You can get the package directly from PyPI
pip install tradingview-screener
This package allows you to create stock screeners with TradingView's, and retrieve the data directly from the official API (without doing any kind of web-scraping).
Import the function and the Enum class
from tradingview_screener import get_scanner_data, ScannerAll the scanners available:
>>> Scanner.names()['premarket_gainers',
'premarket_losers',
'premarket_most_active',
'premarket_gappers',
'postmarket_gainers',
'postmarket_losers',
'postmarket_most_active']
Then, to retrieve the data you must provide the scanner type
df = Scanner.premarket_gainers.get_data()And we get a DataFrame with the data:
>>> df name premarket_change close volume market_cap_basic
0 VIRI 77.514793 0.6253 4047431 1.146199e+07
1 CXAI 45.310016 6.2900 1407773 5.398518e+07
2 BXRX 45.212766 1.8800 469498 4.861120e+06
3 SPPI 35.264301 0.6905 478063 1.417606e+08
4 MORF 30.944176 43.5300 239678 1.720921e+09
5 PTPI 29.166667 3.8400 828932 8.020600e+06
6 ARWR 21.918721 30.0200 630642 3.251471e+09
7 EDUC 21.076233 2.2300 44927 1.943064e+07
8 ASTS 20.183486 4.3600 1814082 8.723593e+08
9 ZFOX 18.348624 1.0900 460906 1.292519e+08
10 MEDP 16.511802 187.2600 598436 5.814453e+09
...
[50 rows x 5 columns]
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')[
{'name': 'VIRI', 'premarket_change': 77.5147929, 'close': 0.6253, 'volume': 4047431, 'market_cap_basic': 11461993.0},
{'name': 'CXAI', 'premarket_change': 45.3100159, 'close': 6.29, 'volume': 1407773, 'market_cap_basic': 53985175.00000001},
{'name': 'BXRX', 'premarket_change': 45.21276596, 'close': 1.88, 'volume': 469498, 'market_cap_basic': 4861120.0},
{'name': 'SPPI', 'premarket_change': 35.26430123, 'close': 0.6905, 'volume': 478063, 'market_cap_basic': 141760626.0},
{'name': 'MORF', 'premarket_change': 30.94417643, 'close': 43.53, 'volume': 239678, 'market_cap_basic': 1720920967.0},
...
]
Or to get the most active during the pre-market session:
>>> Scanner.premarket_most_active.get_data() name premarket_volume close volume market_cap_basic
0 MULN 27981441 0.0960 756912573 3.640486e+08
1 VIRI 16779935 0.6253 4047431 1.146199e+07
2 BBBY 12464514 0.1888 539779156 8.082502e+07
3 SPPI 8098728 0.6905 478063 1.417606e+08
4 ZFOX 5510783 1.0900 460906 1.292519e+08
5 FRC 5165413 16.0000 90150729 2.928000e+09
6 BXRX 4524110 1.8800 469498 4.861120e+06
7 XELA 3525650 0.0397 49643555 5.058590e+07
8 CXAI 2464392 6.2900 1407773 5.398518e+07
9 IDEX 2250652 0.0399 67497209 2.787359e+07
10 AGFY 2250428 0.2123 5197584 1.884452e+06
...
[50 rows x 5 columns]
Once you have the data you can filter the results however you want.
In our case we only want stocks that have a pre-market change above 10% and a price bigger than $3
>>> filt = (df['premarket_change'] > 10) & (df['close'] > 3)
>>> df[filt] name premarket_change close volume market_cap_basic
1 CXAI 45.310016 6.290 1407773 5.398518e+07
4 MORF 30.944176 43.530 239678 1.720921e+09
5 PTPI 29.166667 3.840 828932 8.020600e+06
6 ARWR 21.918721 30.020 630642 3.251471e+09
8 ASTS 20.183486 4.360 1814082 8.723593e+08
10 MEDP 16.511802 187.260 598436 5.814453e+09
15 GDHG 12.159612 3.709 3492 1.854500e+08
16 PCT 11.368015 5.190 1294479 8.494532e+08
19 APLM 10.643564 4.040 159000 2.590733e+07
[9 rows x 5 columns]
You can also override the settings that are being passed to the API.
For example if you want to get the first 200 results instead of just 50:
>>> Scanner.premarket_losers.get_data(range=[0, 200]) name premarket_change close volume market_cap_basic
0 TCON -51.123596 1.7800 18817 4.240412e+07
1 VYNT -31.709091 0.5500 194750 3.447239e+06
2 NCPL -27.626818 1.5199 6111277 9.228493e+06
3 FRC -25.937500 16.0000 90150729 2.928000e+09
4 SFR -25.500000 2.0000 16709427 9.651445e+07
.. ... ... ... ... ...
195 RBOT -2.643172 2.2700 123525 2.859286e+08
196 HPCO -2.640264 0.6060 151701 1.416420e+07
197 BLDP -2.608696 4.6000 1776254 1.372613e+09
198 TRX -2.601535 0.5343 205843 1.479665e+08
199 SOI -2.573529 8.1600 185698 3.803123e+08
[200 rows x 5 columns]
You can also specify the columns you want to get
>>> Scanner.premarket_gainers.get_data(range=[0, 10], columns=['name', 'close', 'premarket_change', 'logoid', 'description', 'type', 'VWAP', 'MACD.macd']) name close premarket_change ... type VWAP MACD.macd
0 VIRI 0.6253 91.907884 ... stock 0.644767 0.086156
1 BXRX 1.8800 43.617021 ... stock 2.018500 0.082743
2 CXAI 6.2900 39.745628 ... stock 7.100400 1.106645
3 SPPI 0.6905 34.047791 ... stock 0.696000 -0.018506
4 MORF 43.5300 30.484723 ... stock 43.910000 1.133547
5 ASTS 4.3600 25.688073 ... stock 4.308333 -0.396030
6 ARWR 30.0200 21.585610 ... stock 30.348067 0.864783
7 PTPI 3.8400 21.093750 ... stock 4.450000 0.941922
8 EDUC 2.2300 21.076233 ... stock 2.280000 -0.257153
9 AGFY 0.2123 17.192652 ... stock 0.217233 -0.016040
[10 rows x 8 columns]
For the full list of columns have a look at the following dictionary:
>>> from tradingview_screener.screener import COLUMNS
>>> COLUMNS{'1-Month High': 'High.1M',
'1-Month Low': 'Low.1M',
'1-Year Beta': 'beta_1_year',
'3-Month High': 'High.3M',
'3-Month Low': 'Low.3M',
'3-Month Performance': 'Perf.3M',
'52 Week High': 'price_52_week_high',
'52 Week Low': 'price_52_week_low',
'5Y Performance': 'Perf.5Y',
'6-Month High': 'High.6M',
'6-Month Low': 'Low.6M',
'6-Month Performance': 'Perf.6M',
'All Time High': 'High.All',
'All Time Low': 'Low.All',
'All Time Performance': 'Perf.All',
'Aroon Down (14)': 'Aroon.Down',
'Aroon Up (14)': 'Aroon.Up',
...}
- Create a SQL-like query language to create custom screeners
- Add tests