tradingview_screener.screener

  1from __future__ import annotations
  2
  3import requests
  4
  5from tradingview_screener.query import Query
  6from tradingview_screener.constants import URL
  7
  8
  9DEFAULT_COLUMNS = ['name', 'close', 'volume', 'market_cap_basic']  # for the scanners
 10
 11
 12class Scanner:
 13    """
 14    This class contains some of the most common stock-screeners, to use them you just need to call
 15    `get_scanner_data()` on any given stock-screener.
 16
 17    Examples:
 18
 19    Get the full list of scanners:
 20    >>> from tradingview_screener import Scanner
 21    >>> Scanner.names()
 22    ['premarket_gainers',
 23     'premarket_losers',
 24     'premarket_most_active',
 25     'premarket_gappers',
 26     'postmarket_gainers',
 27     'postmarket_losers',
 28     'postmarket_most_active']
 29
 30    Get the Pre-Market gainers
 31    >>> Scanner.premarket_gainers.get_scanner_data()
 32    (18060,
 33              ticker  name  ...  premarket_change_abs  premarket_volume
 34     0   NASDAQ:APLM  APLM  ...               0.72200          30551043
 35     1      OTC:RNVA  RNVA  ...               0.00005            200000
 36     2      OTC:OCLN  OCLN  ...               0.00690            220000
 37     3   NASDAQ:BKYI  BKYI  ...               0.09740           8826676
 38     4    NASDAQ:ICU   ICU  ...               0.28790           7527703
 39     ..          ...   ...  ...                   ...               ...
 40     45     OTC:BSEM  BSEM  ...               0.25000               200
 41     46     NYSE:SWI   SWI  ...               0.76000              5425
 42     47     NYSE:BPT   BPT  ...               0.45000               380
 43     48    NYSE:HOUS  HOUS  ...               0.39000               200
 44     49   NASDAQ:HCM   HCM  ...               1.40000              1950
 45     [50 rows x 8 columns])
 46
 47    Get the most active tickers during the Post-Market session (highest volume)
 48    >>> Scanner.postmarket_most_active.get_scanner_data()
 49    (18060,
 50              ticker  name  ...  postmarket_change_abs  postmarket_volume
 51     0   NASDAQ:MCOM  MCOM  ...                -0.0001           12432509
 52     1   NASDAQ:AAPL  AAPL  ...                -0.2000           10364140
 53     2      AMEX:XLF   XLF  ...                -0.0100            8813170
 54     3      NYSE:BAC   BAC  ...                -0.0100            8741713
 55     4   NASDAQ:INTC  INTC  ...                -0.0600            8493758
 56     ..          ...   ...  ...                    ...                ...
 57     45      NYSE:PG    PG  ...                 0.2200            1895116
 58     46     NYSE:BMY   BMY  ...                -0.0700            1875806
 59     47  NASDAQ:TQQQ  TQQQ  ...                -0.0200            1870908
 60     48   NASDAQ:WBD   WBD  ...                -0.0003            1865705
 61     49    NYSE:SNAP  SNAP  ...                -0.0100            1852715
 62     [50 rows x 8 columns])
 63
 64    """
 65
 66    premarket_gainers = (
 67        Query()
 68        .select(*DEFAULT_COLUMNS, 'premarket_change', 'premarket_change_abs', 'premarket_volume')
 69        .order_by('premarket_change', ascending=False)
 70    )
 71    premarket_losers = (
 72        Query()
 73        .select(*DEFAULT_COLUMNS, 'premarket_change', 'premarket_change_abs', 'premarket_volume')
 74        .order_by('premarket_change', ascending=True)
 75    )
 76    premarket_most_active = (
 77        Query()
 78        .select(*DEFAULT_COLUMNS, 'premarket_change', 'premarket_change_abs', 'premarket_volume')
 79        .order_by('premarket_volume', ascending=False)
 80    )
 81    premarket_gappers = (
 82        Query()
 83        .select(*DEFAULT_COLUMNS, 'premarket_change', 'premarket_change_abs', 'premarket_volume')
 84        .order_by('premarket_gap', ascending=False)
 85    )
 86
 87    postmarket_gainers = (
 88        Query()
 89        .select(*DEFAULT_COLUMNS, 'postmarket_change', 'postmarket_change_abs', 'postmarket_volume')
 90        .order_by('postmarket_change', ascending=False)
 91    )
 92    postmarket_losers = (
 93        Query()
 94        .select(*DEFAULT_COLUMNS, 'postmarket_change', 'postmarket_change_abs', 'postmarket_volume')
 95        .order_by('postmarket_change', ascending=True)
 96    )
 97    postmarket_most_active = (
 98        Query()
 99        .select(*DEFAULT_COLUMNS, 'postmarket_change', 'postmarket_change_abs', 'postmarket_volume')
100        .order_by('postmarket_volume', ascending=False)
101    )
102
103    @classmethod
104    def names(cls) -> list[str]:
105        return [x for x in cls.__dict__.keys() if not x.startswith('_') and x != 'names']
106
107
108def get_all_symbols(market: str = 'america') -> list[str]:
109    """
110    Get all the symbols of a given market.
111
112    Examples:
113
114    >>> from tradingview_screener import get_all_symbols
115    >>> get_all_symbols()
116    ['OTC:BMVVF',
117     'OTC:BRQL',
118     'NYSE:EFC/PA',
119     'NASDAQ:NVCR',
120     'NASDAQ:OMIC',
121     ...
122
123    >>> len(get_all_symbols())
124    18060
125
126    The default market is `america`, but you can change it with any market from
127    `tradingview_screener.constants.MARKETS`:
128    >>> get_all_symbols(market='switzerland')
129    ['BX:UN01',
130     'BX:XFNT',
131     'BX:ZPDE',
132     'BX:0QF',
133     'BX:BSN',
134     ...
135
136    For instance, to get all the crypto tickers:
137    >>> get_all_symbols(market='crypto')
138    ['KRAKEN:KNCEUR',
139     'TRADERJOE:WETHEWAVAX_FE15C2',
140     'UNISWAP:DBIWETH_DEDF7B',
141     'KUCOIN:DIABTC',
142     'QUICKSWAP:WIXSWMATIC_F87B83.USD',
143     ...
144
145    >>> len(get_all_symbols(market='futures'))
146    75205
147
148    >>> len(get_all_symbols(market='bonds'))
149    1090
150
151    >>> len(get_all_symbols(market='germany'))
152    13251
153
154    >>> len(get_all_symbols(market='israel'))
155    1034
156
157    :param market: any market from `tradingview_screener.constants.MARKETS`, default 'america'
158    :return: list of tickers
159    """
160    r = requests.get(URL.format(market=market))
161    r.raise_for_status()
162    data = r.json()['data']  # [{'s': 'NYSE:HKD', 'd': []}, {'s': 'NASDAQ:ALTY', 'd': []}...]
163
164    return [dct['s'] for dct in data]