我正在尝试使用数据框/时间序列的股票价格在熊猫中构建滚动OLS模型。我想要做的是在过去的N天内执行OLS计算并返回预测的价格和斜率，并将它们添加到数据框中各自的列中。据我所知，我唯一的选择是使用来自PandasRollingOLS的pyfinance所以我将在我的例子中使用它，但如果有另一种方式我很乐意使用它。
我的数据框如下所示：
Date Price
....
2019-03-31 08:59:59.999 1660
2019-03-31 09:59:59.999 1657
2019-03-31 10:59:59.999 1656
2019-03-31 11:59:59.999 1652
2019-03-31 12:59:59.999 1646
2019-03-31 13:59:59.999 1645
2019-03-31 14:59:59.999 1650
2019-03-31 15:59:59.999 1669
2019-03-31 16:59:59.999 1674
我想使用Date列作为自变量来执行滚动回归。通常我会这样做：
X = df['Date']
y = df['Price']
model = ols.PandasRollingOLS(y, X, window=250)
然而，不出所料，使用df['Date']作为我的X会返回错误。
所以我的第一个问题是，我需要对我的Date专栏做些什么来让PandasRollingOLS工作。我的下一个问题是我需要调用什么来返回预测值和斜率？有了常规的OLS，我会做像model.predict和model.slope这样的事情，但这些选项显然不适用于PandasRollingOLS。
我实际上想要将这些值添加到我的df中的新列中，所以正在考虑像df['Predict'] = model.predict这样的东西，但显然这不是答案。理想的出来df将是这样的：
Date Price Predict Slope
....
2019-03-31 08:59:59.999 1660 1665 0.10
2019-03-31 09:59:59.999 1657 1663 0.10
2019-03-31 10:59:59.999 1656 1661 0.09
2019-03-31 11:59:59.999 1652 1658 0.08
2019-03-31 12:59:59.999 1646 1651 0.07
2019-03-31 13:59:59.999 1645 1646 0.07
2019-03-31 14:59:59.999 1650 1643 0.07
2019-03-31 15:59:59.999 1669 1642 0.07
2019-03-31 16:59:59.999 1674 1645 0.08
任何帮助都会非常感激，欢呼。
您可以使用datetime.datetime.strptime和time.mktime将日期转换为整数，然后使用statsmodels和自定义函数为数据框的所需子集构建模型来处理滚动窗口：
输出：
Price Predict Slope
Date
2019-03-31 10:59:59.999 1656 1657.670504 0.000001
2019-03-31 11:59:59.999 1652 1655.003830 0.000001
2019-03-31 12:59:59.999 1646 1651.337151 0.000001
2019-03-31 13:59:59.999 1645 1647.670478 0.000001
2019-03-31 14:59:59.999 1650 1647.003818 0.000001
2019-03-31 15:59:59.999 1669 1654.670518 0.000001
2019-03-31 16:59:59.999 1674 1664.337207 0.000001
码：
#%%
# imports
import datetime, time
import pandas as pd
import numpy as np
import statsmodels.api as sm
from collections import OrderedDict
# your data in a more easily reprodicible format
data = {'Date': ['2019-03-31 08:59:59.999', '2019-03-31 09:59:59.999', '2019-03-31 10:59:59.999',
'2019-03-31 11:59:59.999', '2019-03-31 12:59:59.999', '2019-03-31 13:59:59.999',
'2019-03-31 14:59:59.999', '2019-03-31 15:59:59.999', '2019-03-31 16:59:59.999'],
'Price': [1660, 1657, 1656, 1652, 1646, 1645, 1650, 1669, 1674]}
# function to make a useful time structure as independent variable
def myTime(date_time_str):
date_time_obj = datetime.datetime.strptime(date_time_str, '%Y-%m-%d %H:%M:%S.%f')
return(time.mktime(date_time_obj.timetuple()))
# add time structure to dataset
data['Time'] = [myTime(obs) for obs in data['Date']]
# time for pandas
df = pd.DataFrame(data)
# Function for rolling OLS of a desired window size on a pandas dataframe
def RegressionRoll(df, subset, dependent, independent, const, win):
"""
RegressionRoll takes a dataframe, makes a subset of the data if you like,
and runs a series of regressions with a specified window length, and
returns a dataframe with BETA or R^2 for each window split of the data.
Parameters:
===========
df -- pandas dataframe
subset -- integer - has to be smaller than the size of the df or 0 if no subset.
dependent -- string that specifies name of denpendent variable
independent -- LIST of strings that specifies name of indenpendent variables
const -- boolean - whether or not to include a constant term
win -- integer - window length of each model
Example:
========
df_rolling = RegressionRoll(df=df, subset = 0,
dependent = 'Price', independent = ['Time'],
const = False, win = 3)
"""
# Data subset
if subset != 0:
df = df.tail(subset)
else:
df = df
# Loopinfo
end = df.shape[0]+1
win = win
rng = np.arange(start = win, stop = end, step = 1)
# Subset and store dataframes
frames = {}
n = 1
for i in rng:
df_temp = df.iloc[:i].tail(win)
newname = 'df' + str(n)
frames.update({newname: df_temp})
n += 1
# Analysis on subsets
df_results = pd.DataFrame()
for frame in frames:
#debug
#print(frames[frame])
# Rolling data frames
dfr = frames[frame]
y = dependent
x = independent
# Model with or without constant
if const == True:
x = sm.add_constant(dfr[x])
model = sm.OLS(dfr[y], x).fit()
else:
model = sm.OLS(dfr[y], dfr[x]).fit()
# Retrieve price and price prediction
Prediction = model.predict()[-1]
d = {'Price':dfr['Price'].iloc[-1], 'Predict':Prediction}
df_prediction = pd.DataFrame(d, index = dfr['Date'][-1:])
# Retrieve parameters (constant and slope, or slope only)
theParams = model.params[0:]
coefs = theParams.to_frame()
df_temp = pd.DataFrame(coefs.T)
df_temp.index = dfr['Date'][-1:]
# Build dataframe with Price, Prediction and Slope (+constant if desired)
df_temp2 = pd.concat([df_prediction, df_temp], axis = 1)
df_temp2=df_temp2.rename(columns = {'Time':'Slope'})
df_results = pd.concat([df_results, df_temp2], axis = 0)
return(df_results)
# test run
df_rolling = RegressionRoll(df=df, subset = 0,
dependent = 'Price', independent = ['Time'],
const = False, win = 3)
print(df_rolling)
通过不指定这么多变量可以很容易地缩短代码，而是将更多表达式直接放入字典和函数中，但是如果生成的输出实际上代表了您想要的输出，我们可以看一下。此外，您没有指定是否在分析中包含常量术语，因此我也包含了一个处理它的选项。