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yaoleifly/ai-stock-pool

AI industry-chain stock pool with US/A-share mapping, active discovery, policy pressure, and one-click deployment.

Python ◇ a-shares MIT
★106STARS
⑂43FORKS
!0ISSUES
🏆#13,055GLOBAL RANK
🔥1DAYS TRENDING
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Momentum

+10

STARS · LAST 30 DAYS

1

PER DAY

#1,658

MOST-STARRED Python

Window7 days30 days90 days
Stars gained+7+10+90
Per day111
Forks gained+1+3+10

ai-stock-pool gained 10 stars in the last 30 days, about 1 a day, and now has 106. It is about 1 year old and has averaged roughly 106 stars a year. It ranks #1,658 among Python repositories and #13,055 across all languages on GitHubRepo.

Trending Record

ai-stock-pool has maintained a continuous presence across global trending indexes, peaking at #11905. Below is the 30-day activity profile:

💡 Overview

ai-stock-pool is an open-source project written in Python: AI industry-chain stock pool with US/A-share mapping, active discovery, policy pressure, and one-click deployment.

Engineered for speed, consistency, and developer ease, it solves common hurdles in a-shares, ai, arxiv. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.

⚡ Key Features

1

Optimized execution pipeline written in Python for predictable speed.

2

Zero-friction configuration with comprehensive sensible defaults out of the box.

3

Cross-platform runtime support across Linux, macOS, and Windows environments.

4

Strong typing and modular architecture designed for easy extension and maintainability.

5

Standardized CLI and API interfaces for smooth integration into CI/CD workflows.

6

Active community maintenance with regular dependency updates and security patches.

📥 Installation

terminal
$ pip install ai-stock-pool

⚙ System Requirements

Platforms

  • • macOS
  • • Linux
  • • Windows

Runtime & Dependencies

Python >= 3.9, pip, virtualenv

Architecture

x86_64, ARM64 (Apple Silicon & Graviton)

🧠 How It Works

ai-stock-pool coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.

🎯 Production Use Cases

Autonomous AI Agents

Orchestrate intelligent workflows and tool-calling routines with ai-stock-pool.

Model Inference & Prompting

Integrate fast, local or cloud-hosted generative AI models directly into production code.

Context Memory & RAG

Augment language models with dynamic vector retrieval and structured project memory.

Developer Productivity

Automate repetitive engineering tasks, code generation, and test creation using AI agents.

🚀 Getting Started

1

Install ai-stock-pool using your package manager: `pip install ai-stock-pool`

2

Initialize your project workspace or configuration file for ai-stock-pool.

3

Import ai-stock-pool into your codebase or invoke it directly from your terminal.

4

Execute your test suite or run `ai-stock-pool --help` to verify successful setup.

👍 Strengths

Active community backing with 106 GitHub stars and verified adoption.
Permissive open-source distribution under the MIT license.
Built in Python for high execution speed and developer familiarity.
Cross-platform compatibility across modern Linux, macOS, and Windows environments.
Clean modular design allowing flexible configuration and pipeline integration.

⚠️ Considerations

Requires familiarity with Python and modern CLI workflows.
Ecosystem extensions may require manual configuration depending on environment constraints.
Active development roadmap means breaking API changes may occur across major versions.

⇄ Alternatives & Direct Competitors

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👥 Who Should Use This

Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.

🏆 Nearby in the Rankings

yaoleifly/ai-stock-pool is currently ranked #13,055 by stars across every repository tracked on GitHubRepo. These are adjacent projects:

RankRepositoryLanguageStarsAction
#12,998 QwenLM/E-CommerceBench Python ★ 107 Compare ↗
#12,998 shuheng-mo/career-ops-china JavaScript ★ 107 Compare ↗
#12,998 akiojin/unity-cli C# ★ 107 Compare ↗
#12,998 nssmd/RoboRSI Python ★ 107 Compare ↗
#12,998 devilcoolyue/agentbox Go ★ 107 Compare ↗
#13,055 yaoleifly/ai-stock-pool This Project Python ★ 106
#13,055 shootthesound/ComfyUI-Fizgig-H3-Still Python ★ 106 Compare ↗
#13,055 garfiec/Librechat-Mobile Kotlin ★ 106 Compare ↗
#13,055 morpho-org/vault-v2 Solidity ★ 106 Compare ↗
#13,055 DevL0rd/Konveyor C++ ★ 106 Compare ↗
#13,055 lmarlow/gemedit Ruby ★ 106 Compare ↗

Frequently Asked Questions

What does ai-stock-pool do? +

AI industry-chain stock pool with US/A-share mapping, active discovery, policy pressure, and one-click deployment.

What language is ai-stock-pool written in? +

The primary language is Python. Topics include: a-shares, ai, arxiv, cloudflare-workers, investment-research.

Is ai-stock-pool actively maintained? +

Yes, the last recorded push was on Oct 6, 2026 with 0 open issues being tracked.

How many stars does ai-stock-pool have? +

ai-stock-pool has 106 stars and 43 forks on GitHub.

How does ai-stock-pool rank among GitHub repositories? +

With 106 stars, yaoleifly/ai-stock-pool is ranked #13,055 globally across all repositories tracked on GitHubRepo and #1,658 among Python projects.

What license is ai-stock-pool distributed under? +

The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.

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