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HunterCode

HunterCode is an open-source, self-hosted AI investment-research terminal for A-share, Hong Kong and U.S. equities. It organizes market and news research, stock analysis, watchlists and holdings around a model key supplied by the operator, while storing conversations and user data locally.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

HunterCode — OpenViST project profile in Trading & Crypto Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use HunterCode when you want a self-hosted workspace for AI-assisted equity research and are prepared to supply a model key and run the Docker-based service.

Who it is for

  • Professional individual investors seeking a locally run AI research assistant.
  • Private-fund researchers and small quantitative teams using Docker-based deployments.

Documented uses

  • Research individual equities using AI-generated analysis and market information.
  • Scan stocks across A-share, Hong Kong and U.S. markets.
  • Manage watchlists, holdings and investment theses in a local database.
  • Apply personal research methods written as SKILLs.
Before you choose

Requirements & limits

The project documents a Docker Compose self-hosting route and a separate desktop launcher. The README lists Docker Desktop on Windows or macOS, or Docker Engine with Compose v2 on Linux, plus 10 GB disk space, 4 GB memory and access to ghcr.io. A model key is required for the described AI workflow.

  • Research coverage is described for A-share, Hong Kong and U.S. equities.
  • The project says inference and user data run on the operator’s machine, with conversations and portfolio-related data held in a local database.
  • The service is for research; the README says it does not connect to broker trading.
  • AI outputs are presented as research reference, not investment advice or a guarantee of prediction accuracy.
  • HunterCode is open source under the Apache-2.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a self-hosted HunterCode installation

Check the listed requirements and prepare a machine for HunterCode’s Docker-based self-hosted setup.

Before you start

  • Docker Desktop on Windows or macOS, or Docker Engine with Compose v2 on Linux.
  • 10 GB of disk space and 4 GB of memory.
  • Network access to ghcr.io.

Steps

  1. Choose a host machine that meets the listed platform, disk, and memory requirements.

  2. Ensure the host can access ghcr.io.

  3. Continue with the repository’s 5-minute setup instructions to proceed with installation.

Expected outcome

A host is prepared for the Docker-based self-hosted setup; the README describes HunterCode as keeping data and conversations on the operator’s machine.

Guide limitations

  • These preparation steps do not install or start HunterCode.
  • HunterCode is a research tool: its AI outputs are for research reference, not investment advice; it does not connect to brokerage trading or make decisions for the user.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
agentpit-io/hunter-community · Official homepage title · retained in its source language
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