| name | fpa-portfolio-learn |
| description | Use when you run FP&A for several clients and want your practice to compound - mines patterns that generalize across your same-type clients, validates them by leave-one-out cross-client backtesting, and promotes ratified priors and skills into a local library that seeds every new client. All local; nothing leaves your machine. |
Portfolio Learn (Loop B)
Overview
Loop A makes the model better at one client. This makes your practice compound:
client #10 starts smarter than client #1 because your library carries what generalized
across #1–9. Everything is local - your own book, on your own machine.
Core principle: self-improving, never self-ratifying - propose, you accept. The
objective metric is cross-client: does a pattern learned on some clients fail to
degrade the others' backtest?
Setup
A portfolio manifest ~/.fpa/portfolio.yaml lists your clients + a business-type tag:
library: ~/.fpa/library
clients:
- { path: ~/clients/acme, type: d2c-inventory }
- { path: ~/clients/peak, type: d2c-inventory }
- { path: ~/clients/haul, type: trucking }
Workflow
- Load the manifest (
pyfpa.load_portfolio).
- For each business-type with at least 3 clients:
- Priors: let
type_clients = pyfpa.portfolio.clients_of_type(portfolio, type).
pyfpa.mine_priors(portfolio, type) finds drivers that cluster tightly; validate each
with pyfpa.validate_prior(driver, type_clients) (leave-one-out). Surface validated
ones first (by cross-client delta), then unvalidated/judgment.
- Skills:
pyfpa.find_recurring_skills(portfolio, type) for recurring generated
skills. Also weigh recurring structural corrections across clients (read each
.fpa/corrections/ for type: structural) - a human-authored pattern that repeats
is strong signal.
- Present candidates ranked by evidence (support count + cross-client delta).
- Ratify. On your acceptance,
pyfpa.promote_prior / pyfpa.promote_skill writes the
~/.fpa/library/ and library-log.md. Reversible.
Guardrails
- Local-only; nothing phones home.
- At least 3 clients to propose; tight-cluster only; a prior must not degrade held-out clients.
- You ratify everything; priors are seeds, not mandates - each client's Loop A refines.
The payoff
New clients inherit the library: fpa-learn-business seeds their starting model from
your promoted priors (pyfpa.seed_from_library) and offers the promoted skills.
Next
Promoted → the next new client onboarded via fpa-learn-business starts smarter.
