What happens if a model version is missing from a checkpoint?

The protocol includes a fallback mechanism that infers the version by analyzing the structural dimensions of the model, such as the observation space and action count, to maintain backward compatibility with legacy models.

Is this skill compatible with CI/CD workflows?

Yes, it includes a strict-version flag that can be enabled in automated testing environments to force a failure if a version mismatch is detected, ensuring only compatible models reach production.

How does the protocol handle different action meanings across versions?

It uses a version-specific action interpretation function that reads the model specification to map integers to specific actions, such as varying position sizes (e.g., 25% vs 50% buy orders).

Why should I embed metadata instead of using versioned filenames?

Embedding metadata directly in the checkpoint ensures the configuration is physically tied to the model weights, preventing accidental mismatches that occur when files are renamed or moved.

Model Version Protocol

Name: Model Version Protocol
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Manages version compatibility between trained trading models and live trading environments by embedding metadata into checkpoints.

About

The Model Version Protocol is a specialized framework designed to prevent silent failures in algorithmic trading by ensuring strict compatibility between training code and live trading bots. By embedding explicit version metadata—including observation dimensions, action spaces, and specific feature mappings—directly into model checkpoints, it enables automated validation at load time. This skill is particularly useful for quant developers and ML engineers who frequently update training architectures and need a reliable way to manage breaking changes, ensuring that the live trader interprets model outputs correctly regardless of which version is deployed.

Key Features

Legacy model detection using structural inference (obs_dim/n_actions)
Compatibility matrix mapping model versions to trader requirements
Strict and soft validation modes for CI/CD and manual testing
0 GitHub stars
Dynamic action interpretation based on version-specific schemas
Automated version metadata embedding in model checkpoints

Use Cases

Managing multiple generations of PPO models during rapid experimentation
Preventing live trading bots from loading models with incompatible action spaces
Automating deployment checks to verify model-trader alignment in production

About

Key Features

Legacy model detection using structural inference (obs_dim/n_actions)
Compatibility matrix mapping model versions to trader requirements
Strict and soft validation modes for CI/CD and manual testing
0 GitHub stars
Dynamic action interpretation based on version-specific schemas
Automated version metadata embedding in model checkpoints

Use Cases

Managing multiple generations of PPO models during rapid experimentation
Preventing live trading bots from loading models with incompatible action spaces
Automating deployment checks to verify model-trader alignment in production