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hkscc-screener

Filter HKSCC holdings to quarterly snapshots with multi-quarter continuity.

Updated Jan 12, 2026
Search Tags:#pandas#screening#duckdb#holdings#universe#quarters#hkscc
One-click install
npx skills add https://github.com/chinawrj/agent-skills-stock --skill hkscc-screener
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hkscc-screener
Source: https://github.com/chinawrj/agent-skills-stock/tree/main/.github/skills/hkscc-screener
Command: npx skills add https://github.com/chinawrj/agent-skills-stock --skill hkscc-screener

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires duckdb, pandas, akshare, and includes scripts (resource) components.

What problem does it solve?

This skill filters HKSCC (港股通) holdings to surface stock candidates that exhibit durable, multi-quarter ownership patterns by foreign or institutional investors.

Core Features & Use Cases

  • Quarterly snapshot generation: down-samples daily HKSCC holdings to the quarter-end snapshot for stable comparison.
  • Multi-quarter continuity check: requires a stock to have at least N consecutive quarters of holding data.
  • Market-cap filtering: enforces minimum holding market cap and total market cap ranges to focus on investable candidates.
  • Universe intersection: retains only codes present in a provided universe (non-SOE) dataset.

Quick Start

Run the screening pipeline locally by executing the fetch_hkscc.py, hkscc_quarterly.py, and screen_hkscc.py scripts in sequence.

Frequently Asked Questions about hkscc-screener

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I filter HKSCC holdings to find stocks with durable multi-quarter ownership?▼

Filter HKSCC holdings by down-sampling daily data to quarterly snapshots, enforcing minimum N consecutive quarters of holding continuity, and intersecting with a target stock universe to identify durable candidates.

What is the best way to down-sample daily Hong Kong Stock Connect data for quarterly analysis?▼

Down-sample daily HKSCC data to quarterly snapshots using pandas and pd.Period('Q') for quarter arithmetic, creating stable quarter-end snapshots for consistent multi-quarter ownership comparison.

Does this HKSCC screening workflow require DuckDB and pandas?▼

Yes, the HKSCC screening workflow requires both DuckDB and pandas to process quarterly holdings data, enforce continuity checks, apply market-cap filters, and output results to a parquet file.

Can I apply market-cap filtering and universe intersection to HKSCC quarterly snapshots?▼

Yes, you can enforce minimum holding market cap and total market cap ranges on quarterly HKSCC snapshots, then intersect the results with a provided non-SOE universe dataset to retain investable candidates.

What scripts do I need to run for screening HKSCC holdings locally?▼

Run the fetch_hkscc.py, hkscc_quarterly.py, and screen_hkscc.py scripts in sequence to fetch data, generate quarterly snapshots, and screen for durable foreign-led HKSCC holdings.

Why use quarterly snapshots instead of daily data for identifying continuous foreign holdings?▼

Quarterly snapshots provide stable comparison points by down-sampling volatile daily HKSCC data, ensuring that multi-quarter continuity checks accurately surface durable institutional ownership patterns.

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