alpha-discover

To convert natural language factor ideas into computable factor expressions, input your English or Chinese prompt describing the factor logic. The system translates the text into an executable Python expression and provides a concise design specification for immediate evaluation.

Yes, you can design quantitative factors for multi-market contexts including A-share, HK, and US stocks. The system applies scope-aware mapping to handle different market data fields, ensuring the generated factor definitions are accurate across specified regional markets.

The best way to prepare a custom factor definition for backtesting is to generate an evaluation-ready expression. You provide the factor design idea, and the system outputs the required data fields alongside an executable Python expression ready for immediate testing.

Yes, the factor design process supports both English and Chinese prompts. The system processes bilingual natural language inputs to generate factor definitions, allowing users to describe their quantitative research ideas in their preferred language.

To start generating factor expressions from text descriptions, you only need a clear idea of your factor logic. The system requires no specific environment setup or dependencies, directly mapping your text prompt to either built-in factors or new expression code.

Using natural language processing for quantitative factor design eliminates manual coding errors by directly mapping text to executable Python expressions. It transforms vague ideas into precise, evaluation-ready factor definitions with required data fields, accelerating multi-market research.