VeighNa 4.0 Plans for Multi-Factor Research and Trading

Summary

This project plan outlines VeighNa’s intended 4.0 development, including support for Python 3.13, framework refactoring, improved logging and API bindings, updated packaging and code-quality tools, and expanded trading gateways and applications. It describes these as planned engineering changes rather than completed capabilities. The plan notes that Python’s no-global-interpreter-lock mode remains experimental and that current community testing indicates a single-thread performance cost.

A central quantitative feature is AlphaStrategy, a planned module for cross-sectional multi-factor research. Its workflow covers data cleaning, factor discovery, machine-learning model prediction, and event-driven backtesting, with phased integration into VeighNa. The authors explain that a planned Qlib integration proved impractical and that the new module grew from community exploration. The document also proposes sharing applications of large language models in quantitative work. It presents a roadmap, not measured strategy results or evidence that the planned tools have already been delivered.

Key ideas

• VeighNa 4.0 is planned to prioritize Python 3.13 while No-GIL remains experimental.
• The roadmap includes framework, packaging, logging, API compatibility, and code-quality updates.
• AlphaStrategy is intended to cover data preparation, factor discovery, model prediction, and event-driven backtesting.
• The plan describes phased integration and future LLM-related community activity, not completed outcomes.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.