What to do in this article

--Create your own stock price dataset and implement the AR (1) model.

Creating a stock price dataset

--You can get the csv of the stock price by accessing the following URL and searching for the stock name.

https://kabuoji3.com/stock/

Implementation of AR (1)

--Implement the AR (1) model based on the stock price csv obtained above.

AR (1) in one word

--A model that predicts the stock price of the day based on the stock price of the previous day.

Implementation

Library module

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

Preprocessing

#Read the data as df.
df = pd.read_csv(file_nm, encoding='cp932') # file_Specify the path of the stock price csv file in nm.
#Since the above csv has useless information, format it as df.
date = df.index[1:] #Generate a date to store the value of the date.
df = pd.DataFrame(df.values[1:,:], columns=df.loc['date'].values) #Delete extra information.
df['date'] = date
df = df[['date', 'Open price', 'High price', 'Low price', 'closing price', 'Volume', 'closing price調整値']]

#Narrow the df column to date and closing price.
df = df[['date','closing price']]

#Change the data type of the value stored in the closing price from string to float32.
df.loc[:,'closing price'] = df['closing price'].astype(np.float32)

#June 28, 2013 0,July 1, 2013 1,July 2, 2013 2, ...Generate a date number column.
date_num = list(range(len(df.date)))
df['Date number'] = date_num
df = df[['date', 'date番号', 'closing price']]

#Generate a column of closing prices one day ago.
df_2 = df.copy()
df_2['Date number'] = df_2['Date number'] + 1
df = pd.merge(df, df_2[['Date number' ,'closing price']], on='Date number', how='left')
df = df.rename(columns={'closing price_x':'Stock price of the day', 'closing price_y':'Stock price of the previous day'})

#Exclude rows that contain missing values.
df = df.dropna()

#Feature value,X to store the objective variable,Generate y.
y = df.Stock price of the day.values
X = df.Stock price of the previous day.values

def reshape_row(arr):
    """X,Change the shape of y.(Change to row vector.)
    """
    return arr.reshape(-1, 1)

X, y = reshape_row(X), reshape_row(y)

# X,7 for train data and validation data:Divide by a ratio of 3.
train_X, val_X, train_y, val_y = train_test_split(X, y, test_size=0.3)

Learning ~ Evaluation

#Linear regression model(AR(1))Calculate the coefficient estimate of.
model = LinearRegression()
model.fit(train_X, train_y)

#Display the coefficient estimates of the intercept and the stock price of the previous day.
print("The intercept is", model.intercept_[0])
print("The coefficient estimate of the stock price on the previous day is", model.coef_[0][0])

#Evaluate the model.
true_y = val_y
pred_y = model.predict(val_X)
mean_squared_error(true_y, pred_y)

[PYTHON] "Creating a stock price dataset" and "Implementing AR (1)"