Haochenhust Stock Analyzer
Standardized deep-dive analysis for individual stocks, producing a structured research note.
When to Use
- User asks to analyze a specific stock
- A research task identifies a potential trading candidate
- Updating analysis for a stock already in your research notes
Prerequisites
Data commands below use the sibling tushare-data and stock-market-data skills. Install them, then set once per shell:
export TUSHARE_TOKEN=<your Tushare Pro token> # never hard-code; see the tushare-data skill
PY=python3 # a python3 with tushare/pandas/akshare (e.g. stock-market-data/.venv/bin/python3)
SKILLS=~/.claude/skills # where you installed these skills
TS=$SKILLS/tushare-data/scripts/fetch_tushare.py
AK=$SKILLS/stock-market-data/scripts
Analysis Workflow
Step 1: Gather Data
# Primary: Tushare Pro (richer, more stable)
$PY $TS --mode fina_indicator --symbols {CODE}.SZ
$PY $TS --mode daily_basic --symbols {CODE}.SZ --start 20260101
$PY $TS --mode income --symbols {CODE}.SZ
$PY $TS --mode forecast --symbols {CODE}.SZ
$PY $TS --mode top10_holders --symbols {CODE}.SZ
$PY $TS --mode daily --symbols {CODE}.SZ --start {3_MONTHS_AGO}
$PY $TS --mode adj_factor --symbols {CODE}.SZ --start {3_MONTHS_AGO}
# Fallback: AKShare
$PY $AK/fetch_financial_data.py --mode stock --symbols {CODE}
Step 2: Web Research
Search for recent news, analyst reports, and industry developments:
- Use WebSearch for "{stock name} 券商研报 一致预期" (analyst consensus, earnings forecasts)
- Use WebSearch for "{stock name} 最新研报 2026"
- Use WebSearch for "{stock name} {industry keyword} 进展"
- Check Xueqiu/Eastmoney for recent discussions
Step 2.5: Price & Volume Analysis (近3个月日K)
Using the daily OHLCV + adj_factor data from Step 1:
- 走势形态: 上升趋势/下降趋势/震荡盘整？近3个月涨跌幅？
- 均线位置: 当前价格相对5/10/20/60日均线的位置
- 量价关系: 近期量能变化趋势（放量上涨？缩量回调？）
- 关键价位: 近3个月高点/低点，支撑位/阻力位
- 波动率: 近期日均振幅，判断当前波动是否正常
This step bridges fundamentals (Step 1-2) and the 5-dimension analysis (Step 3), providing price context for valuation and entry timing.
Step 3: Analyze & Synthesize
Evaluate across 5 dimensions:
Business Model Quality
- What does the company do? Revenue composition?
- Competitive advantages (moat)? Barriers to entry?
- Customer concentration risk?
Financial Quality
- Revenue growth trend (YoY)
- Profit margin trend (gross margin, net margin)
- ROE level and trend
- Balance sheet health (debt ratio, current ratio)
- Cash flow quality (operating cash flow vs net profit)
Valuation
- Current PE/PB vs historical range
- PE/PB vs peers
- Target price derivation (if applicable)
- Is the current price pricing in good news or bad news?
Catalysts
- What events could trigger re-rating?
- Timeline for each catalyst
- Probability assessment
Risks
- What could go wrong?
- Sector/market risks
- Company-specific risks
Step 4: Output Research Note
Save to research-{stock_code}-{stock_name}.md in your research notes directory.
Output Template
# {Stock Name}({Stock Code}) — 研究笔记
> 分析日期：{date}
> 数据截止：{latest financial period}
## 一句话结论
{做多/做空/观望} | 置信度 {X}% | 目标涨幅 {X}%
## 核心逻辑（≤3条）
1. {logic 1}
2. {logic 2}
3. {logic 3}
## 关键数据
### 财务概况
- 营收（TTM）：{X} 亿 | 同比 {X}%
- 净利润（TTM）：{X} 亿 | 同比 {X}%
- 毛利率：{X}% | 净利率：{X}%
- ROE：{X}%
- 资产负债率：{X}%
### 估值
- 当前股价：{X} 元
- PE(TTM)：{X}x | 历史分位 {X}%
- PB：{X}x
- 目标价：{X} 元（推导逻辑：...）
## 催化剂
- {date}: {event} — {expected impact}
## 风险点
1. {risk 1}
2. {risk 2}
## 证伪条件
出现以下任一信号则论点失效，需重新评估：
1. {condition 1}
2. {condition 2}
## 交易建议
- **建仓价位**：{X} 元附近
- **目标价位**：{X} 元
- **止损价位**：{X} 元（-12%）
- **建议仓位**：{X}%（根据置信度）
Quality Checklist
Before saving the research note, verify:
- Conclusion has clear direction (做多/做空/观望), not "可能涨也可能跌"
- Confidence level is justified by evidence
- All financial data cites source and reporting period
- At least 1 catalyst with specific timeline
- At least 2 risk points identified
- At least 1 falsification condition that is measurable
- Trading suggestion includes entry, target, stop-loss