def calculate(time_s, time_e):
try:
tasks = pre.generate_tasks(time_s, time_e, freq="w", processes=7, conn=engine_rd)
tasks = {k: v for k, v in tasks.items() if k >= dt.date(2015, 1, 1)}
print(time_e, len(tasks))
except ValueError as e:
print(time_e, e)
for statistic_date, ids_used in sorted(tasks.items(), key=lambda x: x[0]):
print("UPDATE TIME:{ut}: STATISTIC DATE:{sd}, LENGTH:{l}".format(ut=time_e, sd=statistic_date, l=len(ids_used)))
result_return = []
result_risk = []
result_sub = []
data = pre.ProcessedData(statistic_date, list(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.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(
cols_return_sorted, _freq, prefix=["fund_id", "fund_name", "statistic_date", "benchmark"]
)
cols_risk = cal.format_cols(
cols_risk_sorted, _freq, prefix=["fund_id", "fund_name", "statistic_date", "benchmark"]
)
cols_sub = cal.format_cols(
cols_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_weekly_return", conn=engine_rd, dataframe=df_return, chunksize=5000)
io.to_sql("fund_weekly_risk", conn=engine_rd, dataframe=df_risk, chunksize=5000)
io.to_sql("fund_subsidiary_weekly_index", conn=engine_rd, dataframe=df_sub, chunksize=5000)
except Exception as e:
time.sleep(10)
io.to_sql("fund_weekly_return", conn=engine_rd, dataframe=df_return, chunksize=5000)
io.to_sql("fund_weekly_risk", conn=engine_rd, dataframe=df_risk, chunksize=5000)
io.to_sql("fund_subsidiary_weekly_index", conn=engine_rd, dataframe=df_sub, chunksize=5000)
print("TASK DONE: {ut}".format(ut=time_e))
_intervals = calargs.intervals
_funcs_return = [
"accumulative_return", "return_a", "sharpe_a", "calmar_a", "sortino_a"
]
_funcs_risk = [
"stdev", "stdev_a", "downside_deviation_a", "max_drawdown"
]
_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")
_funcs_return = [
"accumulative_return", "return_a", "excess_return_a", "odds", "sharpe_a",
"calmar_a", "sortino_a", "info_a", "jensen_a", "treynor_a"
]
_funcs_risk = [
"stdev", "stdev_a", "downside_deviation_a", "max_drawdown", "beta", "corr"
]
_funcs_sub = [
"con_rise_periods", "con_fall_periods", "ability_timing",
"ability_security", "persistence", "unsystematic_risk", "tracking_error_a",
"VaR", "p_earning_periods", "n_earning_periods", "min_return",
"max_return", "mdd_repair_time", "mdd_time", "skewness", "kurtosis"
]
cols_return = cal.format_cols(
_funcs_return,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_risk = cal.format_cols(
_funcs_risk,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
cols_sub = cal.format_cols(
_funcs_sub,
_freq,
prefix=["fund_id", "fund_name", "statistic_date", "benchmark"])
_bms_used = ["hs300", "csi500", "sse50", "cbi", "strategy", "FI01", "nfi"]
for year in range(last_month.year, last_month.year + 1):
for month in range(last_month.month, last_month.month + 1):
month_range = cld.monthrange(year, month)[1]
for fid, attrs in data.funds.items():
fund = cal.Fund(attrs)
res_return, funcs_return_sorted = cal.calculate(_funcs_return, _intervals, _bms_used, _freq, statistic_date, fund, bms, tbond, with_func_names=True)
res_risk, funcs_risk_sorted = cal.calculate(_funcs_risk, _intervals, _bms_used, _freq, statistic_date, fund, bms, tbond, with_func_names=True)
res_sub, funcs_sub_sorted = cal.calculate(_funcs_sub, _intervals, _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_private(
funcs_return_sorted, _freq, prefix=["fund_id", "fund_name", "statistic_date", "benchmark"]
)
cols_risk = cal.format_cols(
funcs_risk_sorted, _freq, prefix=["fund_id", "fund_name", "statistic_date", "benchmark"]
)
cols_sub = cal.format_cols(
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
# io.to_sql("fund_weekly_return", conn=engine_rd, dataframe=df_return, chunksize=5000)
# io.to_sql("fund_weekly_risk", conn=engine_rd, dataframe=df_risk, chunksize=5000)
# io.to_sql("fund_subsidiary_weekly_index", conn=engine_rd, dataframe=df_sub, chunksize=5000)
if bm.id == "y1_treasury_rate":
continue
res_return, funcs_return_sorted = cal.calculate(_funcs_return, _intervals, None, _freq, statistic_date, bm, None, tbond, with_func_names=True)
res_risk, funcs_risk_sorted = cal.calculate(_funcs_risk, _intervals, None, _freq, statistic_date, bm, None, tbond, with_func_names=True)
res_sub, funcs_sub_sorted = cal.calculate(_funcs_sub, _intervals, None, _freq, statistic_date, bm, None, 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(funcs_return_sorted, _freq, prefix=["index_id", "index_name", "statistic_date"])
cols_risk = cal.format_cols(funcs_risk_sorted, _freq, prefix=["index_id", "index_name", "statistic_date"])
cols_sub = cal.format_cols(funcs_sub_sorted, _freq, prefix=["index_id", "index_name", "statistic_date"])
df_return.columns = cols_return
df_risk.columns = cols_risk
df_sub.columns = cols_sub
df_return["index_id"] = df_return["index_id"].apply(str.upper)
df_risk["index_id"] = df_risk["index_id"].apply(str.upper)
df_sub["index_id"] = df_sub["index_id"].apply(str.upper)
io.to_sql("index_monthly_return", conn=engine_rd, dataframe=df_return, chunksize=5000)
io.to_sql("index_monthly_risk", conn=engine_rd, dataframe=df_risk, chunksize=5000)
io.to_sql("index_monthly_subsidiary", conn=engine_rd, dataframe=df_sub, chunksize=5000)
# res_return = cal.calculate(_funcs_return, _intervals, _bms_used, _freq, statistic_date, fund, bms, tbond)