pip install tradingview-screener

tradingview-screener is a Python package that allows you to create custom stock screeners using TradingView's official API. This package retrieves data directly from TradingView without the need for web scraping or HTML parsing.

• Markets: Stocks, crypto, forex, CFDs, futures, bonds, and more.
• 3000+ Data Fields: OHLC data, technical indicators, fundamental metrics (e.g. P/E, EPS), and even internal TradingView-only fields.
• Timeframes: Use 1m, 5m, 15m, 30m, 1h, 2h, 4h, 1d, 1w, and 1mo — freely mix timeframes per field, no subscription required.
• Filtering: SQL-like syntax with full support for AND/OR logic.

• GitHub Repository
• Documentation
• Fields
• Screeners

Note that throughout the documentation, "field" and "column" are used interchangeably. Same with "Scanner" and "Screener".

Here’s a simple example to get you started:

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

Output:

(17580,
 ticker name close volume market_cap_basic
 0 NASDAQ:NVDA NVDA 127.25 298220762 3.130350e+12
 1 AMEX:SPY SPY 558.70 33701795 NaN
 2 NASDAQ:TSLA TSLA 221.10 73869589 7.063350e+11
 3 NASDAQ:QQQ QQQ 480.26 29102854 NaN
 4 NASDAQ:AMD AMD 156.40 76693809 2.531306e+11
 .. ... ... ... ... ...
 45 NASDAQ:PDD PDD 144.22 8653323 2.007628e+11
 46 NYSE:JPM JPM 214.52 5639973 6.103447e+11
 47 NYSE:JNJ JNJ 160.16 7274621 3.855442e+11
 48 NASDAQ:SQQQ SQQQ 7.99 139721164 NaN
 49 NASDAQ:ASTS ASTS 34.32 32361315 9.245616e+09
 
 [50 rows x 5 columns])

By default, the result is limited to 50 rows. You can adjust this limit, but be mindful of server load and potential bans.

A more advanced query:

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())

To access real-time data, you need to pass your session cookies, as even free real-time data requires authentication.

You can run this query to get an overview on the update_mode you get for each exchange:

from tradingview_screener import Query
_, df = Query().select('exchange', 'update_mode').limit(1_000_000).get_scanner_data()
df = df.groupby('exchange')['update_mode'].value_counts()
print(df)

exchange update_mode 
AMEX delayed_streaming_900 3255
NASDAQ delayed_streaming_900 4294
NYSE delayed_streaming_900 2863
OTC delayed_streaming_900 7129

rookiepy is a library that loads the cookies from your local browser. So if you are logged in on Chrome (or whatever browser you use), it will use the same session.

1. Install rookiepy:

   pip install rookiepy

2. Load the cookies:

   import rookiepy
   cookies = rookiepy.to_cookiejar(rookiepy.chrome(['.tradingview.com'])) # replace chrome() with your browser

3. Pass the cookies when querying:

   Query().get_scanner_data(cookies=cookies)

Now, if you re-run the update mode check:

_, df = Query().select('exchange', 'update_mode').limit(1_000_000).get_scanner_data(cookies=cookies)
df = df.groupby('exchange')['update_mode'].value_counts()
print(df)

exchange update_mode 
AMEX streaming 3256
NASDAQ streaming 4286
NYSE streaming 2860
OTC delayed_streaming_900 7175

We now get live-data for all exchanges except OTC (because my subscription dosent include live-data for OTC tickers).

from http.cookiejar import CookieJar
import requests
from tradingview_screener import Query
def authenticate(username: str, password: str) -> CookieJar:
 session = requests.Session()
 r = session.post(
 'https://www.tradingview.com/accounts/signin/', 
 headers={'User-Agent': 'Mozilla/5.0', 'Referer': 'https://www.tradingview.com'}, 
 data={'username': username, 'password': password, 'remember': 'on'}, 
 timeout=60,
 )
 r.raise_for_status()
 if r.json().get('error'):
 raise Exception(f'Failed to authenticate: \n{r.json()}')
 return session.cookies
cookies = authenticate('<your-username-or-email>', '<your-password>')
Query().get_scanner_data(cookies=cookies)

Unlike other Python libraries that have specific features like extracting the sentiment, or what not. This package is but a (low-level) wrapper around TradingView's /screener API endpoint.

It merely documents the endpoint, by listing all the functions and operations available, the different fields you can use, the markets, instruments (even some that you wont find on TradingView's website), and so on.

This library is also a wrapper that makes it easier to generate those verbose JSON payloads.

This package is designed to be future-proof. There are no hard-coded values in the package, all fields/columns and markets are documented on the website, which is updated daily via a GitHub Actions script.

When using methods like select() or where(), the Query object constructs a dictionary representing the API request. Here’s an example of the dictionary generated:

{
 'markets': ['america'],
 'symbols': {'query': {'types': []}, 'tickers': []},
 'options': {'lang': 'en'},
 'columns': ['name', 'close', 'volume', 'relative_volume_10d_calc'],
 'sort': {'sortBy': 'volume', 'sortOrder': 'desc'},
 'range': [5, 25],
 'filter': [
 {'left': 'market_cap_basic', 'operation': 'in_range', 'right': [1000000, 50000000]},
 {'left': 'relative_volume_10d_calc', 'operation': 'greater', 'right': 1.2},
 {'left': 'MACD.macd', 'operation': 'egreater', 'right': 'MACD.signal'},
 ],
}

The get_scanner_data() method sends this dictionary as a JSON payload to the TradingView API, allowing you to query data using SQL-like syntax without knowing the specifics of the API.

If this package has bought value to your projects, please consider starring it.

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