Stock Data Preparation

Stock Data Preparation#

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

There are various methods to import stock data for a specific period using its symbol. One of the most popular tools is yfinance, a Python module that allows users to fetch historical stock price data conveniently via the Yahoo Finance API.

Description of Data#

Each stock has a symbol, which is unique to it and consists of up to five characters, including letters, ‘.’, and ‘-‘.”

Examples:

Company	Symbol
Alphabet	AALP
Amazon	AMZN
Apple	AAPL
Visa	V
Allstate	ALL
Tesla	TSLA

The symbol is used to import historical data.
history() method returns a dataframe with date as index and 7 columns:
- Open : The intial price of the stock in the beginning of the day
- High : The highest price of the stock during the day
- Low : The lowest price of the stock during the day
- Close : The final price of the stock at the end of the day
- Volume: The number of stocks traded during the day
- Dividens: This is the share of company earnings distributed among its investors
- Stock Splits: It is subdividing each share of its stock into a fixed number of units.

The history() method by default returns data for the business days of the last month.
- Some dates may be missing, representing days when the market is closed.
- The index includes dates and times.
- start and end parameters allow access to data within a specific range.
  - Dates should be in the format ‘YEAR-MONTH-DAY’, where the month is numerical.

Import Data#

By default, it returns only the last month of historical stock price data.

df = yf.Ticker('AAPL').history()
df.head().round(2)

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2024-12-09 00:00:00-05:00	241.83	247.24	241.75	246.75	44649200	0.0	0.0
2024-12-10 00:00:00-05:00	246.89	248.21	245.34	247.77	36914800	0.0	0.0
2024-12-11 00:00:00-05:00	247.96	250.80	246.26	246.49	45205800	0.0	0.0
2024-12-12 00:00:00-05:00	246.89	248.74	245.68	247.96	32777500	0.0	0.0
2024-12-13 00:00:00-05:00	247.82	249.29	246.24	248.13	33155300	0.0	0.0

Data is available only for business days when the stock market is open.

# data of 22 days
df.shape

(21, 7)

Setting period='max' returns all available data for a stock.

df = yf.Ticker('AAPL').history(period='max')
df.head().round(2)

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
1980-12-12 00:00:00-05:00	0.10	0.10	0.10	0.10	469033600	0.0	0.0
1980-12-15 00:00:00-05:00	0.09	0.09	0.09	0.09	175884800	0.0	0.0
1980-12-16 00:00:00-05:00	0.09	0.09	0.09	0.09	105728000	0.0	0.0
1980-12-17 00:00:00-05:00	0.09	0.09	0.09	0.09	86441600	0.0	0.0
1980-12-18 00:00:00-05:00	0.09	0.09	0.09	0.09	73449600	0.0	0.0

Daily data between ‘1995-1-1’ and ‘2000-12-31’, in the form of ‘YEAR-MONTH-DAY’.

df = yf.Ticker('AAPL').history(start='1995-1-1', end='2000-12-31')
df.head().round(2)

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
1995-01-03 00:00:00-05:00	0.29	0.29	0.28	0.29	103868800	0.0	0.0
1995-01-04 00:00:00-05:00	0.29	0.30	0.29	0.29	158681600	0.0	0.0
1995-01-05 00:00:00-05:00	0.29	0.29	0.29	0.29	73640000	0.0	0.0
1995-01-06 00:00:00-05:00	0.31	0.32	0.31	0.31	1076622400	0.0	0.0
1995-01-09 00:00:00-05:00	0.31	0.31	0.31	0.31	274086400	0.0	0.0

Remove the last the two columns.

df = yf.Ticker('AAPL').history(start='1995-1-1', end='2000-12-31').iloc[:,:-2]
df.head().round(2)

	Open	High	Low	Close	Volume
Date
1995-01-03 00:00:00-05:00	0.29	0.29	0.28	0.29	103868800
1995-01-04 00:00:00-05:00	0.29	0.30	0.29	0.29	158681600
1995-01-05 00:00:00-05:00	0.29	0.29	0.29	0.29	73640000
1995-01-06 00:00:00-05:00	0.31	0.32	0.31	0.31	1076622400
1995-01-09 00:00:00-05:00	0.31	0.31	0.31	0.31	274086400

Remove Time#

In this part, we will remove the time from the index dates.

# Reset the index and set the previous index as the Date column.
df.reset_index(inplace=True)
df.head().round(2)

	Date	Open	High	Low	Close	Volume
0	1995-01-03 00:00:00-05:00	0.29	0.29	0.28	0.29	103868800
1	1995-01-04 00:00:00-05:00	0.29	0.30	0.29	0.29	158681600
2	1995-01-05 00:00:00-05:00	0.29	0.29	0.29	0.29	73640000
3	1995-01-06 00:00:00-05:00	0.31	0.32	0.31	0.31	1076622400
4	1995-01-09 00:00:00-05:00	0.31	0.31	0.31	0.31	274086400

# Use date() method to access only the date part and assign it as the new values for the 'Date' column.
df['Date'] = [i.date() for i in df.Date]
df.head().round(2)

	Date	Open	High	Low	Close	Volume
0	1995-01-03	0.29	0.29	0.28	0.29	103868800
1	1995-01-04	0.29	0.30	0.29	0.29	158681600
2	1995-01-05	0.29	0.29	0.29	0.29	73640000
3	1995-01-06	0.31	0.32	0.31	0.31	1076622400
4	1995-01-09	0.31	0.31	0.31	0.31	274086400

Stock Data Preparation

Contents

Stock Data Preparation#

Description of Data#

Import Data#

Remove Time#