openscreener 0.1.1

A Playwright-powered Python package for extracting structured financial data from Screener.in.

openscreener

openscreener is a Playwright-powered Python library for extracting structured financial data from Screener.in.

It loads live stock and index pages, detects the page type, and returns normalized Python dictionaries and lists for the sections you care about.

Highlights

• High-level APIs for single stocks, indexes, and batch stock fetches
• Normalized outputs for summary, analysis, peers, quarterly results, profit and loss, balance sheet, cash flow, ratios, and shareholding
• Index support with constituent pagination handling
• Pretty terminal output, JSON export, and optional pandas DataFrame conversion
• Helpful errors when a section is missing or the wrong class is used for a page

Installation

openscreener requires Python 3.10+.

Install the package:

pip install openscreener

Install Playwright browser binaries:

python -m playwright install chromium

Install pandas if you want to_dataframe() support:

pip install pandas

Install development dependencies when working on the repo:

pip install -e .[dev]

Quick Start

Stock

from openscreener import Stock

stock = Stock("TCS")

summary = stock.summary()
print(summary["company_name"])
print(summary["current_price"])
print(summary["ratios"]["market_cap"])

analysis = stock.pros_cons()
print(analysis["pros"][0])

payload = stock.fetch(["summary", "ratios", "shareholding"])
print(payload["ratios"]["roce_percent"])

stock.pretty("summary")
stock.pretty("cash_flow")
print(stock.metadata())

Index

from openscreener import Index

index = Index("CNX500")

print(index.page_type())  # index
print(index.summary()["company_name"])

constituents = index.constituents(limit=70)
print(constituents["returned_companies"])
print(constituents["companies"][0]["symbol"])

index.pretty("constituents", constituents_limit=20)

Batch

from openscreener import Stock

batch = Stock.batch(["TCS", "INFY"])

ratios_by_symbol = batch.fetch("ratios")
print(ratios_by_symbol["TCS"]["roce_percent"])

payload_by_symbol = batch.fetch(["summary", "shareholding"])
print(payload_by_symbol["INFY"]["summary"]["company_name"])

JSON And DataFrame Helpers

from openscreener import Stock

stock = Stock("TCS")

print(stock.to_json())

frame = stock.to_dataframe("peers")
print(frame.head())

Public API

from openscreener import BatchStock, Index, PlaywrightScraper, Stock

Stock

Stock(symbol: str, consolidated: bool = False, scraper: PlaywrightScraper | None = None)

Main methods:

• summary()
• pros_cons()
• pros()
• cons()
• peers()
• quarterly_results()
• profit_loss()
• balance_sheet()
• cash_flow()
• ratios()
• shareholding(frequency="quarterly")
• shareholding_quarterly()
• shareholding_yearly()
• fetch(sections, constituents_limit=None)
• all()
• available_sections()
• page_type()
• is_stock()
• is_index()
• pretty(section=None, constituents_limit=None)
• print_section(section, constituents_limit=None)
• to_json(indent=2, constituents_limit=None)
• to_dataframe(section)
• metadata()

Index

Index(symbol: str, scraper: PlaywrightScraper | None = None)

Main methods:

• summary()
• constituents(limit=None)
• fetch(sections, constituents_limit=None)
• all(constituents_limit=None)
• available_sections()
• page_type()
• pretty(section=None, constituents_limit=None)
• print_section(section, constituents_limit=None)
• to_json(indent=2, constituents_limit=None)
• to_dataframe(section)
• metadata()

BatchStock

BatchStock(
    symbols,
    consolidated: bool = False,
    scraper: PlaywrightScraper | None = None,
)

Main method:

• fetch(sections)

Shortcut constructor:

from openscreener import Stock

batch = Stock.batch(["TCS", "INFY"])

PlaywrightScraper

PlaywrightScraper(
    base_url="https://www.screener.in/company/{symbol}{path_suffix}",
    consolidated=False,
    headless=True,
    timeout_ms=30000,
)

Main methods:

• fetch_page(symbol)
• fetch_pages(symbols)
• fetch_constituent_pages(symbol, page_numbers, page_size=50)

Supported Sections

Stock Sections

Canonical section	Accepted aliases	Method	Return shape
summary	summary	summary()	dict
analysis	analysis, pros_cons	pros_cons()	dict
peers	peers	peers()	dict
quarterly_results	quarters, quarterly_results	quarterly_results()	list[dict]
profit_loss	profit-loss, profit_loss	profit_loss()	list[dict]
balance_sheet	balance-sheet, balance_sheet	balance_sheet()	list[dict]
cash_flow	cash-flow, cash_flow	cash_flow()	list[dict]
ratios	ratios	ratios()	dict
shareholding	shareholding	shareholding()	list[dict]

Index Sections

Canonical section	Accepted aliases	Method	Return shape
summary	summary	summary()	dict
constituents	constituents, companies	constituents(limit=None)	dict

Helper-Only Section Names

These work with pretty(), print_section(), and to_dataframe() where applicable:

• pros
• cons
• shareholding_quarterly
• shareholding_yearly

Behavior Notes

• Stock("TCS") is for stock pages.
• Index("CNX500") or Index("NIFTY") is for index pages.
• page_type() returns stock, index, or unknown.
• Using the wrong class for a page raises EntityTypeMismatchError.
• available_sections() depends on whether the resolved page is a stock or an index.
• stock.fetch("ratios") returns {"ratios": {...}}.
• index.all(constituents_limit=100) limits the returned constituent rows in the payload.
• summary()["ratios"] contains top-card metrics such as market cap, current price, high/low, and similar values.
• ratios() returns the latest annual ratios row, not the whole historical ratios table.
• shareholding() defaults to quarterly data.
• metadata() returns source metadata such as symbol, entity type, currency, units, and company or index name when available.

Output Helpers

Pretty-print one section or the full payload:

from openscreener import Stock

stock = Stock("TCS")

stock.pretty()
stock.pretty("summary")
stock.print_section("pros")

Index pretty-printing works the same way:

from openscreener import Index

index = Index("CNX500")
index.pretty("constituents", constituents_limit=50)

If pandas is installed, you can convert tabular sections to DataFrames:

from openscreener import Stock

stock = Stock("TCS")
frame = stock.to_dataframe("cash_flow")
print(frame.head())

If pandas is not installed, to_dataframe() raises an ImportError with an install hint.

Data Conventions

• Numeric values are converted to int or float where possible.
• Missing values are returned as None.
• Period labels remain strings such as Dec 2025, Mar 2025, or TTM.
• Monetary values and units follow Screener's presentation.
• Default metadata reports INR currency and crores units.

Development

Repository layout:

src/openscreener/         Package source
src/openscreener/parsers/ Section parsers
tests/                    Automated tests

Run the local checks:

python -m pytest -q
python -m build --no-isolation
python -m twine check dist/*

Releasing A New Version

1. Bump the version in pyproject.toml.
2. Run the test and build checks.
3. Upload the new distribution files.

python -m pytest -q
rm -rf dist/
python -m build --no-isolation
python -m twine check dist/*
python -m twine upload dist/*

Each PyPI upload must use a new version number. Clear dist/ before building so old artifacts are not uploaded again.

Limitations

• Parsing depends on Screener.in's current HTML structure.
• Live usage requires Playwright and installed browser binaries.
• Missing sections raise SectionNotFoundError.
• Large index fetches depend on Screener's pagination remaining accessible.
• The project exposes a Python API; it does not currently provide a packaged CLI.

Use the live scraper responsibly and in a way that respects Screener.in's terms and rate limits.

License

MIT. See LICENSE.

Legal Disclaimer

openscreener is distributed under the MIT License. See the LICENSE file for details.

openscreener is not affiliated with, endorsed by, or vetted by Screener.in. It is an open-source tool that uses Screener.in's publicly available pages and is intended for research and educational purposes. Refer to Screener.in's terms of use for details on your rights to access and use the underlying data. You are responsible for ensuring your usage complies with applicable terms, laws, and regulations.