def compute_applications(data, base_date):
previous_month_data = rs.records_for_month(data,
dth.months_ago(base_date, 1))
current_month_data = rs.records_for_month(data, base_date)
difference = rs.total_amount_by(
APPLICATIONS_GROUPBY, current_month_data) - rs.total_amount_by(
APPLICATIONS_GROUPBY, previous_month_data)
return difference.amount
def food_expenses(expenses, base_date):
data = rs.total_amount_by('category',
rs.records_for_month(expenses, base_date))
if 'mercado' in data.index and 'restaurante' in data.index:
return pd.Series(data.loc['mercado'].amount +
data.loc['restaurante'].amount) * -1
else:
return pd.Series(0)
def absolute_return_for_month(invest, base_date):
"""Total return on current month given base_date"""
past_month = rs.records_for_previous_month(invest, base_date)
current_month = rs.records_for_month(invest, base_date)
invested_previous_month = total_invested_by('title', past_month)
invested_for_month = total_invested_by('title', current_month)
applications_month = total_applications_by('title', current_month)
discounts_month = total_discounts_by('title', current_month)
return invested_for_month \
.sub(invested_previous_month, fill_value=0) \
.sub(applications_month, fill_value=0) \
.add(discounts_month, fill_value=0)
def total_monthly_return(invest, base_date):
past_month = rs.records_for_previous_month(invest, base_date)
current_month = rs.records_for_month(invest, base_date)
invested_past_month = ft.invested(past_month).amount.sum()
invested_current_month = ft.invested(current_month).amount.sum()
applications_current_month = ft.applications(current_month).amount.sum()
discounts_current_month = ft.discounts(current_month).amount.sum()
if invested_current_month == 0:
return 0.0
return monthly_return_percentage(
invested_past_month, invested_current_month,
applications_current_month + discounts_current_month)
def monthly_return_by_title(invest, base_date):
"""Monthly return percentage given investments.
Even though it has a different calculation, it returns the same results
as absolute_return_for_month_percentage"""
past_month = rs.records_for_previous_month(invest, base_date)
current_month = rs.records_for_month(invest, base_date)
invested_previous_month = total_invested_by('title', past_month)
invested_for_month = total_invested_by('title', current_month)
applications_month = total_applications_by('title', current_month)
discounts_month = total_discounts_by('title', current_month)
return invested_for_month\
.sub(applications_month, fill_value=0)\
.add(discounts_month, fill_value=0)\
.div(invested_previous_month, fill_value=0) - 1
def summary(invest, base_date):
past_month = rs.records_for_previous_month(invest, base_date)
current_month = rs.records_for_month(invest, base_date)
invest_return_for_month = tt.absolute_return_for_month(invest, base_date)
invested_previous_month = tt.total_invested_by('title', past_month)
invest_return_for_month_perc = invest_return_for_month / invested_previous_month
summary = {'Total': tt.total_invested_by('title', current_month).amount,
'Total last month': invested_previous_month.amount,
'Return for month': invest_return_for_month.amount,
'Return for month (%)': invest_return_for_month_perc.amount,
'Return with inflation (%)': tt.return_with_inflation(
invest_return_for_month_perc, base_date).amount}
summary_invest = pd.DataFrame(summary, columns=list(summary.keys()))
return summary_invest[summary_invest['Total'] > 0]
def earned_for_month(incomes, base_date):
return rs.records_for_month(incomes[incomes.category == 'renda'], base_date).amount.sum()
def applications_for_month(incomes, base_date):
"""Total applications on current month given base_date"""
applications = rs.records_for_month(ft.applications(incomes), base_date)
return rs.total_amount_by('title', applications)
def invested_for_month_by(column, invest, base_date):
"""Total invested on current month given base_date"""
invested = rs.records_for_month(invest, base_date)
return total_invested_by(column, invested)
def describe_over_time(expenses, incomes, post_describe_fn):
"""Computes expenses distribution over time, for all months available in expenses"""
return rs.describe_over_time(
expenses, lambda exps, date: post_describe_fn(
distribution(rs.records_for_month(expenses, date),
rs.records_for_month(incomes, date))))
def describe(invest, column, base_date):
"""Returns relevant data about types"""
current_month = rs.records_for_month(invest, base_date)
return tt.total_invested_by(column, current_month).amount
