从Pandas到Statsmodels的OLS中不推荐使用的滚动窗口选项

Question

正如标题所暗示的那样,Pandas中ols命令中的滚动功能选项在statsmodels中迁移到哪里？我似乎找不到它.熊猫告诉我厄运正在起作用:

FutureWarning: The pandas.stats.ols module is deprecated and will be removed in a future version. We refer to external packages like statsmodels, see some examples here: http://statsmodels.sourceforge.net/stable/regression.html
  model = pd.ols(y=series_1, x=mmmm, window=50)

事实上,如果你做的事情如下:

import statsmodels.api as sm

model = sm.OLS(series_1, mmmm, window=50).fit()

print(model.summary())

你得到结果(窗口不会影响代码的运行)但你只得到整个时期的回归运行参数,而不是应该应该处理的每个滚动周期的一系列参数.

Answer 1

我创建了一个ols模块,旨在模仿大熊猫的弃用MovingOLS; 它就在这里.

它有三个核心类:

• OLS:静态(单窗口)普通最小二乘回归.输出是NumPy数组
• RollingOLS:滚动(多窗口)普通最小二乘回归.输出是更高维度的NumPy数组.
• PandasRollingOLS:包装RollingOLSpandas Series&DataFrames 的结果.旨在模仿已弃用的pandas模块的外观.

请注意,该模块是包的一部分(我目前正在上传到PyPi),它需要一个包间导入.

上面的前两个类完全在NumPy中实现,主要使用矩阵代数. RollingOLS广泛利用广播.属性很大程度上模仿了statsmodels的OLS RegressionResultsWrapper.

一个例子:

import urllib.parse
import pandas as pd
from pyfinance.ols import PandasRollingOLS

# You can also do this with pandas-datareader; here's the hard way
url = "https://fred.stlouisfed.org/graph/fredgraph.csv"

syms = {
    "TWEXBMTH" : "usd", 
    "T10Y2YM" : "term_spread", 
    "GOLDAMGBD228NLBM" : "gold",
}

params = {
    "fq": "Monthly,Monthly,Monthly",
    "id": ",".join(syms.keys()),
    "cosd": "2000-01-01",
    "coed": "2019-02-01",
}

data = pd.read_csv(
    url + "?" + urllib.parse.urlencode(params, safe=","),
    na_values={"."},
    parse_dates=["DATE"],
    index_col=0
).pct_change().dropna().rename(columns=syms)
print(data.head())
#                  usd  term_spread      gold
# DATE                                       
# 2000-02-01  0.012580    -1.409091  0.057152
# 2000-03-01 -0.000113     2.000000 -0.047034
# 2000-04-01  0.005634     0.518519 -0.023520
# 2000-05-01  0.022017    -0.097561 -0.016675
# 2000-06-01 -0.010116     0.027027  0.036599

y = data.usd
x = data.drop('usd', axis=1)

window = 12  # months
model = PandasRollingOLS(y=y, x=x, window=window)

print(model.beta.head())  # Coefficients excluding the intercept
#             term_spread      gold
# DATE                             
# 2001-01-01     0.000033 -0.054261
# 2001-02-01     0.000277 -0.188556
# 2001-03-01     0.002432 -0.294865
# 2001-04-01     0.002796 -0.334880
# 2001-05-01     0.002448 -0.241902

print(model.fstat.head())
# DATE
# 2001-01-01    0.136991
# 2001-02-01    1.233794
# 2001-03-01    3.053000
# 2001-04-01    3.997486
# 2001-05-01    3.855118
# Name: fstat, dtype: float64

print(model.rsq.head())  # R-squared
# DATE
# 2001-01-01    0.029543
# 2001-02-01    0.215179
# 2001-03-01    0.404210
# 2001-04-01    0.470432
# 2001-05-01    0.461408
# Name: rsq, dtype: float64

Answer 2

用sklearn滚动测试版

import pandas as pd
from sklearn import linear_model

def rolling_beta(X, y, idx, window=255):

    assert len(X)==len(y)

    out_dates = []
    out_beta = []

    model_ols = linear_model.LinearRegression()

    for iStart in range(0, len(X)-window):        
        iEnd = iStart+window

        model_ols.fit(X[iStart:iEnd], y[iStart:iEnd])

        #store output
        out_dates.append(idx[iEnd])
        out_beta.append(model_ols.coef_[0][0])

    return pd.DataFrame({'beta':out_beta}, index=out_dates)


df_beta = rolling_beta(df_rtn_stocks['NDX'].values.reshape(-1, 1), df_rtn_stocks['CRM'].values.reshape(-1, 1), df_rtn_stocks.index.values, 255)