PortLearn

PortLearn is a finance-first research framework for controlled, reproducible, and modular experimentation in machine-learned portfolio choice.

Change the research component without accidentally changing the financial experiment.

PortLearn is being developed to support comparable portfolio-learning research across classical methods, forecasting-based machine learning, direct deep learning, reinforcement learning, optimization-based approaches, and user-defined research components under common financial experiment contracts.

Status

PortLearn is in early development and has not yet been published as a stable package release. The current public surface exposes the research foundation and API contracts. The public API is not yet stable.

Design Direction

PortLearn aims to provide reusable infrastructure for:

• financial data and information-set construction;
• modular feature engineering;
• portfolio strategy composition;
• common portfolio accounting;
• controlled comparison of alternative methods;
• research reproducibility and provenance.

PortLearn is a research toolkit, not a repository for individual paper-specific models or unpublished research architectures.

Roadmap

PortLearn is under active research development. This roadmap is intentionally high-level: it communicates broad direction only, is subject to change as the research framework develops, and does not promise dates or specific functionality.

Available now

• Research foundation: time/chronology contracts, observation handling, information sets, and the core research interfaces that define how portfolio research components compose.
• Validation utilities for detecting violations of point-in-time information contracts.
• Canonical run manifests for recording environment and command provenance.

Next

• Datasets and data access: adapters and alignment for market and macro data.
• Experiment and reproducibility infrastructure.

Planned

• Forecasting methods.
• Portfolio construction and accounting.
• Later deep-learning and reinforcement-learning research capabilities.

Entries move forward on this roadmap as the underlying research foundation stabilizes; nothing here is a dated commitment.

Development

The local development battery, from a fresh clone:

uv sync --locked
uv run ruff check .
uv run pytest
uv build
uv run python scripts/verify_built_wheel.py