def get_top_performance_entities(
entity_type="stock",
start_timestamp=None,
end_timestamp=None,
pct=0.1,
return_type=None,
adjust_type: Union[AdjustType, str] = None,
entity_filters=None,
kdata_filters=None,
show_name=False,
list_days=None,
entity_provider=None,
data_provider=None,
):
if not adjust_type:
adjust_type = default_adjust_type(entity_type=entity_type)
data_schema = get_kdata_schema(entity_type=entity_type,
adjust_type=adjust_type)
if not entity_filters:
entity_filters = []
if list_days:
entity_schema = get_entity_schema(entity_type=entity_type)
list_date = next_date(start_timestamp, -list_days)
entity_filters += [entity_schema.list_date <= list_date]
filter_entities = get_entity_ids(
provider=entity_provider,
entity_type=entity_type,
filters=entity_filters,
)
if not filter_entities:
logger.warning(f"no entities selected")
return None, None
if not kdata_filters:
kdata_filters = []
kdata_filters = kdata_filters + [
data_schema.entity_id.in_(filter_entities)
]
return get_top_entities(
data_schema=data_schema,
start_timestamp=start_timestamp,
end_timestamp=end_timestamp,
column="close",
pct=pct,
method=WindowMethod.change,
return_type=return_type,
kdata_filters=kdata_filters,
show_name=show_name,
data_provider=data_provider,
)
def get_top_performance_entities(
entity_type="stock",
start_timestamp=None,
end_timestamp=None,
pct=0.1,
return_type=None,
adjust_type: Union[AdjustType, str] = None,
filters=None,
show_name=False,
list_days=None,
entity_provider=None,
data_provider=None,
):
if not adjust_type:
adjust_type = default_adjust_type(entity_type=entity_type)
data_schema = get_kdata_schema(entity_type=entity_type,
adjust_type=adjust_type)
if list_days:
entity_schema = get_entity_schema(entity_type=entity_type)
list_date = next_date(start_timestamp, -list_days)
ignore_entities = get_entity_ids(
provider=entity_provider,
entity_type=entity_type,
filters=[entity_schema.list_date >= list_date],
)
if ignore_entities:
logger.info(f"ignore size: {len(ignore_entities)}")
logger.info(f"ignore entities: {ignore_entities}")
f = [data_schema.entity_id.notin_(ignore_entities)]
if filters:
filters = filters + f
else:
filters = f
return get_top_entities(
data_schema=data_schema,
start_timestamp=start_timestamp,
end_timestamp=end_timestamp,
column="close",
pct=pct,
method=WindowMethod.change,
return_type=return_type,
filters=filters,
show_name=show_name,
data_provider=data_provider,
)
def get_entity_ids_by_filter(provider="em",
ignore_st=True,
ignore_new_stock=True,
target_date=None,
entity_schema=Stock,
entity_ids=None):
filters = []
if ignore_new_stock:
if not target_date:
target_date = current_date()
pre_year = next_date(target_date, -365)
filters += [entity_schema.timestamp <= pre_year]
if ignore_st:
filters += [
entity_schema.name.not_like("%退%"),
entity_schema.name.not_like("%ST%"),
entity_schema.name.not_like("%*ST%"),
]
return get_entity_ids(provider=provider,
entity_schema=entity_schema,
filters=filters,
entity_ids=entity_ids)
def get_top_performance_entities(entity_type='stock', start_timestamp=None, end_timestamp=None, pct=0.1,
return_type=None, adjust_type: Union[AdjustType, str] = None, filters=None,
show_name=False, list_days=None):
if not adjust_type and entity_type == 'stock':
adjust_type = AdjustType.hfq
data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type)
if list_days:
entity_schema = get_entity_schema(entity_type=entity_type)
list_date = next_date(start_timestamp, -list_days)
ignore_entities = get_entity_ids(entity_type=entity_type, filters=[entity_schema.list_date >= list_date])
if ignore_entities:
logger.info(f'ignore size: {len(ignore_entities)}')
logger.info(f'ignore entities: {ignore_entities}')
f = [data_schema.entity_id.notin_(ignore_entities)]
if filters:
filters = filters + f
else:
filters = f
return get_top_entities(data_schema=data_schema, start_timestamp=start_timestamp, end_timestamp=end_timestamp,
column='close', pct=pct, method=WindowMethod.change, return_type=return_type,
filters=filters, show_name=show_name)
def report_top_stats(periods=[7, 30, 180, 365], ignore_new_stock=True):
latest_day: Stock1dHfqKdata = Stock1dHfqKdata.query_data(order=Stock1dHfqKdata.timestamp.desc(), limit=1,
return_type='domain')
current_timestamp = latest_day[0].timestamp
email_action = EmailInformer()
# 至少上市一年
filters = None
if ignore_new_stock:
pre_year = next_date(current_timestamp, -365)
stocks = get_entity_ids(provider='joinquant', entity_schema=Stock, filters=[Stock.timestamp <= pre_year])
filters = [Stock1dHfqKdata.entity_id.in_(stocks)]
stats = []
ups = []
downs = []
msg = ''
for period in periods:
start = next_date(current_timestamp, -period)
df, _ = get_top_performance_entities(start_timestamp=start, filters=filters, pct=1, show_name=True)
df.rename(columns={'score': f'score_{period}'}, inplace=True)
ups.append(tabulate(df.iloc[:50], headers='keys'))
downs.append(tabulate(df.iloc[:-100], headers='keys'))
stats.append(tabulate(df.describe(), headers='keys'))
# 最近一个月最靓仔的
if period == 30:
# add them to eastmoney
try:
try:
eastmoneypy.del_group('最靓仔')
except:
pass
eastmoneypy.create_group('最靓仔')
for entity_id in df.index[:50]:
_, _, code = decode_entity_id(entity_id)
eastmoneypy.add_to_group(code=code, group_name='最靓仔')
except Exception as e:
logger.exception(e)
email_action.send_message("*****@*****.**", f'report_top_stats error',
'report_top_stats error:{}'.format(e))
# 一年内没怎么动的
if period == 365:
stable_df = df[(df['score_365'] > -0.1) & (df['score_365'] < 0.1)]
vol_df = get_top_volume_entities(entity_ids=stable_df.index.tolist(), start_timestamp=start)
# add them to eastmoney
try:
try:
eastmoneypy.del_group('躺尸一年')
except:
pass
eastmoneypy.create_group('躺尸一年')
for entity_id in vol_df.index[:50]:
_, _, code = decode_entity_id(entity_id)
eastmoneypy.add_to_group(code=code, group_name='躺尸一年')
except Exception as e:
logger.exception(e)
email_action.send_message("*****@*****.**", f'report_top_stats error',
'report_top_stats error:{}'.format(e))
for s in stats:
msg = msg + s + '\n'
for up in ups:
msg = msg + up + '\n'
for down in downs:
msg = msg + down + '\n'
email_action.send_message('*****@*****.**', f'{current_timestamp} 统计报告', msg)
StockActorSummary.actor_type == ActorType.raised_fund.value,
StockActorSummary.holding_ratio >= fund_ratio_nums
],
columns=['entity_id', 'holding_ratio', 'actor_type'],
)
# fund_cap_df = FundStock.query_data(filters=[FundStock.report_date >= report_date, FundStock.timestamp <= current_timestamp, FundStock.proportion >= 0.01],
# columns=['stock_id', 'market_cap', 'proportion'])
top_holding_stocks = fund_cap_df['entity_id'].tolist()
# 至少上市
least_start_day_nums = 60
filters = None
pre_date = next_date(current_timestamp, -least_start_day_nums)
stocks = get_entity_ids(provider='joinquant',
entity_schema=Stock,
filters=[Stock.timestamp <= pre_date])
filters = [Stock1dHfqKdata.entity_id.in_(stocks)]
# 任一rps 大于90
top_rps_stocks = []
rps_rank_limit = 1000
periods = [50, 120, 250]
for period in periods:
start = next_date(current_timestamp, -period)
updf, _ = get_top_performance_entities(start_timestamp=start,
filters=filters,
pct=1,
show_name=True)
top_rps_stocks.extend(updf.iloc[:rps_rank_limit].index.tolist())
def report_top_stats(
entity_provider,
data_provider,
periods=[7, 30, 180, 365],
ignore_new_stock=True,
entity_type="stock",
adjust_type=None,
top_count=30,
turnover_threshold=100000000,
turnover_rate_threshold=0.02,
em_group_over_write=True,
):
if not adjust_type:
adjust_type = default_adjust_type(entity_type=entity_type)
kdata_schema = get_kdata_schema(entity_type=entity_type,
adjust_type=adjust_type)
entity_schema = get_entity_schema(entity_type=entity_type)
latest_day = kdata_schema.query_data(provider=data_provider,
order=kdata_schema.timestamp.desc(),
limit=1,
return_type="domain")
current_timestamp = latest_day[0].timestamp
email_action = EmailInformer()
# 至少上市一年
filter_entity_ids = []
if ignore_new_stock:
pre_year = next_date(current_timestamp, -365)
entity_ids = get_entity_ids(
provider=entity_provider,
entity_schema=entity_schema,
filters=[entity_schema.timestamp <= pre_year])
if not entity_ids:
msg = f"{entity_type} no entity_ids listed one year"
logger.error(msg)
email_action.send_message(zvt_config["email_username"],
"report_top_stats error", msg)
return
filter_entity_ids = entity_ids
filter_turnover_df = kdata_schema.query_data(
filters=[
kdata_schema.turnover >= turnover_threshold,
kdata_schema.turnover_rate >= turnover_rate_threshold,
],
provider=data_provider,
start_timestamp=current_timestamp,
index="entity_id",
columns=["entity_id", "code"],
)
if filter_entity_ids:
filter_entity_ids = set(filter_entity_ids) & set(
filter_turnover_df.index.tolist())
else:
filter_entity_ids = filter_turnover_df.index.tolist()
if not filter_entity_ids:
msg = f"{entity_type} no entity_ids selected"
logger.error(msg)
email_action.send_message(zvt_config["email_username"],
"report_top_stats error", msg)
return
logger.info(
f"{entity_type} filter_entity_ids size: {len(filter_entity_ids)}")
filters = [kdata_schema.entity_id.in_(filter_entity_ids)]
stats = []
ups = []
downs = []
for period in periods:
start = next_date(current_timestamp, -period)
df, _ = get_top_performance_entities(
entity_type=entity_type,
start_timestamp=start,
filters=filters,
pct=1,
show_name=True,
entity_provider=entity_provider,
data_provider=data_provider,
)
df.rename(columns={"score": f"score_{period}"}, inplace=True)
ups.append(tabulate(df.iloc[:top_count], headers="keys"))
downs.append(tabulate(df.iloc[-top_count:], headers="keys"))
stats.append(tabulate(df.describe(), headers="keys"))
# 最近一个月和一周最靓仔的
if period == 7 or period == 30:
try:
codes = [
decode_entity_id(entity_id)[2]
for entity_id in df.index[:top_count]
]
add_to_eastmoney(codes=codes,
entity_type=entity_type,
group="最靓仔",
over_write=em_group_over_write)
except Exception as e:
logger.exception(e)
email_action.send_message(
zvt_config["email_username"], f"report_top_stats error",
"report_top_stats error:{}".format(e))
# 一年内跌幅最大的
if period == 365:
try:
codes = [
decode_entity_id(entity_id)[2]
for entity_id in df.index[-top_count:]
]
add_to_eastmoney(codes=codes,
entity_type=entity_type,
group="谁有我惨",
over_write=em_group_over_write)
except Exception as e:
logger.exception(e)
email_action.send_message(
zvt_config["email_username"], f"report_top_stats error",
"report_top_stats error:{}".format(e))
msg = "\n"
for s in stats:
msg = msg + s + "\n"
email_action.send_message(zvt_config["email_username"],
f"{current_timestamp} {entity_type}统计报告", msg)
msg = "\n"
for up in ups:
msg = msg + up + "\n"
email_action.send_message(zvt_config["email_username"],
f"{current_timestamp} {entity_type}涨幅统计报告", msg)
msg = "\n"
for down in downs:
msg = msg + down + "\n"
email_action.send_message(zvt_config["email_username"],
f"{current_timestamp} {entity_type}跌幅统计报告", msg)