finvizfinance is an MIT-licensed Python library that scrapes the FinViz website to deliver stock, forex, and crypto data as pandas dataframes, made for analysts, quants, and developers who want programmatic access to FinViz screens, fundamentals, and news instead of reading the site by hand.

What it is

finvizfinance is a Python package published on PyPI that collects financial information from the FinViz website and hands it back as dictionaries and pandas dataframes. It lives in the Python data-extraction and web-scraping ecosystem, sitting alongside pandas as its output format and relying on the standard Python packaging chain for distribution. The library covers three asset classes in one interface: stocks, forex, and crypto, with dedicated modules for quotes, news, insider trading, and screeners.

The concrete problem it solves is that FinViz presents a large amount of useful financial data — fundamentals, technicals, insider activity, ratings, peers, ETF holders — only through a website, with no official API for the data its users want. finvizfinance replaces manual copying, ad-hoc scraping scripts, and screenshot-driven research with named functions that return structured results. A screener filter such as an index and sector combination becomes a dataframe, and a ticker lookup becomes a dictionary of fields including Company, Sector, Industry, Country, P/E, EPS (ttm), Insider Own, Shs Outstand, Perf Week, Market Cap, and Forward P/E.

Key capabilities

• Pull per-ticker fundamentals through finvizfinance.quote.finvizfinance and ticker_fundament(), returning fields such as P/E, Forward P/E, EPS (ttm), Insider Own, Shs Outstand, Market Cap, and Perf Week.
• Retrieve charts with ticker_charts(), company text with ticker_description(), and peer lists with ticker_peer().
• Collect ETF holder lists with ticker_etf_holders(), and dataframe outputs for analyst ratings with ticker_outer_ratings(), headlines with ticker_news(), and trades with ticker_inside_trader().
• Aggregate market-wide news via finvizfinance.news.News and get_news(), which returns separate news and blogs dataframes.
• Query insider trading through finvizfinance.insider.Insider with the options latest, top week, and top owner trade, served by get_insider().
• Screen multiple tickers across the Overview, Valuation, Financial, Ownership, Performance, and Technical views, setting criteria with set_filter(filters_dict=...) and retrieving results with screener_view().
• Cover forex charts and performance and crypto charts and performance alongside equities, with a Ticker screener returning ticker lists.

Who uses it and how

• Individual analysts and developers working in Python who need FinViz fundamentals and technicals loaded straight into pandas for further computation.
• Research workflows that compare stocks against user-defined filters, such as an Index of S&P 500 combined with a Sector, then read the resulting dataframe.
• Insider-trading monitoring, using the latest, top week, or top owner trade views to track disclosed transactions.
• ETF and holdings research, where a ticker's ETF holder list supports exposure checking.
• News and sentiment intake, pulling the FinViz news and blogs feeds into a single call for downstream processing.
• Multi-asset monitoring across equities, forex, and crypto using the same library rather than separate tools.

Getting started

Install from PyPI with pip install finvizfinance, or clone the repository from GitHub if a source checkout is preferred. Documentation lives at https://finvizfinance.readthedocs.io/en/latest/.

How it compares

No comparable tools are named in the facts for this entry, and no list of paid products it replaces is provided, so finvizfinance stands alone in this registry at present.

When to use it — and when not to

Anyone relying on this library must accept that it reads data from a third-party website rather than an official data feed, so availability and field coverage follow FinViz itself. Self-hosters take on nothing beyond a Python environment — no database, storage service, or SMTP setup is described — which makes adoption cheap but also means there is no server-side component to operate or scale. It is a poor fit for anyone needing tick-level, real-time, or contractually guaranteed market data, and for teams that cannot tolerate a scraper breaking when the upstream page structure changes; the README is functional and example-driven rather than exhaustive, so some behaviours must be discovered from the source.

$ git clone https://github.com/lit26/finvizfinance.git

$ pip install finvizfinance

from finvizfinance.quote import finvizfinance

stock = finvizfinance('tsla')

stock.ticker_charts()

stock_fundament = stock.ticker_fundament()

# result
# stock_fundament = {'Company': 'Tesla, Inc.', 'Sector': 'Consumer Cyclical',
# 'Industry': 'Auto Manufacturers', 'Country': 'USA', 'Index': '-', 'P/E': '849.57',
# 'EPS (ttm)': '1.94', 'Insider Own': '0.10%', 'Shs Outstand': '186.00M',
# 'Perf Week': '13.63%', 'Market Cap': '302.10B', 'Forward P/E': '106.17',
# ...}

stock_description = stock.ticker_description()

# stock_description
# stock_description = 'Tesla, Inc. designs, develops, manufactures, ...'

stock_peer = stock.ticker_peer()

# stock_peer
# stock_peer = ['LI', 'XPEV', 'NIO', 'RIVN', 'LCID', 'TM', 'HMC', 'GM', 'STLA', 'F']

stock_etf_holders = stock.ticker_etf_holders()

# stock_etf_holders
# stock_etf_holders = ['VTI', 'VOO', 'IVV', 'SPY', 'VUG', 'QQQ', 'VGT', 'IWF', 'XLK', 'SPLG']

outer_ratings_df = stock.ticker_outer_ratings()

news_df = stock.ticker_news()

inside_trader_df = stock.ticker_inside_trader()

from finvizfinance.news import News

fnews = News()
all_news = fnews.get_news()

all_news['news'].head()

all_news['blogs'].head()

from finvizfinance.insider import Insider

finsider = Insider(option='top owner trade')
# option: latest, top week, top owner trade
# default: latest

insider_trader = finsider.get_insider()

from finvizfinance.screener.overview import Overview

foverview = Overview()
filters_dict = {'Index':'S&P 500','Sector':'Basic Materials'}
foverview.set_filter(filters_dict=filters_dict)
df = foverview.screener_view()
df.head()

from finvizfinance.calendar import Calendar

fcalendar = Calendar()
df = fcalendar.calendar()
df.head()

from finvizfinance.earnings import Earnings

# period: This Week (default), Next Week, Previous Week, This Month
fearnings = Earnings(period='This Week')

# mode: financial (default), overview, valuation, ownership, performance, technical
days = fearnings.partition_days(mode='financial')

# optionally export the partitioned tables
fearnings.output_excel('earning_days.xlsx')
fearnings.output_csv('earning_days')

from finvizfinance.future import Future

ffuture = Future()
# timeframe: D (default), W, M, Q, HY, Y
df = ffuture.performance(timeframe='D')
df.head()

from finvizfinance.util import set_proxy

proxies={'http': 'http://127.0.0.1:8080'}
set_proxy(proxies)