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Code Issues Releases Activity Actions

Add income and spending insight cards to dashboard (#668)

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* Generate time series for rolling 30 day sum of income and spending

* Highlight accounts with most income and spending in the last 30 days

* Aggregate chips after 3 top accounts in insight card

* Refactor aggregation filter

I think this is easier to read and understand whats happening at a
glance

* Refactor and tidy

* Use family currency for insight cards

* Further reduce risk of sql injection

* Fix lint

* Refactor rolling total queries

* Add test for transaction snapshot
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Josh Brown 2024-04-24 13:34:50 +01:00 • committed by GitHub
parent 1f6e83ee91
commit 461fa672ff
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8 changed files with 207 additions and 64 deletions
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39
app/models/transaction.rb
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@ -11,6 +11,45 @@ class Transaction < ApplicationRecord
scope :inflows, -> { where("amount > 0") }
scope :outflows, -> { where("amount < 0") }
scope :active, -> { where(excluded: false) }
scope :with_converted_amount, ->(currency = Current.family.currency) {
# Join with exchange rates to convert the amount to the given currency
# If no rate is available, exclude the transaction from the results
select(
"transactions.*",
"transactions.amount * COALESCE(er.rate, 1) AS converted_amount"
)
.joins(sanitize_sql_array([ "LEFT JOIN exchange_rates er ON transactions.date = er.date AND transactions.currency = er.base_currency AND er.converted_currency = ?", currency ]))
.where("er.rate IS NOT NULL OR transactions.currency = ?", currency)
}
def self.daily_totals(transactions, period: Period.last_30_days, currency: Current.family.currency)
# Sum spending and income for each day in the period with the given currency
select(
"gs.date",
"COALESCE(SUM(converted_amount) FILTER (WHERE converted_amount > 0), 0) AS spending",
"COALESCE(SUM(-converted_amount) FILTER (WHERE converted_amount < 0), 0) AS income"
)
.from(transactions.with_converted_amount(currency), :t)
.joins(sanitize_sql([ "RIGHT JOIN generate_series(?, ?, interval '1 day') AS gs(date) ON t.date = gs.date", period.date_range.first, period.date_range.last ]))
.group("gs.date")
end
def self.daily_rolling_totals(transactions, period: Period.last_30_days, currency: Current.family.currency)
# Extend the period to include the rolling window
period_with_rolling = period.extend_backward(period.date_range.count.days)
# Aggregate the rolling sum of spending and income based on daily totals
rolling_totals = from(daily_totals(transactions, period: period_with_rolling, currency: currency))
.select(
"*",
sanitize_sql_array([ "SUM(spending) OVER (ORDER BY date RANGE BETWEEN INTERVAL ? PRECEDING AND CURRENT ROW) as rolling_spend", "#{period.date_range.count} days" ]),
sanitize_sql_array([ "SUM(income) OVER (ORDER BY date RANGE BETWEEN INTERVAL ? PRECEDING AND CURRENT ROW) as rolling_income", "#{period.date_range.count} days" ])
)
.order("date")
# Trim the results to the original period
select("*").from(rolling_totals).where("date >= ?", period.date_range.first)
end
def self.ransackable_attributes(auth_object = nil)
%w[name amount date]