finvizfinance Documentation

The finvizfinance.group module provides access to aggregated financial data categorized by groups. You can retrieve information across four main categories:

• Overview: General group summary data.
• Valuation: Metrics related to the valuation of companies within the group.
• Performance: Historical and recent performance metrics for the group.
• Spectrum: Data regarding the distribution or range of metrics within the group.

Each category is implemented as a separate submodule within finvizfinance.group.

The finvizfinance library provides access to various categories of stock screener data. You can retrieve data by importing specific modules from finvizfinance.screener. The available data categories are:

• Overview: General company overview data.
• Valuation: Metrics related to company valuation (e.g., P/E ratios).
• Financial: Fundamental financial data.
• Ownership: Data regarding company ownership and insider holdings.
• Performance: Historical and recent performance metrics.
• Technical: Technical analysis indicators.
• Ticker: Data associated with specific tickers.

Install finvizfinance using pip. It is recommended to use a virtual environment. Requires Python >= v3.9.

> virtualenv -p python3 virtualenvironment
> source virtualenvironment/bin/activate
> pip install finvizfinance

To install the latest version of finvizfinance from PyPI, use the following command:

$ pip install finvizfinance

Use the finvizfinance class from finvizfinance.quote to instantiate a stock object by its ticker symbol. Once instantiated, you can download ticker charts or retrieve various fundamental and news data points.

import pandas as pd
from finvizfinance.quote import finvizfinance

stock = finvizfinance('tsla')

# Download chart to a directory
stock.TickerCharts(out_dir='asset')

# Get individual ticker information
stock_fundament = stock.ticker_fundament()
stock_description = stock.ticker_description()
stock_peers = stock.ticker_peer()
stock_etf_holders = stock.ticker_etf_holders()
outer_ratings_df = stock.ticker_outer_ratings()
news_df = stock.ticker_news()
inside_trader_df = stock.ticker_inside_trader()

Use the finvizfinance class from finvizfinance.quote to fetch comprehensive data for a specific ticker. You can retrieve fundamental data, company descriptions, outer ratings, news, and insider trader information. The class also supports downloading ticker charts as image files.

import pandas as pd
from finvizfinance.quote import finvizfinance

stock = finvizfinance('tsla')

# Download chart image
stock.ticker_charts()

# Retrieve data
stock_fundament = stock.ticker_fundament()
stock_description = stock.ticker_description()
outer_ratings_df = stock.ticker_outer_ratings()
news_df = stock.ticker_news()
inside_trader_df = stock.ticker_inside_trader()
signal = stock.ticker_signal()

Use the Insider class from finvizfinance.insider to retrieve insider trading data. You can specify the type of data via the option parameter (e.g., 'top owner trade') when initializing the class.

from finvizfinance.insider import Insider

finsider = Insider(option='top owner trade')
finsider.get_insider().head()

Use the News class from finvizfinance.news to fetch news articles. The get_news() method returns a dictionary containing news and blog data.

from finvizfinance.news import News

fnews = News()
all_news = fnews.get_news()
# Access news via all_news['news'] or blogs via all_news['blogs']

Use the Overview class from finvizfinance.screener.overview to filter stocks based on specific criteria. Use set_filter with a dictionary of filters and call screener_view() to retrieve the results as a DataFrame.

from finvizfinance.screener.overview import Overview

foverview = Overview()
filters_dict = {'Exchange':'AMEX','Sector':'Basic Materials'}
foverview.set_filter(filters_dict=filters_dict)
df = foverview.screener_view()
df.head()