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249 lines (221 loc) · 6.94 KB
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from collections import defaultdict
from typing import Union
import polars as pl
from quantdata import mongo_get_data
dbname = "finance"
# 要排除的概率
exclude_concept = [
"883300.TI", # 沪深300样本股
"883301.TI", # 上证50样本股
"883302.TI", # 上证180成份股
"883303.TI", # 上证380成份股
"883304.TI", # 中证500成份股
"885338.TI", # 融资融券
"885472.TI", # 上海自贸区
"885487.TI", # 天津自贸区
"885514.TI", # 京津冀一体化
"885520.TI", # 沪股通
"885521.TI", # 粤港澳大湾区
"885587.TI", # 举牌
"885591.TI", # 中韩自贸区
"885598.TI", # 新股与次新股
"885617.TI", # 福建自贸区
"885694.TI", # 深股通
"885699.TI", # ST板块
"885701.TI", # 杭州亚运会
"885729.TI", # 参股新三板
"885734.TI", # 广东自贸区
"885739.TI", # 股权转让
"885742.TI", # 摘帽
"885796.TI", # 送转填权
"885849.TI", # 黑龙江自贸区
"885855.TI", # 创业板重组松绑
"885867.TI", # 标普道琼斯A股
"885873.TI", # 分拆上市意愿
"885905.TI", # 注册制次新股
"885906.TI", # 核准制次新股
"885907.TI", # 科创次新股
]
def get_ths_concepts_names():
index_list = mongo_get_data(
dbname,
"basic_info_ths_concepts",
projection={"symbol": 1, "name": 1},
)
return {doc["symbol"]: doc["name"] for doc in index_list}
def get_stocks_of_index(collection_name, index_symbol, dt):
"""
获取截止{dt}时的概念或指数的成分股
"""
df = pl.DataFrame(
mongo_get_data(
dbname,
collection_name,
query={"$and": [{"index_code": index_symbol}, {"tradedate": {"$lte": dt}}]},
)
)
stocks = set()
for row in df.iter_rows(named=True):
if row["op"] == 1:
stocks.add(row["stock_code"])
else:
try:
stocks.remove(row["stock_code"])
except KeyError:
pass
return list(stocks)
def _get_indexes_of_stock(sub_df):
indexes = set()
for row in sub_df.iter_rows(named=True):
if row["op"] == 1:
if row["index_code"] in exclude_concept:
continue
indexes.add(row["index_code"])
else:
try:
indexes.remove(row["index_code"])
except KeyError:
pass
return list(indexes)
def get_indexes_of_stock(collection_name, symbol, dt):
"""
获取截止{dt}时的股票所属的概念板块或指数
"""
df = pl.DataFrame(
mongo_get_data(
dbname,
collection_name,
query={"$and": [{"stock_code": symbol}, {"tradedate": {"$lte": dt}}]},
)
)
return _get_indexes_of_stock(df)
def include_in_ths_concepts(stock_list, dt, concept_codes, logger):
"""
在概念集{concept_codes}中的股票才能被选中
"""
if not stock_list or not concept_codes:
return stock_list
ret = []
df = pl.DataFrame(mongo_get_data(dbname, "constituent_ths_index"))
for stock in stock_list:
sub_df = df.filter(
(pl.col("tradedate") <= dt) & (pl.col("stock_code") == stock)
)
concepts = _get_indexes_of_stock(sub_df)
found = False
for one_c in concepts:
if one_c in concept_codes:
ret.append(stock)
found = True
break
if not found:
logger.debug(f"{stock}被排除: 不在概念集中")
return ret
def top_concepts_of_stocks(stock_list, dt, limit=None):
"""
统计股票所属概念前{limit}名
"""
df = pl.DataFrame(mongo_get_data(dbname, "constituent_ths_index"))
concepts_count = defaultdict(int)
for stock in stock_list:
sub_df = df.filter(
(pl.col("tradedate") <= dt) & (pl.col("stock_code") == stock)
)
concepts = _get_indexes_of_stock(sub_df)
for _c in concepts:
concepts_count[_c] += 1
sorted_concepts = sorted(concepts_count.items(), key=lambda x: x[1], reverse=True)
if limit:
return sorted_concepts[:limit]
else:
return sorted_concepts
def get_indexes_of_stocks(stock_list, dt):
"""
统计股票所属概念前{limit}名
"""
df = pl.DataFrame(mongo_get_data(dbname, "constituent_ths_index"))
all_concepts = {}
for stock in stock_list:
sub_df = df.filter(
(pl.col("tradedate") <= dt) & (pl.col("stock_code") == stock)
)
concepts = _get_indexes_of_stock(sub_df)
all_concepts[stock] = concepts
return all_concepts
def ths_hot_stocks(top_n, dt):
"""
获取{dt}当天的同花顺热股前{top_n}名
"""
hot_stocks = pl.DataFrame(
mongo_get_data(dbname, "hot_stocks_ths", query={"date": dt})
)
return hot_stocks.bottom_k(top_n, by="order")
def get_finance_data(tablename, fields) -> pl.DataFrame:
projection = {
"_id": 0,
"f_ann_date": 1,
"end_date": 1,
}
for f in fields:
projection[f] = 1
df = pl.DataFrame(
mongo_get_data(
dbname,
tablename,
query={},
projection=projection,
)
)
if df.is_empty():
return df
# drop duplicates of finance_data
df = df.with_columns(
pl.col("f_ann_date").cast(pl.Datetime("ms")),
pl.col("end_date").cast(pl.Datetime("ms")),
)
df = df.sort(["ts_code", "f_ann_date", "end_date"], maintain_order=True)
df = df.filter(
(pl.col("ts_code") != pl.col("ts_code").shift(1))
| (
pl.col("end_date")
>= pl.col("end_date").shift(1, fill_value=pl.datetime(1990, 1, 1))
)
)
return df
def merge_finance_data(*data) -> Union[pl.DataFrame, None]:
l = len(data)
if l < 1:
return None
elif l == 1:
return data[0]
else:
left = data[0]
right = merge_finance_data(*data[1:])
if (
left is not None
and not left.is_empty()
and right is not None
and not right.is_empty()
):
df = left.join(
right,
on=["ts_code", "f_ann_date", "end_date"],
how="full",
coalesce=True,
)
return df.sort(["ts_code", "f_ann_date", "end_date"], maintain_order=True)
else:
return None
if __name__ == "__main__":
from quantdata import mongo_connect, mongo_close
from datetime import datetime
mongo_connect("localhost")
try:
dt = datetime(2025, 11, 21)
print(get_indexes_of_stocks(["002493.SZ", "600605.SH"], dt))
# print(get_ths_concepts_names())
hot_stocks = ths_hot_stocks(50, dt)
hot_stock_codes = set(hot_stocks["code"].to_list())
top_concepts_of_stocks(hot_stock_codes, dt, limit=6)
finally:
mongo_close()