def test_performance(factor, prices):
import matplotlib.pyplot as plt
from alphalens import utils, performance, plotting
# 持股收益-逐只
stocks_holding_return = utils.get_clean_factor_and_forward_returns(factor,
prices,
quantiles=5,
periods=(1, 5, 10))
print("因子的IC值：")
ic = performance.factor_information_coefficient(stocks_holding_return)
print(ic)
plotting.plot_ic_hist(ic)
plt.show()
plotting.plot_ic_ts(ic)
plt.show()
print("平均IC值-月：")
mean_ic = performance.mean_information_coefficient(stocks_holding_return,
by_time="M")
plotting.plot_monthly_ic_heatmap(mean_ic)
plt.show()
# 按quantile区分的持股平均收益（减去了总体平均值）
mean_return_by_q = performance.mean_return_by_quantile(stocks_holding_return,
by_date=True,
demeaned=True)[0]
# 按quantile画出累积持有收益
for i in [1, 5, 10]:
plotting.plot_cumulative_returns_by_quantile(mean_return_by_q,
period=i)
plt.show()
def create_ic_tear_sheet(self):
factor_and_return = self._get_clean_factor_and_fwd_return(
self._factor, self._factor_freq)
ic = factor_information_coefficient(factor_and_return)
plot_ic_hist(ic)
self.ic_bar_tear_sheet(ic)
plot_monthly_ic_heatmap(ic)
plt.show()
return
# 3)计算加权合成的因子
new_factor = factor_admin.ic_cov_weighted_factor(factors_dict, ic_weight_df)
# 4)查看合成的因子表现
perf = factor_admin.calculate_performance(new_factor.name,
new_factor.multifactor_value,
start,
end,
periods=(1, 5, 10),
quantiles=5,
price=prices)
from alphalens import plotting
import matplotlib.pyplot as plt
plotting.plot_ic_hist(perf.ic)
plt.show()
plotting.plot_ic_ts(perf.ic)
plt.show()
plotting.plot_monthly_ic_heatmap(perf.mean_ic_by_M)
plt.show()
# 按quantile画出累积持有收益
for i in [1, 5, 10]:
plotting.plot_cumulative_returns_by_quantile(perf.mean_return_by_q,
period=i)
plt.show()
# 5) shrink_weight_df合成
holding_period = 10
ic_weight_shrink_df = factor_admin.get_ic_weight_shrink_df(
"""
import pandas as pd
import numpy as np
import scipy.stats as st
from alphalens import tears, performance, plotting, utils
df = pd.DataFrame([[1, 2], [4, 5]], columns=["A", "B"])
# 计算斯皮尔相关系数Rank IC，取值 [-1, 1]之间
print(st.spearmanr(df["A"], df["B"]))
"""使用alphalens更简易的做因子分析"""
# 输入因子表和收盘价表到返回到期收益率表，再将因子表和到期收益表整合返回综合因子数据表
factor_data = utils.get_clean_factor_and_forward_returns("factor", "price")
# 因子IC的计算
IC = performance.factor_information_coefficient(factor_data)
# 因子时间序列和移动平均图，看出一个因子在时间上的正负性、
plotting.plot_ic_ts(IC)
# 因子分布直方图，IC平均值，标准差
plotting.plot_ic_hist(IC)
# 热力图
mean_monthly_ic = performance.mean_information_coefficient(factor_data, by_time="1m")
plotting.plot_monthly_ic_heatmap(mean_monthly_ic)
# IC分析合集
tears.create_information_tear_sheet(factor_data)
# 收益率分析
tears.create_returns_tear_sheet(factor_data)
# 因子的每一期的收益（因子收益）
performance.factor_returns(factor_data).iloc[:, 0].mean()