python - 有谁知道如何在python中绘制线性回归曲线或相关函数来绘制它？

Question

我刚刚开始学习Python，想知道如何用价格数据的时间序列绘制线性回归曲线(例如，收盘价，其中<强>只有 y 因素)

import pandas as pd
import pandas_datareader.data as web
import matplotlib.pyplot as plt
from datetime import datetime

start=datetime(2015,1,1)
end=datetime(2015,12,31)

df = web.DataReader("AMZN", "yahoo", start, end)
close = df['Close']

我提到了这个web page掌握了绘制线性回归曲线的基本思想，但不知道用什么函数在python中再次编写。

Answer 1

试试这个:

import pandas_datareader.data as web
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm

start = datetime(2015, 1, 1)
end = datetime(2015, 12, 31)

df = web.DataReader("AMZN", "yahoo", start, end)

df['day'] = df.index.map(lambda observation_timestamp: observation_timestamp.dayofyear)
y = df.Close
X = df.day
X = sm.add_constant(X)
est = sm.OLS(y, X)
est = est.fit()

X_prime = np.linspace(X.day.min(), X.day.max(), 100)
X_prime = sm.add_constant(X_prime)
y_hat = est.predict(X_prime)

plt.plot(X_prime[:,1], y_hat)
plt.scatter(X.day, y)
plt.show()

执行此est.summary():

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Close   R-squared:                       0.935
Model:                            OLS   Adj. R-squared:                  0.934
Method:                 Least Squares   F-statistic:                     3570.
Date:                Mon, 05 Dec 2016   Prob (F-statistic):          5.06e-150
Time:                        00:27:53   Log-Likelihood:                -1199.8
No. Observations:                 252   AIC:                             2404.
Df Residuals:                     250   BIC:                             2411.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
const        289.9491      3.622     80.053      0.000       282.816   297.083
day            1.0212      0.017     59.748      0.000         0.988     1.055
==============================================================================
Omnibus:                       15.313   Durbin-Watson:                   0.117
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                6.134
Skew:                           0.007   Prob(JB):                       0.0466
Kurtosis:                       2.236   Cond. No.                         429.
==============================================================================

另一个例子:

import pandas_datareader.data as web
from datetime import datetime
import statsmodels.api as sm
from patsy.highlevel import dmatrices

import matplotlib.pyplot as plt

start = datetime(2015, 1, 1)
end = datetime(2015, 12, 31)

df = web.DataReader("AMZN", "yahoo", start, end)

df['day'] = df.index.map(lambda observation_timestamp: observation_timestamp.dayofyear)

y, X = dmatrices('Close ~ day', data=df, return_type='dataframe')

mod = sm.OLS(y, X)

res = mod.fit()
sm.stats.linear_rainbow(res)
sm.graphics.plot_regress_exog(res, "day")
plt.show()

将sm.graphics.plot_regress_exog(res, "day")更改为sm.graphics.plot_fit(res, "day")

执行这个:res.summary()

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  Close   R-squared:                       0.935
Model:                            OLS   Adj. R-squared:                  0.934
Method:                 Least Squares   F-statistic:                     3570.
Date:                Mon, 05 Dec 2016   Prob (F-statistic):          5.06e-150
Time:                        00:26:04   Log-Likelihood:                -1199.8
No. Observations:                 252   AIC:                             2404.
Df Residuals:                     250   BIC:                             2411.
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept    289.9491      3.622     80.053      0.000       282.816   297.083
day            1.0212      0.017     59.748      0.000         0.988     1.055
==============================================================================
Omnibus:                       15.313   Durbin-Watson:                   0.117
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                6.134
Skew:                           0.007   Prob(JB):                       0.0466
Kurtosis:                       2.236   Cond. No.                         429.
==============================================================================