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Python kernel

The importable package is pyfpa. The distribution name is au-fpa-pack.

The account-and-amount CSV readers refuse duplicate or unnamed column headings, extra row fields and non-blank rows without an account. These cases can otherwise discard or misassign amounts. Entirely blank rows and declared metadata columns remain supported. Quote amounts that contain thousands separators, such as "1,234".

import pyfpa

config = pyfpa.load_config("examples/ridgeline/config.yaml")
monthly = pyfpa.cashflow_from_config(config)

cash_config = pyfpa.load_cash13_config("examples/ridgeline/cash13.yaml")
weekly = pyfpa.cash13_forecast(cash_config)
runway = pyfpa.runway_summary(weekly)

print(pyfpa.to_briefing_md(monthly, title="My Company", runway=runway))

Named scenarios change the declared 13-week flows instead of editing them. A FlowChange scales every base flow of one name (amount_factor), pushes it later (delay_weeks), or both; add_receipts and add_disbursements add new flows. A name that matches no base flow is refused, so a misspelling cannot leave a scenario identical to the base. A flow pushed past the horizon leaves the forecast.

scenarios = pyfpa.load_cash13_scenarios("examples/ridgeline/scenarios.yaml")
print(pyfpa.compare_scenarios(cash_config, scenarios))

compare_scenarios returns one row per scenario, base first, with the cash trough (min_cash), its week, the first negative week and closing cash. It compares declared assumptions; it does not say which scenario is likely.

The base kernel includes:

  • monthly P&L and indirect cash-flow modelling
  • 13-week direct-method cash forecasting, with named scenario comparison
  • revenue, COGS, operating-expense, debt, tax, and working-capital primitives
  • reconciliation, segment, SKU, and divestiture analysis helpers
  • CSV ingestion and Markdown or Excel reporting: static value export via forecast_to_excel, and a live-formula model workbook via model_to_excel (named assumption cells, real formulas, verified against the engine in CI);
  • forecast snapshots, scoring, and holdout backtests
  • workspace, intake, correction, experiment, retrieval, and research records
  • experimental cross-company prior and skill mining.

EntityConfig is a starting point; it does not prescribe every model. A company with cohorts, projects, contracts, fleets, stores, or complex inventory may need a different generated model.

The model_to_excel function compiles an EntityConfig into a 2-sheet workbook: an Assumptions sheet of named, editable driver cells and a Model sheet where every P&L and cash-flow line is a real formula referencing those names. The verify_workbook function evaluates the workbook with a Python formula engine and compares every line and month to cashflow_from_config, so the workbook is verified against the engine before it is used. For cadences or layouts the kernel does not cover, compose pyfpa.excel.toolkit in a company-specific exporter and verify it the same way.