Python API to load data from Fundamentus website.
Main functions are named after each website functionality:
get_resultado
- https://www.fundamentus.com.br/resultado.phpget_papel
- https://www.fundamentus.com.br/detalhes.php?papel=WEGE3
A specific list
function is built from the following setor
parameter:
list_papel_setor
- https://www.fundamentus.com.br/resultado.php?setor=27
Return: -> DataFrame
>>> import fundamentus
>>> df = fundamentus.get_resultado()
>>> print(df.columns)
Index(['cotacao', 'pl', 'pvp', 'psr', 'dy', 'pa', 'pcg', 'pebit', 'pacl',
'evebit', 'evebitda', 'mrgebit', 'mrgliq', 'roic', 'roe', 'liqc',
'liq2m', 'patrliq', 'divbpatr', 'c5y'],
dtype='object', name='Multiples')
>>> print( df[ df.pl > 0] )
papel cotacao pl pvp ... divbpatr c5y
ABCB4 15.81 9.66 0.83 ... 0.00 -0.5287
ABEV3 15.95 28.87 3.22 ... 0.09 0.0455
AEDU11 37.35 20.13 1.13 ... 0.30 0.2090
... ... ... ... ... ... ...
WIZS3 7.95 5.96 3.61 ... 0.00 0.1737
WSON33 45.45 34.29 1.34 ... 1.11 0.0131
YDUQ3 33.26 39.71 3.10 ... 1.45 0.0449
Columns names were simplified from the original web page to allow DataFrame filtering in a simplified way:
# filter on DataFrame
df = df[ df.pl > 0 ]
df = df[ df.pl < 100 ]
df = df[ df.pvp > 0 ]
Return: -> DataFrame
>>> import fundamentus
>>> df = fundamentus.get_resultado_raw()
>>> print(df.columns)
Index(['Cotação', 'P/L', 'P/VP', 'PSR', 'Div.Yield', 'P/Ativo', 'P/Cap.Giro',
'P/EBIT', 'P/Ativ Circ.Liq', 'EV/EBIT', 'EV/EBITDA', 'Mrg Ebit',
'Mrg. Líq.', 'Liq. Corr.', 'ROIC', 'ROE', 'Liq.2meses', 'Patrim. Líq',
'Dív.Brut/ Patrim.', 'Cresc. Rec.5a'],
dtype='object', name='Multiples')
>>> print( df[ df['P/L'] > 0] )
papel Cotação P/L P/VP ... Dív.Brut/ Patrim. Cresc. Rec.5a
ABCB4 15.81 9.66 0.83 ... 0.00 -0.5287
ABEV3 15.95 28.87 3.22 ... 0.09 0.0455
AEDU11 37.35 20.13 1.13 ... 0.30 0.2090
... ... ... ... ... ... ...
WIZS3 7.95 5.96 3.61 ... 0.00 0.1737
WSON33 45.45 34.29 1.34 ... 1.11 0.0131
YDUQ3 33.26 39.71 3.10 ... 1.45 0.0449
In the _raw
function, columns names are preserved as captured from the web page. Be aware that names are in pt-br
and contain spaces and accents. Filtering must be made explicitly:
# filter on DataFrame
df = df[ df['P/L'] > 0 ]
df = df[ df['P/L'] < 100 ]
df = df[ df['P/VP'] > 0 ]
The renaming list can be found here.
Return: -> DataFrame
>>> import fundamentus
>>> df = fundamentus.get_papel('WEGE3') ## or...
>>> df = fundamentus.get_papel(['ITSA4','WEGE3'])
>>> print(df)
Tipo Empresa Setor ... Receita_Liquida_3m EBIT_3m Lucro_Liquido_3m
ITSA4 PN N1 ITAÚSA PN N1 Financeiros ... 1778000000 257000000 1784000000
WEGE3 ON N1 WEG SA ON N1 Máquinas e ... 4801260000 946670000 644246000
Return: -> list
>>> import fundamentus
>>> fin = fundamentus.list_papel_setor(35) # finance
>>> seg = fundamentus.list_papel_setor(38) # seguradoras
>>> print(fin)
['ABCB4', 'BBAS3', 'BBDC3', 'BBDC4', ... ]
>>> print(seg)
['BBSE3', 'IRBR3', 'SULA4', 'WIZS3', ... ]
The full list of companies by setor
can be found here
The MIT License (MIT)
fundamentus-api's People
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