t_min = pd.merge(fdate, fnvdate, "outer")
print("Some foundation_date missed, use first nv date...", len(ids_diff), len(ids_fdate), len(t_min))
t_min = t_min.sort_values("fund_id", ascending=True) # Sort the df by fund id ASC
sql_nav = sf.SQL.nav(ids_used) # Get their navs
d = pd.read_sql(sf.SQL.nav(ids_used), engine_rd)
tic("Preproessing")
d["statistic_date"] = d["statistic_date"].apply(su.date2tstp)
tic("Grouping")
idx4slice = su.idx4slice(d, slice_by="fund_id") # Grouping the datas By fund_id
navs = su.slice(d, idx4slice, "nav")
t_reals = su.slice(d, idx4slice, "statistic_date")
t_mins = t_min["t_min"].tolist()
t_mins_tstp = [su.date2tstp(x) for x in t_mins]
print("length of Data: {0}".format(len(d)))
conn.close()
#
t_stds = [tu.timeseries_std(date_s.today, interval, periods_y=12, use_lastday=True, extend=1) for interval
in intervals] # 标准序列 ###w->m
t_std_y5 = t_stds[6]
t_stds_len = [len(x) - 1 for x in t_stds] # 标准序列净值样本个数
t_std_alls = [tu.timeseries_std(date_s.today, tu.periods_in_interval(date_s.today, t_min, 12), periods_y=12,
use_lastday=True, extend=6) for t_min in t_mins] # 标准序列_成立以来
t_std_alls = [t_std_all[:len([x for x in t_std_all if x >= t_min]) + 1] for t_std_all, t_min in
zip(t_std_alls, t_mins_tstp)]
# 基金标准序列_成立以来
def calculate():
conn = engine_rd.connect()
year, month = yesterday.year, yesterday.month
month_range = cld.monthrange(year, month)[1]
time_to_fill = sf.Time(dt.datetime(year, month, month_range))
year, month = time_to_fill.year, time_to_fill.month
bms_used = [
"hs300", "csi500", "sse50", "ssia", "cbi", "y1_treasury_rate", "nfi"
]
sql_bm = sf.SQL.market_index(date=time_to_fill.today,
benchmarks=bms_used,
whole=True) # Get benchmark prices
bm = pd.read_sql(sql_bm, conn)
bm.loc[bm["statistic_date"] == dt.date(1995, 8, 16),
"y1_treasury_rate"] = 2.35
bm["y1_treasury_rate"] = bm["y1_treasury_rate"].fillna(method="backfill")
bm["y1_treasury_rate"] = bm["y1_treasury_rate"].apply(su.annually2monthly)
bm["statistic_date"] = bm["statistic_date"].apply(su.date2tstp)
prices_bm = [
bm.dropna(subset=[bm_name])[bm_name].tolist() for bm_name in bms_used
]
ts_bm = [
bm.dropna(subset=[bm_name])["statistic_date"].tolist()
for bm_name in bms_used
]
prices = prices_bm.copy()
ts = ts_bm.copy()
t_mins_pe_all = sf.PEIndex().firstyear # 寻找指数中可被计算的
t_mins_pe_all = {
k: dt.datetime(x - 1, 12, 31)
for (k, x) in t_mins_pe_all.items()
}
pesid_used = []
for k in t_mins_pe_all:
if t_mins_pe_all[k].year < year:
pesid_used.append(k)
elif t_mins_pe_all[k].year == year:
if t_mins_pe_all[k].month < month: # w -> m
pesid_used.append(k)
else:
continue
else:
continue
prices_pe = []
ts_pe = []
pes_used = []
for idx in pesid_used:
PE = sf.PEIndex(idx)
pes_used.append(PE.id)
sql_pe = sf.SQL.pe_index(time_to_fill.today, index_id=PE.id, freq="m")
pe = pd.read_sql(sql_pe, conn)
pe["statistic_date"] = pe["statistic_date"].apply(
lambda x: su.date2tstp(x) - 864000)
prices_pe.append(pe["index_value"].tolist())
ts_pe.append(pe["statistic_date"].tolist())
conn.close()
prices.extend(prices_pe)
ts.extend(ts_pe)
t_mins_tstp = [min(x) for x in ts]
t_mins = tu.tr(t_mins_tstp)
intervals = table.intervals
intervals1 = [1, 2, 3, 4, 5, 6, 9, 10, 11]
intervals3 = [1, 2, 3, 4, 5, 6, 10, 11]
index_used = bms_used.copy()
index_used.extend(pes_used)
index_name = {
"FI01": "私募全市场指数",
"FI02": "阳光私募指数",
"FI03": "私募FOF指数",
"FI04": "股票多头策略私募指数",
"FI05": "股票多空策略私募指数",
"FI06": "市场中性策略私募指数",
"FI07": "债券基金私募指数",
"FI08": "管理期货策略私募指数",
"FI09": "宏观策略私募指数",
"FI10": "事件驱动策略私募指数",
"FI11": "相对价值策略私募指数",
"FI12": "多策略私募指数",
"FI13": "组合投资策略私募指数",
"hs300": "沪深300指数",
"csi500": "中证500指数",
"sse50": "上证50指数",
"ssia": "上证A股指数",
"cbi": "中债指数",
"nfi": "南华商品指数",
"y1_treasury_rate": "y1_treasury_rate"
}
result = []
for mday in range(7, 8):
print("Day {0}: {1}".format(mday, dt.datetime.now()))
date_s = sf.Time(dt.datetime(year, month, mday) -
dt.timedelta(mday)) # Generate statistic_date
#
t_stds = [
tu.timeseries_std(date_s.today,
interval,
periods_y=12,
use_lastday=True,
extend=1) for interval in intervals
] # 标准序列
t_std_lens = [len(x) - 1 for x in t_stds] # 标准序列净值样本个数
t_std_y5 = t_stds[6]
ts_std_total = [
tu.timeseries_std(date_s.today,
tu.periods_in_interval(date_s.today, t_min, 12),
periods_y=12,
use_lastday=True,
extend=6) for t_min in t_mins
] # 标准序列_成立以来
ts_std_total = [
t_std_total[:len([x for x in t_std_total if x >= t_min]) + 1]
for t_std_total, t_min in zip(ts_std_total, t_mins_tstp)
]
# 基准指数的标准序列_成立以来
matchs = [
tu.outer_match4indicator_m(t, t_std_all, False)
for t, t_std_all in zip(ts, ts_std_total)
]
idx_matchs = [x[1] for x in matchs]
prices_total = [[
price[ix] if ix is not None else None for ix in idx.values()
] for price, idx in zip(prices, idx_matchs)]
# 基准指标的收益率_不同频率
rs_total = [
fi.gen_return_series(price_total) for price_total in prices_total
]
# 无风险国债的收益率
r_f_total = prices_total[5][
1:] # the list `y1_treasury_rate` in prices_total is not price, but return
r_f_total = pd.DataFrame(r_f_total).fillna(
method="backfill")[0].tolist()
r_f_all = [r_f_total[:length - 1] for length in t_std_lens]
r_f_all.append(r_f_total)
for i in range(len(index_used)):
if index_name[index_used[i]] == "y1_treasury_rate": continue
price_all = []
r_all = []
for j in range(7):
if dt.date.fromtimestamp(
(t_mins[i] + relativedelta(months=intervals[j])
).timestamp()) <= date_s.today:
price_all.append(prices_total[i][:t_std_lens[j]])
r_all.append(rs_total[i][:t_std_lens[j] - 1])
else:
price_all.append([])
r_all.append([])
for j in range(7, 11):
price_all.append(prices_total[i][:t_std_lens[j]])
if rs_total[i] is not None:
r_all.append(rs_total[i][:t_std_lens[j] - 1])
else:
r_all.append([])
price_all.append(prices_total[i])
r_all.append(rs_total[i])
price_all1 = [price_all[i] for i in intervals1]
price_all3 = [price_all[i] for i in intervals3]
r_all1 = [r_all[i] for i in intervals1]
r_all3 = [r_all[i] for i in intervals3]
r_f_all1 = [r_f_all[i] for i in intervals1][:-1]
r_f_all3 = [r_f_all[i] for i in intervals3][:-1]
r_f_all1.append(r_f_all[-1][:len(r_all[-1])])
r_f_all3.append(r_f_all[-1][:len(r_all[-1])])
ir = [fi.accumulative_return(price) for price in price_all1]
ir_a = [fi.return_a(r, 12) for r in r_all1]
stdev_a = [fi.standard_deviation_a(r, 12) for r in r_all3]
dd_a = [
fi.downside_deviation_a(r, r_f, 12)
for r, r_f in zip(r_all3, r_f_all3)
]
mdd = [fi.max_drawdown(price)[0] for price in price_all3]
sharpe_a = [
fi.sharpe_a(r, r_f, 12) for r, r_f in zip(r_all3, r_f_all3)
]
calmar_a = [
fi.calmar_a(price, r_f, 12)
for price, r_f in zip(price_all3, r_f_all3)
]
sortino_a = [
fi.sortino_a(r, r_f, 12) for r, r_f in zip(r_all3, r_f_all3)
]
p_earning_months = [fi.periods_positive_return(r) for r in r_all3]
n_earning_months = [fi.periods_npositive_return(r) for r in r_all3]
con_rise_months = [
fi.periods_continuous_rise(r)[0] for r in r_all3
]
con_fall_months = [
fi.periods_continuous_fall(r)[0] for r in r_all3
]
tmp = [
ir, ir_a, stdev_a, dd_a, mdd, sharpe_a, calmar_a, sortino_a,
p_earning_months, n_earning_months, con_rise_months,
con_fall_months
]
result_i = [index_used[i], index_name[index_used[i]], date_s.today]
for x in tmp:
result_i.extend(x)
result.append(result_i)
df = pd.DataFrame(result)
df[list(range(3, 101))] = df[list(range(3, 101))].astype(np.float64)
df[list(range(3, 101))] = df[list(range(3,
101))].apply(lambda x: round(x, 6))
df.columns = columns
df.index_id = df.index_id.apply(lambda x: x.upper())
return df