VernonOY

VernonOY / alpha-skills

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Quantitative factor research skills for AI coding assistants

ai-coding-assistant ai-tools alpha backtesting china-stock
12
0
100% credibility
Found Apr 09, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A set of structured guides that enable AI coding assistants to perform professional quantitative factor research across A-share, HK, and US stock markets.

How It Works

1
📚 Discover Alpha Skills

You hear about Alpha Skills, a set of smart guides that turn your AI helper into a stock market research expert.

2
💾 Grab the Guides

Download the simple instruction files that teach your AI new research tricks.

3
🧠 Teach Your AI Buddy

Copy the guides into your AI coding assistant's special instructions spot so it learns quant skills.

4
Pick Your Stock World
🇨🇳
China Stocks

Focus on A-shares with their unique trading rules.

🇭🇰
Hong Kong Stocks

Dive into HK market data seamlessly.

🇺🇸
US Stocks

Research American stocks with easy access.

5
💬 Chat and Research

Simply tell your AI 'test this momentum idea' or 'check factor health' and it springs into action.

6
📈 Watch the Magic

Your AI loads data, crunches numbers, evaluates ideas, and runs tests automatically.

🏆 Unlock Pro Insights

You now have powerful reports, backtests, and strategies to spot winning stock factors effortlessly.

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AI-Generated Review

What is alpha-skills?

Alpha-skills turns AI coding assistants like Cursor, Claude Code, or Continue into quantitative factor research workstations using structured Markdown prompts. You describe a factor in natural language—say, momentum or price-volume divergence—and it handles discovery, multi-level evaluation (IC, ICIR, quintiles), backtesting, monitoring, and reporting across A-share, HK, and US markets. It pulls data via Tushare or Yahoo Finance, applies market-specific rules like T+1 settlement or price limits, and spits out standardized Python workflows with pandas and numpy.

Why is it gaining traction?

Unlike scattered GitHub quantitative finance notebooks, alpha-skills packs 25+ built-in factors (RSI, PE, ROE composites) into conversational triggers, with custom data adapters for AkShare or Binance. Developers love the zero-setup copy-paste into .cursorrules, plus optional qtype linting for look-ahead bias—skipping hours of debugging fake alpha. Multi-platform support and a clear research pipeline (discover-evaluate-backtest-report) make it a quick win for github quantitative analysis without building from scratch.

Who should use this?

Quant traders prototyping factors for quantitative factor investing, especially A-share or cross-market strategies. Python devs at alpha skills development institute lahore-style teams doing github quantitative trading systems, or solo researchers running IC decay checks on momentum/reversal signals. Skip if you're deep into production pipelines needing genetic programming.

Verdict

Promising early prototype for AI-driven quantitative factor analysis—docs are solid, setup takes minutes—but 12 stars and 1.0% credibility score scream unproven; test on toy data first. Worth starring for alpha skills github watchers eyeing the 2025 summit roadmap. (187 words)

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