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abundance_every_year_market_notes

Automate stock market analysis and generate structured daily commentary.

5|1|Updated Feb 13, 2026
Search Tags:#data-driven#market-analysis#technical-analysis#a股#fund-flow#news-generation
One-click install
npx skills add https://github.com/cyhzzz/finance_aigc_skills --skill abundance-every-year-market-notes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: abundance_every_year_market_notes
Source: https://github.com/cyhzzz/finance_aigc_skills/tree/main/abundance-every-year
Command: npx skills add https://github.com/cyhzzz/finance_aigc_skills --skill abundance-every-year-market-notes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires akshare>=1.18.0, pandas>=1.3.0, requests>=2.22.0, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a repeatable, data-driven workflow for generating professional A-share market commentary in the "年年有鱼" style. It integrates market data collection, multi-source analysis, and narrative generation to produce compliant, data-backed observations that help investors understand market context and potential actions.

Core Features & Use Cases

  • Data aggregation: gather prices for the three major indices (Shanghai Composite, Shenzhen Component, and ChiNext), market-wide statistics, and sector data from multiple sources.
  • Structured analysis: perform index, funds, statistics, sectors analyses to inform a cohesive commentary.
  • Narrative generation: produce six-section market commentaries in the "年年有鱼" voice, including fish/wale/ballast metaphors and data-driven conclusions.
  • Use Case: generate a daily market commentary for the previous trading day to be published as a market wrap, or a pre-market brief that previews the day’s potential dynamics.

Quick Start

Run the provided scripts to fetch market data, analyze it, and generate a ready-to-publish market commentary.

Frequently Asked Questions about abundance_every_year_market_notes

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

FAQPage Schema
How do I automate A-share market commentary generation from daily index and fund flow data?▼

Automated market commentary generation integrates data gathering, structured index and fund flow analysis, and narrative synthesis to produce ready-to-publish daily market wraps. It aggregates prices for major A-share indices and sector statistics to drive data-driven insights.

Can I generate pre-market briefings for A-share equities and sectors using Python?▼

Pre-market briefings for A-share equities and sectors can be generated by running Python scripts that fetch market-wide statistics and sector data. The workflow applies structured analysis to preview potential daily dynamics and outputs a narrative briefing.

What is the best way to perform structured technical analysis on A-share market statistics for news generation?▼

Structured technical analysis for news generation applies multi-source data aggregation across indices, funds, and sectors. By processing market-wide statistics through Python, it produces cohesive, data-backed observations suitable for automated narrative generation.

Does this market analysis workflow require akshare and pandas to fetch A-share index prices?▼

Yes, fetching A-share index prices and market data requires akshare and pandas as core dependencies. These libraries handle data collection and tabular data processing to support the structured analysis and commentary generation pipeline.

How to generate data-driven daily market wrap reports with built-in safety checks?▼

Daily market wrap reports are generated by executing scripts that apply structured analysis to aggregated market data, incorporating built-in safety checks. The process outputs six-section commentaries featuring data-driven conclusions and specific narrative styles.

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