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Selic: Divergência entre biblioteca e site do Banco Central #54

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JulioNegreiro opened this issue Dec 1, 2022 · 13 comments
Open

Selic: Divergência entre biblioteca e site do Banco Central #54

JulioNegreiro opened this issue Dec 1, 2022 · 13 comments
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bug Something isn't working

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@JulioNegreiro
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Encontrei uma divergência no uso da biblioteca para ajustar um valor pela selic,

utilizei o código abaixo :

from calculadora_do_cidadao import Selic
selic = Selic()
v = selic.adjust("2017-10", 800000,"2022-11")
print(v)

v = 1087869.386433910510912246451

Simulando no site da calculadora do cidadão encontra-se :

data inicial : 01/10/2017
data final : 01/11/2022
valor: 800000

valor corrigido = R$ 1.083.812,34 (REAL)

Os valores apresentam um diferença de R$ 4057,04

Captura de Tela 2022-11-30 às 21 37 55
@cuducos cuducos changed the title [Divergência entre biblioteca e site do banco central] [Selic] Selic: Divergência entre biblioteca e site do Banco Central Dec 1, 2022
cuducos added a commit that referenced this issue Dec 1, 2022
@cuducos
Removes Selic
d14c4b9 
See #54

To avoid this bug to cause any miscalculation, let's remove Selic from
the API temporarily. We can bring it back when the bug is fixed.
@cuducos cuducos added the bug Something isn't working label Dec 1, 2022
@cuducos
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cuducos commented Dec 1, 2022

Obrigado por reportar esse erro, @JulioNegreiro.

Temporariamente removi a Selic e soltei uma nova versão do PyPI sem a Selic para evitar que pessoas usem a conversão “bugada”.

@turicas
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turicas commented Dec 1, 2022

Acho que descobri onde está o erro: quantidade de casas decimais consideradas depois de todas as multiplicações acumuladas.

Se você faz a pesquisa por dia, ele devolve o fator acumulado com 16 casas decimais:

2022-12-01_11-04

fator_diario_2017 = decimal.Decimal("1.0993932466542384")

Se você faz por mês, cada fator possui apenas 8 casas decimais:

2022-12-01_11-04_1

Se multiplicarmos esses fatores mensais usando o decimal.Decimal do Python acabamos com um número com 27 casas decimais:

fator_mensal_2017 = (
    1
    * decimal.Decimal("1.01086120")
    * decimal.Decimal("1.00865084")
    * decimal.Decimal("1.01052056")
    * decimal.Decimal("1.00786581")
    * decimal.Decimal("1.00927132")
    * decimal.Decimal("1.00808869")
    * decimal.Decimal("1.00797923")
    * decimal.Decimal("1.00802289")
    * decimal.Decimal("1.00638460")
    * decimal.Decimal("1.00643930")
    * decimal.Decimal("1.00568188")
    * decimal.Decimal("1.00538400")
)
# resultado: Decimal('1.099393246654238379628934669')

Se arredondarmos o resultado para 16 dígitos os valores ficam iguais:

fator_mensal_2017_arredondado = fator_mensal_2017.quantize(decimal.Decimal("0.0000000000000001"))

fator_mensal_2017_arredondado == fator_diario_2017 # True

Então, adicionar um .quantize(decimal.Decimal("0.0000000000000001")) no multiplicador final da Selic deve resolver (não cheguei a testar com o período completo citado, mas acho que dá pra criar um caso de teste com esses valores).

@cuducos
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cuducos commented Dec 1, 2022

Vou levantar outra hipótese aqui, que é o do período de vigência e como o cálculo é ffeito.

  1. No exemplo do @JulioNegreiro, a consulta no site é feita com dia, mês e ano — tanto que o site mesmo já corrige do dia 1º para o dia 2 do mês.
  2. O adpatador usa os dados da API REST do Banco do Central, pedindo o fator acumulado mensal.
  3. Apesar disso, o Selic não é mensal.

No esse pacote, o fator acumulado dá 35,983673%, sendo que a do site é de 35,476542% — a diferença é de pouco mais de 0,5% — será que as datas de referência, e a forma como é calculado o acumúlo mensal dá conta dessa diferença?

Se sim, acho que temos que deixar claro essa questão de datas aqui:

  • Talvez renomear de Selic para SelicMensal (ou SelicAcumuladoMensal) — esse seria algo de curto prazo
  • Talvez implementar outro adaptador Selic com a precisão do prazo de vigência de cada taxa de juros — esse pode ser um objetivo mais de médio-prazo

Acho que isso mais o cuidado com as casas decimais que o @turicas mencionou seria a solução mais definitiva. O que acham?

@cuducos
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cuducos commented Dec 1, 2022 •
edited
Loading

Compartilhando aqui um output meio bagunçado, mas que me ajuda a debugar — caso interesse a mais alguém
  • As linhas com cara de JSON é o corpo das respostas da API
  • As linhas com uma tupla nomeada, é o que extraímos do JSON e passamos para o serializer_
  • As demais linhas, são o antes e depois de agregar cada linha
$ poetry run python -c "from calculadora_do_cidadao.adapters.selic import Selic; Selic()"
{"totalItems":12,"registros":[{"periodo":"Jan / 2022","fator":1.00732270,"fatorFormatado":"1,00732270"},{"periodo":"Feb / 2022","fator":1.00755041,"fatorFormatado":"1,00755041"},{"periodo":"Mar / 2022","fator":1.00927054,"fatorFormatado":"1,00927054"},{"periodo":"Apr / 2022","fator":1.00834321,"fatorFormatado":"1,00834321"},{"periodo":"May / 2022","fator":1.01034592,"fatorFormatado":"1,01034592"},{"periodo":"Jun / 2022","fator":1.01015316,"fatorFormatado":"1,01015316"},{"periodo":"Jul / 2022","fator":1.01034842,"fatorFormatado":"1,01034842"},{"periodo":"Aug / 2022","fator":1.01169361,"fatorFormatado":"1,01169361"},{"periodo":"Sep / 2022","fator":1.01071982,"fatorFormatado":"1,01071982"},{"periodo":"Oct / 2022","fator":1.01020676,"fatorFormatado":"1,01020676"},{"periodo":"Nov / 2022","fator":1.01020676,"fatorFormatado":"1,01020676"},{"periodo":"Dec / 2022","fator":1.00050788,"fatorFormatado":"1,00050788"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:23"}
Row(periodo='Jan / 2022', fator=1.0073227, fatorFormatado='1,00732270')
Row(periodo='Feb / 2022', fator=1.00755041, fatorFormatado='1,00755041')
Row(periodo='Mar / 2022', fator=1.00927054, fatorFormatado='1,00927054')
Row(periodo='Apr / 2022', fator=1.00834321, fatorFormatado='1,00834321')
Row(periodo='May / 2022', fator=1.01034592, fatorFormatado='1,01034592')
Row(periodo='Jun / 2022', fator=1.01015316, fatorFormatado='1,01015316')
Row(periodo='Jul / 2022', fator=1.01034842, fatorFormatado='1,01034842')
Row(periodo='Aug / 2022', fator=1.01169361, fatorFormatado='1,01169361')
Row(periodo='Sep / 2022', fator=1.01071982, fatorFormatado='1,01071982')
Row(periodo='Oct / 2022', fator=1.01020676, fatorFormatado='1,01020676')
Row(periodo='Nov / 2022', fator=1.01020676, fatorFormatado='1,01020676')
Row(periodo='Dec / 2022', fator=1.00050788, fatorFormatado='1,00050788')
{"totalItems":12,"registros":[{"periodo":"Jan / 2021","fator":1.00149486,"fatorFormatado":"1,00149486"},{"periodo":"Feb / 2021","fator":1.00134527,"fatorFormatado":"1,00134527"},{"periodo":"Mar / 2021","fator":1.00201080,"fatorFormatado":"1,00201080"},{"periodo":"Apr / 2021","fator":1.00207785,"fatorFormatado":"1,00207785"},{"periodo":"May / 2021","fator":1.00270326,"fatorFormatado":"1,00270326"},{"periodo":"Jun / 2021","fator":1.00307779,"fatorFormatado":"1,00307779"},{"periodo":"Jul / 2021","fator":1.00355616,"fatorFormatado":"1,00355616"},{"periodo":"Aug / 2021","fator":1.00427952,"fatorFormatado":"1,00427952"},{"periodo":"Sep / 2021","fator":1.00441999,"fatorFormatado":"1,00441999"},{"periodo":"Oct / 2021","fator":1.00485996,"fatorFormatado":"1,00485996"},{"periodo":"Nov / 2021","fator":1.00586749,"fatorFormatado":"1,00586749"},{"periodo":"Dec / 2021","fator":1.00769083,"fatorFormatado":"1,00769083"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:24"}
Row(periodo='Jan / 2021', fator=1.00149486, fatorFormatado='1,00149486')
Row(periodo='Feb / 2021', fator=1.00134527, fatorFormatado='1,00134527')
Row(periodo='Mar / 2021', fator=1.0020108, fatorFormatado='1,00201080')
Row(periodo='Apr / 2021', fator=1.00207785, fatorFormatado='1,00207785')
Row(periodo='May / 2021', fator=1.00270326, fatorFormatado='1,00270326')
Row(periodo='Jun / 2021', fator=1.00307779, fatorFormatado='1,00307779')
Row(periodo='Jul / 2021', fator=1.00355616, fatorFormatado='1,00355616')
Row(periodo='Aug / 2021', fator=1.00427952, fatorFormatado='1,00427952')
Row(periodo='Sep / 2021', fator=1.00441999, fatorFormatado='1,00441999')
Row(periodo='Oct / 2021', fator=1.00485996, fatorFormatado='1,00485996')
Row(periodo='Nov / 2021', fator=1.00586749, fatorFormatado='1,00586749')
Row(periodo='Dec / 2021', fator=1.00769083, fatorFormatado='1,00769083')
{"totalItems":12,"registros":[{"periodo":"Jan / 2020","fator":1.00376633,"fatorFormatado":"1,00376633"},{"periodo":"Feb / 2020","fator":1.00293729,"fatorFormatado":"1,00293729"},{"periodo":"Mar / 2020","fator":1.00338369,"fatorFormatado":"1,00338369"},{"periodo":"Apr / 2020","fator":1.00284925,"fatorFormatado":"1,00284925"},{"periodo":"May / 2020","fator":1.00235810,"fatorFormatado":"1,00235810"},{"periodo":"Jun / 2020","fator":1.00212332,"fatorFormatado":"1,00212332"},{"periodo":"Jul / 2020","fator":1.00194346,"fatorFormatado":"1,00194346"},{"periodo":"Aug / 2020","fator":1.00159890,"fatorFormatado":"1,00159890"},{"periodo":"Sep / 2020","fator":1.00156966,"fatorFormatado":"1,00156966"},{"periodo":"Oct / 2020","fator":1.00156966,"fatorFormatado":"1,00156966"},{"periodo":"Nov / 2020","fator":1.00149486,"fatorFormatado":"1,00149486"},{"periodo":"Dec / 2020","fator":1.00164447,"fatorFormatado":"1,00164447"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:24"}
Row(periodo='Jan / 2020', fator=1.00376633, fatorFormatado='1,00376633')
Row(periodo='Feb / 2020', fator=1.00293729, fatorFormatado='1,00293729')
Row(periodo='Mar / 2020', fator=1.00338369, fatorFormatado='1,00338369')
Row(periodo='Apr / 2020', fator=1.00284925, fatorFormatado='1,00284925')
Row(periodo='May / 2020', fator=1.0023581, fatorFormatado='1,00235810')
Row(periodo='Jun / 2020', fator=1.00212332, fatorFormatado='1,00212332')
Row(periodo='Jul / 2020', fator=1.00194346, fatorFormatado='1,00194346')
Row(periodo='Aug / 2020', fator=1.0015989, fatorFormatado='1,00159890')
Row(periodo='Sep / 2020', fator=1.00156966, fatorFormatado='1,00156966')
Row(periodo='Oct / 2020', fator=1.00156966, fatorFormatado='1,00156966')
Row(periodo='Nov / 2020', fator=1.00149486, fatorFormatado='1,00149486')
Row(periodo='Dec / 2020', fator=1.00164447, fatorFormatado='1,00164447')
{"totalItems":12,"registros":[{"periodo":"Jan / 2019","fator":1.00543042,"fatorFormatado":"1,00543042"},{"periodo":"Feb / 2019","fator":1.00493553,"fatorFormatado":"1,00493553"},{"periodo":"Mar / 2019","fator":1.00468818,"fatorFormatado":"1,00468818"},{"periodo":"Apr / 2019","fator":1.00518295,"fatorFormatado":"1,00518295"},{"periodo":"May / 2019","fator":1.00543042,"fatorFormatado":"1,00543042"},{"periodo":"Jun / 2019","fator":1.00468818,"fatorFormatado":"1,00468818"},{"periodo":"Jul / 2019","fator":1.00567796,"fatorFormatado":"1,00567796"},{"periodo":"Aug / 2019","fator":1.00501719,"fatorFormatado":"1,00501719"},{"periodo":"Sep / 2019","fator":1.00463760,"fatorFormatado":"1,00463760"},{"periodo":"Oct / 2019","fator":1.00479264,"fatorFormatado":"1,00479264"},{"periodo":"Nov / 2019","fator":1.00380386,"fatorFormatado":"1,00380386"},{"periodo":"Dec / 2019","fator":1.00374704,"fatorFormatado":"1,00374704"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:24"}
Row(periodo='Jan / 2019', fator=1.00543042, fatorFormatado='1,00543042')
Row(periodo='Feb / 2019', fator=1.00493553, fatorFormatado='1,00493553')
Row(periodo='Mar / 2019', fator=1.00468818, fatorFormatado='1,00468818')
Row(periodo='Apr / 2019', fator=1.00518295, fatorFormatado='1,00518295')
Row(periodo='May / 2019', fator=1.00543042, fatorFormatado='1,00543042')
Row(periodo='Jun / 2019', fator=1.00468818, fatorFormatado='1,00468818')
Row(periodo='Jul / 2019', fator=1.00567796, fatorFormatado='1,00567796')
Row(periodo='Aug / 2019', fator=1.00501719, fatorFormatado='1,00501719')
Row(periodo='Sep / 2019', fator=1.0046376, fatorFormatado='1,00463760')
Row(periodo='Oct / 2019', fator=1.00479264, fatorFormatado='1,00479264')
Row(periodo='Nov / 2019', fator=1.00380386, fatorFormatado='1,00380386')
Row(periodo='Dec / 2019', fator=1.00374704, fatorFormatado='1,00374704')
{"totalItems":12,"registros":[{"periodo":"Jan / 2018","fator":1.00584205,"fatorFormatado":"1,00584205"},{"periodo":"Feb / 2018","fator":1.00465602,"fatorFormatado":"1,00465602"},{"periodo":"Mar / 2018","fator":1.00532345,"fatorFormatado":"1,00532345"},{"periodo":"Apr / 2018","fator":1.00518295,"fatorFormatado":"1,00518295"},{"periodo":"May / 2018","fator":1.00518295,"fatorFormatado":"1,00518295"},{"periodo":"Jun / 2018","fator":1.00518295,"fatorFormatado":"1,00518295"},{"periodo":"Jul / 2018","fator":1.00543042,"fatorFormatado":"1,00543042"},{"periodo":"Aug / 2018","fator":1.00567796,"fatorFormatado":"1,00567796"},{"periodo":"Sep / 2018","fator":1.00468818,"fatorFormatado":"1,00468818"},{"periodo":"Oct / 2018","fator":1.00543042,"fatorFormatado":"1,00543042"},{"periodo":"Nov / 2018","fator":1.00493553,"fatorFormatado":"1,00493553"},{"periodo":"Dec / 2018","fator":1.00493553,"fatorFormatado":"1,00493553"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:25"}
Row(periodo='Jan / 2018', fator=1.00584205, fatorFormatado='1,00584205')
Row(periodo='Feb / 2018', fator=1.00465602, fatorFormatado='1,00465602')
Row(periodo='Mar / 2018', fator=1.00532345, fatorFormatado='1,00532345')
Row(periodo='Apr / 2018', fator=1.00518295, fatorFormatado='1,00518295')
Row(periodo='May / 2018', fator=1.00518295, fatorFormatado='1,00518295')
Row(periodo='Jun / 2018', fator=1.00518295, fatorFormatado='1,00518295')
Row(periodo='Jul / 2018', fator=1.00543042, fatorFormatado='1,00543042')
Row(periodo='Aug / 2018', fator=1.00567796, fatorFormatado='1,00567796')
Row(periodo='Sep / 2018', fator=1.00468818, fatorFormatado='1,00468818')
Row(periodo='Oct / 2018', fator=1.00543042, fatorFormatado='1,00543042')
Row(periodo='Nov / 2018', fator=1.00493553, fatorFormatado='1,00493553')
Row(periodo='Dec / 2018', fator=1.00493553, fatorFormatado='1,00493553')
{"totalItems":12,"registros":[{"periodo":"Jan / 2017","fator":1.01086120,"fatorFormatado":"1,01086120"},{"periodo":"Feb / 2017","fator":1.00865084,"fatorFormatado":"1,00865084"},{"periodo":"Mar / 2017","fator":1.01052056,"fatorFormatado":"1,01052056"},{"periodo":"Apr / 2017","fator":1.00786581,"fatorFormatado":"1,00786581"},{"periodo":"May / 2017","fator":1.00927132,"fatorFormatado":"1,00927132"},{"periodo":"Jun / 2017","fator":1.00808869,"fatorFormatado":"1,00808869"},{"periodo":"Jul / 2017","fator":1.00797923,"fatorFormatado":"1,00797923"},{"periodo":"Aug / 2017","fator":1.00802289,"fatorFormatado":"1,00802289"},{"periodo":"Sep / 2017","fator":1.00638460,"fatorFormatado":"1,00638460"},{"periodo":"Oct / 2017","fator":1.00643930,"fatorFormatado":"1,00643930"},{"periodo":"Nov / 2017","fator":1.00568188,"fatorFormatado":"1,00568188"},{"periodo":"Dec / 2017","fator":1.00538400,"fatorFormatado":"1,00538400"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:25"}
Row(periodo='Jan / 2017', fator=1.0108612, fatorFormatado='1,01086120')
Row(periodo='Feb / 2017', fator=1.00865084, fatorFormatado='1,00865084')
Row(periodo='Mar / 2017', fator=1.01052056, fatorFormatado='1,01052056')
Row(periodo='Apr / 2017', fator=1.00786581, fatorFormatado='1,00786581')
Row(periodo='May / 2017', fator=1.00927132, fatorFormatado='1,00927132')
Row(periodo='Jun / 2017', fator=1.00808869, fatorFormatado='1,00808869')
Row(periodo='Jul / 2017', fator=1.00797923, fatorFormatado='1,00797923')
Row(periodo='Aug / 2017', fator=1.00802289, fatorFormatado='1,00802289')
Row(periodo='Sep / 2017', fator=1.0063846, fatorFormatado='1,00638460')
Row(periodo='Oct / 2017', fator=1.0064393, fatorFormatado='1,00643930')
Row(periodo='Nov / 2017', fator=1.00568188, fatorFormatado='1,00568188')
Row(periodo='Dec / 2017', fator=1.005384, fatorFormatado='1,00538400')
{"totalItems":12,"registros":[{"periodo":"Jan / 2016","fator":1.01055880,"fatorFormatado":"1,01055880"},{"periodo":"Feb / 2016","fator":1.01002822,"fatorFormatado":"1,01002822"},{"periodo":"Mar / 2016","fator":1.01162079,"fatorFormatado":"1,01162079"},{"periodo":"Apr / 2016","fator":1.01055880,"fatorFormatado":"1,01055880"},{"periodo":"May / 2016","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Jun / 2016","fator":1.01162079,"fatorFormatado":"1,01162079"},{"periodo":"Jul / 2016","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Aug / 2016","fator":1.01215220,"fatorFormatado":"1,01215220"},{"periodo":"Sep / 2016","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Oct / 2016","fator":1.01048842,"fatorFormatado":"1,01048842"},{"periodo":"Nov / 2016","fator":1.01038286,"fatorFormatado":"1,01038286"},{"periodo":"Dec / 2016","fator":1.01123315,"fatorFormatado":"1,01123315"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:25"}
Row(periodo='Jan / 2016', fator=1.0105588, fatorFormatado='1,01055880')
Row(periodo='Feb / 2016', fator=1.01002822, fatorFormatado='1,01002822')
Row(periodo='Mar / 2016', fator=1.01162079, fatorFormatado='1,01162079')
Row(periodo='Apr / 2016', fator=1.0105588, fatorFormatado='1,01055880')
Row(periodo='May / 2016', fator=1.01108965, fatorFormatado='1,01108965')
Row(periodo='Jun / 2016', fator=1.01162079, fatorFormatado='1,01162079')
Row(periodo='Jul / 2016', fator=1.01108965, fatorFormatado='1,01108965')
Row(periodo='Aug / 2016', fator=1.0121522, fatorFormatado='1,01215220')
Row(periodo='Sep / 2016', fator=1.01108965, fatorFormatado='1,01108965')
Row(periodo='Oct / 2016', fator=1.01048842, fatorFormatado='1,01048842')
Row(periodo='Nov / 2016', fator=1.01038286, fatorFormatado='1,01038286')
Row(periodo='Dec / 2016', fator=1.01123315, fatorFormatado='1,01123315')
{"totalItems":12,"registros":[{"periodo":"Jan / 2015","fator":1.00935075,"fatorFormatado":"1,00935075"},{"periodo":"Feb / 2015","fator":1.00822411,"fatorFormatado":"1,00822411"},{"periodo":"Mar / 2015","fator":1.01039967,"fatorFormatado":"1,01039967"},{"periodo":"Apr / 2015","fator":1.00951792,"fatorFormatado":"1,00951792"},{"periodo":"May / 2015","fator":1.00985322,"fatorFormatado":"1,00985322"},{"periodo":"Jun / 2015","fator":1.01066676,"fatorFormatado":"1,01066676"},{"periodo":"Jul / 2015","fator":1.01178198,"fatorFormatado":"1,01178198"},{"periodo":"Aug / 2015","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Sep / 2015","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Oct / 2015","fator":1.01108965,"fatorFormatado":"1,01108965"},{"periodo":"Nov / 2015","fator":1.01055880,"fatorFormatado":"1,01055880"},{"periodo":"Dec / 2015","fator":1.01162079,"fatorFormatado":"1,01162079"}],"observacoes":null,"dataAtual":"01/12/2022 às 19:50:25"}
Row(periodo='Jan / 2015', fator=1.00935075, fatorFormatado='1,00935075')
Row(periodo='Feb / 2015', fator=1.00822411, fatorFormatado='1,00822411')
Row(periodo='Mar / 2015', fator=1.01039967, fatorFormatado='1,01039967')
Row(periodo='Apr / 2015', fator=1.00951792, fatorFormatado='1,00951792')
Row(periodo='May / 2015', fator=1.00985322, fatorFormatado='1,00985322')
Row(periodo='Jun / 2015', fator=1.01066676, fatorFormatado='1,01066676')
Row(periodo='Jul / 2015', fator=1.01178198, fatorFormatado='1,01178198')
Row(periodo='Aug / 2015', fator=1.01108965, fatorFormatado='1,01108965')
Row(periodo='Sep / 2015', fator=1.01108965, fatorFormatado='1,01108965')
Row(periodo='Oct / 2015', fator=1.01108965, fatorFormatado='1,01108965')
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2013-06-01: 1.006052729999999950649680613423697650432586669921875 => 12.39146037226327832085965191
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2013-12-01: 1.0078974599999999117727611519512720406055450439453125 => 12.95537326403350253075340199
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2015-02-01: 1.008224110000000006692744136671535670757293701171875 => 14.62169483554047315801177262
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2016-11-01: 1.01038285999999999376086634583771228790283203125 => 18.34977550477357113603145707
2016-12-01: 1.0112331500000000250594212047872133553028106689453125 => 18.55590128548501883647292222
2017-01-01: 1.01086119999999990426431395462714135646820068359375 => 18.75744064052692694659768217
2017-02-01: 1.008650840000000048490846893400885164737701416015625 => 18.91970825831762381686856947
2017-03-01: 1.0105205600000000121241328088217414915561676025390625 => 19.11875418423175010667641990
2017-04-01: 1.0078658099999999731011257608770392835140228271484375 => 19.26913867208162153471005194
2017-05-01: 1.0092713200000000828282509246491827070713043212890625 => 19.44778902283486691010629297
2017-06-01: 1.0080886899999998984611693231272511184215545654296875 => 19.60509615942597909502764401
2017-07-01: 1.0079792299999998927972910678363405168056488037109375 => 19.76152973085415354848264427
2017-08-01: 1.008022889999999893717586019192822277545928955078125 => 19.92007431011652392814214644
2017-09-01: 1.006384600000000073549699663999490439891815185546875 => 20.04725601655689535192924558
2017-10-01: 1.0064393000000000366611629942781291902065277099609375 => 20.17634631222431090312464398
2017-11-01: 1.00568188000000002801925802486948668956756591796875 => 20.29098589080881253608514315
2017-12-01: 1.0053840000000000554081225345726124942302703857421875 => 20.40023255884492830706485815
2018-01-01: 1.0058420500000000430418367614038288593292236328125 => 20.51941173746532919854462610
2018-02-01: 1.004656020000000093972403192310594022274017333984375 => 20.61495052890320444885806691
2018-03-01: 1.0053234499999998963204461688292212784290313720703125 => 20.72469318729629207523246730
2018-04-01: 1.0051829500000000194859239854849874973297119140625 => 20.83210823585138979608358949
2018-05-01: 1.0051829500000000194859239854849874973297119140625 => 20.94008001123239616276007847
2018-06-01: 1.0051829500000000194859239854849874973297119140625 => 21.04861139892661351848866317
2018-07-01: 1.0054304199999999358539071181439794600009918212890625 => 21.16291419923957122888555255
2018-08-01: 1.0056779600000000929327370613464154303073883056640625 => 21.28307637954628751146785629
2018-09-01: 1.004688180000000041758312363526783883571624755859375 => 21.38285527256734971439872118
2018-10-01: 1.0054304199999999358539071181439794600009918212890625 => 21.49897315749660353000816589
2018-11-01: 1.00493553000000002128899723174981772899627685546875 => 21.60508198448462319932020713
2018-12-01: 1.00493553000000002128899723174981772899627685546875 => 21.71171451477150705160967855
2019-01-01: 1.0054304199999999358539071181439794600009918212890625 => 21.82961824350681114621122489
2019-02-01: 1.00493553000000002128899723174981772899627685546875 => 21.93735897923618678255836713
2019-03-01: 1.004688180000000041758312363526783883571624755859375 => 22.04020526685546320477571030
2019-04-01: 1.0051829500000000194859239854849874973297119140625 => 22.15443854874331215726666702
2019-05-01: 1.0054304199999999358539071181439794600009918212890625 => 22.27474645492717739335105818
2019-06-01: 1.004688180000000041758312363526783883571624755859375 => 22.37917447576223881801883903
2019-07-01: 1.0056779600000000929327370613464154303073883056640625 => 22.50624253326863985929593448
2019-08-01: 1.0050171900000000047015191739774309098720550537109375 => 22.61916062824413005232512625
2019-09-01: 1.0046375999999999084622004374978132545948028564453125 => 22.72405924757367295934759740
2019-10-01: 1.0047926400000000146661705002770759165287017822265625 => 22.83296748288596478059441245
2019-11-01: 1.003803860000000103269712781184352934360504150390625 => 22.91982089457541774453871821
2019-12-01: 1.003747039999999923765017229015938937664031982421875 => 23.00570238026022587055206356
2020-01-01: 1.003766329999999928901388557278551161289215087890625 => 23.09234944730606973138160541
2020-02-01: 1.0029372899999999813047679708688519895076751708984375 => 23.16017837441414694522606427
2020-03-01: 1.0033836899999999392463223557570017874240875244140625 => 23.23854523837786694303714511
2020-04-01: 1.0028492499999999143511786314775235950946807861328125 => 23.30475766339831309007058371
2020-05-01: 1.002358099999999918594539849436841905117034912109375 => 23.35971261244437075503375787
2020-06-01: 1.00212331999999992859784470056183636188507080078125 => 23.40931275742862446841150844
2020-07-01: 1.0019434599999998969366288292803801596164703369140625 => 23.45480782040017429027819790
2020-08-01: 1.0015989000000000697099267199519090354442596435546875 => 23.49230971262421376398385810
2020-09-01: 1.001569659999999917232571533531881868839263916015625 => 23.52918465148772954296256935
2020-10-01: 1.001569659999999917232571533531881868839263916015625 => 23.56611747146778182506686846
2020-11-01: 1.0014948599999999867549149712431244552135467529296875 => 23.60134551783117984127065821
2020-12-01: 1.001644470000000008980123311630450189113616943359375 => 23.64015722249488789352722564
2021-01-01: 1.0014948599999999867549149712431244552135467529296875 => 23.67549594792050628852785124
2021-02-01: 1.0013452700000000650248921374441124498844146728515625 => 23.70734588235436684751918942
2021-03-01: 1.0020108000000000902929286894504912197589874267578125 => 23.75501661345460714898187218
2021-04-01: 1.00207785000000004771436579176224768161773681640625 => 23.80437597472487493790193625
2021-05-01: 1.002703260000000096141548056039027869701385498046875 => 23.86872539212231199191612576
2021-06-01: 1.0030777900000000801838950792443938553333282470703125 => 23.94218831644693403642909781
2021-07-01: 1.003556160000000030407818485400639474391937255859375 => 24.02733056885035069340180198
2021-08-01: 1.0042795200000000921392029340495355427265167236328125 => 24.13015601056635936006031611
2021-09-01: 1.0044199899999999292532493200269527733325958251953125 => 24.23681105883150085563805796
2021-10-01: 1.00485996000000010752728485385887324810028076171875 => 24.35460099110498220265491137
2021-11-01: 1.0058674899999999752253643237054347991943359375 => 24.49750136887428017130280044
2021-12-01: 1.007690830000000037358631743700243532657623291015625 => 24.68590748732706046666579344
2022-01-01: 1.007322700000000015307932699215598404407501220703125 => 24.86667498208451071023525748
2022-02-01: 1.007550409999999896371036811615340411663055419921875 => 25.05442857353598884383917853
2022-03-01: 1.0092705399999999382742998932371847331523895263671875 => 25.28669665580409562335339891
2022-04-01: 1.008343210000000045312162910704500973224639892578125 => 25.49766887620976805779403556
2022-05-01: 1.0103459200000000084429530033958144485950469970703125 => 25.76146571858952443661414805
2022-06-01: 1.0101531599999999944117234917939640581607818603515625 => 26.02302600186487870858080766
2022-07-01: 1.0103484199999999137986606001504696905612945556640625 => 26.29232320460309501348656286
2022-08-01: 1.0116936100000000209320205613039433956146240234375 => 26.59977537815167436171866939
2022-09-01: 1.0107198200000000465337279820232652127742767333984375 => 26.88492018224589348136162025
2022-10-01: 1.01020675999999998140310708549804985523223876953125 => 27.15932811016523307713546114
2022-11-01: 1.01020675999999998140310708549804985523223876953125 => 27.43653685394694266641872778
2022-12-01: 1.0005078800000000160963509188150055706501007080078125 => 27.45047132228432568128827372

@vmesel
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vmesel commented Dec 2, 2022 •
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Eu tenho a impressão que a nossa chamada a API do BCB está muito fixada em meses fechados e isso não permite a gente comparar corretamente o dado, já que na teoria a gente estaria pegando a taxa referente ao mês inteiro de novembro também.

Só a critério de comparação, estou utilizando mais 1 site para vermos corretamente o que acontece, a referência de vários bancos e sistemas do mercado: Com Dinheiro e a taxa deles também dá um acumulado de 35,476542% no período.

image

Para esse valor bater com um código simples que fiz utilizando a API do BCB com a série 11 (dados da Selic).

import pandas as pd
import requests
from datetime import datetime
from decimal import Decimal, getcontext

req = requests.get("https://api.bcb.gov.br/dados/serie/bcdata.sgs.11/dados?formato=json")
selic_json = req.json()

df_selic = pd.DataFrame(selic_json)
df_selic["data"] = pd.to_datetime(df_selic["data"])
df_selic["data"].iloc[0] # So para confirmar que convertemos o valor certo

# Utilizando Float

df_divergencia = df_selic[(df_selic["data"] >= datetime(2017, 10, 1)) & (df_selic["data"] <= datetime(2022, 11, 1))]
df_divergencia = df_divergencia.sort_values(by=['data'])
df_divergencia["valor"] = df_divergencia["valor"].apply(lambda x: 1 + (float(x.replace(",", ".")) / 100))
df_divergencia["valor"] = df_divergencia["valor"].cumprod()
df_divergencia["valor_monetario"] =  df_divergencia["valor"] * 800000

print(df_divergencia["valor"].iloc[-2]) # 1.3547654246160838
print(df_divergencia["valor_monetario"].iloc[-2]) # 1083812.3396928671

# Utilizando decimal

getcontext().prec = 8
df_divergencia = df_selic[(df_selic["data"] >= datetime(2017, 10, 1)) & (df_selic["data"] <= datetime(2022, 11, 1))]
df_divergencia = df_divergencia.sort_values(by=['data'])
df_divergencia["valor"] = df_divergencia["valor"].apply(lambda x: 1 + (Decimal(x.replace(",", ".")) / 100))
df_divergencia["valor"] = df_divergencia["valor"].cumprod()
df_divergencia["valor_monetario"] =  df_divergencia["valor"] * 800000

print(df_divergencia["valor"].iloc[-2]) # 1.3553309
print(df_divergencia["valor_monetario"].iloc[-1]) # 1084641.5

Então aqui temos alguns pontos:

  • Não podemos pegar o fator mês a mês para calcular coisas sem mês fechado, isso complica a vida do usuário e a nossa depois na hora de explicar para o usuário como chegamos neste cálculo.

  • Utilizando Decimal, temos uma divergência neste dia e ele acrua um valor a mais de alguns bps (base percentage points).. O cálculo utilizando float com truncamento funciona e o valor dá exato.

@cuducos
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cuducos commented Dec 2, 2022

O cálculo utilizando float com truncamento funciona e o valor dá exato.

Talvez a gente apenas reproduza um erro do BC ao fazer isso, dado que cientificamente o cálculo mais preciso seria usando o decimal, não?

@vmesel
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vmesel commented Dec 2, 2022

O cálculo do BACEN é o cálculo que os bancos usam, então não seria errado

@turicas
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turicas commented Dec 2, 2022

Mais algumas informações que podem ajudar:

Simulei manualmente no site novo (vejam prints abaixo) e encontrei o seguinte:

  • 01/10/2017 a 01/11/2022: fator = decimal.Decimal("1.35476542461604"). R$ 800.000,00 ajustados dariam R$ 1.083.812,34
  • 01/10/2017 a 31/10/2022: fator = decimal.Decimal("1.35407771562583"). R$ 800.000,00 ajustados dariam R$ 1.083.262,17

2022-12-02_18-44
2022-12-02_18-45

Fiquei com dúvida se o simulador inclui a taxa do último dia escolhido (ou se termina no dia útil anterior), mas parece que inclui sim, por conta de duas outras simulações que fiz: uma de 1 dia e outra de 0 dias:

2022-12-02_19-04
2022-12-02_19-04_1

Se baixarmos os dados mensais e compararmos com o valor que não inclui o dia 01/11, temos o seguinte:

import datetime
import decimal

dados = [
 (datetime.date(2017, 1, 1), Decimal('1.01086120')),
 (datetime.date(2017, 2, 1), Decimal('1.00865084')),
 (datetime.date(2017, 3, 1), Decimal('1.01052056')),
 (datetime.date(2017, 4, 1), Decimal('1.00786581')),
 (datetime.date(2017, 5, 1), Decimal('1.00927132')),
 (datetime.date(2017, 6, 1), Decimal('1.00808869')),
 (datetime.date(2017, 7, 1), Decimal('1.00797923')),
 (datetime.date(2017, 8, 1), Decimal('1.00802289')),
 (datetime.date(2017, 9, 1), Decimal('1.00638460')),
 (datetime.date(2017, 10, 1), Decimal('1.00643930')),
 (datetime.date(2017, 11, 1), Decimal('1.00568188')),
 (datetime.date(2017, 12, 1), Decimal('1.00538400')),
 (datetime.date(2018, 1, 1), Decimal('1.00584205')),
 (datetime.date(2018, 2, 1), Decimal('1.00465602')),
 (datetime.date(2018, 3, 1), Decimal('1.00532345')),
 (datetime.date(2018, 4, 1), Decimal('1.00518295')),
 (datetime.date(2018, 5, 1), Decimal('1.00518295')),
 (datetime.date(2018, 6, 1), Decimal('1.00518295')),
 (datetime.date(2018, 7, 1), Decimal('1.00543042')),
 (datetime.date(2018, 8, 1), Decimal('1.00567796')),
 (datetime.date(2018, 9, 1), Decimal('1.00468818')),
 (datetime.date(2018, 10, 1), Decimal('1.00543042')),
 (datetime.date(2018, 11, 1), Decimal('1.00493553')),
 (datetime.date(2018, 12, 1), Decimal('1.00493553')),
 (datetime.date(2019, 1, 1), Decimal('1.00543042')),
 (datetime.date(2019, 2, 1), Decimal('1.00493553')),
 (datetime.date(2019, 3, 1), Decimal('1.00468818')),
 (datetime.date(2019, 4, 1), Decimal('1.00518295')),
 (datetime.date(2019, 5, 1), Decimal('1.00543042')),
 (datetime.date(2019, 6, 1), Decimal('1.00468818')),
 (datetime.date(2019, 7, 1), Decimal('1.00567796')),
 (datetime.date(2019, 8, 1), Decimal('1.00501719')),
 (datetime.date(2019, 9, 1), Decimal('1.00463760')),
 (datetime.date(2019, 10, 1), Decimal('1.00479264')),
 (datetime.date(2019, 11, 1), Decimal('1.00380386')),
 (datetime.date(2019, 12, 1), Decimal('1.00374704')),
 (datetime.date(2020, 1, 1), Decimal('1.00376633')),
 (datetime.date(2020, 2, 1), Decimal('1.00293729')),
 (datetime.date(2020, 3, 1), Decimal('1.00338369')),
 (datetime.date(2020, 4, 1), Decimal('1.00284925')),
 (datetime.date(2020, 5, 1), Decimal('1.00235810')),
 (datetime.date(2020, 6, 1), Decimal('1.00212332')),
 (datetime.date(2020, 7, 1), Decimal('1.00194346')),
 (datetime.date(2020, 8, 1), Decimal('1.00159890')),
 (datetime.date(2020, 9, 1), Decimal('1.00156966')),
 (datetime.date(2020, 10, 1), Decimal('1.00156966')),
 (datetime.date(2020, 11, 1), Decimal('1.00149486')),
 (datetime.date(2020, 12, 1), Decimal('1.00164447')),
 (datetime.date(2021, 1, 1), Decimal('1.00149486')),
 (datetime.date(2021, 2, 1), Decimal('1.00134527')),
 (datetime.date(2021, 3, 1), Decimal('1.00201080')),
 (datetime.date(2021, 4, 1), Decimal('1.00207785')),
 (datetime.date(2021, 5, 1), Decimal('1.00270326')),
 (datetime.date(2021, 6, 1), Decimal('1.00307779')),
 (datetime.date(2021, 7, 1), Decimal('1.00355616')),
 (datetime.date(2021, 8, 1), Decimal('1.00427952')),
 (datetime.date(2021, 9, 1), Decimal('1.00441999')),
 (datetime.date(2021, 10, 1), Decimal('1.00485996')),
 (datetime.date(2021, 11, 1), Decimal('1.00586749')),
 (datetime.date(2021, 12, 1), Decimal('1.00769083')),
 (datetime.date(2022, 1, 1), Decimal('1.00732270')),
 (datetime.date(2022, 2, 1), Decimal('1.00755041')),
 (datetime.date(2022, 3, 1), Decimal('1.00927054')),
 (datetime.date(2022, 4, 1), Decimal('1.00834321')),
 (datetime.date(2022, 5, 1), Decimal('1.01034592')),
 (datetime.date(2022, 6, 1), Decimal('1.01015316')),
 (datetime.date(2022, 7, 1), Decimal('1.01034842')),
 (datetime.date(2022, 8, 1), Decimal('1.01169361')),
 (datetime.date(2022, 9, 1), Decimal('1.01071982')),
 (datetime.date(2022, 10, 1), Decimal('1.01020676')),
 (datetime.date(2022, 11, 1), Decimal('1.01020676')),]

data_inicial = datetime.date(2017, 10, 1)
data_final = datetime.date(2022, 10, 31)
fator_total = 1
for data, fator_mensal in dados:
    if data_inicial <= data <= data_final:
        fator_total *= fator_mensal
fator_total = fator_total.quantize(Decimal('0.0000000000000001'))
# fator_total: Decimal('1.3547653647828167')

Nota: para chegar nesses dados baixei manualmente, copiei, colei e formatei, ou seja, não usei a biblioteca, pra ter certeza de que o dado é comparável com o que eu peguei nas 2 simulações que fiz por período (usei a mesma interface Web, trocando apenas a opção "período" para "mensal").

O valor ajustado para R$ 800.000,00 por esse fator final que calculei com base no mensal (e arredondado para 16 casas decimais) é de: R$ 1.083.812,29, que é muito próximo da simulação que inclui o dia 01/11/2022 (diferença de 15 centaos).

Ponto importante: o valor relatado pelo @JulioNegreiro (~ R$ 1.087.869,39) é BEM diferente dos valores que obtive acima, então mesmo que ele quisesse somente o ajuste do mês fechado (ou seja, excluindo a possibilidade de 1 dia influenciar) e se a questão das 16 casas decimais fosse resolvida, isso ainda não resolveria o problema. Será que existe diferença entre os valores que estou usando (desse simulador "novoselic" - que é diferente do que foi usado pelo @JulioNegreiro) com os que a biblioteca usa?

@turicas
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turicas commented Dec 2, 2022 •
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Mais um detalhe interessante: a API que o @vmesel citou tem um número bem menor de casas decimais comparado àquele simulador que utilizei: a que possui taxa diária tem 6 dígitos (dividindo por 100 ficariam 8) e a mensal tem apenas 2 dígitos (dividindo por 100 ficariam 4).
Sendo assim, não dá pra confiar em outros simuladores (fora do BCB) pois não sabemos de qual lugar no BCB eles pegam a informação (pode ser com mais precisão ou menos precisão).

Pegando a mensal de 11/2022 temos 1.02. Pegando a diária e multiplicando teríamos 1.01021 (arredondando) -- são 20 dias úteis com taxa igual a 0.050788, que daria (1 + (0.050788 / 100)) ** 20.

@turicas
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turicas commented Dec 3, 2022

Além da correção com relação à conta (para ficar igual a do simulador), acho que o ideal seria a biblioteca sempre pegar os valores diários e, caso o usuário passe só o mês, ela utiliza o primeiro dia do mês inicial e o último dia do mês final (calendar.monthrange(year, month)[1]).

@turicas
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turicas commented Dec 3, 2022

Li em vários manuais que usam a Selic para alguns conta (do BCB, da ANBIMA etc.) que deve-se usar o valor da taxa anual com 2 dígitos, fazendo a conta diária ((1 + taxa_anual) ** (1/252)) e pegando o resultado dessa conta com 8 dígitos.

@JulioNegreiro
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Não quero poluir esse Issue, com outro issue.. mas apenas gostaria de levantar um ponto de atenção.

Validando os adaptadores do IPCA também é possível encontrar diferença entre o valor do banco central e o valor do adaptador.

Talvez o problema esteja mais profundo no código do que apenas no tratamento da Selic. ( Por favor conferir as refêrencias utilizadas)

Simulando o IPCA 2017-10 a 2022-10 - 800.000,00 no site- > R$ 1.054.615,28

Captura de Tela 2022-12-05 às 22 11 08

simulando o IPCA 2017-10 a 2022-10 - 800.000,00 no adaptador -> R$1.050.211,318822023047375160051

from calculadora_do_cidadao import Ipca
ipca = Ipca()
v = ipca.adjust("2017-10", 800000,'2022-10')
print(v)

Captura de Tela 2022-12-05 às 22 11 33

A diferença é de 4404 reais.

@vmesel
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vmesel commented Dec 6, 2022

@JulioNegreiro não acho que você está poluindo a issue não, inclusive esse problema é correlato a correção da SELIC. Só para deixar uma projeção registrada que eu fiz usando a data de novembro ao invés de outubro de 2017.

image

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