def create_summary_tear_sheet(
self,
by_datetime=True,
by_group: bool = False,
long_short: bool = True,
group_neutral: bool = False,
):
"""
创建一个小型的汇总表格，包括因子的收益率分析，IC 值，换手率等分析
参数
---
:param factor_data: 因子数据
:param long_short: 是否构建多空组合，在该组合上进行进行分析。
:param group_neutral: 是否进行行业中性
"""
# Returns Analysis
mean_quant_ret, std_quant = self.calc_mean_return_by_quantile(
by_group=by_group, demeaned=long_short, group_adjust=group_neutral)
mean_quant_rateret = mean_quant_ret.apply(
utils.rate_of_return,
axis=0,
base_period=mean_quant_ret.columns[0]
)
mean_quant_ret_bydatetime, std_quant_bydatetime = self.calc_mean_return_by_quantile(
by_datetime=True, demeaned=long_short, group_adjust=group_neutral)
mean_quant_rateret_bydatetime = mean_quant_ret_bydatetime.apply(
utils.rate_of_return,
axis=0,
base_period=mean_quant_ret_bydatetime.columns[0],
)
std_quant_rate_bydatetime = std_quant_bydatetime.apply(
utils.rate_of_return,
axis=0,
base_period=std_quant_bydatetime.columns[0]
)
alpha_beta = self.calc_factor_alpha_beta(
demeaned=long_short,
group_adjust=group_neutral
)
mean_ret_spread_quant, std_spread_quant = self.calc_mean_returns_spread(
)
fr_cols = utils.get_forward_returns_columns(
self._clean_factor_data.columns
)
vertical_sections = 2 + len(fr_cols) * 3
gf = GridFigure(rows=vertical_sections, cols=1)
plotting.plot_quantile_statistics_table(self._clean_factor_data)
plotting.plot_returns_table(
alpha_beta,
mean_quant_rateret,
mean_ret_spread_quant
)
plotting.plot_quantile_returns_bar(
mean_quant_rateret,
by_group=False,
ylim_percentiles=None,
ax=gf.next_row()
)
# Information Analysis
ic = perf.factor_information_coefficient(self._clean_factor_data)
plotting.plot_information_table(ic)
# Turnover Analysis
# FIXME: 股票是 T+1，意味着频率只能是 Day 及以上频率
quantile_factor = self._clean_factor_data["factor_quantile"]
quantile_turnover = {
p: pd.concat(
[
perf.quantile_turnover(quantile_factor,
q,
p)
for q in range(1,
int(quantile_factor.max()) + 1)
],
axis=1,
)
for p in self.periods
}
autocorrelation = pd.concat(
[
perf.factor_rank_autocorrelation(
self._clean_factor_data,
period
) for period in self.periods
],
axis=1,
)
plotting.plot_turnover_table(autocorrelation, quantile_turnover)
plt.show()
gf.close()
def create_summary_tear_sheet(
factor_data: pd.DataFrame,
by_datetime: bool = True, # 按日期计算
by_group: bool = False, # 按分组计算
long_short: bool = True, # 多空组合
group_neutral: bool = False, # 分组中性
periods: Union[int, Tuple[int], List[int]] = 1,
frequence: str = '1d'):
"""
创建一个小型的汇总表格，包括因子的收益率分析，IC 值，换手率等分析
参数
---
:param factor_data: 因子数据
:param long_short: 是否构建多空组合，在该组合上进行进行分析。
:param group_neutral: 是否进行行业中性
"""
if isinstance(periods, int):
periods = [
periods,
]
# 收益分析
mean_quant_ret, std_quant = perf.mean_return_by_quantile(
factor_data,
by_group=by_group,
demeaned=long_short,
group_adjust=group_neutral)
mean_quant_rateret = mean_quant_ret.apply(
utils.rate_of_return, axis=0, base_period=mean_quant_ret.columns[0])
std_quant_rate = std_quant.apply(utils.std_conversion,
axis=0,
base_period=std_quant.columns[0])
mean_quant_ret_bydatetime, std_quant_bydatetime = perf.mean_return_by_quantile(
factor_data,
by_datetime=by_datetime,
by_group=by_group,
demeaned=long_short,
group_adjust=group_neutral)
mean_quant_rateret_bydatetime = mean_quant_ret_bydatetime.apply(
utils.rate_of_return,
axis=0,
base_period=mean_quant_ret_bydatetime.columns[0])
std_quant_rate_bydatetime = std_quant_bydatetime.apply(
utils.std_conversion,
axis=0,
base_period=std_quant_bydatetime.columns[0])
alpha_beta = perf.factor_alpha_beta(factor_data=factor_data,
demeaned=long_short,
group_adjust=group_neutral)
mean_ret_spread_quant, std_spread_quant = perf.mean_returns_spread(
mean_quant_rateret,
upper_quant=factor_data.factor_quantile.max(),
lower_quant=factor_data.factor_quantile.min(),
std_err=std_quant_rate)
fr_cols = utils.get_forward_returns_columns(factor_data.columns)
vertical_sections = 2 + len(fr_cols) * 3
gf = GridFigure(rows=vertical_sections, cols=1)
plotting.plot_quantile_statistics_table(factor_data)
plotting.plot_returns_table(alpha_beta, mean_quant_rateret,
mean_ret_spread_quant)
plotting.plot_quantile_returns_bar(mean_quant_rateret,
by_group=by_group,
ylim_percentiles=None,
ax=gf.next_row())
# Information Analysis
ic = perf.factor_information_coefficient(factor_data)
plotting.plot_information_table(ic)
# Turnover Analysis
# FIXME: 股票是 T+1，意味着频率只能是 Day 及以上频率
quantile_factor = factor_data["factor_quantile"]
quantile_turnover = {
p: pd.concat(
[
perf.quantile_turnover(quantile_factor, q, p)
for q in range(1,
int(quantile_factor.max()) + 1)
],
axis=1,
)
for p in periods
}
autocorrelation = pd.concat(
[
perf.factor_rank_autocorrelation(factor_data, period)
for period in periods
],
axis=1,
)
plotting.plot_turnover_table(autocorrelation, quantile_turnover)
plt.show()
gf.close()
