Fpa Portfolio Learn

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

1. Load the manifest (pyfpa.load_portfolio).
2. 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.

1. Present candidates ranked by evidence (support count + cross-client delta).
2. 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: JeffBrines
• Source: JeffBrines/openfpa
• License: MIT
• Homepage: https://www.guiderail.io

Install and usage instructions live in the source repository linked above.