如何在此图中绘制线性回归线？

Question

enter image description here如何在此图中绘制线性回归线？

这里是我的代码：

import numpy as np 
import pandas_datareader.data as web 
import pandas as pd 
import datetime 
import matplotlib.pyplot as plt 
#get adjusted close price of Tencent from yahoo 
start = datetime.datetime(2007, 1, 1) 
end = datetime.datetime(2017, 12, 27) 
tencent = pd.DataFrame() 
tencent = web.DataReader('0700.hk', 'yahoo', start, end)['Adj Close'] 
nomalized_return=np.log(tencent/tencent.iloc[0]) 
nomalized_return.plot() 
plt.show() 

Pic 1 Jupiter Notebook

Pic 2 my Jupiter Notebook

Answer 1

您可以使用scikit-learn来计算线性回归。

以下添加到您的文件的底部：

# Create dataframe 
df = pd.DataFrame(data=nomalized_return) 

# Resample by day 
# This needs to be done otherwise your x-axis for linear regression will be incorrectly scaled since you have missing days. 
df = df.resample('D').asfreq() 

# Create a 'x' and 'y' column for convenience 
df['y'] = df['Adj Close']  # create a new y-col (optional) 
df['x'] = np.arange(len(df)) # create x-col of continuous integers 

# Drop the rows that contain missing days 
df = df.dropna() 

# Fit linear regression model using scikit-learn 
from sklearn.linear_model import LinearRegression 
lin_reg = LinearRegression() 
lin_reg.fit(X=df['x'].values[:, np.newaxis], y=df['y'].values[:, np.newaxis]) 

# Make predictions w.r.t. 'x' and store it in a column called 'y_pred' 
df['y_pred'] = lin_reg.predict(df['x'].values[:, np.newaxis]) 

# Plot 'y' and 'y_pred' vs 'x' 
df[['y', 'y_pred', 'x']].plot(x='x') # Remember 'y' is 'Adj Close' 

# Plot 'y' and 'y_pred' vs 'DateTimeIndex` 
df[['y', 'y_pred']].plot()