_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
result_return.extend(res_return)
result_risk.extend(res_risk)
result_sub.extend(res_sub)
df_return = pd.DataFrame(result_return)
df_risk = pd.DataFrame(result_risk)
df_sub = pd.DataFrame(result_sub)
cols_return = cal.format_cols_mutual(
_funcs_return_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_risk = cal.format_cols_mutual(
_funcs_risk_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_sub = cal.format_cols_mutual(
_funcs_sub_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
df_return.columns = cols_return
df_risk.columns = cols_risk
df_sub.columns = cols_sub
try:
io.to_sql("fund_daily_return",
def cal_by_date(statistic_date, fund_ids):
"""
:param statistic_date: datetime.date
:param fund_ids: list
:return:
"""
print("STATISTIC_DATE:{sd}, LENGTH:{l}".format(sd=statistic_date,
l=len(fund_ids)))
result_1 = []
result_2 = []
result_3 = []
data = pre.ProcessedData(statistic_date,
fund_ids,
_freq,
pe=[],
conn=engine_rd,
conn_mkt=engine_mkt,
weekday=True)
bms = {
index_name: cal.Benchmark(attr_dict, index_name)
for index_name, attr_dict in data.index.items()
}
tbond = cal.Tbond(data.index["y1_treasury_rate"], "y1_treasury_rate")
for fid, attrs in data.funds.items():
fund = cal.Fund(attrs)
res_1, _funcs_1_sourted = cal.calculate(_funcs_1,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
res_2, _funcs_2_sorted = cal.calculate(_funcs_2,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
res_3, _funcs_3_sorted = cal.calculate(_funcs_3,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
result_1.extend(res_1)
result_2.extend(res_2)
result_3.extend(res_3)
df_1 = pd.DataFrame(result_1)
df_2 = pd.DataFrame(result_2)
df_3 = pd.DataFrame(result_3)
# 删除空行
df_1.dropna(how='all', inplace=True)
df_2.dropna(how='all', inplace=True)
df_3.dropna(how='all', inplace=True)
cols_1 = cal.format_cols_mutual(
_funcs_1_sourted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_2 = cal.format_cols_mutual(
_funcs_2_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_3 = cal.format_cols_mutual(
_funcs_3_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
if len(df_1):
df_1.columns = cols_1
io.to_sql("fund_daily_return",
conn=engine_wt,
dataframe=df_1,
chunksize=500)
if len(df_2):
df_2.columns = cols_2
io.to_sql("fund_daily_risk",
conn=engine_wt,
dataframe=df_2,
chunksize=500)
if len(df_3):
df_3.columns = cols_3
io.to_sql("fund_daily_subsidiary",
conn=engine_wt,
dataframe=df_3,
chunksize=500)
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
result_1.extend(res_1)
result_2.extend(res_2)
result_3.extend(res_3)
df_1 = pd.DataFrame(result_1)
df_2 = pd.DataFrame(result_2)
df_3 = pd.DataFrame(result_3)
cols_1 = cal.format_cols_mutual(
_funcs_1_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_2 = cal.format_cols_mutual(
_funcs_2_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_3 = cal.format_cols_mutual(
_funcs_3_sorted,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
df_1.columns = cols_1
df_2.columns = cols_2
df_3.columns = cols_3
try:
io.to_sql("fund_daily_risk2",