Fpa backtest learn

Backtest & Learn (Operate)

Overview

The model should get measurably better at this business over time. This skill scores past forecasts against the company's actuals, surfaces what keeps missing, and proposes improvements a human ratifies. The objective metric is reconciliation error against the user's own books (pyfpa.score_forecast) - the FP&A analog of a validation loss.

Core principle: self-experimenting, but never self-promoting. The AI may run and discard bounded challengers autonomously; a human approves replacement of the champion. Everything learned lives as plain files in .fpa/.

Memory (.fpa/)

• forecasts/<period>.snapshot.yaml - each forecast's assumptions + predictions, and (after close) its score.
• scorecard.md - the running track record (rendered, never hand-edited).
• experiments/<slug>.experiment.yaml - each tested model change, its evidence, changed files, checks, before/after metrics, and decision.
• learnings.md - every accepted change: what, the evidence, the backtest delta, the date.

Workflow

1. Snapshot every forecast. When you produce a forecast, persist it: snapshot_forecast(cfg, forecast_df, label=<period>, created=<today>) → save_snapshot(..., ".fpa/forecasts/<period>.snapshot.yaml").
2. Score at close. When a period closes (actuals via fpa-configure-actuals), load that period's snapshot, score_forecast(snap.predicted, actuals), write the score back into the snapshot, and re-render scorecard.md with render_scorecard.
3. Attribute each material per-line miss to a driver (volume / price / cost ratio / working-capital timing). Run the fpa-cfo-judgment one-time-item screen first - never blame the model for a one-off.
   • Monitor applied corrections: if a type: parametric correction's target line keeps missing, flag it as possibly stale (applied → superseded) for the human - never auto-revert.
4. Create an experiment before changing the model. State the financial hypothesis, CFO question, evidence, fit periods, holdout periods, and files expected to change. Save it with pyfpa.save_experiment.
5. Propose, tagged by type:
   • Parametric (an assumption change): re-score it with holdout_backtest on the company's history. Surface it only if it lowers holdout fitness (not in-sample), ranked by the delta. Clamp the proposed move with magnitude_cap (±25%/cycle).
   • Structural (a methodology/skill change, e.g. a revenue-recognition lag): surface only when persistent_miss is true for the line (same-signed across K≥2 closes) and it survived the one-time screen. Hand it to fpa-learn-business to generate the skill on approval - propose, don't auto-write.
6. Evaluate. Record before/after metrics and explicit checks in the experiment. A model change that breaks reconciliation or another accounting invariant is failed even if one headline metric improves.
7. Ratify + log. Present proposals; the human accepts/rejects. On accept, add an ExperimentDecision, set status: accepted, save with explicit overwrite=True, update the company model, and append to learnings.md. Rejected and reverted experiments remain in memory.
8. Run fpa-research-loop when the miss warrants multiple autonomous challenger epochs instead of one manually proposed change.

Bootstrap (day one)

If the company already has ~12+ months of actuals, you don't have to wait: run holdout_backtest immediately (fit on the earlier months, hold out the recent ones) to report current accuracy and the first round of parametric proposals.

Guardrails (always)

• Never score on data the model was fit on (holdout_backtest enforces this).
• No proposal off a single period - require a persistent, same-signed miss.
• A parametric change must improve holdout fitness, not in-sample fit.
• Cap how far any assumption moves per cycle (magnitude_cap).
• Human ratifies champion promotion; autonomous failed epochs are logged and reversible.
• Missing or zero actuals are insufficient evidence, not perfect forecast performance.

Next

Scored + learned → fpa-board-briefing (report the forecast and how it's tracking).