycle_tag(name))
strong_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
"strong_cycle"]["stock_id"]
weak_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
"weak_cycle"]["stock_id"]
non_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
"non_cycle"]["stock_id"]
strong_cycle_domains = self.get_tag_domains(
entity_ids=strong_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.strong_cycle.value)
weak_cycle_domains = self.get_tag_domains(
entity_ids=weak_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.weak_cycle.value)
non_cycle_domains = self.get_tag_domains(
entity_ids=non_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.non_cycle.value)
self.session.add_all(strong_cycle_domains)
self.session.add_all(weak_cycle_domains)
self.session.add_all(non_cycle_domains)
self.session.commit()
def tag(self, timestamp):
stock_df = Stock.query_data(filters=[Stock.list_date <= timestamp],
index='entity_id')
block_df = Block.query_data(provider='eastmoney',
filters=[Block.category == 'industry'],
index='entity_id')
block_ids = block_df.index.tolist()
block_stock_df = BlockStock.query_data(
provider='eastmoney',
entity_ids=block_ids,
filters=[BlockStock.stock_id.in_(stock_df.index.tolist())],
index='stock_id')
block_stock_df['cycle_tag'] = block_stock_df['name'].apply(
lambda name: get_cycle_tag(name))
strong_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
'strong_cycle']['stock_id']
weak_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
'weak_cycle']['stock_id']
non_cycle_stocks = block_stock_df[block_stock_df.cycle_tag ==
'non_cycle']['stock_id']
strong_cycle_domains = self.get_tag_domains(
entity_ids=strong_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.strong_cycle.value)
weak_cycle_domains = self.get_tag_domains(
entity_ids=weak_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.weak_cycle.value)
non_cycle_domains = self.get_tag_domains(
entity_ids=non_cycle_stocks,
timestamp=timestamp,
cycle_tag=CycleTag.non_cycle.value)
self.session.add_all(strong_cycle_domains)
self.session.add_all(weak_cycle_domains)
self.session.add_all(non_cycle_domains)
self.session.commit()
def report_tm():
while True:
error_count = 0
discord_informer = DiscordInformer()
try:
# 抓取k线数据
# StockTradeDay.record_data(provider='baostock', sleeping_time=2)
# Stock1dKdata.record_data(provider='baostock', sleeping_time=1.5)
latest_day: StockTradeDay = StockTradeDay.query_data(order=StockTradeDay.timestamp.desc(), limit=1,provider='joinquant',
return_type='domain')
if latest_day:
target_date = latest_day[0].timestamp
else:
target_date = now_pd_timestamp()
start_date = target_date - timedelta(60)
# 计算
my_selector = TargetSelector(entity_schema=Stock, provider='joinquant',
start_timestamp=start_date, end_timestamp=target_date)
# add the factors
tm_factor = TMFactor(entity_schema=Stock, provider='joinquant',
start_timestamp=start_date,
end_timestamp=target_date)
my_selector.add_filter_factor(tm_factor)
my_selector.run()
long_targets = my_selector.get_open_long_targets(timestamp=target_date)
logger.info(long_targets)
msg = 'no targets'
# 过滤亏损股
# check StockValuation data
pe_date = target_date - timedelta(10)
if StockValuation.query_data(start_timestamp=pe_date, limit=1, return_type='domain'):
positive_df = StockValuation.query_data(provider='joinquant', entity_ids=long_targets,
start_timestamp=pe_date,
filters=[StockValuation.pe > 0],
columns=['entity_id'])
bad_stocks = set(long_targets) - set(positive_df['entity_id'].tolist())
if bad_stocks:
stocks = get_entities(provider='joinquant', entity_schema=Stock, entity_ids=bad_stocks,
return_type='domain')
info = [f'{stock.name}({stock.code})' for stock in stocks]
msg = '亏损股:' + ' '.join(info) + '\n'
long_stocks = set(positive_df['entity_id'].tolist())
if long_stocks:
# use block to filter
block_selector = BlockSelector(start_timestamp='2020-01-01', long_threshold=0.8)
block_selector.run()
long_blocks = block_selector.get_open_long_targets(timestamp=target_date)
if long_blocks:
blocks = Block.query_data(provider='sina', entity_ids=long_blocks,
return_type='domain')
info = [f'{block.name}({block.code})' for block in blocks]
msg = ' '.join(info) + '\n'
block_stocks = BlockStock.query_data(provider='sina', filters=[
BlockStock.stock_id.in_(long_stocks)],
entity_ids=long_blocks, return_type='domain')
block_map_stocks = {}
for block_stock in block_stocks:
stocks = block_map_stocks.get(block_stock.name)
if not stocks:
stocks = []
block_map_stocks[block_stock.name] = stocks
stocks.append(f'{block_stock.stock_name}({block_stock.stock_code})')
for block in block_map_stocks:
stocks = block_map_stocks[block]
stock_msg = ' '.join(stocks)
msg = msg + f'{block}:\n' + stock_msg + '\n'
discord_informer.send_message(f'{target_date} TM选股结果 {msg}')
break
except Exception as e:
logger.exception('report_tm error:{}'.format(e))
time.sleep(60 * 3)
error_count = error_count + 1
if error_count == 10:
discord_informer.send_message(f'report_tm error',
'report_tm error:{}'.format(e))
def report_real(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 = '2020-02-04'
# 计算均线
my_selector = TargetSelector(region=region,
start_timestamp='2018-01-01',
end_timestamp=target_date)
# add the factors
factor1 = VolumeUpMa250Factor(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)
msg = 'no targets'
# 过滤亏损股
# check StockValuation data
pe_date = target_date - datetime.timedelta(10)
if StockValuation.query_data(region=region,
start_timestamp=pe_date,
limit=1,
return_type='domain'):
positive_df = StockValuation.query_data(
region=region,
provider=Provider.JoinQuant,
entity_ids=long_stocks,
start_timestamp=pe_date,
filters=[StockValuation.pe > 0],
columns=['entity_id'])
bad_stocks = set(long_stocks) - set(
positive_df['entity_id'].tolist())
if bad_stocks:
stocks = get_entities(region=region,
provider=Provider.JoinQuant,
entity_schema=Stock,
entity_ids=bad_stocks,
return_type='domain')
info = [f'{stock.name}({stock.code})' for stock in stocks]
msg = '亏损股:' + ' '.join(info) + '\n'
long_stocks = set(positive_df['entity_id'].tolist())
if long_stocks:
# use block to filter
block_selector = BlockSelector(region=region,
start_timestamp='2020-01-01',
long_threshold=0.8)
block_selector.run()
long_blocks = block_selector.get_open_long_targets(
timestamp=target_date)
if long_blocks:
blocks: List[Block] = Block.query_data(
region=region,
provider=Provider.Sina,
entity_ids=long_blocks,
return_type='domain')
info = [f'{block.name}({block.code})' for block in blocks]
msg = ' '.join(info) + '\n'
block_stocks: List[BlockStock] = BlockStock.query_data(
region=region,
provider=Provider.Sina,
filters=[BlockStock.stock_id.in_(long_stocks)],
entity_ids=long_blocks,
return_type='domain')
if block_stocks:
# add them to eastmoney
try:
try:
eastmoneypy.del_group('real')
except:
pass
eastmoneypy.create_group('real')
for block_stock in block_stocks:
eastmoneypy.add_to_group(
block_stock.stock_code, group_name='real')
except Exception as e:
email_action.send_message(
"*****@*****.**", f'report_real error',
'report_real error:{}'.format(e))
block_map_stocks = {}
for block_stock in block_stocks:
stocks = block_map_stocks.get(block_stock.name)
if not stocks:
stocks = []
block_map_stocks[block_stock.name] = stocks
stocks.append(
f'{block_stock.stock_name}({block_stock.stock_code})'
)
for block in block_map_stocks:
stocks = block_map_stocks[block]
stock_msg = ' '.join(stocks)
msg = msg + f'{block}:\n' + stock_msg + '\n'
logger.info(msg)
email_action.send_message('*****@*****.**',
f'{target_date} 放量突破年线real选股结果', msg)
break
except Exception as e:
logger.exception('report_real error:{}'.format(e))
time.sleep(60 * 3)
error_count = error_count + 1
if error_count == 10:
email_action.send_message("*****@*****.**",
f'report_real error',
'report_real error:{}'.format(e))
values[0] if str(x) + relocation_date <= date.fromtimestamp(time.time(
)).strftime("%Y%m%d") else np.NaN)
buy_signal = buy_signal.query("timestamp <= @end")
code_dict = {}
for stock_data in buy_signal[['股票代码', 'timestamp',
'股票名称']].to_dict('records'):
stock_n = stock_data['股票代码'] + '_' + stock_data['股票名称']
if stock_n not in code_dict:
code_dict.update({stock_n: stock_data['timestamp']})
else:
if code_dict[stock_n] >= stock_data['timestamp']:
code_dict.update({stock_n: stock_data['timestamp']})
buy_signal['CODES'] = buy_signal['股票代码'] + '_' + buy_signal['股票名称']
list(set(buy_signal['股票代码'].tolist()))
data = BlockStock.query_data(filters=[
BlockStock.stock_code.in_(list(set(buy_signal['股票代码'].tolist()))),
BlockStock.block_type == 'swl1'
])
buy_signaldata = buy_signal[buy_signal.year == buy_signal.year.max()]
data = BlockStock.query_data(filters=[
BlockStock.stock_code.in_(list(set(buy_signal['股票代码'].tolist()))),
BlockStock.block_type == 'swl1'
])
data["股票代码"] = data['stock_code']
data["行业分类"] = data['name']
data = data[["行业分类", "股票代码"]]
buy_signaldata = pd.merge(data, buy_signaldata, on=['股票代码'])
buy_signaldata['CODES'] = buy_signaldata.apply(
lambda x: x.CODES + '_' + x.行业分类, axis=1)