Fetch the stock prices from yahoo and save the data frame to the local machine. For more detailed explanation, check out my blog post https://www.gsrikar.com/2020/02/how-to-fetch-and-save-stock-price.html

Fetch Stock Prices and Save to Local Folder

1. Fetch the stock prices using pandas_datareader libray.

2. Use pandas to save the data frame to csv file or xlsx file.

3. Fetch and save for the desired ticker symbol between any date range.

main.py

# Get the dates
import datetime

# Find relative dates of the past or of the future
from dateutil.relativedelta import relativedelta

from stock_price import StockPrice


def stock_price():
    # Assign a ticker symbol
    ts = 'BAJFINANCE.NS'

    # Create an instance of the class
    stockPrice = StockPrice()
    # Fetch the stock price
    df_ts = stockPrice.fetch(
        ts,
        datetime.date.today() - relativedelta(years=10),
        datetime.date.today()
    )
    # Print the first 5 rows
    print(df_ts.head())
    # Print the last 5 rows
    print(df_ts.tail())
    # Save the stock price
    stockPrice.save(df_ts, ts.replace(".", "_"))


if __name__ == '__main__':
    stock_price()

stock_prices.py

# Create files in the operating system
import os

# Convert data frames to file formats
import pandas as pd
# Fetch the stock prices
import pandas_datareader as web


# Find relative dates of the past or of the future

class StockPrice:
    """
    Provide functionality to fetch the historical stock prices and save it to a path
    """

    def __init__(self):
        """
        Entry point to the class
        """
        self.data_source = 'yahoo'
        self.path = 'data/'

    def fetch(self, ticker_symbol, start, end):
        """
        Fetch the data from pre-defined source between the start and end time
        :param ticker_symbol:
        :param start: start time in yyyy-mm-dd
        :param end: end time in yyyy-mm-dd
        :return: data frame with the historical stock prices
        """
        return web.DataReader(ticker_symbol, self.data_source, start, end)

    def save(self, df, file_name):
        """
        Save the data frame to a csv file in the local folderz
        :param df: data frame with historical stock prices
        :param file_name: name of the csv files
        """
        # Create the path
        created = self.create_path()
        print('New Path created', created)

        # Create and write to the csv file
        self.write_csv(df, file_name)
        self.write_excel(df, file_name)

    def create_path(self):
        """
        Create a path in the system
        :return: True if the path was created because it doesn't exist and false otherwise
        """
        if not os.path.exists(self.path):
            # Path doesn't exists, create the path
            os.makedirs(self.path)
            return True
        return False

    def write_excel(self, df, file_name):
        """
        Create a file of format xlsx
        :param df: data frame with historical stock prices
        :param file_name: name of the csv files
        """
        writer = pd.ExcelWriter(self.path + file_name + ".xlsx", datetime_format='dd/mm/yyy')
        df.to_excel(writer)
        writer.close()

    def write_csv(self, df, file_name):
        """
        Create a file of format csv
        :param df: data frame with historical stock prices
        :param file_name: name of the csv files
        """
        df.to_csv(self.path + file_name + ".csv")