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How to build a searchable factor library and export a research report with Alpha Research OS

Keep useful features in one library and export a full report for review.

Who it is forA research lead who runs several factor searches and needs to share results and reuse strong features across projects.

The problemFactor ideas and their results get lost across runs, which makes it hard to reuse what worked or explain past decisions.

The outcomeA searchable factor library and an exported research report that together document the features, fitted-model settings, review decisions, and development results.

What you need first

  • Python 3.11 to 3.13 on macOS, Linux, or Windows through WSL2
  • The uv package manager
  • A daily OHLCV CSV with columns date, symbol, open, high, low, close, volume, covering 3 to 100 assets and at least 468 common trading days for default research settings
  • An authenticated Codex CLI, or an API key and model ID for a DeepSeek, Gemini, Ollama, or custom compatible endpoint

Steps

  1. Run uv sync --locked and then uv run alphaos serve in the repository folder.
  2. Complete one or more research runs from the workbench on an imported dataset.
  3. Open the factor library and search for features by name or by the research run that produced them.
  4. Save the features you want to keep so they stay available for later runs.
  5. Export the complete research report for the run and store it with the dataset and model settings used.

Prompt for your agent

In the AlphaResearchOS workbench, search the factor library for features from <research-run-name> and export the full research report for that run. Return a summary of the kept features with their development scores. Verify that the exported report includes the fitted-model settings and review decisions.

Caveat. The README says the library is searchable and exportable but does not describe its file format or how features move between separate installations.

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