def test_merge_operacoes_other_df_vazio(self):
data = [{
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 3, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'gcgs',
'qtd': -100,
'data': datetime.date(2019, 4, 12),
'preco': 200,
'aquisicao_via': 'HomeBroker'
}]
df = create_testing_dataframe(data)
other_df = create_testing_dataframe([])
result = merge_operacoes(df, other_df)
assert len(result) == len(df)
def test_split_operations_com_venda(self):
data = [
{
'ticker': 'id2',
'qtd': 3,
'operacao': 'Compra',
'data': datetime.date(2019, 3, 11),
'preco': 50,
'taxas': 0,
'aquisicao_via': 'teste'
},
{
'ticker': 'id1',
'qtd': 1,
'operacao': 'Compra',
'data': datetime.date(2019, 4, 11),
'preco': 100,
'taxas': 0,
'aquisicao_via': 'teste'
},
{
'ticker': 'id2',
'qtd': -3,
'operacao': 'Venda',
'data': datetime.date(2019, 4, 11),
'preco': 100,
'taxas': 0,
'aquisicao_via': 'teste'
},
]
notas = [
{
'ticker': '@SPLIT',
'qtd': 0,
'data': datetime.date(2019, 4, 11),
'preco': 0,
'taxas': 4.00,
'aquisicao_via': 'teste'
},
]
df = create_testing_dataframe(data)
dfNotas = create_testing_dataframe(notas)
split_data = check_split_operations(dfNotas, df)
print(split_data)
assert len(split_data) == 2
assert split_data['ticker'][0] == 'id1'
assert split_data['ticker'][1] == 'id2'
assert split_data['taxas'][0] == 1
assert split_data['taxas'][1] == 3
assert split_data['valor'][0] == 0
assert split_data['valor'][1] == 0
def test_merge_operacoes_df_vazio(self):
ticker = random_ticker()
df = create_testing_dataframe([])
other_data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 12), 'preco': 100, 'aquisicao_via': 'HomeBroker'}]
other_df = create_testing_dataframe(other_data)
result = merge_operacoes(df, other_df)
assert len(result) == len(other_df)
def test_envia_relatorio_html_por_email(self):
data = [{
'ticker': 'MAXR11',
'qtd': 100,
'data': datetime.date(2019, 3, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'PETR4',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': 100,
'data': datetime.date(2019, 4, 12),
'preco': 200,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': -50,
'data': datetime.date(2019, 5, 12),
'preco': 220,
'aquisicao_via': 'HomeBroker'
}]
df = create_testing_dataframe(data)
calcula_custodia(df)
calculo_ir = CalculoIr(df=df)
calculo_ir.calcula()
assert envia_relatorio_html_por_email('assunto - teste unitário',
relatorio_html(calculo_ir))
def test_calcula_precos_medios_quando_operacoes_no_mesmo_dia_estao_fora_de_ordem(
self):
# dentro de um mesmo dia, nao existe a informacao de horas.
# logo, nao há garantia de que as operacoes ocorreram na ordem informada (dentro um mesmo dia)
data = [{
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100
}, {
'ticker': 'gcgs',
'qtd': -100,
'data': datetime.date(2019, 4, 12),
'preco': 200
}, {
'ticker': 'gcgs',
'qtd': -200,
'data': datetime.date(2019, 4, 13),
'preco': 500
}, {
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 13),
'preco': 300
}, {
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 13),
'preco': 400
}, {
'ticker': 'gcgs',
'qtd': -1,
'data': datetime.date(2019, 4, 15),
'preco': 1
}, {
'ticker': 'gcgs',
'qtd': 2,
'data': datetime.date(2019, 4, 15),
'preco': 5
}, {
'ticker': 'gcgs',
'qtd': 3,
'data': datetime.date(2019, 4, 15),
'preco': 2
}, {
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 16),
'preco': 2
}]
df = create_testing_dataframe(data)
precos_medio_de_compra = calcula_precos_medio_de_compra(df)
assert precos_medio_de_compra['gcgs']['valor'] == pytest.approx(
2.96, 0.001)
assert precos_medio_de_compra['gcgs'][
'data_primeira_compra'] == datetime.date(2019, 4, 15)
def test_merge_operacoes_com_ipo_em_uma_data_futura(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'}]
other_data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 13), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 14), 'preco': 100, 'aquisicao_via': 'HomeBroker'}]
other_data.extend(data)
other_df = create_testing_dataframe(other_data)
data.extend([{'ticker': ticker, 'qtd': -100, 'data': date(2099, 4, 20), 'preco': 200, 'aquisicao_via': 'IPO'}])
df = create_testing_dataframe(data)
result = merge_operacoes(df, other_df)
assert len(result) == (len(df) + 2)
def test_merge_operacoes_append_puro(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': -100, 'data': date(2019, 4, 12), 'preco': 200, 'aquisicao_via': 'HomeBroker'}]
df = create_testing_dataframe(data)
ticker2 = random_ticker()
other_data = [{'ticker': ticker2, 'qtd': 100, 'data': date(2019, 10, 14), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker2, 'qtd': 100, 'data': date(2019, 10, 15), 'preco': 100, 'aquisicao_via': 'HomeBroker'}]
other_df = create_testing_dataframe(other_data)
result = merge_operacoes(df, other_df)
assert len(result) == (len(df) + len(other_df))
def test_merge_operacoes_df_eh_none(self):
ticker = random_ticker()
other_data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 11), 'preco': 100, 'aquisicao_via': 'HomeBroker'},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 10, 12), 'preco': 100, 'aquisicao_via': 'HomeBroker'}]
other_df = create_testing_dataframe(other_data)
with pytest.raises(AssertionError):
merge_operacoes(None, other_df)
def test_calcula_precos_medios_quando_varios_ciclo_em_varios_meses(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 11), 'preco': 5},
{'ticker': ticker, 'qtd': -1, 'data': date(2019, 4, 12), 'preco': 6},
{'ticker': ticker, 'qtd': 2, 'data': date(2019, 4, 13), 'preco': 3},
{'ticker': ticker, 'qtd': -1, 'data': date(2019, 4, 14), 'preco': 2},
{'ticker': ticker, 'qtd': -1, 'data': date(2019, 4, 15), 'preco': 4},
{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 15), 'preco': 2},
{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 15), 'preco': 3},
{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 15), 'preco': 4},
{'ticker': ticker, 'qtd': -2, 'data': date(2019, 4, 16), 'preco': 5}]
df = create_testing_dataframe(data)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 11))['valor'] == pytest.approx(5.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 13))['valor'] == pytest.approx(3.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 14))['valor'] == pytest.approx(3.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 15))['valor'] == pytest.approx(3.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 20))['valor'] == pytest.approx(3.0, 0.001)
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 11), 'preco': 200},
{'ticker': ticker, 'qtd': -100, 'data': date(2019, 4, 12), 'preco': 200},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 13), 'preco': 300},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 14), 'preco': 400},
{'ticker': ticker, 'qtd': -200, 'data': date(2019, 4, 15), 'preco': 500},
{'ticker': ticker, 'qtd': 2, 'data': date(2019, 4, 16), 'preco': 5},
{'ticker': ticker, 'qtd': -1, 'data': date(2019, 4, 16), 'preco': 1},
{'ticker': ticker, 'qtd': 3, 'data': date(2019, 4, 17), 'preco': 2},
{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 17), 'preco': 20}]
df = create_testing_dataframe(data)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 15))['valor'] == pytest.approx(350.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 16))['valor'] == pytest.approx(5.0, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 17))['valor'] == pytest.approx(6.2, 0.001)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 11))['data_primeira_compra'] == date(2019, 4, 11)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 12))['data_primeira_compra'] == date(2019, 4, 11)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 15))['data_primeira_compra'] == date(2019, 4, 13)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 16))['data_primeira_compra'] == date(2019, 4, 16)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 17))['data_primeira_compra'] == date(2019, 4, 16)
def test_descobre_vendas_no_mes_quando_mais_de_um_ciclo_no_mesmo_mes(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 100},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 12), 'preco': 100},
{'ticker': ticker, 'qtd': -200, 'data': date(2019, 3, 13), 'preco': 200},
{'ticker': ticker, 'qtd': 500, 'data': date(2019, 3, 14), 'preco': 300},
{'ticker': ticker, 'qtd': -500, 'data': date(2019, 3, 15), 'preco': 400}]
df = create_testing_dataframe(data)
vendas_no_mes_abril = vendas_no_mes(df, 2019, 3)
assert type(vendas_no_mes_abril) is list
assert len(vendas_no_mes_abril) == 2
def test_deve_retornar_precos_de_compra_diferentes_quando_varias_operacoes_de_compra_venda_mesmo_mes(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 10), 'preco': 100},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 11), 'preco': 200},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 12), 'preco': 200},
{'ticker': ticker, 'qtd': -100, 'data': date(2019, 4, 13), 'preco': 250},
{'ticker': ticker, 'qtd': -150, 'data': date(2019, 4, 14), 'preco': 250},
{'ticker': ticker, 'qtd': 400, 'data': date(2019, 4, 15), 'preco': 400},
{'ticker': ticker, 'qtd': -250, 'data': date(2019, 4, 16), 'preco': 250}]
df = create_testing_dataframe(data)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 14))['valor'] == pytest.approx(180.00, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 16))['valor'] == pytest.approx(315.38, 0.01)
def test_descobre_vendas_no_mes_quando_mais_de_uma_compra_venda_e_dentro_do_mesmo_ciclo_mas_com_mesmo_preco_de_compra(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 100},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 12), 'preco': 100},
{'ticker': ticker, 'qtd': -50, 'data': date(2019, 3, 13), 'preco': 200},
{'ticker': ticker, 'qtd': 500, 'data': date(2019, 3, 14), 'preco': 100},
{'ticker': ticker, 'qtd': -400, 'data': date(2019, 3, 15), 'preco': 400}]
df = create_testing_dataframe(data)
vendas_no_mes_abril = vendas_no_mes(df, 2019, 3)
assert type(vendas_no_mes_abril) is list
assert len(vendas_no_mes_abril) == 1
assert vendas_no_mes_abril[0]['preco_medio_compra'] == pytest.approx(100.0, 0.001)
assert vendas_no_mes_abril[0]['qtd_vendida'] == 450
def test_split_operations_by_defs(self):
data = [{
'ticker': 'id1:1|id2:3',
'qtd': 0,
'data': datetime.date(2019, 4, 11),
'preco': 0,
'taxas': 4,
'aquisicao_via': 'teste'
}]
df = create_testing_dataframe(data)
split_data = check_split_operations(df)
assert len(split_data) == 2
assert split_data['ticker'][0] == 'id1'
assert split_data['ticker'][1] == 'id2'
assert split_data['taxas'][0] == 1
assert split_data['taxas'][1] == 3
def test_calcula_precos_medios_quando_um_ciclo(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 20), 'preco': 100},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 13), 'preco': 200}]
df = create_testing_dataframe(data)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 14))['valor'] == pytest.approx(200.00, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 16))['valor'] == pytest.approx(200.00, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 19))['valor'] == pytest.approx(200.00, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 20))['valor'] == pytest.approx(166.66, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 21))['valor'] == pytest.approx(166.66, 0.01)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 14))['data_primeira_compra'] == date(2019, 4, 13)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 16))['data_primeira_compra'] == date(2019, 4, 13)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 19))['data_primeira_compra'] == date(2019, 4, 13)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 20))['data_primeira_compra'] == date(2019, 4, 13)
assert calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 21))['data_primeira_compra'] == date(2019, 4, 13)
def test_descobre_vendas_no_mes_deve_considerar_varios_ciclos_dentro_de_um_mesmo_mes(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 150},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 11), 'preco': 100},
{'ticker': ticker, 'qtd': -200, 'data': date(2019, 4, 12), 'preco': 200},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 13), 'preco': 300},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 14), 'preco': 400},
{'ticker': ticker, 'qtd': -300, 'data': date(2019, 4, 15), 'preco': 500}]
df = create_testing_dataframe(data)
vendas_no_mes_abril = vendas_no_mes(df, 2019, 4)
assert type(vendas_no_mes_abril) is list
assert len(vendas_no_mes_abril) == 2
assert vendas_no_mes_abril[0] == {'ticker': ticker, 'qtd_vendida': 200, 'preco_medio_venda': 200.0,
'preco_medio_compra': 125.0, 'resultado_apurado': 15000.0}
assert vendas_no_mes_abril[1] == {'ticker': ticker, 'qtd_vendida': 300, 'preco_medio_venda': 500.0,
'preco_medio_compra': 350.0, 'resultado_apurado': 45000.0}
def test_calcula_precos_medios_quando_varios_tickers_juntos(self):
ticker = random_ticker()
ticker2 = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 20), 'preco': 100},
{'ticker': ticker2, 'qtd': 100, 'data': date(2019, 4, 20), 'preco': 100},
{'ticker': ticker2, 'qtd': 100, 'data': date(2019, 4, 20), 'preco': 200},
{'ticker': ticker2, 'qtd': 100, 'data': date(2019, 4, 20), 'preco': 300},
{'ticker': ticker, 'qtd': 200, 'data': date(2019, 4, 13), 'preco': 200}]
df = create_testing_dataframe(data)
preco_medio_de_compra = calcula_preco_medio_de_compra(df, ticker, date(2019, 4, 21))
assert preco_medio_de_compra['valor'] == pytest.approx(166.66, 0.01)
assert preco_medio_de_compra['data_primeira_compra'] == date(2019, 4, 13)
preco_medio_de_compra = calcula_preco_medio_de_compra(df, ticker2, date(2019, 4, 21))
assert preco_medio_de_compra['valor'] == pytest.approx(200.00, 0.01)
assert preco_medio_de_compra['data_primeira_compra'] == date(2019, 4, 20)
def test_calcula_precos_medios_quando_um_ciclo(self):
data = [{
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 20),
'preco': 100
}, {
'ticker': 'gcgs',
'qtd': 200,
'data': datetime.date(2019, 4, 13),
'preco': 200
}]
df = create_testing_dataframe(data)
precos_medio_de_compra = calcula_precos_medio_de_compra(df)
assert precos_medio_de_compra['gcgs']['valor'] == pytest.approx(
166.66, 0.01)
assert precos_medio_de_compra['gcgs'][
'data_primeira_compra'] == datetime.date(2019, 4, 13)
def test_descobre_vendas_no_mes(self):
ticker = random_ticker()
data = [{'ticker': ticker, 'qtd': 100, 'data': date(2019, 3, 11), 'preco': 100},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 11), 'preco': 100},
{'ticker': ticker, 'qtd': -100, 'data': date(2019, 4, 12), 'preco': 200},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 13), 'preco': 300},
{'ticker': ticker, 'qtd': 100, 'data': date(2019, 4, 14), 'preco': 400},
{'ticker': ticker, 'qtd': -200, 'data': date(2019, 4, 15), 'preco': 500},
{'ticker': random_ticker(), 'qtd': 200, 'data': date(2019, 4, 15), 'preco': 500},
{'ticker': random_ticker(), 'qtd': -200, 'data': date(2019, 4, 15), 'preco': 500},
{'ticker': ticker, 'qtd': -200, 'data': date(2019, 4, 15), 'preco': 500},
{'ticker': ticker, 'qtd': 2, 'data': date(2019, 4, 15), 'preco': 5},
{'ticker': ticker, 'qtd': -1, 'data': date(2019, 4, 15), 'preco': 1},
{'ticker': ticker, 'qtd': 3, 'data': date(2019, 4, 15), 'preco': 2},
{'ticker': ticker, 'qtd': 1, 'data': date(2019, 4, 16), 'preco': 2}]
df = create_testing_dataframe(data)
vendas_no_mes_abril = vendas_no_mes(df, 2019, 4)
assert type(vendas_no_mes_abril) is list
assert len(vendas_no_mes_abril) == 2
def test_relatorio_html(self):
data = [{
'ticker': 'MAXR11',
'qtd': 100,
'data': datetime.date(2019, 3, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'PETR4',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': 100,
'data': datetime.date(2019, 4, 12),
'preco': 200,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': -50,
'data': datetime.date(2019, 5, 12),
'preco': 220,
'aquisicao_via': 'HomeBroker'
}]
df = create_testing_dataframe(data)
custodia = calcula_custodia(df)
calculo_ir = CalculoIr(df=df)
calculo_ir.calcula()
from py_w3c.validators.html.validator import HTMLValidator
assert HTMLValidator().validate_fragment(
relatorio_html(custodia, calculo_ir, datetime.date.today(), False))
def test_deve_retornar_relatorio_html_com_todos_os_meses(self):
data = [{
'ticker': 'MAXR11',
'qtd': 100,
'data': datetime.date(2019, 3, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'PETR4',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': 100,
'data': datetime.date(2019, 4, 12),
'preco': 200,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': -50,
'data': datetime.date(2019, 5, 12),
'preco': 220,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'MAXR11',
'qtd': 100,
'data': datetime.date(2019, 6, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'PETR4',
'qtd': 100,
'data': datetime.date(2019, 10, 11),
'preco': 100,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': 100,
'data': datetime.date(2019, 10, 12),
'preco': 200,
'aquisicao_via': 'HomeBroker'
}, {
'ticker': 'XPLG11',
'qtd': -50,
'data': datetime.date(2019, 11, 12),
'preco': 220,
'aquisicao_via': 'HomeBroker'
}]
df = create_testing_dataframe(data)
calcula_custodia(df)
calculo_ir = CalculoIr(df=df)
calculo_ir.calcula()
from py_w3c.validators.html.validator import HTMLValidator
html = relatorio_html(calculo_ir)
assert HTMLValidator().validate_fragment(html)
assert 'MES : 05/2019' in html
assert 'MES : 11/2019' in html
def test_calcula_precos_medios_quando_varios_ciclo(self):
data = [{
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 11),
'preco': 5
}, {
'ticker': 'gcgs',
'qtd': -1,
'data': datetime.date(2019, 4, 12),
'preco': 6
}, {
'ticker': 'gcgs',
'qtd': 2,
'data': datetime.date(2019, 4, 13),
'preco': 3
}, {
'ticker': 'gcgs',
'qtd': -1,
'data': datetime.date(2019, 4, 14),
'preco': 2
}, {
'ticker': 'gcgs',
'qtd': -1,
'data': datetime.date(2019, 4, 15),
'preco': 4
}, {
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 15),
'preco': 2
}, {
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 15),
'preco': 3
}, {
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 15),
'preco': 4
}, {
'ticker': 'gcgs',
'qtd': -2,
'data': datetime.date(2019, 4, 16),
'preco': 5
}]
df = create_testing_dataframe(data)
precos_medio_de_compra = calcula_precos_medio_de_compra(df)
assert precos_medio_de_compra['gcgs']['valor'] == pytest.approx(
3.0, 0.001)
data = [{
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 11),
'preco': 100
}, {
'ticker': 'gcgs',
'qtd': -100,
'data': datetime.date(2019, 4, 12),
'preco': 200
}, {
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 13),
'preco': 300
}, {
'ticker': 'gcgs',
'qtd': 100,
'data': datetime.date(2019, 4, 14),
'preco': 400
}, {
'ticker': 'gcgs',
'qtd': -200,
'data': datetime.date(2019, 4, 15),
'preco': 500
}, {
'ticker': 'gcgs',
'qtd': 2,
'data': datetime.date(2019, 4, 16),
'preco': 5
}, {
'ticker': 'gcgs',
'qtd': -1,
'data': datetime.date(2019, 4, 16),
'preco': 1
}, {
'ticker': 'gcgs',
'qtd': 3,
'data': datetime.date(2019, 4, 17),
'preco': 2
}, {
'ticker': 'gcgs',
'qtd': 1,
'data': datetime.date(2019, 4, 17),
'preco': 2
}]
df = create_testing_dataframe(data)
precos_medio_de_compra = calcula_precos_medio_de_compra(df)
assert precos_medio_de_compra['gcgs']['valor'] == pytest.approx(
2.6, 0.001)
assert precos_medio_de_compra['gcgs'][
'data_primeira_compra'] == datetime.date(2019, 4, 16)