numerai-model-implementation

name	numerai-model-implementation
description	Add a new Numerai model type to the agents training pipeline. Use when you need to register a model in `agents/code/modeling/utils/model_factory.py`, handle fit/predict quirks in `agents/code/modeling/utils/numerai_cv.py`, and update configs so the model can run via `python -m agents.code.modeling`.

Numerai Model Implementation

Overview

Add a new model type so it can be selected in configs and trained/evaluated by the base pipeline.

Note: run commands from numerai/ (so agents is importable), or from repo root with PYTHONPATH=numerai.

Implement a New Model Type

1. Define the model API and output shape.

   • Implement fit(X, y, sample_weight=...) and predict(X).
   • Put custom wrappers in agents/code/modeling/models/ so model-specific code stays isolated.
   • Accept pandas DataFrames or convert to NumPy inside the model wrapper.

2. Register the model constructor in agents/code/modeling/utils/model_factory.py.

   • Use lazy imports so optional dependencies do not break other workflows.
   • Raise a clear ImportError when the dependency is missing.

if model_type == "XGBRegressor":
    try:
        from xgboost import XGBRegressor
    except ImportError as exc:
        raise ImportError(
            "xgboost is required for XGBRegressor. Install with `.venv/bin/pip install xgboost`."
        ) from exc
    return XGBRegressor(**model_params)

3. Add or update a config to use the new model type.

CONFIG = {
    "model": {"type": "XGBRegressor", "params": {"n_estimators": 500}},
    "training": {"cv": {"n_splits": 5}},
    "data": {"data_version": "v5.2", "feature_set": "small", "target_col": "target", "era_col": "era"},
    "output": {},
    "preprocessing": {},
}

4. Add extra data columns if the model needs them.
   • Update load_and_prepare_data in agents/code/modeling/utils/pipeline.py to pass extra columns into load_full_data.
   • Add corresponding config entries so experiments stay reproducible.

Validate

• Run a smoke test: .venv/bin/python -m agents.code.modeling --config <config_path>.
• Run metrics on the smoke test and make sure corr_mean is > 0.005 and < 0.04. If it's less then something is probably fundamentally wrong. If it's higher than there is likely leakage and you need to find the problem.
• Double check that any early stopping mechanisms or modifications to the fit/predict loop don't over-estimate accuracy. Accurately estimating performance is of paramount importance on Numerai because we need to be able to decide if we should stake or not.
• Run unit tests after refactors: .venv/bin/python -m unittest.

Next Steps

After validating the model implementation:

1. Use the numerai-experiment-design skill to run multiple rounds of experiments (4–5 configs per round), then scale winners until you hit a plateau.
2. Use the numerai-model-upload skill to create a pkl file only after you have a stable, scaled “best model” you intend to deploy.
3. Deploy to Numerai using the MCP server (see numerai-model-upload skill for deployment workflow).