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 georgezouq · Agent Tool · ★ 6.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-in-finance safe to install? View the security audit →
Awesome AI in Finance There are millions of trades made in the global financial market every day. Data grows very quickly and people are hard to understand. With the power of the latest artificial intelligence research, people analyze & trade automatically and intelligently. This list contains the research, tools and code that people use to beat the market. [中文资源] Contents Agents LLMs Papers Courses & Books Strategies & Research Time Series Data Portfolio Management High Frequency Trading Event Drive Crypto Currencies Strategies Technical Analysis Lottery & Gamble Arbitrage Data Sources Research Tools Trading System TA Lib Exchange API Articles Others Agents 🌟🌟 nofx - A multi-exchange Al trading platform with multi-Ai competition self-evolution, and real-time dashboard. [
| Stars | 6,495 |
| Forks | 793 |
| Category | Agent Tool |
| License | CC0-1.0 |
| Quality Score | 51.7163742786252/100 |
| Open Issues | 26 |
| Last Updated | 2026-09-08 |
| Created | 2018-08-29 |
| Est. Tokens | ~22k |
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awesome-ai-in-finance is 🔬 A curated list of awesome LLMs & deep learning strategies & tools in financial market.. It is categorized as a Agent Tool with 6.5k GitHub stars.
You can find installation instructions and usage details in the awesome-ai-in-finance GitHub repository at github.com/georgezouq/awesome-ai-in-finance. The project has 6.5k stars and 793 forks, indicating an active community.
awesome-ai-in-finance is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-ai-in-finance on Agent Skills Hub include awesome-claude-skills, QuantDinger, awesome-agent-skills. 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: