2017-12-27 486 views
2

enter image description here如何在此圖中繪製線性迴歸線?如何在此圖中繪製線性迴歸線?

這裏是我的代碼:

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

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可否請你()'tencent.head的'的輸出中添加到您的問題? – grovina

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請參閱PIC –

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您是在問如何計算,繪製它,還是兩者兼而有之?無論哪種情況,在堆棧交換中有很多關於python線性迴歸的已經回答的問題。同樣,有很多關於使用matplotlib繪製線條的回答問題。 –

回答

1

您可以使用scikit-learn來計算線性迴歸。

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' 

The linear regression fit using integers as the x-axis

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

The linear regression fit using DateTimeIndex as the x-axis

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哇,奇妙的代碼和清晰的解釋!謝謝。 –

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不客氣。萬一它起作用,請考慮選擇它作爲正確的答案:) – Nitred