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How to run an AI-guided factor search on your own price CSV with Alpha Research OS

Import daily OHLCV data and let a model propose, train, and review candidate strategies.

Credentials. Model API keys for DeepSeek, Gemini, or a custom endpoint are entered in the workbench, so store them carefully and do not paste them into shared logs or agent prompts.

Who it is forA quant researcher or analyst with daily price data for a set of assets who wants to test strategy ideas without building the pipeline from scratch.

The problemTurning a raw price file into tested trading hypotheses usually means writing feature code, model training, validation, and review logic by hand.

The outcomeA local research run that proposes feature expressions, fits predictive models, reviews each candidate, and shows development and holdout results in the workbench.

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 in the repository folder.
  2. Run uv run alphaos serve, then open http://127.0.0.1:8765.
  3. Click Import CSV / 导入 CSV, review coverage and validation results, and confirm the import.
  4. Connect a model: choose your authenticated Codex CLI, a DeepSeek, Gemini, or Ollama preset, or a custom compatible endpoint, enter a model ID, save, and test the structured response.
  5. Select the imported dataset, enter a research direction and budget (defaults are 6 candidates, 12 model requests, and 900 seconds), and follow the candidates as they are proposed, trained, reviewed, and evaluated.

Prompt for your agent

In the AlphaResearchOS repo, start the workbench with `uv run alphaos serve`, import the CSV at <path-to-csv> with the standard columns, and start a research run with the default budget. Report the final candidate list with development scores and holdout results. Verify that every candidate has a recorded review decision before you report it.

Caveat. The README describes the research loop and benchmark, but it does not promise that any strategy found by the search will hold up on new data.

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