By koreal6803
Develop, backtest, and analyze quantitative trading strategies for global stock markets (TW, US, KR, JP, HK) using the FinLab Python package, handling data access, FinLabDataFrame operations, factor research, and US market specifics.
npx claudepluginhub koreal6803/finlab-aiLet AI discover your next alpha.
curl -sSf https://ai.finlab.finance/install.sh | sh
Auto-detects your CLI (Claude Code / Codex / Gemini), installs uv if needed, and sets up the skill.
| Document | Content |
|---|---|
| Data Reference | 900+ columns across 80+ tables |
| Backtesting Reference | sim() API, resampling, metrics |
| Factor Examples | 60+ complete strategy examples |
| Best Practices | Patterns, anti-patterns, tips |
| ML Reference | Feature engineering, labels |
FinLab quantitative trading skills for Taiwan stock market (台股) - includes strategy development, backtesting, data analysis, and factor research
Share bugs, ideas, or general feedback.
Backtest trading strategies with historical data, performance metrics, and risk analysis
Quantitative research metrics: SOTA evaluation for range bars, Sharpe ratios with daily aggregation, ML prediction quality (IC, autocorrelation), crypto-specific considerations
Orchestration, data validation, risk geometry, hypothesis execution, and forensic audit for quantitative research
Integration skills for Longbridge financial API and trading platform connectivity.
Build and deploy agentic finance applications on the Alva platform. Access 250+ financial data sources, run cloud-side analytics, backtest trading strategies, and publish interactive playbooks.