def get_holdings_dict(code, aim=95):
"""
通过天天基金的股票持仓数据来生成实时预测所需的持仓字典，不保证稳定性和可靠性以及 API 的连续性，慎用
:param code:
:param aim:
:return:
"""
df = get_fund_holdings(code)
if df.ratio.sum() < 60:
d = dt.datetime.now()
if d.month > 3 and d.month < 8:
year = d.year - 1
season = 4
elif d.month <= 3:
year = d.year - 1
season = 2
else:
year = d.year
season = 2
# season 只选 2，4, 具有更详细的持仓信息
df = get_fund_holdings(code, year, season)
if df is None:
if season == 4:
season = 2
else:
year -= 1
season = 4
df = get_fund_holdings(code, year, season)
df["scode"] = df["code"].apply(ttjjcode)
d = pd.Series(df.ratio.values, index=df.scode).to_dict()
d = scale_dict(d, aim=aim)
return d
def get_stock_holdings(
self, year=None, season=None, date=yesterdayobj(), threhold=100
):
"""
获取整个基金组合的底层股票持仓总和和细节，组合穿透
:param year: 基于的基金季报年份
:param season: 基于的基金季报季度
:param date: 默认昨天
:param threhold: 默认100。小于100元的底层股票将不在最后的结果中展示
:return: pd.DataFrame column: name, code, value, ratio
"""
d = {}
if year is None or season is None:
rd = convert_date(date) - pd.Timedelta(days=120)
if not year:
year = rd.year
if not season:
season = int((rd.month - 0.1) / 3) + 1
logger.debug("use %s, %s for fund report" % (year, season))
for f in self.fundtradeobj:
if isinstance(f, itrade):
if f.get_type() == "股票":
code = f.code
elif f.get_type() == "场内基金":
code = f.code[2:]
else:
continue
else:
code = f.code
value = f.briefdailyreport(date).get("currentvalue", 0)
if value > 0:
if code.startswith("SH") or code.startswith("SZ"):
stock = code
d[stock] = d.get(stock, 0) + value
elif code == "mf":
continue
else:
df = get_fund_holdings(code, year, season)
if df is None:
continue
for _, row in df.iterrows():
stock = row["code"]
stock = ttjjcode(stock)
d[stock] = d.get(stock, 0) + row["ratio"] / 100 * value
# print("%s has %s contribution from %s" %(stock, row["ratio"] / 100 * value, f.name))
l = []
for code, value in sorted(d.items(), key=lambda item: -item[1]):
if value >= threhold:
try:
name = get_rt(code)["name"]
except:
name = code
l.append([name, code, value])
fdf = pd.DataFrame(l, columns=["name", "code", "value"])
fdf["ratio"] = fdf["value"] / fdf["value"].sum()
return fdf
