pip install tradingview-screener
This package allows you to create stock screeners with TradingView, and retrieve the data directly from the official API (without doing any kind of web-scraping/HTML-parsing).
Some of its main features are:
- Get data from over 3000 fields, including OHLC, indicators, and fundamental data.
- Variety of markets, including equities, crypto, forex, futures, and bonds.
- Choose the timeframe for each field, such as 1 minute, 5 minutes, 1 hour, or 1 day.
- Filter and sort the results using SQL, a common database language.
- Create and save screeners to easily monitor the markets and identify trading opportunities.
You can find the docs here, and the source on GitHub.
from tradingview_screener import ScannerSome of the pre-built scanners:
>>> Scanner.names()['premarket_gainers',
'premarket_losers',
'premarket_most_active',
'premarket_gappers',
'postmarket_gainers',
'postmarket_losers',
'postmarket_most_active']
Say we want to get the Pre-Market Gainers:
n_rows, df = Scanner.premarket_gainers.get_scanner_data()And we get a DataFrame with the data:
>>> df ticker name ... premarket_change_abs premarket_volume
0 NASDAQ:APLM APLM ... 0.72200 30551043
1 OTC:RNVA RNVA ... 0.00005 200000
2 OTC:OCLN OCLN ... 0.00690 220000
3 NASDAQ:BKYI BKYI ... 0.09740 8826676
4 NASDAQ:ICU ICU ... 0.28790 7527703
.. ... ... ... ... ...
45 OTC:BSEM BSEM ... 0.25000 200
46 NYSE:SWI SWI ... 0.76000 5425
47 NYSE:BPT BPT ... 0.45000 380
48 NYSE:HOUS HOUS ... 0.39000 200
49 NASDAQ:HCM HCM ... 1.40000 1950
[50 rows x 8 columns]
With the following columns:
>>> df.columnsIndex(['ticker', 'name', 'close', 'volume', 'market_cap_basic',
'premarket_change', 'premarket_change_abs', 'premarket_volume'],
dtype='object')
If you aren't yet familiar with Pandas DataFrames, you can convert the output to a list of dictionaries like so:
>>> df.to_dict('records')[:10][
{'ticker': 'NASDAQ:APLM', 'name': 'APLM', 'close': 0.862, 'volume': 94235502, 'market_cap_basic': 146422699.0, 'premarket_change': 127.11267606, 'premarket_change_abs': 0.722, 'premarket_volume': 30551043}
{'ticker': 'OTC:RNVA', 'name': 'RNVA', 'close': 0.0001, 'volume': 11050007, 'market_cap_basic': 2993432.0, 'premarket_change': 100.0, 'premarket_change_abs': 5e-05, 'premarket_volume': 200000}
{'ticker': 'OTC:OCLN', 'name': 'OCLN', 'close': 0.0076, 'volume': 2543494, 'market_cap_basic': 9864586.0, 'premarket_change': 86.25, 'premarket_change_abs': 0.0069, 'premarket_volume': 220000}
{'ticker': 'NASDAQ:BKYI', 'name': 'BKYI', 'close': 0.22, 'volume': 39926873, 'market_cap_basic': 2036156.0, 'premarket_change': 57.05916813, 'premarket_change_abs': 0.0974, 'premarket_volume': 8826676}
{'ticker': 'NASDAQ:ICU', 'name': 'ICU', 'close': 1.02, 'volume': 46835892, 'market_cap_basic': 19834890.0, 'premarket_change': 36.3510101, 'premarket_change_abs': 0.2879, 'premarket_volume': 7527703}
...
]
from tradingview_screener import Query, ColumnCreate a query (like you would in a SQL database):
(Query()
.select('name', 'close', 'volume', 'relative_volume_10d_calc', 'market_cap_basic')
.get_scanner_data())(18060,
ticker name ... relative_volume_10d_calc market_cap_basic
0 AMEX:SPY SPY ... 1.112917 NaN
1 NASDAQ:QQQ QQQ ... 1.050254 NaN
2 NASDAQ:TSLA TSLA ... 0.784272 6.589904e+11
3 NASDAQ:NVDA NVDA ... 0.819148 1.000350e+12
4 NASDAQ:AMZN AMZN ... 2.206912 1.310658e+12
.. ... ... ... ... ...
45 NYSE:UNH UNH ... 0.898852 4.859952e+11
46 NASDAQ:DXCM DXCM ... 2.763555 3.449933e+10
47 NYSE:MA MA ... 1.254684 3.429080e+11
48 NYSE:ABBV ABBV ... 2.007460 2.452179e+11
49 AMEX:XLK XLK ... 1.041988 NaN
[50 rows x 6 columns])
Our dataframe only contains 50 rows, even though there are 5271 rows in total. This is because the default LIMIT is 50, but you can change that if you need to. Just keep in mind that the more rows you request, the heavier the load you're putting on the server, and the longer it will take to respond. And if you request too many rows, you might even get banned, so don't get crazy.
A more elaborate query:
(Query()
.select('name', 'close', 'volume', 'relative_volume_10d_calc')
.where(
Column('market_cap_basic').between(1_000_000, 50_000_000),
Column('relative_volume_10d_calc') > 1.2,
Column('MACD.macd') >= Column('MACD.signal')
)
.order_by('volume', ascending=False)
.offset(5)
.limit(25)
.get_scanner_data())(393,
ticker name close volume relative_volume_10d_calc
0 OTC:YCRM YCRM 0.012000 19626514 1.887942
1 OTC:PLPL PLPL 0.000200 17959914 3.026059
2 NASDAQ:ABVC ABVC 1.380000 16295824 1.967505
3 OTC:TLSS TLSS 0.000900 15671157 1.877976
4 OTC:GVSI GVSI 0.012800 14609774 2.640792
.. ... ... ... ... ...
15 AMEX:TPET TPET 0.483999 2707793 3.141248
16 OTC:PWDY PWDY 0.000700 2138674 1.802687
17 NASDAQ:FGEN FGEN 0.476100 1846644 1.385978
18 NASDAQ:VVPR VVPR 1.930000 1541197 64.668412
19 OTC:NAVB NAVB 0.052000 1475558 2.491307
[20 rows x 5 columns])
For more examples have a look here.
When you call a method like select() or where() on the Query object,
it updates a dictionary that contains all the data to send to the API.
For example, the previous query creates the following dictionary:
{
'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'},
],
}When the get_scanner_data() method is called, it will dump that dictionary as a JSON and send it to the API.
Using this package, you can access and query TradingView data with a simple SQL syntax, without needing to know the details of TradingView's API.
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