Math Helpers¶
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quant.common.math_helpers.cal_mdd(netvalue, compound=True)[源代码]¶ 计算最大回撤
Parameters: - netvalue: pd.Series
净值序列
- compound: bool, optional
是否为复利制，如果为真，回撤计算方法为 \(\frac{N_j}{N_i}-1\) (j > i); 如果为假，回撤计算方法为 \(N_j-N_i\) (j > i)
Returns: - 最大回撤值(正值)
Examples
In [1]: import pandas as pd In [2]: from quant.common.math_helpers import cal_mdd In [3]: series = pd.Series([1.0, 1.1, 1.11, 1.04, 1.02, 1.08]) In [4]: cal_mdd(series) Out[4]: 0.08108108108108114 In [5]: 1.02 / 1.11 - 1 Out[5]: -0.08108108108108114
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quant.common.math_helpers.get_ic(table1, table2, method='spearman')[源代码]¶ 求两组数据之间的IC score
Parameters: - table1, table2: pd.DataFrame
要计算IC的两个数据框
- method: {‘spearman’, ‘pearson’, ‘kendall’}
- pearson : standard correlation coefficient
- kendall : Kendall Tau correlation coefficient
- spearman : Spearman rank correlation
Returns: - pd.Series
每期的相关系数，求平均可得IC分数。
参见
pd.Series.corrExamples
In [1]: import numpy as np In [2]: import pandas as pd In [3]: from quant.common.math_helpers import get_ic In [4]: df1 = pd.DataFrame(np.random.randn(10, 10)) In [5]: df2 = pd.DataFrame(np.random.randn(10, 10)) In [6]: get_ic(df1, df2) Out[6]: 0 0.103030 1 -0.151515 2 -0.090909 3 0.212121 4 0.333333 5 0.418182 6 0.503030 7 0.442424 8 -0.115152 9 0.151515 dtype: float64