How to do Financial Analysis with Pandas using Python• 2 min
Financial analysis can be performed in Pandas using Python by loading financial data into a Pandas DataFrame, cleaning and pre-processing the data, and then using functions to perform operations like calculating returns, aggregating data, generating financial metrics, and visualizing the results. Common financial metrics include daily returns, cumulative returns, moving averages, volatility, and Sharpe ratios. Pandas can also be used to perform time series analysis on financial data to understand trends and patterns over time.
Fetching data using yfinance
yfinance is a Python library that provides an easy-to-use interface to the Yahoo Finance API, allowing you to retrieve financial market data such as stock prices, dividends, and other historical financial information. With yfinance, you can fetch financial data for a specific stock or index, as well as perform data analysis using tools like Pandas, to extract insights and make informed decisions. yfinance is a convenient library for financial analysis and can be useful for a range of applications including stock market analysis, portfolio management, and algorithmic trading.
Here's an example of fetching stock prices using yfinance in Python:
import yfinance as yf # fetch data for Microsoft (MSFT) stock msft = yf.Ticker("MSFT") # retrieve historical stock prices df = msft.history(period="max") print(df.head())
This will output the first 5 rows of the Microsoft stock price data, including the date and the opening, high, low, close, and volume prices.
Open High Low Close Adj Close Volume Date 1986-03-13 0.06 0.07 0.06 0.07 0.066 187258400 1986-03-14 0.07 0.07 0.07 0.07 0.066 40502400 1986-03-17 0.07 0.07 0.07 0.07 0.066 40502400 1986-03-18 0.07 0.07 0.07 0.07 0.066 40502400 1986-03-19 0.07 0.07 0.07 0.07 0.066 40502400
With the yfinance library, you can fetch a variety of financial market data including:
- Stock prices: Historical and real-time stock prices for a specific company or index.
- Company information: Information such as company name, ticker symbol, market capitalization, sector, and industry.
- Financial statements: Balance sheets, income statements, and cash flow statements for a company.
- Dividends: Dividend history and yield information for a company.
- Options data: Options data for a specific stock or index, including option chains and option contract information.
- Recommendations: Analyst recommendations and target prices for a company.
- Earnings data: Earnings report data including earnings date, earnings per share (EPS), and consensus estimate.
- Sustainability data: Environmental, social, and governance (ESG) scores and metrics for a company.
These data can be fetched using yfinance functions such as
sustainability. The data can then be further analyzed and visualized using libraries such as Pandas and Matplotlib.
If you want to learn more about financial analysis in Python you can read the following articles: