Symbolic alpha mining and allocation

An adapted AlphaPROBE research workspace with local point-in-time feature infrastructure and alpha-allocation additions.

PythonSymbolic expressionsGFlowNetPPOPoint-in-time data

AdaptationRank 09 of 13 in this field ↗

Why it sits here. Placed by the scope of implementation, the available contribution evidence, and the distinct technical capability it demonstrates.

03 / Somewhere between data and understanding.STUDY IN SPACE

01 / IMPLEMENTATION & CONTRIBUTION

What the work involves

Local adaptations extend the upstream AlphaPROBE codebase with a point-in-time feature registry, dataset adapters, alpha-allocation modules, and targeted tests. The original algorithm and research belong to the paper authors.

Technical depth

Symbolic expression trees, alpha pools, knowledge-guided search, GFlowNet/PPO search entrypoints, point-in-time feature access and allocation after discovery.

The project family

A_Share/factorResearch/AlphaPROBE

02 / RESULTS

What came out of it

The workspace connects symbolic-alpha search to explicit feature-availability controls and allocation code, extending the upstream discovery workflow.

03 / SUPPORTING EVIDENCE

Follow the source

Implementation notes, project records, and supporting artifacts.

Source context & project scope

Original AlphaPROBE paper/model is not Donald’s publication. Working-tree additions establish local work, not validated alpha or exact sole authorship. No research outputs inspected.

README.md

Names the original AlphaPROBE authors and approach.

SOURCE · 2026-09-17

pit.py

Local added PIT layer identified in Git status.

SOURCE · 2026-09-17

feature_registry.py

Local feature-registry extension identified alongside allocation additions.

SOURCE · 2026-09-17

CONTINUE IN MACHINE LEARNING & MODEL RESEARCH

What the work involves

Technical depth

The project family

What came out of it

Follow the source

A-share representation and attention-model adaptations