def cal_obj(signal, name, period=5, quantile=5):
obj = SignalDigger()
obj.process_signal_before_analysis(
signal,
price=price,
n_quantiles=quantile,
period=period,
can_enter=can_enter,
can_exit=can_exit,
mask=mask,
)
obj.create_full_report()
return obj
def cal_obj(signal, name, period, quantile):
price_bench = dv.data_benchmark
obj = SignalDigger(output_folder="hs300/%s" % name, output_format='pdf')
obj.process_signal_before_analysis(signal,
price=price,
n_quantiles=quantile,
period=period,
mask=mask,
group=sw1,
can_enter=can_enter,
can_exit=can_exit,
commission=0.0003)
obj.create_full_report()
return obj
ic_20_mean = dict()
ic_20_std = dict()
ir_20 = dict()
for name in ic_20.columns:
ic_20_mean[name] = ic_20[name].loc[start:].mean()
ic_20_std[name] = ic_20[name].loc[start:].std()
ir_20[name] = ic_20_mean[name] / ic_20_std[name]
import datetime
trade_date = pd.Series(ic_20.index)
trade_date = trade_date.apply(
lambda x: datetime.datetime.strptime(str(x), '%Y%m%d'))
ic_20.index = trade_date
obj = SignalDigger(output_folder='./output', output_format='pdf')
obj.process_signal_before_analysis(
signal=comb_factors["equal_weight"],
price=dv.get_ts("close_adj"),
high=dv.get_ts("high_adj"), # 可为空
low=dv.get_ts("low_adj"), # 可为空
n_quantiles=5, # quantile分类数
mask=mask, # 过滤条件
can_enter=can_enter, # 是否能进场
can_exit=can_exit, # 是否能出场
period=period, # 持有期
benchmark_price=dv.data_benchmark, # 基准价格 可不传入，持有期收益（return）计算为绝对收益
commission=0.0008,
)
obj.create_full_report()
plt.show()
price=dv.get_ts("close_adj"),
high=dv.get_ts("high_adj"), # 可为空
low=dv.get_ts("low_adj"), # 可为空
group=dv.get_ts("sw1"), # 可为空
n_quantiles=5, # quantile分类数
mask=mask, # 过滤条件
can_enter=can_enter, # 是否能进场
can_exit=can_exit, # 是否能出场
period=5, # 持有期
benchmark_price=dv.data_benchmark, # 基准价格 可不传入，持有期收益（return）计算为绝对收益
commission=0.0008,
)
signal_data = obj.signal_data
import matplotlib.pyplot as plt
print(obj.create_full_report())
plt.show()
from jaqs_fxdayu.research.signaldigger.analysis import analysis
result = analysis(signal_data, is_event=False, period=5)
print("——ic分析——")
print(result["ic"])
print("——选股收益分析——")
print(result["ret"])
print("——最大潜在盈利/亏损分析——")
print(result["space"])
import matplotlib.pyplot as plt
obj.create_full_report()
plt.show()
import matplotlib.pyplot as plt
obj = SignalDigger()
def draw_analysis(signal_data,period):
obj.signal_data = signal_data
obj.period = period
obj.create_full_report()
plt.show()
#全样本可视化操作:
draw_analysis(optimizer.all_signals[ret_best[0]["signal_name"]], period=30)
#当然,这里也可以仿制第二章中的方法,不定义函数,直接运行：
''''
obj.signal_data=optimizer.all_signals[ret_best[0]["signal_name"]]
obj.period = 30
obj.create_full_report()
plt.show()
''''
#样本内可视化:
draw_analysis(optimizer.all_signals[ret_best[0]["signal_name"]].loc[20140101:20160101], period=30)
A=optimizer.all_signals[ret_best[0]["signal_name"]].loc[20140101:20160101] #！！！,选择时间进行操作
#样本外可视化:
draw_analysis(optimizer.all_signals[ret_best[0]["signal_name"]].loc[20160101:], period=30)
#step4:运用参数优化器例2:
# 以持有期mean_ic为最优化目标
ic_best = optimizer.enumerate_optimizer(target_type="return_ic",#优化目标类型
target = "IC Mean",
in_sample_range=[20140101,20160101],#样本内范围 默认为None,在全样本上优化