No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by OpenByteInc · MCP Server · ★ 12.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is QuantDinger safe to install? View the security audit →
QuantDinger Open-source AI Trading OS Turn trading ideas into Python strategies, backtests, paper trading, live execution, and monitoring — all in one self-hosted stack. QuantDinger is a product of Open Byte Inc. AI research → Strategy code → Backtest → Paper/Live execution → Monitoring English · 简体中文 · API · AI Agents & MCP Live App · Website · Video Demo · Official Support Email <img src="https://img.shields.io/badge/Discord-Server-5865F2?style=flat-square&logo=discord&lo
| Stars | 12,678 |
| Forks | 2,574 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 63.6633934884414/100 |
| Open Issues | 48 |
| Last Updated | 2026-10-09 |
| Created | 2025-12-28 |
| Platforms | mcp, python |
| Est. Tokens | ~21k |
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QuantDinger is Open-source AI Trading OS, agent trading, and vibe trading, with Jev System One integration. Research, build Python strategies, backtest, and paper/live trade across crypto, stocks, and forex. Launch . It is categorized as a MCP Server with 12.7k GitHub stars.
QuantDinger is primarily written in Python. It covers topics such as agent, alpaca, backtesting.
You can find installation instructions and usage details in the QuantDinger GitHub repository at github.com/OpenByteInc/QuantDinger. The project has 12.7k stars and 2574 forks, indicating an active community.
QuantDinger is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to QuantDinger on Agent Skills Hub include QuantDinger, Vibe-Trading, gpt-researcher. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: