def record(self, entity_item, start, end, size, timestamps):
self.seed += 1
timestamp = timestamps[0]
the_url = self.url.format(to_time_str(timestamp),
to_time_str(timestamp))
items = get_all_results(url=the_url,
token=GithubAccount.get_token(seed=self.seed))
current_time = now_pd_timestamp()
results = [{
'id': f'user_github_{item["login"]}',
'entity_id': f'user_github_{item["login"]}',
'timestamp': timestamp,
'exchange': 'github',
'entity_type': 'user',
'code': item['login'],
'node_id': item['node_id'],
'created_timestamp': current_time,
'updated_timestamp': None
} for item in items]
# for save faster
df = pd.DataFrame(data=results[:-1])
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force=True)
return results[-1:]
def generate_kdata_id(entity_id, timestamp, level):
if level >= IntervalLevel.LEVEL_1DAY:
return "{}_{}".format(entity_id,
to_time_str(timestamp, fmt=TIME_FORMAT_DAY))
else:
return "{}_{}".format(entity_id,
to_time_str(timestamp, fmt=TIME_FORMAT_ISO8601))
def fetch_cumulative_net_value(self, security_item, start, end) -> pd.DataFrame:
query_url = 'http://api.fund.eastmoney.com/f10/lsjz?' \
'fundCode={}&pageIndex={}&pageSize=200&startDate={}&endDate={}'
page = 1
df = pd.DataFrame()
while True:
url = query_url.format(security_item.code, page, to_time_str(start), to_time_str(end))
response = requests.get(url, headers=EASTMONEY_ETF_NET_VALUE_HEADER)
response_json = demjson.decode(response.text)
response_df = pd.DataFrame(response_json['Data']['LSJZList'])
# 最后一页
if response_df.empty:
break
response_df['FSRQ'] = pd.to_datetime(response_df['FSRQ'])
response_df['JZZZL'] = pd.to_numeric(response_df['JZZZL'], errors='coerce')
response_df['LJJZ'] = pd.to_numeric(response_df['LJJZ'], errors='coerce')
response_df = response_df.fillna(0)
response_df.set_index('FSRQ', inplace=True, drop=True)
df = pd.concat([df, response_df])
page += 1
self.sleep()
return df
def record(self, entity, start, end, size, timestamps):
if self.start_timestamp:
start = max(self.start_timestamp, to_pd_timestamp(start))
end = now_pd_timestamp() + timedelta(days=1)
start_timestamp = to_time_str(start)
end_timestamp = to_time_str(end)
# 不复权
df = get_price(to_jq_entity_id(entity), start_date=to_time_str(start_timestamp),
end_date=end_timestamp,
frequency=self.jq_trading_level,
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
skip_paused=True, fq=None)
df.index.name = 'timestamp'
df.reset_index(inplace=True)
df['name'] = entity.name
df.rename(columns={'money': 'turnover'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = 'joinquant'
df['level'] = self.level.value
# remove the unfinished kdata
if is_in_trading(entity_type='stock', exchange='sh', timestamp=df.iloc[-1, :]['timestamp']):
df = df.iloc[:-1, :]
return df.to_dict(orient='records')
def generate_kdata_id(se):
if self.level >= IntervalLevel.LEVEL_1DAY:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_DAY))
else:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_ISO8601))
def on_finish_entity(self, entity):
kdatas = get_kdata(
provider=self.provider,
entity_id=entity.id,
level=self.level.value,
order=self.data_schema.timestamp.asc(),
return_type='domain',
session=self.session,
filters=[
self.data_schema.hfq_close.is_(None),
self.data_schema.timestamp >= to_pd_timestamp('2005-01-01')
])
if kdatas:
start = kdatas[0].timestamp
end = kdatas[-1].timestamp
# get hfq from joinquant
df = get_price(to_jq_entity_id(entity),
start_date=to_time_str(start),
end_date=now_time_str(),
frequency='daily',
fields=['factor', 'open', 'close', 'low', 'high'],
skip_paused=True,
fq='post')
if df_is_not_null(df):
# fill hfq data
for kdata in kdatas:
time_str = to_time_str(kdata.timestamp)
if time_str in df.index:
kdata.hfq_open = df.loc[time_str, 'open']
kdata.hfq_close = df.loc[time_str, 'close']
kdata.hfq_high = df.loc[time_str, 'high']
kdata.hfq_low = df.loc[time_str, 'low']
kdata.factor = df.loc[time_str, 'factor']
self.session.add_all(kdatas)
self.session.commit()
latest_factor = df.factor[-1]
# factor not change yet, no need to reset the qfq past
if latest_factor == self.current_factors.get(entity.id):
sql = 'UPDATE {} SET qfq_close=hfq_close/{},qfq_high=hfq_high/{}, qfq_open= hfq_open/{}, qfq_low= hfq_low/{} where ' \
'entity_id=\'{}\' and level=\'{}\' and (qfq_close isnull or qfq_high isnull or qfq_low isnull or qfq_open isnull)'.format(
self.data_schema.__table__, latest_factor, latest_factor, latest_factor, latest_factor,
entity.id, self.level.value)
else:
sql = 'UPDATE {} SET qfq_close=hfq_close/{},qfq_high=hfq_high/{}, qfq_open= hfq_open/{}, qfq_low= hfq_low/{} where ' \
'entity_id=\'{}\' and level=\'{}\''.format(self.data_schema.__table__, latest_factor,
latest_factor, latest_factor, latest_factor,
entity.id,
self.level.value)
self.logger.info(sql)
self.session.execute(sql)
self.session.commit()
def record(self, entity, start, end, size, timestamps):
# 只要前复权数据
if not self.end_timestamp:
df = get_bars(to_jq_entity_id(entity),
count=size,
unit=self.jq_trading_level,
fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'],
fq_ref_date=to_time_str(now_pd_timestamp()),
include_now=True)
else:
end_timestamp = to_time_str(self.end_timestamp)
df = get_bars(to_jq_entity_id(entity),
count=size,
unit=self.jq_trading_level,
fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'],
end_dt=end_timestamp,
fq_ref_date=to_time_str(now_pd_timestamp()),
include_now=False)
if pd_is_not_null(df):
df['name'] = entity.name
df.rename(columns={'money': 'turnover', 'date': 'timestamp'}, inplace=True)
df['entity_id'] = entity.id
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = 'joinquant'
df['level'] = self.level.value
df['code'] = entity.code
# 判断是否需要重新计算之前保存的前复权数据
check_df = df.head(1)
check_date = check_df['timestamp'][0]
current_df = get_kdata(entity_id=entity.id, provider=self.provider, start_timestamp=check_date,
end_timestamp=check_date, limit=1, level=self.level)
if pd_is_not_null(current_df):
old = current_df.iloc[0, :]['close']
new = check_df['close'][0]
# 相同时间的close不同，表明前复权需要重新计算
if round(old, 2) != round(new, 2):
self.factor = new / old
self.last_timestamp = pd.Timestamp(check_date)
def generate_kdata_id(se):
if self.level >= IntervalLevel.LEVEL_1DAY:
return "{}_{}".format(se['entity_id'], to_time_str(se['timestamp'], fmt=TIME_FORMAT_DAY))
else:
return "{}_{}".format(se['entity_id'], to_time_str(se['timestamp'], fmt=TIME_FORMAT_ISO8601))
df['id'] = df[['entity_id', 'timestamp']].apply(generate_kdata_id, axis=1)
df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)
return None
def record(self, entity, start, end, size, timestamps):
if self.start_timestamp:
start = max(self.start_timestamp, to_pd_timestamp(start))
# if self.level < IntervalLevel.LEVEL_1HOUR:
# start = '2019-01-01'
end = now_pd_timestamp()
start_timestamp = to_time_str(start)
# 聚宽get_price函数必须指定结束时间，否则会有未来数据
end_timestamp = to_time_str(end, fmt=TIME_FORMAT_MINUTE2)
# 不复权
df = get_price(
to_jq_entity_id(entity),
start_date=to_time_str(start_timestamp),
end_date=end_timestamp,
frequency=self.jq_trading_level,
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
skip_paused=True,
fq=None)
if df_is_not_null(df):
df.index.name = 'timestamp'
df.reset_index(inplace=True)
df['name'] = entity.name
df.rename(columns={'money': 'turnover'}, inplace=True)
df['entity_id'] = entity.id
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = 'joinquant'
df['level'] = self.level.value
df['code'] = entity.code
def generate_kdata_id(se):
if self.level >= IntervalLevel.LEVEL_1DAY:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_DAY))
else:
return "{}_{}".format(
se['entity_id'],
to_time_str(se['timestamp'], fmt=TIME_FORMAT_ISO8601))
df['id'] = df[['entity_id', 'timestamp']].apply(generate_kdata_id,
axis=1)
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force=self.force_update)
return None
def record(self, entity, start, end, size, timestamps):
if self.start_timestamp:
start = max(self.start_timestamp, to_pd_timestamp(start))
start_timestamp = to_time_str(start)
ccxt_exchange = CCXTAccount.get_ccxt_exchange(entity.exchange)
if ccxt_exchange.has['fetchOHLCV']:
limit = CCXTAccount.get_kdata_limit(entity.exchange)
limit = min(size, limit)
kdata_list = []
if CCXTAccount.exchange_conf[entity.exchange]['support_since']:
kdatas = ccxt_exchange.fetch_ohlcv(
entity.code,
timeframe=self.ccxt_trading_level,
since=start_timestamp)
else:
kdatas = ccxt_exchange.fetch_ohlcv(
entity.code,
timeframe=self.ccxt_trading_level,
limit=limit)
# always ignore the latest one,because it's not finished
for kdata in kdatas[0:-1]:
current_timestamp = kdata[0]
if self.level == IntervalLevel.LEVEL_1DAY:
current_timestamp = to_time_str(current_timestamp)
kdata_json = {
'timestamp': to_pd_timestamp(current_timestamp),
'open': kdata[1],
'high': kdata[2],
'low': kdata[3],
'close': kdata[4],
'volume': kdata[5],
'name': entity.name,
'provider': 'ccxt',
'level': self.level.value
}
kdata_list.append(kdata_json)
return kdata_list
else:
self.logger.warning("exchange:{} not support fetchOHLCV".format(
entity.exchange))
def test_china_stock_reader():
data_reader = DataReader(codes=['002572', '000338'], data_schema=Stock1dKdata, provider='joinquant',
start_timestamp='2019-01-01',
end_timestamp='2019-06-10')
categories = data_reader.get_categories()
df = data_reader.get_data_df()
assert 'stock_sz_002572' in categories
assert 'stock_sz_000338' in categories
assert ('stock_sz_002572', '2019-01-02') in df.index
assert ('stock_sz_000338', '2019-01-02') in df.index
assert ('stock_sz_002572', '2019-06-10') in df.index
assert ('stock_sz_000338', '2019-06-10') in df.index
for timestamp in iterate_timestamps(entity_type='stock', exchange='sz',
level=IntervalLevel.LEVEL_1DAY,
start_timestamp='2019-06-11',
end_timestamp='2019-06-14'):
data_reader.move_on(to_timestamp=timestamp, timeout=0)
df = data_reader.get_data_df()
assert ('stock_sz_002572', timestamp) in df.index
assert ('stock_sz_000338', to_time_str(timestamp)) in df.index
data_reader.data_drawer().draw_table()
data_reader.data_drawer().draw_kline()
def select_by_finance(timestamp=now_pd_timestamp(), entity_ids=None):
if timestamp.dayofweek in (5, 6):
logger.info(f'today:{timestamp} is {timestamp.day_name()},just ignore')
today = to_time_str(timestamp)
my_selector = TargetSelector(start_timestamp='2015-01-01',
end_timestamp=today,
entity_ids=entity_ids)
# add the factors
good_factor1 = GoodCompanyFactor(start_timestamp='2015-01-01',
end_timestamp=today,
entity_ids=entity_ids)
good_factor2 = GoodCompanyFactor(
start_timestamp='2015-01-01',
end_timestamp=today,
entity_ids=entity_ids,
data_schema=CashFlowStatement,
columns=[
CashFlowStatement.report_period,
CashFlowStatement.net_op_cash_flows
],
filters=[CashFlowStatement.net_op_cash_flows > 0],
col_threshold={'net_op_cash_flows': 100000000})
my_selector.add_filter_factor(good_factor1)
my_selector.add_filter_factor(good_factor2)
my_selector.run()
long_targets = my_selector.get_open_long_targets(today)
logger.info(f'selected:{len(long_targets)}')
return long_targets
def generate_request_param(self, security_item, start, end, size,
timestamp):
return {
"color": "w",
"fc": get_fc(security_item),
"BaoGaoQi": to_time_str(timestamp)
}
def record(self, entity, start, end, size, timestamps):
q = query(valuation).filter(valuation.code == to_jq_entity_id(entity))
count: pd.Timedelta = now_pd_timestamp() - start
df = get_fundamentals_continuously(q,
end_date=now_time_str(),
count=count.days + 1,
panel=False)
df['entity_id'] = entity.id
df['timestamp'] = pd.to_datetime(df['day'])
df['code'] = entity.code
df['name'] = entity.name
df['id'] = df['timestamp'].apply(
lambda x: "{}_{}".format(entity.id, to_time_str(x)))
df = df.rename(
{
'pe_ratio_lyr': 'pe',
'pe_ratio': 'pe_ttm',
'pb_ratio': 'pb',
'ps_ratio': 'ps',
'pcf_ratio': 'pcf'
},
axis='columns')
df['market_cap'] = df['market_cap'] * 100000000
df['circulating_cap'] = df['circulating_cap'] * 100000000
df['capitalization'] = df['capitalization'] * 10000
df['circulating_cap'] = df['circulating_cap'] * 10000
df_to_db(df=df,
data_schema=self.data_schema,
provider=self.provider,
force_update=self.force_update)
return None
def record(self, entity, start, end, size, timestamps):
jq_code = code_map_jq.get(entity.code)
q = query(finance.STK_EXCHANGE_TRADE_INFO).filter(
finance.STK_EXCHANGE_TRADE_INFO.exchange_code == jq_code,
finance.STK_EXCHANGE_TRADE_INFO.date >= to_time_str(start)).limit(2000)
df = finance.run_query(q)
print(df)
json_results = []
for item in df.to_dict(orient='records'):
result = {
'provider': self.provider,
'timestamp': item['date'],
'name': entity.name,
'pe': item['pe_average'],
'total_value': multiple_number(item['total_market_cap'], 100000000),
'total_tradable_vaule': multiple_number(item['circulating_market_cap'], 100000000),
'volume': multiple_number(item['volume'], 10000),
'turnover': multiple_number(item['money'], 100000000),
'turnover_rate': item['turnover_ratio']
}
json_results.append(result)
if len(json_results) < 100:
self.one_shot = True
return json_results
def generate_request_param(self, security_item, start, end, size, timestamps):
if len(timestamps) <= 10:
param = {
"color": "w",
"fc": get_fc(security_item),
"corpType": company_type_flag(security_item),
# 0 means get all types
"reportDateType": 0,
"endDate": '',
"latestCount": size
}
else:
param = {
"color": "w",
"fc": get_fc(security_item),
"corpType": company_type_flag(security_item),
# 0 means get all types
"reportDateType": 0,
"endDate": to_time_str(timestamps[10]),
"latestCount": 10
}
if self.finance_report_type == 'LiRunBiaoList' or self.finance_report_type == 'XianJinLiuLiangBiaoList':
param['reportType'] = 1
return param
def on_trading_open(self, timestamp):
self.logger.info('on_trading_open:{}'.format(timestamp))
if is_same_date(timestamp, self.start_timestamp):
return
# get the account for trading at the date
accounts = get_account(session=self.session,
trader_name=self.trader_name,
return_type='domain',
end_timestamp=to_time_str(timestamp),
limit=1,
order=SimAccount.timestamp.desc())
if accounts:
account = accounts[0]
else:
return
positions = []
# FIXME:dump all directly
for position_domain in account.positions:
position_dict = position_schema.dump(position_domain).data
self.logger.info('current position:{}'.format(position_dict))
del position_dict['sim_account']
positions.append(position_dict)
self.latest_account = sim_account_schema.dump(account).data
self.latest_account['positions'] = positions
self.logger.info('on_trading_open:{},latest_account:{}'.format(
timestamp, self.latest_account))
def record(self, entity, start, end, size, timestamps):
q = query(finance.STK_ML_QUOTA).filter(
finance.STK_ML_QUOTA.link_id == entity.code,
finance.STK_ML_QUOTA.day >= to_time_str(start)).limit(2000)
df = finance.run_query(q)
print(df)
json_results = []
for item in df.to_dict(orient='records'):
result = {
'provider': self.provider,
'timestamp': item['day'],
'name': entity.name,
'buy_amount': multiple_number(item['buy_amount'], 100000000),
'buy_volume': item['buy_volume'],
'sell_amount': multiple_number(item['sell_amount'], 100000000),
'sell_volume': item['sell_volume'],
'quota_daily': multiple_number(item['quota_daily'], 100000000),
'quota_daily_balance': multiple_number(item['quota_daily_balance'], 100000000)
}
json_results.append(result)
if len(json_results) < 100:
self.one_shot = True
return json_results
def load_data(self):
if self.entity_ids:
self.data_df = get_data(data_schema=self.data_schema, entity_ids=self.entity_ids,
provider=self.provider, columns=self.columns,
start_timestamp=self.start_timestamp,
end_timestamp=self.end_timestamp, filters=self.filters, order=self.order,
limit=self.limit,
level=self.level,
time_field=self.time_field,
index=self.time_field)
else:
self.data_df = get_data(data_schema=self.data_schema, codes=self.codes,
provider=self.provider, columns=self.columns,
start_timestamp=self.start_timestamp,
end_timestamp=self.end_timestamp, filters=self.filters, order=self.order,
limit=self.limit,
level=self.level,
time_field=self.time_field,
index=self.time_field)
if self.trip_timestamp:
if self.level == IntervalLevel.LEVEL_1DAY:
self.data_df[self.time_field] = self.data_df[self.time_field].apply(
lambda x: to_pd_timestamp(to_time_str(x)))
if df_is_not_null(self.data_df):
self.normal_data = NormalData(df=self.data_df, category_field=self.category_field,
index_field=self.time_field, is_timeseries=True)
self.data_df = self.normal_data.data_df
for listener in self.data_listeners:
listener.on_data_loaded(self.data_df)
def record(self, entity, start, end, size, timestamps):
start = to_time_str(start, fmt=TIME_FORMAT_DAY1)
end = now_time_str(fmt=TIME_FORMAT_DAY1)
if entity.exchange == 'sh':
exchange_flag = 0
else:
exchange_flag = 1
url = self.url.format(exchange_flag, entity.code, start, end)
response = requests.get(url=url)
df = read_csv(io.BytesIO(response.content), encoding='GB2312', na_values='None')
if df is None:
return []
df['name'] = entity.name
# 指数数据
if entity.entity_type == 'index':
df = df.loc[:,
['日期', 'name', '最低价', '开盘价', '收盘价', '最高价', '成交量', '成交金额', '涨跌幅']]
df.columns = ['timestamp', 'name', 'low', 'open', 'close', 'high', 'volume', 'turnover', 'change_pct']
# 股票数据
else:
df = df.loc[:,
['日期', 'name', '最低价', '开盘价', '收盘价', '最高价', '成交量', '成交金额', '涨跌幅', '换手率']]
df.columns = ['timestamp', 'name', 'low', 'open', 'close', 'high', 'volume', 'turnover', 'change_pct',
'turnover_rate']
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = 'netease'
df['level'] = self.level.value
return df.to_dict(orient='records')
def test_china_stock_reader():
data_reader = DataReader(codes=['002572', '000338'],
data_schema=Stock1dKdata,
entity_schema=Stock,
start_timestamp='2019-01-01',
end_timestamp='2019-06-10',
entity_provider='eastmoney')
categories = data_reader.data_df.index.levels[0].to_list()
df = data_reader.data_df
assert 'stock_sz_002572' in categories
assert 'stock_sz_000338' in categories
assert ('stock_sz_002572', '2019-01-02') in df.index
assert ('stock_sz_000338', '2019-01-02') in df.index
assert ('stock_sz_002572', '2019-06-10') in df.index
assert ('stock_sz_000338', '2019-06-10') in df.index
for timestamp in iterate_timestamps(entity_type='stock',
exchange='sz',
level=IntervalLevel.LEVEL_1DAY,
start_timestamp='2019-06-11',
end_timestamp='2019-06-14'):
data_reader.move_on(to_timestamp=timestamp)
df = data_reader.data_df
assert ('stock_sz_002572', timestamp) in df.index
assert ('stock_sz_000338', to_time_str(timestamp)) in df.index
def record(self, entity, start, end, size, timestamps):
jq_code = code_map_jq.get(entity.code)
q = query(finance.STK_MT_TOTAL).filter(
finance.STK_MT_TOTAL.exchange_code == jq_code,
finance.STK_MT_TOTAL.date >= to_time_str(start)).limit(2000)
df = finance.run_query(q)
print(df)
json_results = []
for item in df.to_dict(orient='records'):
result = {
'provider': self.provider,
'timestamp': item['date'],
'name': entity.name,
'margin_value': item['fin_value'],
'margin_buy': item['fin_buy_value'],
'short_value': item['sec_value'],
'short_volume': item['sec_sell_volume'],
'total_value': item['fin_sec_value']
}
json_results.append(result)
if len(json_results) < 100:
self.one_shot = True
return json_results
def generate_request_param(self, security_item, start, end, size, timestamp):
return {
'security_item': security_item,
'start': to_time_str(start, fmt=TIME_FORMAT_DAY1),
'end': now_time_str(fmt=TIME_FORMAT_DAY1),
'level': self.level.value
}
def test_000778_cash_flow_statement():
correct_timestamps = ['2018-09-30', '2018-06-30', '2018-03-31', '2017-12-31', '2017-09-30', '2017-06-30',
'2017-03-31', '2016-12-31', '2016-09-30', '2016-06-30', '2016-03-31', '2015-12-31',
'2015-09-30', '2015-06-30', '2015-03-31', '2014-12-31', '2014-09-30', '2014-06-30',
'2014-03-31', '2013-12-31', '2013-09-30', '2013-06-30', '2013-03-31', '2012-12-31',
'2012-09-30', '2012-06-30', '2012-03-31', '2011-12-31', '2011-09-30', '2011-06-30',
'2011-03-31', '2010-12-31', '2010-09-30', '2010-06-30', '2010-03-31', '2009-12-31',
'2009-09-30', '2009-06-30', '2009-03-31', '2008-12-31', '2008-09-30', '2008-06-30',
'2008-03-31', '2007-12-31', '2007-09-30', '2007-06-30', '2007-03-31', '2006-12-31',
'2006-09-30', '2006-06-30', '2006-03-31', '2005-12-31', '2005-09-30', '2005-06-30',
'2005-03-31', '2004-12-31', '2004-09-30', '2004-06-30', '2004-03-31', '2003-12-31',
'2003-09-30', '2003-06-30', '2003-03-31', '2002-12-31', '2002-06-30', '2001-12-31',
'2001-06-30', '2000-12-31', '2000-06-30', '1999-12-31', '1998-12-31', '1998-06-30']
result = get_cash_flow_statement(session=session, provider='eastmoney', return_type='domain',
codes=['000778'], end_timestamp='2018-12-30',
order=CashFlowStatement.report_date.desc(), time_field='report_date')
assert len(correct_timestamps) == len(result)
timestamps = [to_time_str(item.report_date) for item in result]
assert set(correct_timestamps) == set(timestamps)
latest: CashFlowStatement = result[0]
assert latest.cash_from_selling == 27784000000
assert latest.tax_refund == 60700000
assert latest.cash_from_other_op == 1463000000
assert latest.total_op_cash_inflows == 29310000000
assert latest.cash_to_goods_services == 21210000000
assert latest.cash_to_employees == 1460000000
assert latest.taxes_and_surcharges == 2016000000
assert latest.cash_to_other_related_op == 573700000
assert latest.total_op_cash_outflows == 25260000000
assert latest.net_op_cash_flows == 4050000000
assert latest.cash_from_disposal_of_investments == 556500000
assert latest.cash_from_returns_on_investments == 44180000
assert latest.cash_from_disposal_fixed_intangible_assets == 457200
assert latest.cash_from_disposal_subsidiaries == 1046000000
assert latest.cash_from_other_investing == 553000000
assert latest.total_investing_cash_inflows == 2201000000
assert latest.cash_to_acquire_fixed_intangible_assets == 2521000000
assert latest.cash_to_investments == 1808000000
assert latest.total_investing_cash_outflows == 4329000000
assert latest.net_investing_cash_flows == -2128000000
assert latest.cash_from_accepting_investment == 24500000
assert latest.cash_from_subsidiaries_accepting_minority_interest == 24500000
assert latest.cash_from_borrowings == 10080000000
assert latest.cash_from_issuing_bonds == 997000000
assert latest.cash_from_other_financing == 200000000
assert latest.total_financing_cash_inflows == 11300000000
assert latest.cash_to_repay_borrowings == 11940000000
assert latest.cash_to_pay_interest_dividend == 892100000
assert latest.cash_to_other_financing == 328500000
assert latest.total_financing_cash_outflows == 13160000000
assert latest.net_financing_cash_flows == -1862000000
assert latest.foreign_exchange_rate_effect == 21350000
assert latest.net_cash_increase == 81240000
assert latest.cash_at_beginning == 5078000000
assert latest.cash == 5159000000
def marshal_object_for_ui(object):
if isinstance(object, Enum):
return object.value
if isinstance(object, pd.Timestamp):
return to_time_str(object)
return object
def marshal_data_for_ui(cls, data):
if isinstance(data, Enum):
return data.value
if isinstance(data, pd.Timestamp):
return to_time_str(data)
return data
def china_stock_finished_timestamp(timestamp: pd.Timestamp, level: IntervalLevel):
timestamp = to_pd_timestamp(timestamp)
if timestamp.microsecond != 0:
return False
return to_time_str(timestamp, fmt=TIME_FORMAT_MINUTE1) in china_stock_level_map_finished_timestamps.get(
level.value)
def record(self, entity, start, end, size, timestamps):
df = pd.DataFrame()
dates = get_trade_days(start_date=start)
df['timestamp'] = pd.to_datetime(dates)
df['id'] = [to_time_str(date) for date in dates]
df['entity_id'] = 'stock_sz_000001'
df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update)
def report_core_company():
while True:
error_count = 0
email_action = EmailInformer()
try:
# StockTradeDay.record_data(provider='joinquant')
# Stock.record_data(provider='joinquant')
# FinanceFactor.record_data(provider='eastmoney')
# BalanceSheet.record_data(provider='eastmoney')
target_date = to_time_str(now_pd_timestamp())
my_selector: TargetSelector = FundamentalSelector(
start_timestamp='2015-01-01', end_timestamp=target_date)
my_selector.run()
long_targets = my_selector.get_open_long_targets(
timestamp=target_date)
if long_targets:
stocks = get_entities(provider='joinquant',
entity_schema=Stock,
entity_ids=long_targets,
return_type='domain')
# add them to eastmoney
try:
try:
eastmoneypy.del_group('core')
except:
pass
eastmoneypy.create_group('core')
for stock in stocks:
eastmoneypy.add_to_group(stock.code, group_name='core')
except Exception as e:
email_action.send_message(
"*****@*****.**", f'report_core_company error',
'report_core_company error:{}'.format(e))
info = [f'{stock.name}({stock.code})' for stock in stocks]
msg = ' '.join(info)
else:
msg = 'no targets'
logger.info(msg)
email_action.send_message(get_subscriber_emails(),
f'{to_time_str(target_date)} 核心资产选股结果',
msg)
break
except Exception as e:
logger.exception('report_core_company error:{}'.format(e))
time.sleep(60 * 3)
error_count = error_count + 1
if error_count == 10:
email_action.send_message(
"*****@*****.**", f'report_core_company error',
'report_core_company error:{}'.format(e))
def test_000001_finance_factor():
correct_timestamps = ['2018-09-30', '2018-06-30', '2018-03-31', '2017-12-31', '2017-09-30', '2017-06-30',
'2017-03-31', '2016-12-31', '2016-09-30', '2016-06-30', '2016-03-31', '2015-12-31',
'2015-09-30', '2015-06-30', '2015-03-31', '2014-12-31', '2014-09-30', '2014-06-30',
'2014-03-31', '2013-12-31', '2013-09-30', '2013-06-30', '2013-03-31', '2012-12-31',
'2012-09-30', '2012-06-30', '2012-03-31', '2011-12-31', '2011-09-30', '2011-06-30',
'2011-03-31', '2010-12-31', '2010-09-30', '2010-06-30', '2010-03-31', '2009-12-31',
'2009-09-30', '2009-06-30', '2009-03-31', '2008-12-31', '2008-09-30', '2008-06-30',
'2008-03-31', '2007-12-31', '2007-09-30', '2007-06-30', '2007-03-31', '2006-12-31',
'2006-09-30', '2006-06-30', '2006-03-31', '2005-12-31', '2005-09-30', '2005-06-30',
'2005-03-31', '2004-12-31', '2004-09-30', '2004-06-30', '2004-03-31', '2003-12-31',
'2003-09-30', '2003-06-30', '2003-03-31', '2002-12-31', '2002-09-30', '2002-06-30',
'2002-03-31', '2001-12-31', '2001-09-30', '2001-06-30', '2001-03-31', '2000-12-31',
'2000-06-30', '1999-12-31', '1999-06-30', '1998-12-31', '1998-06-30', '1997-12-31',
'1997-06-30', '1996-12-31', '1996-06-30', '1995-12-31', '1995-06-30', '1994-12-31',
'1994-06-30', '1993-12-31', '1993-06-30', '1992-12-31', '1991-12-31', '1990-12-31',
'1989-12-31']
result = get_finance_factor(session=session, provider='eastmoney', return_type='domain',
codes=['000001'], end_timestamp='2018-12-30',
order=FinanceFactor.report_date.desc(), time_field='report_date')
assert len(correct_timestamps) == len(result)
timestamps = [to_time_str(item.report_date) for item in result]
assert set(correct_timestamps) == set(timestamps)
latest: FinanceFactor = result[0]
assert latest.basic_eps == 1.14
assert latest.deducted_eps == 1.13
assert latest.diluted_eps == 1.14
assert latest.bps == 12.538
assert latest.capital_reserve_ps == 3.2886
assert latest.undistributed_profit_ps == 5.3566
assert latest.op_cash_flow_ps == -0.6587
assert latest.total_op_income == 86660000000
assert latest.net_profit == 20460000000
assert latest.deducted_net_profit == 20350000000
assert latest.op_income_growth_yoy == 0.0856
assert latest.net_profit_growth_yoy == 0.068
assert latest.deducted_net_profit_growth_yoy == 0.0636
assert latest.op_income_growth_qoq == 0.0336
assert latest.net_profit_growth_qoq == 0.0202
assert latest.deducted_net_profit_growth_qoq == 0.0168
assert latest.roe == 0.0948
assert latest.deducted_roe == 0.0943
assert latest.rota == 0.0062
assert latest.net_margin == 0.2360
assert latest.debt_asset_ratio == 0.9298
assert latest.em == 14.25
assert latest.equity_ratio == 13.25
assert latest.fi_total_deposit == 2130000000000
assert latest.fi_total_loan == 1920000000000
assert latest.fi_loan_deposit_ratio == 0.9004
assert latest.fi_npl_ratio == 0.0168
assert latest.fi_npl_provision_coverage == 1.6914
def test_000778_income_statement():
correct_timestamps = ['2018-09-30', '2018-06-30', '2018-03-31', '2017-12-31', '2017-09-30', '2017-06-30',
'2017-03-31', '2016-12-31', '2016-09-30', '2016-06-30', '2016-03-31', '2015-12-31',
'2015-09-30', '2015-06-30', '2015-03-31', '2014-12-31', '2014-09-30', '2014-06-30',
'2014-03-31', '2013-12-31', '2013-09-30', '2013-06-30', '2013-03-31', '2012-12-31',
'2012-09-30', '2012-06-30', '2012-03-31', '2011-12-31', '2011-09-30', '2011-06-30',
'2011-03-31', '2010-12-31', '2010-09-30', '2010-06-30', '2010-03-31', '2009-12-31',
'2009-09-30', '2009-06-30', '2009-03-31', '2008-12-31', '2008-09-30', '2008-06-30',
'2008-03-31', '2007-12-31', '2007-09-30', '2007-06-30', '2007-03-31', '2006-12-31',
'2006-09-30', '2006-06-30', '2006-03-31', '2005-12-31', '2005-09-30', '2005-06-30',
'2005-03-31', '2004-12-31', '2004-09-30', '2004-06-30', '2004-03-31', '2003-12-31',
'2003-09-30', '2003-06-30', '2003-03-31', '2002-12-31', '2002-09-30', '2002-06-30',
'2002-03-31', '2001-12-31', '2001-06-30', '2000-12-31', '2000-06-30', '1999-12-31',
'1999-06-30', '1998-12-31', '1998-06-30', '1997-12-31', '1997-06-30', '1996-12-31',
'1995-12-31', '1994-12-31']
result = get_income_statement(session=session, provider='eastmoney', return_type='domain',
codes=['000778'], end_timestamp='2018-12-30',
order=IncomeStatement.report_date.desc(), time_field='report_date')
assert len(correct_timestamps) == len(result)
timestamps = [to_time_str(item.report_date) for item in result]
assert set(correct_timestamps) == set(timestamps)
latest: IncomeStatement = result[0]
assert latest.operating_income == 31710000000
assert latest.total_operating_costs == 29230000000
assert latest.operating_costs == 26220000000
assert latest.rd_costs == 185500000
assert latest.net_change_in_insurance_contract_reserves == 0
assert latest.business_taxes_and_surcharges == 359700000
assert latest.sales_costs == 771400000
assert latest.managing_costs == 472900000
assert latest.financing_costs == 397500000
assert latest.assets_devaluation == 824400000
assert latest.investment_income == 104100000
assert latest.investment_income_from_related_enterprise == 61290000
assert latest.operating_profit == 2637000000
assert latest.non_operating_income == 38340000
assert latest.non_operating_costs == 221700000
assert latest.total_profits == 2454000000
assert latest.tax_expense == 579600000
assert latest.net_profit == 1874000000
assert latest.net_profit_as_parent == 1811000000
assert latest.net_profit_as_minority_interest == 63570000
assert latest.deducted_net_profit == 1897000000
assert latest.eps == 0.4537
assert latest.diluted_eps == 0.4537
assert latest.other_comprehensive_income == -521000000
assert latest.other_comprehensive_income_as_parent == -522400000
assert latest.other_comprehensive_income_as_minority_interest == 1403000
assert latest.total_comprehensive_income == 1353000000
assert latest.total_comprehensive_income_as_parent == 1288000000
assert latest.total_comprehensive_income_as_minority_interest == 64980000
