GitHub - deepentropy/tvscreener: TradingView Screener API - Stock, Crypto, Forex, Bond, Futures, Coin

TradingView™ Screener API

TradingView™ Screener API: simple Python library to retrieve data from TradingView™ Screener

🚀 Try the Code Generator

Build screener queries visually and get Python code instantly!

The Code Generator lets you:

• Select from 6 screener types (Stock, Crypto, Forex, Bond, Futures, Coin)

• Build filters visually with 13,000+ fields

• Generate ready-to-use Python code

• Copy and run in your environment

Get the results as a Pandas Dataframe

Disclaimer

This is an unofficial, third-party library and is not affiliated with, endorsed by, or connected to TradingView™ in any way. TradingView™ is a trademark of TradingView™, Inc.

This independent project provides a Python interface to publicly available data from TradingView's screener. Use of this library is at your own risk and subject to TradingView's terms of service.

What's New in v0.2.0

MCP Server Integration - This release adds Model Context Protocol (MCP) support, enabling AI assistants like Claude to query market data directly.

MCP Server for AI Assistants

Install with MCP support

pip install tvscreener[mcp]

Run MCP server

tvscreener-mcp

Register with Claude Code

claude mcp add tvscreener -- tvscreener-mcp

MCP Tools:

• discover_fields - Search 3500+ available fields by keyword

• custom_query - Flexible queries with any fields and filters

• search_stocks / search_crypto / search_forex - Simplified screeners

• get_top_movers - Get top gainers/losers

What's New in v0.1.0

Major API Enhancement Release - This release significantly expands the library with new screeners, 13,000+ fields, and a more intuitive API.

New Screeners

• BondScreener - Query government and corporate bonds

• FuturesScreener - Query futures contracts

• CoinScreener - Query coins from CEX and DEX exchanges

Expanded Field Coverage

• 13,000+ fields across all screener types (up from ~300)

• Complete technical indicator coverage with all time intervals

• Fields organized by category with search and discovery methods

Pythonic Comparison Syntax

from tvscreener import StockScreener , StockField

ss = StockScreener () ss . where ( StockField . PRICE > 50 ) ss . where ( StockField . VOLUME >= 1_000_000 ) ss . where ( StockField . MARKET_CAPITALIZATION . between ( 1e9 , 50e9 )) ss . where ( StockField . SECTOR . isin ([ 'Technology' , 'Healthcare' ])) df = ss . get ()

Fluent API

Chain methods for cleaner code

ss = StockScreener () ss . select ( StockField . NAME , StockField . PRICE , StockField . CHANGE_PERCENT ) ss . where ( StockField . PRICE > 100 ) df = ss . get ()

Field Presets

from tvscreener import StockScreener , STOCK_VALUATION_FIELDS , STOCK_DIVIDEND_FIELDS

ss = StockScreener () ss . specific_fields = STOCK_VALUATION_FIELDS + STOCK_DIVIDEND_FIELDS

Type-Safe Validation

The library now validates that you're using the correct field types with each screener, catching errors early.

Main Features

• Query Stock , Forex , Crypto , Bond , Futures , and Coin Screeners

• All the fields available : 13,000+ fields across all screener types

• Any time interval ( no need to be a registered user - 1D, 5m, 1h, etc.)

• Fluent API with select() and where() methods for cleaner code

• Field discovery - search fields by name, get technicals, filter by category

• Field presets - curated field groups for common use cases

• Type-safe validation - catches field/screener mismatches

• Filters by any fields, symbols, markets, countries, etc.

• Get the results as a Pandas Dataframe

• Styled output with TradingView-like colors and formatting

• Streaming/Auto-update - continuously fetch data at specified intervals

Installation

The source code is currently hosted on GitHub at: https://github.com/deepentropy/tvscreener

Binary installers for the latest released version are available at the Python Package Index (PyPI)

or PyPI

pip install tvscreener

From pip + GitHub:

pip install git+https://github.com/deepentropy/tvscreener.git

Usage

Basic Screeners

import tvscreener as tvs

Stock Screener

ss = tvs . StockScreener () df = ss . get () # returns a dataframe with 150 rows by default

Forex Screener

fs = tvs . ForexScreener () df = fs . get ()

Crypto Screener

cs = tvs . CryptoScreener () df = cs . get ()

Bond Screener (NEW)

bs = tvs . BondScreener () df = bs . get ()

Futures Screener (NEW)

futs = tvs . FuturesScreener () df = futs . get ()

Coin Screener (NEW) - CEX and DEX coins

coins = tvs . CoinScreener () df = coins . get ()

Fluent API

Use select() and where() for cleaner, chainable code:

from tvscreener import StockScreener , StockField

ss = StockScreener () ss . select ( StockField . NAME , StockField . PRICE , StockField . CHANGE_PERCENT , StockField . VOLUME , StockField . MARKET_CAPITALIZATION ) ss . where ( StockField . MARKET_CAPITALIZATION > 1e9 ) ss . where ( StockField .

CHANGE_PERCENT > 5 ) df = ss . get ()

Field Discovery

Search and explore the 13,000+ available fields:

from tvscreener import StockField

Search fields by name or label

rsi_fields = StockField . search ( "rsi" ) print ( f"Found { len ( rsi_fields ) } RSI-related fields" )

Get all technical indicator fields

technicals = StockField . technicals () print ( f"Found { len ( technicals ) } technical fields" )

Get recommendation fields

recommendations = StockField . recommendations ()

Field Presets

Use curated field groups for common analysis needs:

from tvscreener import ( StockScreener , get_preset , list_presets , STOCK_PRICE_FIELDS , STOCK_VALUATION_FIELDS , STOCK_DIVIDEND_FIELDS , STOCK_PERFORMANCE_FIELDS , STOCK_OSCILLATOR_FIELDS )

See all available presets

print ( list_presets ())

['stock_price', 'stock_volume', 'stock_valuation', 'stock_dividend', ...]

Use presets directly

ss = StockScreener () ss . specific_fields = STOCK_VALUATION_FIELDS + STOCK_DIVIDEND_FIELDS df = ss . get ()

Or get preset by name

fields = get_preset ( 'stock_performance' )

Available Presets:

Category Presets

Stock stock_price , stock_volume , stock_valuation , stock_dividend , stock_profitability , stock_performance , stock_oscillators , stock_moving_averages , stock_earnings

Crypto crypto_price , crypto_volume , crypto_performance , crypto_technical

Forex forex_price , forex_performance , forex_technical

Bond bond_basic , bond_yield , bond_maturity

Futures futures_price , futures_technical

Coin coin_price , coin_market

Time Intervals for Technical Fields

Apply different time intervals to technical indicators:

from tvscreener import StockScreener , StockField

ss = StockScreener ()

Get RSI with 1-hour interval

rsi_1h = StockField . RELATIVE_STRENGTH_INDEX_14 . with_interval ( "60" )

Available intervals: 1, 5, 15, 30, 60, 120, 240, 1D, 1W, 1M

ss . specific_fields = [ StockField . NAME , StockField . PRICE , rsi_1h , StockField . MACD_LEVEL_12_26 . with_interval ( "240" ), # 4-hour MACD ] df = ss . get ()

Parameters

For detailed usage examples, see the documentation and notebooks below.

Styled Output

You can apply TradingView-style formatting to your screener results using the beautify function. This adds colored text for ratings and percent changes, formatted numbers with K/M/B suffixes, and visual indicators for buy/sell/neutral recommendations.

import tvscreener as tvs

Get raw data

ss = tvs . StockScreener () df = ss . get ()

Apply TradingView styling

styled = tvs . beautify ( df , tvs . StockField )

Display in Jupyter/IPython (shows colored output)

styled

The styled output includes:

• Rating columns with colored text and directional arrows:

• Buy signals: Blue color with up arrow (↑)

• Sell signals: Red color with down arrow (↓)

• Neutral: Gray color with dash (-)

• Percent change columns : Green for positive, Red for negative

• Number formatting : K, M, B, T suffixes for large numbers

• Missing values : Displayed as "--"

Streaming / Auto-Update

You can use the stream() method to continuously fetch screener data at specified intervals. This is useful for monitoring real-time market data.

import tvscreener as tvs

Basic streaming with iteration limit

ss = tvs . StockScreener () for df in ss . stream ( interval = 10 , max_iterations = 5 ): print ( f"Got { len ( df ) } rows" )

Streaming with callback

from datetime import datetime

def on_update ( df ): print ( f"Updated at { datetime . now () } : { len ( df ) } rows" )

ss = tvs . StockScreener () try : for df in ss . stream ( interval = 5 , on_update = on_update ):

Process data

pass except KeyboardInterrupt : print ( "Stopped streaming" )

Stream with filters

ss = tvs . StockScreener () ss . set_markets ( tvs . Market . AMERICA ) for df in ss . stream ( interval = 30 , max_iterations = 10 ): print ( df . head ())

Parameters:

• interval : Refresh interval in seconds (minimum 1.0 to avoid rate limiting)

• max_iterations : Maximum number of refreshes (None = infinite)

• on_update : Optional callback function called with each DataFrame

Documentation

📖 Full Documentation - Complete guides, API reference, and examples.

Quick Links

Guide Description

Quick Start Get up and running in 5 minutes

Filtering Complete filtering reference

Stock Screening Value, momentum, dividend strategies

Technical Analysis RSI, MACD, multi-timeframe

API Reference Screeners, Fields, Enums

Jupyter Notebooks

Interactive examples organized by use case:

Notebook Description

01-quickstart.ipynb Overview of all 6 screeners

02-stocks.ipynb Stock screening strategies

03-crypto.ipynb Crypto analysis

04-forex.ipynb Forex pairs screening

05-bonds-futures.ipynb Bonds and futures

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