_funcs_sub = [
"con_rise_periods", "con_fall_periods", "VaR", "p_earning_periods", "n_earning_periods", "min_return", "max_return", "mdd_repair_time",
"mdd_time", "skewness", "kurtosis", "ERVaR"
]
cols_return = cal.format_cols(_funcs_return, _freq, prefix=["index_id", "index_name", "statistic_date"])
cols_risk = cal.format_cols(_funcs_risk, _freq, prefix=["index_id", "index_name", "statistic_date"])
cols_sub = cal.format_cols(_funcs_sub, _freq, prefix=["index_id", "index_name", "statistic_date"])
date_rng = pd.date_range(today - relativedelta(weeks=1), today)
for statistic_date in date_rng:
result_return = []
result_risk = []
result_sub = []
ids_used = pre.fetch_fids_used(statistic_date=statistic_date, freq=_freq, conn=engine_rd)
data = pre.ProcessedData(statistic_date, [], _freq)
bms = {index_id: cal.Benchmark(attr_dict, index_id) for index_id, attr_dict in data.index.items()}
tbond = cal.Tbond(data.index["y1_treasury_rate"], "y1_treasury_rate")
for bm_name, bm in bms.items():
if bm.id == "y1_treasury_rate":
continue
res_return = cal.calculate(_funcs_return, _intervals, None, _freq, statistic_date, bm, None, tbond)
res_risk = cal.calculate(_funcs_risk, _intervals, None, _freq, statistic_date, bm, None, tbond)
res_sub = cal.calculate(_funcs_sub, _intervals, None, _freq, statistic_date, bm, None, tbond)
result_return.extend(res_return)
result_risk.extend(res_risk)
def calculate(statistic_date):
print(statistic_date)
ids_used = pre.fetch_fids_used(statistic_date=statistic_date,
freq=_freq,
conn=engine_rd)
# TMP EXEC
# tmp = set(['P1001447', 'P1008404', 'P1003197', 'P1000902', 'P1004813', 'P1004746', 'P1001198',
# 'P1001203', 'P1004150', 'P1014451', 'P1008451', 'P1008160', 'P1000277', 'P1028421'])
# ids_used = list(set(ids_used).intersection(tmp))
data = pre.ProcessedData(statistic_date, ids_used, _freq)
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_return, cols_return_sorted = cal.calculate(_funcs_return,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
res_risk, cols_risk_sorted = cal.calculate(_funcs_risk,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
res_sub, cols_sub_sorted = cal.calculate(_funcs_sub,
_intervals,
_bms_used,
_freq,
statistic_date,
fund,
bms,
tbond,
with_func_names=True)
result_return = []
result_risk = []
result_sub = []
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_org4r(
cols_return_sorted,
_freq,
prefix=["org_id", "org_name", "statistic_date", "benchmark"])
cols_risk = cal.format_cols_org4r(
cols_risk_sorted,
_freq,
prefix=["org_id", "org_name", "statistic_date", "benchmark"])
cols_sub = cal.format_cols_org4r(
cols_sub_sorted,
_freq,
prefix=["org_id", "org_name", "statistic_date", "benchmark"])
df_return.columns = cols_return
df_risk.columns = cols_risk
df_sub.columns = cols_sub
df_return["index_id"] = sf.SQL.Org4R.INDEXID
df_risk["index_id"] = sf.SQL.Org4R.INDEXID
df_sub["index_id"] = sf.SQL.Org4R.INDEXID
io.to_sql("org_monthly_return",
conn=engine_rd,
dataframe=df_return,
chunksize=5000)
io.to_sql("org_monthly_risk",
conn=engine_rd,
dataframe=df_risk,
chunksize=5000)
io.to_sql("org_monthly_research",
conn=engine_rd,
dataframe=df_sub,
chunksize=5000)
