Feature Engineering

A fixed Harbor feature-engineering task on public cryptocurrency minute data, with hidden scoring after submission.

The agent phase has two containers:

• main: the public Parquet, three context Markdown files, and Python libraries
• mcp-server: asynchronous train, backtest, and submission tools

Both images derive their public data from one canonical Parquet snapshot. The main image materializes only the agent-visible columns as compressed Parquet; the two scoring columns remain available only to the MCP runtime.

Harbor collects /app/submission directly from the MCP sidecar, tears the agent environment down, and starts a separate verifier. The verifier owns hidden data and sends only feature matrices and the submitted bundle to a networkless, read-only model-worker container. The verifier imports the submitted source, refits it once on all public 2022–2023 rows, serializes and reloads that new estimator, and calls .predict() on hidden 2024 features. Hidden labels never enter the worker; the estimator artifact fitted during public research is not used for the hidden score.

Run with Harbor

From the repository root:

git lfs pull
uvx harbor==0.20.0 run -p . -a codex -m openai/gpt-5.6-luna \
  --agent-kwarg reasoning_effort=medium \
  -e docker -n 1

Scoring

The verifier refits the submitted model on all public 2022–2023 rows, predicts the hidden 2024 rows, and runs the after-cost portfolio backtest. Hidden annualized Sharpe is the primary score.

reward.json contains primary_score, reward, sharpe, cagr, max_drawdown, and pearson_ic. metrics.json contains the full backtest metrics, causal audit, and refit diagnostics.

Layout

.
├── instruction.md
├── task.toml
├── environment/
│   ├── Dockerfile
│   ├── agent/
│   └── mcp-server/
│       ├── Dockerfile
│       ├── feature_engineering/
│       ├── runtime_public.parquet
│       └── task_config.yaml
├── solution/
└── tests/

Data provenance is documented in DATA_PROVENANCE.md.