tradingview-screener

Many fields (like prices and indicators) support multiple timeframes using a pipe | syntax. Use the following mapping for column names:

Install the package using the standard Python package manager:

pip install tradingview-screener

To perform a simple query, instantiate a Query object, use .select() to specify the columns/fields you want to retrieve, and call .get_scanner_data() to execute the request. By default, the result is limited to 50 rows.

from tradingview_screener import Query

x = (Query()
 .select('name', 'close', 'volume', 'market_cap_basic')
 .get_scanner_data())
print(x)

You can build complex queries by chaining methods. Use col() to reference specific fields for logical operations in .where(). Supported methods include:

• .select(*fields): Specify columns to retrieve.
• .where(*conditions): Apply SQL-like filtering (supports AND/OR logic).
• .order_by(field, ascending=True/False): Sort results.
• .offset(n): Skip the first n rows.
• .limit(n): Restrict the number of rows returned.

from tradingview_screener import Query, col

(Query()
 .select('name', 'close', 'close|1', 'close|5', 'volume', 'relative_volume_10d_calc')
 .where(
     col('market_cap_basic').between(1_000_000, 50_000_000),
     col('relative_volume_10d_calc') > 1.2,
     col('MACD.macd|1') >= col('MACD.signal|1')  # 1 minute MACD
 )
 .order_by('volume', ascending=False)
 .offset(5)
 .limit(25)
 .get_scanner_data())

The package provides top-level functions to quickly target specific markets. These functions return a Query object, allowing you to chain further methods like .select() or .where().

Available screener functions:

• stocks(country)
• crypto()
• crypto_dex()
• coin()
• forex()
• futures()
• bond()
• cfd()
• options(symbol)

from tradingview_screener import stocks, crypto, options

# top stocks by market cap in Italy
stocks('italy').limit(5).get_scanner_data()

# top CEX crypto pairs by 24 h volume
crypto().limit(5).get_scanner_data()

# AAPL options chain
options('NASDAQ:AAPL').limit(5).get_scanner_data()

Control the order and pagination of your results using these methods:

• .order_by(column, ascending=True, nulls_first=False): Sorts by the specified column. Set ascending=False for descending order.
• .limit(limit): Sets the maximum number of records to return.
• .offset(offset): Sets the number of records to skip.

(Query()
    .order_by('volume', ascending=False)
    .limit(50)
    .offset(10)
    .get_scanner_data())

Use stocks(market: str) to create a Query object for stocks (including common, preferred, DRs, and non-ETF funds). The results are filtered to primary listings and sorted by market cap in descending order.

Parameters:

• market: The market or country to scan (e.g., 'america', 'italy', 'germany'). Defaults to 'america'.

from tradingview_screener import stocks

# Get US stocks
query = stocks(market='america')

# Get Italian stocks
query = stocks(market='italy')

Use .set_markets(*markets) to define which market or country to scan.

• If a single market is provided, the query targets that specific market URL.
• If multiple markets are provided, the query targets the global market URL.

Supported values include countries (e.g., 'italy', 'america') and asset classes (e.g., 'crypto', 'forex', 'futures', 'options', 'cfd', 'bonds').

# Single market
Query().set_markets('italy')

# Multiple markets (uses global URL)
Query().set_markets('america', 'israel', 'hongkong')

# Asset classes
Query().set_markets('crypto', 'forex')

Use the .select() method to specify which data fields you want to retrieve. You can pass either string names of the columns or Column objects.

Available columns can be found in the project's Fields documentation.

# Using strings
Query().select('open', 'high', 'low', 'VWAP')

# Using Column objects
from tradingview_screener.column import Column
Query().select(Column('open'), Column('high'))