Rank A-share stock candidates using YAML-defined strategies with L1 filters and optional LLM ranking.
npx skills add https://github.com/ZhuLinsen/alphasift --skill alphasift
Please help me install this Agent Skill. Skill: alphasift Source: https://github.com/ZhuLinsen/alphasift/tree/main Command: npx skills add https://github.com/ZhuLinsen/alphasift --skill alphasift
AlphaSift automates discovery and ranking of A-share stock candidates by applying YAML-defined strategies and emitting structured candidate outputs for further analysis.
Run alphasift quickstart to see a minimal end-to-end workflow.
High-intent search queries and answers about installing and using this skill.
Stock screening with YAML strategies works by applying L1 hard filters to the full A-share market, then generating structured ranked candidate lists. AlphaSift automates this pipeline using your defined YAML configuration files for deterministic filtering.
Yes, optional LLM ranking for stock candidates applies as an L2 layer after L1 hard filters. This cross-candidate reasoning generates investment theses, catalysts, risks, and confidence scores to aid portfolio risk evaluation.
Yes, you need a local Python environment with the alphasift package installed. Optional API keys are required for LLM ranking and external data sources to enable cross-candidate reasoning and post-analysis integration.
T+N evaluation in stock screening saves your run results for later review to track candidate performance over time. It operates during the optional L3 post-analysis phase, integrating with DSA or external HTTP analyzers to validate strategy outcomes.
Industry map and board heat context diversify stock candidate exposure by applying local industry maps and board heat scores. This ensures your LLM-ranked results maintain balanced sector distribution across filtered A-share candidates.
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