def FactorTest_deal(codes, self_obj):
import Analysis_Funs as af
import tools.Sample_Tools as smpl
import tools.Pretreat_Tools as pretreat
import dill
import pandas as pd
self_ = dill.loads(self_obj)
data = smpl.get_data(codes,
start=self_.start,
end=self_.end,
gap=self_.gap)
df = smpl.resample_stockdata_low(data.data, freq=self_.freq)
ret_forward_re = smpl.get_forward_return(df, 'close')
# 为保证ind_Model_Class信息的完整性，使用重采样前的数据来生成指标，后面会另外再重采样。
ind_obj = self_.ind_Model_Class(data.data)
ind_obj.fit()
ind = pd.DataFrame(ind_obj.ind_df[self_.main_field])
ind.dropna(axis=0, inplace=True)
if self_.neutralize.get('enable', False):
ind_close = pd.concat([ind, data.close], axis=1) #为了给复权用
ind_close.dropna(axis=0, inplace=True)
ind_added = smpl.add_marketvalue_industry(
ind_close, static_mv=self_.neutralize.get('static_mv', False))
return (ind_added, ret_forward_re)
return (ind, ret_forward_re)
def process(self):
data = smpl.get_sample_by_zs(name=self.sample,
start=self.start,
end=self.end,
gap=self.gap,
only_main=self.only_main)
df = smpl.resample_stockdata_low(data.data, freq=self.freq)
# 后续的重采样依赖于ret_forward，否则不同周期下，resample会出现日期不一致的情况。
ret_forward = smpl.get_forward_return(df, 'close')
# 为保证ind_Model_Class信息的完整性，使用重采样前的数据来生成指标，后面会另外再重采样。
ind_obj = self.ind_Model_Class(data.data)
ind_obj.fit()
ind = pd.DataFrame(ind_obj.ind_df[self.main_field])
ind.dropna(axis=0, inplace=True)
if self.neutralize.get('enable', False):
ind_close = pd.concat([ind, data.close], axis=1) #为了给复权用
ind_close.dropna(axis=0, inplace=True)
ind_added = smpl.add_marketvalue_industry(
ind_close, static_mv=self.neutralize.get('static_mv', False))
# self.indx1 = ind_added
x = ind_added[['totalCapital', 'industry']].sort_index()
# x = ind_added[['liquidity_totalCapital','industry']]
y = ind_added.iloc[:, 0].sort_index()
ind = pretreat.neutralize(y,
x,
categorical=['industry'],
logarithmetics=['totalCapital'])
# 取消因子标准化，很多时候标准化后的rank_ic的结果，与分箱测试观测结果不符
# factor_standardized = pretreat.standardize(ind, multi_code=True)
self.rank_ic = af.get_rank_ic(ind, ret_forward)
else:
# neutralize 最后得到的ind是series，而原来的是dataframe
# get_rank_ic 内部会做交集，这外面就不必resample了
# factor_standardized = pretreat.standardize(ind, multi_code=True)[self.main_field]
self.rank_ic = af.get_rank_ic(ind[self.main_field], ret_forward)
# self.a = ind
# self.b = ret_forward
# self.rank_ic = af.get_rank_ic(factor_standardized, pretreat.standardize(ret_forward, multi_code=True))
self.res = pd.DataFrame(
[af.get_ic_desc(self.rank_ic)],
columns=['rankIC', 'rankIC_std', 'rankIC_T', 'rankIC_P'])
self.res['ICIR'] = round(af.get_ic_ir(self.rank_ic), 6)
self.res['winning'] = round(af.get_winning_rate(self.rank_ic), 6)
common_index = ind.index.get_level_values(0).unique().intersection(
ret_forward.index.get_level_values(0).unique())
ind_resample = ind.loc[common_index]
self.ind_ret_df = pd.concat([ind_resample, ret_forward], axis=1)
self.ind_ret_df.dropna(axis=0, inplace=True)
# 分箱
self.ind_binned = self.ind_ret_df.groupby(
level=0, group_keys=False).apply(lambda x: pretreat.binning(
x, deal_column=self.main_field, box_count=10, inplace=True))
def binned_plot(self, only_binned=False):
# 去除绘图不需要的原始因子和code
ind_binned_noindex = self.ind_binned.reset_index().drop(
['code', self.main_field], axis=1)
# 按日期分组，组内再按分箱分组求总收益,结果会被倒序。
ind_binned_ret_date = ind_binned_noindex.set_index([
'date', 'group_label'
]).groupby(level=0).apply(lambda x: x.groupby(level=1).agg(sum))
fig = plt.figure(figsize=(1420 / 72, 320 / 72))
ind_binned_ret_all = ind_binned_noindex.drop(
['date'], axis=1).dropna().set_index('group_label').groupby(
level=0).apply(lambda x: x['ret_forward'].sum())
plt.bar(ind_binned_ret_all.index, ind_binned_ret_all)
plt.title('分箱平均收益', **PLOT_TITLE)
plt.show()
if only_binned:
return
blenchmark = smpl.get_benchmark(name=self.sample,
start=self.start,
end=self.end,
gap=self.gap)
blenchmark_re = smpl.resample_stockdata_low(blenchmark.data,
freq=self.freq)
blenchmark_ret = smpl.get_forward_return(blenchmark_re, 'close')
blenchmark_ret.reset_index('code', drop=True, inplace=True)
blenchmark_cum = blenchmark_ret.cumsum()
fig = plt.figure(figsize=(1420 / 72, 320 / 72))
lns = ind_binned_ret_date.groupby(level=1).apply(
lambda x: plt.plot(x.index.get_level_values(0).unique().tolist(),
x.values.tolist(),
label=x.index.get_level_values(1)[0]))
ax2 = plt.gca().twinx()
lns = [x[0] for x in lns.values] # lns,为了合并legend
lns += ax2.plot(blenchmark_ret,
linestyle=":",
linewidth=2,
color="black",
label='bm')
labs = [l.get_label() for l in lns]
legend = plt.legend(lns,
labs,
loc='upper left',
fontsize='x-small',
title='反序\n注意\n10最小')
legend.get_title().set_fontsize(fontsize=12)
plt.grid(linestyle="dotted", color="lightgray")
plt.title('分箱收益变化', **PLOT_TITLE)
plt.show()
ind_binned_ret_cum = ind_binned_ret_date.groupby(
level=1).apply(lambda x: x.cumsum())
fig = plt.figure(figsize=(1420 / 72, 320 / 72))
lns = ind_binned_ret_cum.groupby(level=1).apply(
lambda x: plt.plot(x.index.get_level_values(0).unique().tolist(),
x.values.tolist(),
label=x.index.get_level_values(1)[0]))
ax3 = plt.gca().twinx()
lns = [x[0] for x in lns.values] # lns,为了合并legend
lns += ax3.plot(blenchmark_cum,
linestyle=":",
linewidth=2,
color="black",
label='bm')
labs = [l.get_label() for l in lns]
legend = plt.legend(lns,
labs,
loc='upper left',
fontsize='x-small',
title='反序\n注意\n10最小')
legend.get_title().set_fontsize(fontsize=12)
plt.grid(linestyle="dotted", color="lightgray")
plt.title('累计收益率', **PLOT_TITLE)
plt.show()