TradingViewScanner

How It Works

This module uses TradingView's API to retrieve the scanner data. For now there are

The following quick-guide will show you how to get started using the tradingview-package module

Import the function and the Enum class

from tradingview_screener import get_scanner_data, Scanner

All 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',
 ...}

What's next

• Create a SQL-like query language to create custom screeners
• Add tests