by rootSunc · MCP Server · ★ 125
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ASL · 本地可日更的 A 股研究湖 · 42 数据集 · MCP 供数 AI agent · 零 token 零注册 | Local A-share research lake — history for humans & agents
| Stars | 125 |
| Forks | 24 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 55.0341650787553/100 |
| Open Issues | 3 |
| Last Updated | 2026-08-15 |
| Created | 2026-06-28 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
These tools work well together with ashare-lake for enhanced workflows:
Looking for a ashare-lake alternative? If you're comparing ashare-lake with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
A股 AI 金融智能决策中台 · 129 个 MCP 工具 · eltdx 通达信协议 + akshare + 同花顺 + 东财 + 本地数据 · SmartRouter 多源降级路由 · 免费零鉴权
algorithmic trading curated list quant finance trading bots backtesting technical analysis crypto open-source
A股 PEG 估值分析工具 — 彼得·林奇 PEG 投资法本地化实践 | A-share PEG valuation tool with AI-powered analysis
skills:一句话获取 A 股每日市场数据 — 赚钱效应、热门题材、连板天梯、游资龙虎榜。零配置,无需注册,无需 Token。
ApocData · AI-native financial database for China A-share market. Drop-in Skill / MCP for Claude,ChatGPT, Qwen
Agent-driven alpha factory — LLM autonomously designs, backtests, and submits factors to WorldQuant BRAIN
Explore other popular mcp server tools:
ashare-lake is ASL · 本地可日更的 A 股研究湖 · 42 数据集 · MCP 供数 AI agent · 零 token 零注册 | Local A-share research lake — history for humans & agents. It is categorized as a MCP Server with 125 GitHub stars.
ashare-lake is primarily written in Python. It covers topics such as a-share, ai-agent, akshare.
You can find installation instructions and usage details in the ashare-lake GitHub repository at github.com/rootSunc/ashare-lake. The project has 125 stars and 24 forks, indicating an active community.
ashare-lake is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to ashare-lake on Agent Skills Hub include tradex-hub, best-of-algorithmic-trading, astock-peg. 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: