Quantitative Finance

Quantitative finance applies mathematics, statistics, and computer science to financial market analysis and trading decisions. From classic mean-variance portfolio optimization to modern machine-learning-driven alpha factors, quantitative methods have become the backbone of hedge funds, proprietary trading desks, and independent quant researchers alike. This category brings together crystals purpose-built for backtesting strategies, mining alpha factors, implementing signal generation pipelines, and building risk models across A-shares, Hong Kong equities, and US markets. Typical use cases include MACD divergence detection, RSI overbought/oversold screening, multi-factor stock selection, portfolio attribution analysis, and event-driven backtesting. Whether you are a newcomer exploring your first backtest or a seasoned researcher who needs to validate a new hypothesis in minutes, you will find ready-to-run AI-powered recipes here that handle end-to-end data fetching, signal computation, and report generation with minimal setup.