alpha-autopilot

Automated factor lifecycle management streamlines mining, evaluating, registering, monitoring, retiring, and replacing quantitative factors within a single autonomous loop. It uses a multi-phase pipeline with frontmatter-driven configuration to eliminate manual orchestration.

An autonomous factor research loop continuously mines, evaluates, and manages quantitative factors to maintain a healthy library. It enforces reliability through safety guardrails like correlation checks, quality thresholds, and automatic retirement of underperforming factors.

To start factor backtesting and signal generation, prompt the AI to run the full autopilot workflow from mining to retirement. The pipeline applies optional scripts, references, and assets to automatically process and evaluate your quantitative factors.

Automated factor evaluation includes safety guardrails such as correlation checks, quality thresholds, and automatic retirement. These mechanisms ensure that only healthy, non-redundant factors remain active in your managed quantitative factor library.

Yes, autonomous factor management supports ICIR evaluation and backtest automation within its multi-phase pipeline. It applies these quantitative metrics during the evaluation phase to determine factor quality before registration and signal generation.

Use an automated factor replacement loop when you need to efficiently discover, evaluate, and manage quantitative factors at scale without manual orchestration. It is designed to maintain a healthy factor library continuously through automatic retirement and replacement.