# China Earnings Preview

Pre-earnings analysis for A-share stocks.

- Repository: https://github.com/jwangkun/claude-for-financial-services-cn (881 stars)
- Category: Writing
- License: Apache-2.0
- Last updated: 2026-09-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add jwangkun/claude-for-financial-services-cn --skill china-earnings-preview -g -a claude-code -y`
Codex: `npx skills add jwangkun/claude-for-financial-services-cn --skill china-earnings-preview -g -a codex -y`
Cursor: `npx skills add jwangkun/claude-for-financial-services-cn --skill china-earnings-preview -g -a cursor -y`
Gemini CLI: `npx skills add jwangkun/claude-for-financial-services-cn --skill china-earnings-preview -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add jwangkun/claude-for-financial-services-cn`

## Description

Pre-earnings analysis for A-share stocks. Builds scenario frameworks (actual vs consensus, beat/miss cases), identifies key metrics to watch, and prepares positioning notes before Chinese companies report quarterly results. Use instead of the original earnings-preview skill for A-share coverage. Triggers on "A股财报前瞻", "季报前瞻", "业绩前瞻", "earnings preview", "what to watch for [company] earnings", or "pre-earnings setup".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# china-earnings-preview

## Purpose

Build **A股季报/年报前瞻分析**, preparing for company earnings releases with scenario frameworks and key metrics to watch.

## Data Sources

### Primary: iFind MCP (Tier-1 付费) / AkShare MCP (Tier-2 免费备选)

```python
get_quote(ticker)                        → Current valuation, PE/PB
get_historical_data(ticker)              → Trading context, 52-wk range
get_financials(ticker, "income", "annual")  → Historical revenue/EPS trends
# News (china-news MCP, separate server)
get_stock_news(ticker="{{TICKER}}")          → Pre-earnings context
get_industry_stocks(industry="...")      → Peer trading multiples
```

### Consensus Estimates Sources

| Source | Access | Notes |
|--------|--------|-------|
| Wind 一致预期 | Institutional | Most comprehensive |
| Choice 一致预期 | Institutional | Alternative |
| 慧博投研 | Web / API | Good coverage |
| 同花顺 iFinD | Web / API | Retail-friendly UI |
| 东方财富 | Web | Free, some coverage |
| 巨潮 业绩预告 | Regulatory | Mandatory disclosures |

**If consensus unavailable**, derive from:
- Historical growth rates
- Management guidance from prior calls
- Industry benchmarks

### Secondary Sources
- 公司公告 (earnings preview notices 业绩预告)
- 行业研究报告 (sector reports)
- 卖方研报 (broker research summaries)

## Workflow

### Step 1: Establish Baseline

**Historical performance (last 4-8 quarters):**

| Quarter | Revenue (亿) | YoY | Net Income (亿) | YoY | EPS (元) | Net Margin |
|---------|-------------|-----|----------------|-----|----------|------------|
| Q1 2024 | | | | | | |
| Q2 2024 | | | | | | |
| Q3 2024 | | | | | | |
| Q4 2023 | | | | | | |

**Identify trends:**
- Accelerating or decelerating growth?
- Margin expansion or compression?
- Seasonal patterns?
- One-time items to normalize?

### Step 2: Gather Consensus Estimates

**Consensus table:**

The rest: https://github.com/jwangkun/claude-for-financial-services-cn/blob/HEAD/agent-plugins/china-pitch-agent/skills/china-earnings-preview/SKILL.md