Fund Operations & Data Quality Dashboard

Demo for Project Accountants — private equity / fund administration support. Multi-fund portfolio view with a NAV bridge, performance multiples, capital activity and a data-quality exception log. Every headline figure is derived from cleaned transaction data and cross-checked below; the full pipeline, SQL and Excel model are at the bottom of the page.

As at 30 September 2026 · Q3 2026 · USD

NAV Bridge — Q3 2026

Opening NAV + capital activity + income + valuation movement = closing NAV

Performance by Fund

DPI + RVPI stacked = TVPI

Fund Portfolio Overview & Data Quality Status

FundStrategyVintageCommitment% CalledPaid-inNAVDPIRVPITVPINet IRRDQ StatusExceptions

Capital Calls vs Distributions

Trailing 8 quarters, $m

Open Exceptions by Category

Counted from the exception log below

Open Exception Log

RefFundCategoryDescriptionValueAge (days)Owner

How it's built

Every figure above is reproducible from the raw administrator export. Same numbers three ways: Python, SQL and Excel.
1 · Collect

Administrator export

128 raw cash-flow rows: four date formats, eight type spellings, fund names vs IDs, sign-flipped amounts, 3 double-postings.

2 · Clean & validate

pipeline.py (pandas)

Normalises to 125 clean rows, then runs 7 validation rules: bridge closure, over-calls, dating, referential checks.

3 · Aggregate

fund_metrics.sql

Paid-in, distributions, DPI/RVPI/TVPI, DQ status and the 8-quarter cash-flow series, loaded to SQLite.

4 · Report

Dashboard + Excel model

This page, plus a formula-driven workbook (SUMIFS/INDEX-MATCH, as-at date driver, 7 integrity checks).

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fund_ops_model.xlsxExcel model, live formulas, 8 tabs admin_export_cashflows.csvRaw export, before cleaning cashflows_clean.csvpipeline.py output funds.csvFund master nav_bridge.csvQ3 2026 NAV movements exceptions.csvOpen exception log