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)
def report_top_entities(
entity_provider,
data_provider,
periods=None,
ignore_new_stock=True,
ignore_st=True,
entity_ids=None,
entity_type="stock",
adjust_type=None,
top_count=30,
turnover_threshold=100000000,
turnover_rate_threshold=0.02,
informer: EmailInformer = None,
em_group=None,
em_group_over_write=True,
return_type=TopType.positive,
):
error_count = 0
while error_count <= 10:
try:
if periods is None:
periods = [7, 30, 365]
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)
target_date = get_latest_kdata_date(provider=data_provider,
entity_type=entity_type,
adjust_type=adjust_type)
filter_entity_ids = get_entity_ids_by_filter(
provider=entity_provider,
ignore_st=ignore_st,
ignore_new_stock=ignore_new_stock,
entity_schema=entity_schema,
target_date=target_date,
entity_ids=entity_ids,
)
if not filter_entity_ids:
msg = f"{entity_type} no entity_ids selected"
logger.error(msg)
informer.send_message(zvt_config["email_username"],
"report_top_stats error", msg)
return
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=target_date,
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)
informer.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)]
selected = []
for i, period in enumerate(periods):
interval = period
if target_date.weekday() + 1 < interval:
interval = interval + 2
start = next_date(target_date, -interval)
positive_df, negative_df = get_top_performance_entities(
entity_type=entity_type,
start_timestamp=start,
kdata_filters=filters,
pct=1,
show_name=True,
entity_provider=entity_provider,
data_provider=data_provider,
return_type=return_type,
)
if return_type == TopType.positive:
df = positive_df
else:
df = negative_df
selected = selected + df.index[:top_count].tolist()
selected = list(dict.fromkeys(selected))
inform(
informer,
entity_ids=selected,
target_date=target_date,
title=f"{entity_type} {em_group}({len(selected)})",
entity_provider=entity_provider,
entity_type=entity_type,
em_group=em_group,
em_group_over_write=em_group_over_write,
)
break
except Exception as e:
logger.exception("report error:{}".format(e))
time.sleep(30)
error_count = error_count + 1
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())
top_stocks = list(set(top_holding_stocks) & set(top_rps_stocks))
start_day_nums = 420
if latest_day:
target_date = latest_day[0].timestamp
else:
target_date = now_pd_timestamp()
start_date = target_date - timedelta(start_day_nums)
factor = TSIFactor(entity_schema=Stock,
provider='joinquant',
level=IntervalLevel.LEVEL_1DAY,
entity_ids=top_stocks,
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)