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Merge #1037: Fix Finance Tracker aggregate variance and cash-flow scenario ranges

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Michael Sitarzewski
2026-10-06 11:28:27 -05:00
parent 6cca0f9d88 fe06bedf9c
commit 737ef0367c
1 changed files with 12 additions and 9 deletions
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support/support-finance-tracker.md
+12 -9
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@@ -64,9 +64,9 @@ WITH budget_actuals AS (
actual_amount,
DATE_TRUNC('quarter', date) as quarter,
budget_amount - actual_amount as variance,
(actual_amount - budget_amount) / budget_amount * 100 as variance_percentage
(actual_amount - budget_amount) * 100.0 / NULLIF(budget_amount, 0) as variance_percentage
FROM financial_data
WHERE fiscal_year = YEAR(CURRENT_DATE())
WHERE fiscal_year = EXTRACT(YEAR FROM CURRENT_DATE)
),
department_summary AS (
SELECT
@@ -75,7 +75,8 @@ department_summary AS (
SUM(budget_amount) as total_budget,
SUM(actual_amount) as total_actual,
SUM(variance) as total_variance,
AVG(variance_percentage) as avg_variance_pct
(SUM(actual_amount) - SUM(budget_amount)) * 100.0 /
NULLIF(SUM(budget_amount), 0) as variance_pct
FROM budget_actuals
GROUP BY department, quarter
)
@@ -85,10 +86,11 @@ SELECT
total_budget,
total_actual,
total_variance,
avg_variance_pct,
variance_pct,
CASE
WHEN ABS(avg_variance_pct) <= 5 THEN 'On Track'
WHEN avg_variance_pct > 5 THEN 'Over Budget'
WHEN variance_pct IS NULL THEN 'No Budget Baseline'
WHEN ABS(variance_pct) <= 5 THEN 'On Track'
WHEN variance_pct > 5 THEN 'Over Budget'
ELSE 'Under Budget'
END as budget_status,
total_budget - total_actual as remaining_budget
@@ -145,13 +147,14 @@ class CashFlowManager:
'forecasted_payments': forecasted_payments,
'net_cash_flow': net_flow,
'cumulative_cash': cumulative_cash,
'confidence_interval_low': net_flow * 0.85,
'confidence_interval_high': net_flow * 1.15
# Illustrative +/-15% scenarios, not a statistical confidence interval.
'scenario_low': net_flow - abs(net_flow) * 0.15,
'scenario_high': net_flow + abs(net_flow) * 0.15
})
return pd.DataFrame(rows, columns=[
'date', 'forecasted_receipts', 'forecasted_payments', 'net_cash_flow',
'cumulative_cash', 'confidence_interval_low', 'confidence_interval_high'
'cumulative_cash', 'scenario_low', 'scenario_high'
])
def identify_cash_flow_risks(self, forecast_df):