by brokermr810
AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading
# Add to your Claude Code skills
git clone https://github.com/brokermr810/QuantDingerLast scanned: 5/16/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-16T06:21:10.865Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}QuantDinger is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by brokermr810. AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading. It has 9,007 GitHub stars.
Yes. QuantDinger passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/brokermr810/QuantDinger" and add it to your Claude Code skills directory (see the Installation section above).
QuantDinger is primarily written in Python. It is open-source under brokermr810 on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh QuantDinger against similar tools.
No comments yet. Be the first to share your thoughts!
Based on votes and bookmarks from developers who liked this skill
Try in 2 minutes · Why QuantDinger · Safety model · Technical highlights · Repositories · AI agents & MCP · Overview · Features · Architecture · Install · Docs · FAQ · License
Fastest path: one command. No
git clone, nonpm, no Vue source tree. The installer asks for the admin account, writes secure secrets, pulls GHCR images, and starts Docker Compose.
Prerequisites: Docker with Compose v2 (Docker Desktop on Windows/macOS). Node.js is not required.
curl -fsSL https://raw.githubusercontent.com/brokermr810/QuantDinger/main/install.sh | bash
Windows PowerShell:
irm https://raw.githubusercontent.com/brokermr810/QuantDinger/main/install.ps1 | iex
Installs to ~/quantdinger on Linux/macOS and $HOME\quantdinger on Windows by default. Override with ... | bash -s -- /opt/quantdinger or $env:QUANTDINGER_INSTALL_DIR="C:\QuantDinger" before the PowerShell one-liner.
Then open http://localhost:8888 and sign in with the admin username/password you entered during installation. The same stack also serves the mobile H5 client at http://localhost:8889.
Windows (PowerShell) - manual clone path:
git clone https://github.com/brokermr810/QuantDinger.git
Set-Location QuantDinger
Copy-Item backend_api_python\env.example -Destination backend_api_python\.env
$key = & python -c "import secrets; print(secrets.token_hex(32))" 2>$null
if (-not $key) { $key = & py -c "import secrets; print(secrets.token_hex(32))" 2>$null }
(Get-Content backend_api_python\.env) -replace '^SECRET_KEY=.*$', "SECRET_KEY=$key" | Set-Content backend_api_python\.env -Encoding utf8
# Edit backend_api_python\.env before first start:
# ADMIN_USER=your_admin_user
# ADMIN_PASSWORD=your_secure_password
docker compose pull
docker compose up -d
Standard clone (macOS / Linux):
git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
cp backend_api_python/env.example backend_api_python/.env
./scripts/generate-secret-key.sh
# Edit backend_api_python/.env before first start:
# ADMIN_USER=your_admin_user
# ADMIN_PASSWORD=your_secure_password
docker compose pull
docker compose up -d
Slow docker pull (China / VPN): add IMAGE_PREFIX=docker.m.daocloud.io/library/ to a repository-root .env, or configure Docker Desktop → Proxies.
For step-by-step detail and troubleshooting, see Installation & first-time setup.
| Traditional workflow | QuantDinger |
|---|---|
| ChatGPT only generates code | Runs, backtests, and executes strategies in one stack |
| TradingView + Jupyter + exchange bots are fragmented | One self-hosted stack from research to execution |
| SaaS platforms hold API keys | User-owned deployment — your infra, your keys |
| AI agents without scopes or audit | Scoped Agent Gateway, paper-only default, audit logs |
QuantDinger is a self-hosted, local-first quantitative infrastructure layer — not a chatbot with a buy button. It unifies multi-LLM research, Python-native strategy engines, server-side backtesting, and multi-broker live execution across crypto venues, IBKR, and Alpaca in one production-grade stack you fully control.
AGENT_LIVE_TRADING_ENABLED on self-hosted stacks.| Resource | Link |
|---|---|
| Human Web API (OpenAPI) | docs/api/openapi.yaml |
| ReDoc viewer (serve over HTTP) | docs/api/index.html — run python -m http.server from docs/api/ |
| Conventions (auth, envelopes) | docs/API_CONVENTIONS.md |
| Agent Gateway | docs/agent/agent-openapi.json |
| What makes QuantDinger different | |
|---|---|
| Full-stack quant OS | Charting, indicator IDE, AI research, backtests, live bots, quick trade, and broker account management — one product, one Postgres state store. |
| Agent-native | First-class Agent Gateway (/api/agent/v1) + quantdinger-mcp on PyPI — Cursor, Claude Code, and Codex can read markets, run backtests, and trade (paper by default) with full audit logs. |
| Dual strategy runtimes | IndicatorStrategy (four-way dataframe signals + chart overlays) and ScriptStrategy (event-driven on_bar, explicit orders) — research and prod |