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README

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
searchstocks / searchcrypto / search_forex - Simplified screeners
gettopmovers - 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 >= 1000000) 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, STOCKVALUATIONFIELDS, STOCKDIVIDENDFIELDS
ss = StockScreener() ss.specificfields = STOCKVALUATIONFIELDS + STOCKDIVIDEND_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, getpreset, listpresets,
    STOCKPRICEFIELDS, STOCKVALUATIONFIELDS, STOCKDIVIDENDFIELDS,
    STOCKPERFORMANCEFIELDS, STOCKOSCILLATORFIELDS
)
See all available presets
print(list_presets())
['stockprice', 'stockvolume', 'stockvaluation', 'stockdividend', ...]
Use presets directly
ss = StockScreener()
ss.specificfields = STOCKVALUATIONFIELDS + STOCKDIVIDEND_FIELDS
df = ss.get()
Or get preset by name
fields = getpreset('stockperformance')

Available Presets: | Category | Presets | |----------|---------| | Stock | stockprice, stockvolume, stockvaluation, stockdividend, stockprofitability, stockperformance, stockoscillators, stockmovingaverages, stockearnings | | Crypto | cryptoprice, cryptovolume, cryptoperformance, cryptotechnical | | Forex | forexprice, forexperformance, forex_technical | | Bond | bondbasic, bondyield, bond_maturity | | Futures | futuresprice, futurestechnical | | Coin | coinprice, coinmarket |

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
rsi1h = StockField.RELATIVESTRENGTHINDEX14.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.MACDLEVEL1226.withinterval("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, onupdate=onupdate):         # 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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