DEDUPED Hot AI repos, turned into use cases Get the Monday 5

What you can do with Alpha Research OS

AlphaResearchOS is a local, Python-based quant research workbench that takes a standard OHLCV CSV and runs an agent-assisted loop of hypotheses, predictive models, independent review, and holdout evaluation.

Trended earlier

heranliu/AlphaResearchOS on GitHub

Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk.

What do you want to do?

5 use cases

  • Run an AI-guided factor search on your own price CSV

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

    View prompt
    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.

  • Audit the published sector ETF benchmark numbers

    Check the reported returns, Sharpe ratio, and drawdowns against the included result files.

    View prompt
    Using the AlphaResearchOS repo, read docs/BENCHMARK.md and benchmarks/sector-etf/results.json, then list each README benchmark figure next to its source value in the files. Report any figure you cannot find in those files, and verify that the holdout window dates match the README.

  • Stress-test a strategy against trading costs and turnover

    Check whether a strategy's edge survives higher transaction costs and heavier trading.

    View prompt
    In the AlphaResearchOS workbench, take the strategy named <strategy-name> and rerun its analysis at one-way cost levels of <cost-levels>. Return a table of total return, Sharpe ratio, and average turnover for each cost level and baseline. Verify that the baselines use the same cost setting as the strategy at each level.

  • Build a searchable factor library and export a research report

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

    View prompt
    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.

  • Have an AI agent verify changes to Alpha Research OS

    Run the project's test, lint, script, and build checks before accepting a code change.

    View prompt
    In the AlphaResearchOS repo, make the change described in <change-description>, then run the five development checks listed in the README and report each result. Verify that every check passes before you say the change is ready, and list any failure with its output.

How to use Alpha Research OS

  1. Check you have what it needs: 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.
  2. Open heranliu/AlphaResearchOS on GitHub and read the README and license.
  3. Run uv sync --locked in the repository folder.
  4. Run uv run alphaos serve, then open http://127.0.0.1:8765.
  5. Click Import CSV / 导入 CSV, review coverage and validation results, and confirm the import.
  6. 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.

These steps come from our top use case, Run an AI-guided factor search on your own price CSV, which also has a copy-paste agent prompt.

Alpha Research OS alternatives · Best AI repos for Data and analytics

About this repo

Requirements

  • 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

Add this badge to your README

Maintain Alpha Research OS? Show readers where to find these use cases. Use cases on Deduped

Markdown 

[![Use cases on Deduped](https://dedupedai.com/badge/heranliu/AlphaResearchOS.svg)](https://dedupedai.com/use-cases/heranliu-alpharesearchos/?utm_source=github&utm_medium=badge&utm_campaign=heranliu-AlphaResearchOS)

HTML 

<a href="https://dedupedai.com/use-cases/heranliu-alpharesearchos/?utm_source=github&amp;utm_medium=badge&amp;utm_campaign=heranliu-AlphaResearchOS"><img src="https://dedupedai.com/badge/heranliu/AlphaResearchOS.svg" alt="Use cases on Deduped" height="20"></a>

A static image, no tracking script. About the badge

Questions about Alpha Research OS

What is Alpha Research OS?

AlphaResearchOS is a local, Python-based quant research workbench that takes a standard OHLCV CSV and runs an agent-assisted loop of hypotheses, predictive models, independent review, and holdout evaluation. It is an open-source project on GitHub: https://github.com/heranliu/AlphaResearchOS.

Who is Alpha Research OS for?

Quant researchers, data analysts, and developers who want an auditable, local workflow for testing trading strategy ideas on their own daily price data.

What can I do with Alpha Research OS?

5 use cases so far, for example: Run an AI-guided factor search on your own price CSV; Audit the published sector ETF benchmark numbers; Stress-test a strategy against trading costs and turnover. Each has steps and a copy-paste agent prompt.

What do I need to run Alpha Research OS?

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.

What are alternatives to Alpha Research OS?

Other hot AI repos in Data and analytics: Worldmonitor, DBT Charts, Brandpose, Idx, Open Lakehouse Compute Fabric.

Is Alpha Research OS safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.