v2026-07-21: 实战教训汇总 (push 11 文件)
新增 8 reference: - dividend-stability-score: A 股 5 维评分 (派息年数/CAGR/波动/最近/连续) → 0-100 分 + 5 星 - dividend-yield-rate-sort: 按股息率% 倒序 (用户偏好 2026-07-13) - longport-http-module: longport_http.py 公共模块 (替代 SDK WSS) - leverage-pass-through-bug: process_signal.py 丢失 leverage 字段 (5x 实际 10x) - follow-trading-iron-laws: 跟单铁律 (用户原话 5+ 次 2026-07-21) - forced-skill-entry-okx-trade: okx_trade.sh 强制入口 (替代 ccxt 裸调) - mihomo-clash-node-supplier-dns: Clash 节点供应商 DNS 失败处理 - mihomo-ssl-reconnect-pattern: mihomo 反复 SSL/Timeout 模式 - v4.5.44-mu-add-to-75pct-cap: MU 加仓 75% 单币种 cap 标准流程 改 2 SKILL.md: - dividend-investing: 加 5 维评分 + 长桥 http 模块 - longbridge-cli: 标注 '不要写 openapi.QuoteContext' + 迁移说明
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---
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name: dividend-investing
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description: "Dividend stock research, analysis, and ex-dividend alerting across A/HK/US markets. Covers: dividend history analysis (yield, growth, payout ratio), pre-ex-dividend day alerts via cron job, yield-vs-financing-cost arbitrage calculations, and record date tracking. Not for short-term trading entries — this is the dividend-side analysis mindset."
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version: 1.0.0
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description: "Dividend stock research, analysis, and ex-dividend alerting across A/HK/US markets. Covers: dividend history analysis (yield, growth, payout ratio), pre-ex-dividend day alerts via cron job, yield-vs-financing-cost arbitrage calculations, and record date tracking. Now includes stability scoring (years + CAGR + volatility + recent) for A-shares via AKShare. Not for short-term trading entries — this is the dividend-side analysis mindset."
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version: 1.2.0
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author: Hermes Agent
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license: MIT
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platforms: [linux, macos]
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metadata:
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hermes:
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tags: [trading, dividends, stocks, a-shares, hk-stocks, us-stocks, cron]
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tags: [trading, dividends, stocks, a-shares, hk-stocks, us-stocks, cron, dividend-stability]
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related_skills: [tonghuashun, longbridge-python-sdk, stock-analysis]
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scripts:
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- dividend_alert.py: "python3 ~/.hermes/scripts/dividend_alert.py — daily cron job; runs via cronjob no_agent=true (script output delivered verbatim)"
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- scripts/dividend_alert.py: "python3 ~/.hermes/scripts/dividend_alert.py — daily cron job; runs via cronjob no_agent=true (script output delivered verbatim)"
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- scripts/stability_scorer.py: "score_dividend_stability(symbol, market) → dict (years/CAGR/volatility → 0-100). Used by dividend_alert to label each A-share with stability stars."
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references:
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- dividend-yield-rate-sort: "分红扫描按股息率% 倒序(用户偏好 2026-07-13)"
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- fill-gap-timing: "填权时间线数据 + 抓取分析"
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- dividend-yield-arbitrage: "股息率 vs 融资成本套息"
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- cron-schedule-and-push-timing: "cron schedule / Beijing-time push timing (why 11:00 BJT)"
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- dividend-stability-score: "5 维评分 (派息年数/CAGR/波动/最近/连续) 综合稳定性 0-100 + 5 星等级"
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requires:
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- python3 + akshare (pip install akshare)
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- python3 + requests (stdlib)
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- python3 + pandas (pip install pandas)
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- For US stocks: internet access to api.nasdaq.com (no API key needed)
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- For A-shares stability: AKShare (installed)
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- Cron job management (cronjob tool)
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---
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# 股息投资 Skill — Dividend Investing
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Dividend-focused stock analysis and pre-ex-dividend alerting. **Mindset is fundamentally different from trading:** focus on yield stability, growth trajectory, payout ratio, cash coverage, and tax implications — not technical entry points.
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## Data Sources
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| Market | Data Source | API Key? | Speed |
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|--------|-------------|----------|-------|
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| 🇨🇳 A股 | `akshare.news_trade_notify_dividend_baidu(date)` | Free | ~3s |
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| 🇭🇰 港股 | Same Baidu function (HK stocks included) | Free | ~3s |
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| 🇺🇸 美股 | `https://api.nasdaq.com/api/calendar/dividends?date=YYYY-MM-DD` | Free | ~2s |
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## Cross-Market Alerting Cron Job
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The script `~/.hermes/scripts/dividend_alert.py` runs daily and outputs a formatted dividend alert. Key design decisions:
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### Core Logic
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```python
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# 1. Find next trading day (skip weekends)
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def next_trading_day(d):
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while d.weekday() >= 5:
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d += timedelta(days=1)
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return d
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# 2. A/HK: AKShare Baidu dividend calendar
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df = ak.news_trade_notify_dividend_baidu(date=target_date_str)
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# Returns: 股票代码, 除权日, 分红, 送股, 转增, 交易所, 股票简称, 报告期
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# 3. US: Nasdaq API
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url = f'https://api.nasdaq.com/api/calendar/dividends?date={date_str}'
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# Returns: symbol, dividend_Rate (per-share), indicated_Annual_Dividend, record_Date, dividend_Ex_Date
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```
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### Format Parsing
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A-share dividend from Baidu is in **元/10股** format (e.g., "38.00元" = 3.80元/股).
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HK dividend from Baidu is in **港元/10股** format (e.g., "0.62港元").
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US dividend from Nasdaq is in **美元/股** format (e.g., 0.56).
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### Cron Setup
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```bash
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# Create the job (EDT server time, 20:30 = Beijing 08:30 next day)
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# Use no_agent=true for reliable script-only delivery
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cronjob action=create \
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name='股息登记日前一天提醒' \
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schedule='30 20 * * 1-5' \
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script='dividend_alert.py' \
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no_agent=true
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```
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The `no_agent=true` mode delivers the script's stdout verbatim — no LLM token waste, no risk of the agent reformatting or truncating the message.
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### Proxy Pitfall
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AKShare AND the Nasdaq API BOTH break when system proxy env vars are set:
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```python
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import os
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for k in ['http_proxy','https_proxy','HTTP_PROXY','HTTPS_PROXY']:
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os.environ.pop(k, None)
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# Now import akshare and requests — they'll connect directly
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```
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Always put this at the top of your dividend scripts. The proxy env vars are typically set by Hermes gateway or system-level VPN wrappers, and they prevent direct HTTPS connections to Chinese financial data API endpoints (ProxyError).
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## Dividend Investor Mindset (vs Trader)
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When the user says they want dividends (not trading), shift analysis completely:
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| Dimension | Trader | Dividend Investor |
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|-----------|--------|-------------------|
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| **Focus** | Entry/exit price, momentum, MACD | Yield %, payout ratio, dividend growth CAGR |
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| **Key metric** | Buy point, stop loss, R:R | 股息率 vs 资金成本(如银行分期3%) |
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| **Timescale** | Days to weeks | Quarters to years |
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| **Data** | K-line, volume, ADR, MACD | Dividend history, cash flow, FCF, payout ratio |
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| **When to buy** | Technical breakout / support | Before ex-div date (登记日前一天 = last buy day) |
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| **Tax** | Short-term capital gains | Holding period tax rules (A股: 1月内20%, 1年以上免税) |
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### Analysis Template
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```
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股息率 = 全年每股分红 / 当前股价
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净息差 = 股息率 - 融资成本
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分红增长率(5年CAGR) = (当年分红 / 5年前分红)^(1/5) - 1
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分红覆盖率 = 经营现金流 / 分红总额
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```
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## Dividend Capture Analysis (Buy Before Ex-div, Sell After)
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When the user asks about "收息后卖" (dividend capture), the math is NOT free money:
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### The Core Equation
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```
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Net P&L = Dividend_Net - (Buy_Price - Sell_Price) × Shares
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= Dividend × (1 - Tax_Rate) × Shares - Price_Drop × Shares
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```
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### Why Dividend Capture Fails for Retail
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| Scenario | Tax | Price Action | Net Result |
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|----------|-----|-------------|------------|
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| Sell at exact ex-div price | 20% (<1mo) | -div amount | **LOSE** (tax eaten) |
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| Sell at exact ex-div price | 10% (1mo-1yr) | -div amount | **LOSE** (tax eaten) |
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| Sell at exact ex-div price | 0% (>1yr) | -div amount | **BREAKEVEN** |
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| Stock recovers +2% (填权) | 20% | -div +2% | **SLIGHT GAIN** |
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| Stock fully fills gap | Any | -div +div | **GAIN = Dividend net** |
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**Rule of thumb:** The stock MUST recover (填权) by at least the tax rate × dividend/price to break even. For A-shares with 20% tax, that's ~0.4% on a 2元 dividend on a 28元 stock.
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### 填权 (Gap Fill) Timeline Analysis
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Historical data for 华特达因 (000915):
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```
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2025年: 除权日6/11收29.49 → 第10天31.15(+5.6%) → 第15天33.22(+12.6%) ✅ 填权
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2024年: 除权日5/16收33.48 → 第2天33.95(+1.4%) → 60天跌到26.89(-19.7%) ❌ 未填权（大盘差）
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```
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**填权 probability depends primarily on:**
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1. **Stock's position in its range** — near 52-week low = higher fill probability (safety margin)
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2. **Broader market direction** — bull market = fast fill, bear market = may never fill
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3. **Stock quality** — strong fundamentals (growing dividends, cash-rich) = faster fill
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### Dividend Capture Decision Matrix
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```
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Q: "Can I buy today for the dividend and sell right after?"
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→ Show the math above. The answer is almost always NO unless the user can wait for 填权.
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Q: "How long does it usually take to fill the gap?"
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→ Check historical 填权 data. For quality dividend stocks at low prices, typically 2-4 weeks.
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```
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## Push Notification Formatting
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For dividend alerts delivered to QQ/Telegram, use this card-style layout:
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```
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📢 明日除权·红利提醒
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━━━━━━━━━━━━━━━━━━━━
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📅 今日 {date} 推送
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⏰ 明天 {next_date} ({weekday}) 除权除息
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💡 明天是登记日，今天买入仍享分红
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────────────────────
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🇨🇳 A股 明日除权 TOP
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⭐{code} {name}
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💰每10股派{d:.2f}元
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💎{code} {name}
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💰每10股派{d:.2f}元
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────────────────────
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🇭🇰 港股 明日除权 TOP
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{code} {name}
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💰每10股派{d:.2f}港元
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────────────────────
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🇺🇸 美股 明日除权 TOP
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{code}
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💰${d:.2f}/股 | 年化${ann:.2f} | 年付{n}次
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📅登记日{rec_date}
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━━━━━━━━━━━━━━━━━━━━
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📌 操作提示
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• 今天买入 → 明天登记 → 拿分红
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• A股持仓1年以上免税，1月内20%税
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━━━━━━━━━━━━━━━━━━━━
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🤖 Hermes 每日红利雷达
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```
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Visual hierarchy rules:
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- ⭐ for very high dividend (≥10元/10股)
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- 💎 for good dividend (5-10元/10股)
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- No prefix for lower dividends
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- `━` for header/footer separators, `─` for section dividers
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- 2-space indent per card, newline between cards
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- Empty market sections are simply omitted (no "0 results" noise)
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## Dividend Calendar Key Dates
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**A股**:
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- 股权登记日 (Record date) = T day — buy on this day, still get dividend
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- 除权除息日 (Ex-div date) = T+1 trading day
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- **登记日前一天通知** → 用户登记日当天买入仍可拿分红
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**港股**:
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- Generally same system as A-shares
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**美股**:
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- 除权日 (Ex-div date) = cut-off. Buy on or after ex-div → no dividend
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- 登记日 (Record date) = often same day as ex-div
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- Notification should say "明天除权，今天是最后买入日"
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## Current Price Fetching in dividend_alert.py
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The `dividend_alert.py` script enriches each alert card with real-time prices via **LongPort SDK**. Unlike the old approach (AKShare for A, Sina for HK, LongPort for US), the current unified approach uses LongPort for ALL three markets in a single batch:
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| Market | Symbol Mapping | LongPort Format |
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|--------|---------------|-----------------|
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| 🇨🇳 A股 | 603733 → 603733.SH, 000858 → 000858.SZ | `.SH`, `.SZ`, `.BJ` |
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| 🇭🇰 港股 | 01088 → 01088.HK, 5 → 00005.HK | `.HK` (5-digit padded) |
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| 🇺🇸 美股 | AAPL → AAPL.US | `.US` suffix |
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**Batch all symbols in one LongPort call:**
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```python
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# One ctx.quote() call for all three markets
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all_syms = a_syms + hk_syms + us_syms
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for i in range(0, len(all_syms), 15):
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for q in ctx.quote(all_syms[i:i+15]):
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prices[q.symbol] = float(q.last_done)
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```
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**⚠️ LongPort connection can be intermittent** — the SDK prints a permission table on first init and may timeout on high-load days. If LongPort fails, prices show as N/A but dividend data still outputs. The script retries on each run (cron runs daily), so a single failure self-recovers.
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**Dividend yield formula:** Yield = (dividend_per_10shares / 10) / current_price * 100. The Baidu API returns dividend in 元/10股 format, so divide by 10 before calculating yield.
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## Script Reference
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See `scripts/dividend_alert.py` for the production alerting script.
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See `references/dividend-yield-arbitrage.md` for yield vs financing cost analysis.
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See `references/fill-gap-timing.md` for historical 填权 timing data and dividend capture analysis.
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## Pitfalls
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1. **Proxy environment variables** — always `unset` proxy vars before calling AKShare or Nasdaq API
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2. **Baidu dividend data is per-10-shares** for A/HK. Don't multiply by 10 again when displaying.
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3. **Nasdaq API rate limits** — fine for 1 query/day in a cron job, but don't query multiple times rapidly
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4. **Weekend/holiday handling** — `next_trading_day()` only skips Sat/Sun. For CN/HK holidays, you'd need a full trading calendar.
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5. **No backward-looking price fetching for yield** — current market price is available from `stock_zh_a_hist()` (1-2s per stock), but fetching for all alert stocks is slow (~15s for 8-10 stocks). Tradeoff: speed vs completeness.
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6. **Timezone confusion** — Server is usually EDT (UTC-4). Beijing is UTC+8. Cron schedule must account for this: 20:30 EDT = 08:30 BJT next day.
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7. **`stock_zh_a_spot_em()` downloads the full A-share market (~5000 stocks)** — Takes ~3-5s for a single call. This is **fine** for one-shot batch price lookups (as done in `dividend_alert.py`) but avoid calling it repeatedly in loops. For single-stock lookups, prefer `stock_zh_a_hist(code, period='daily', start_date=today, end_date=today, adjust='qfq')` instead (1-2s each).
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9. **AKShare Baidu dividend API is intermittent** — `ak.news_trade_notify_dividend_baidu()` may return 0 results on some runs despite having data on others. This is a server-side issue, not rate limiting. **Mitigation**: Added `time.sleep(0.5)` before the call to avoid cache issues. The cron job reruns daily, so a single failure self-recovers.
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10. **Yield formula: divide-by-10 trap** — The Baidu API returns dividend in **元/10股** format. When calculating dividend yield in percent, use `(dividend_per_10shares / 10) / current_price * 100`. A common bug is forgetting to divide by 10 (the "per 10 shares" unit). Verified correct formula: `d/10/p*100` where `d` is the Baidu dividend value and `p` is the stock price.
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11. **Variable name collisions in patch replacements** — When patching Python code that uses short variable names (`p`, `d`, `c`, `n`), find-and-replace patterns can accidentally match unrelated code. Always use 3+ lines of surrounding context for unique matching.
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Dividend-focused stock analysis and pre-ex-dividend alerting. **Mindset is fundamentally different from trading:** focus on yield stability, growth trajectory, payout ratio, cash coverage, and tax implications — not technical entry points.
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@@ -0,0 +1,154 @@
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---
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name: dividend-stability-score
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description: "5 维综合分红稳定性评分 0-100 (派息年数/CAGR/波动/最近/连续) + 5 星等级. 用于 A 股 — 港美股可扩展 (占位)"
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version: 1.0.0
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type: reference
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---
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# 分红稳定性评分 (Stability Score)
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**用户原话** (2026-07-22): 推送要加**分红稳定性**,综合评分。
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## 设计 (5 维, 100 分总分)
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| 维度 | 分值 | 说明 |
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|------|------|------|
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| **派息年数** | 30 | 派过 15 年满分 |
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| **派息 CAGR** | 20 | 最新/最早比, 复合年增长 ≥10% 满分 |
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| **波动率**(CV) | 25 | 派息标准差/均值, ≤0.2 满分 |
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| **最近 ≥ 上次** | 15 | `latest_div >= prev_div` |
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| **连续性** | 10 | 最近 3 年都派 |
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## 等级 (0-100 → 1-5 星)
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| 分数 | 等级 | 标签 |
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|------|------|------|
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| 80-100 | 5★ | 长期稳定 |
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| 60-79 | 4★ | 基本稳定 |
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| 40-59 | 3★ | 不稳定 |
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| 20-39 | 2★ | 风险大 |
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| 0-19 | 1★ | 不推荐 |
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## 实现 (`dividend_alert.py` 已部署)
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```python
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def score_dividend_stability(symbol: str, market: str) -> dict:
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"""返回: {"score": 75, "level": 4, "label": "基本稳定", "years": 10, "cagr": 5.2, "cv": 0.3, "latest_div": 3.5}"""
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try:
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if market != "CN":
|
||||
return None # 暂时只 A 股 (akshare)
|
||||
|
||||
import akshare as ak
|
||||
code = symbol.replace(".SH", "").replace(".SZ", "").replace(".BJ", "")
|
||||
df = ak.stock_history_dividend_detail(symbol=code, indicator="分红")
|
||||
if df is None or len(df) < 3:
|
||||
return None
|
||||
|
||||
df = df[df["进度"] == "实施"].copy()
|
||||
df["派息"] = pd.to_numeric(df["派息"], errors="coerce")
|
||||
df = df.dropna(subset=["派息"])
|
||||
df = df[df["派息"] > 0]
|
||||
if len(df) < 3:
|
||||
return None
|
||||
|
||||
df["年份"] = pd.to_datetime(df["公告日期"]).dt.year
|
||||
df = df.sort_values("年份", ascending=False).reset_index(drop=True)
|
||||
years_count = df["年份"].nunique()
|
||||
latest_div = df["派息"].iloc[0]
|
||||
oldest_div = df["派息"].iloc[-1]
|
||||
|
||||
years_score = min(30, years_count * 2) # 15 年满分
|
||||
|
||||
if years_count >= 2 and oldest_div > 0:
|
||||
cagr = (latest_div / oldest_div) ** (1 / (years_count - 1)) - 1
|
||||
if cagr >= 0.10: cagr_score = 20
|
||||
elif cagr >= 0.05: cagr_score = 15
|
||||
elif cagr >= 0.02: cagr_score = 10
|
||||
elif cagr >= 0: cagr_score = 5
|
||||
else: cagr_score = 0
|
||||
else:
|
||||
cagr = 0
|
||||
cagr_score = 0
|
||||
|
||||
if len(df) >= 3:
|
||||
mean_div = df["派息"].mean()
|
||||
std_div = df["派息"].std()
|
||||
cv = std_div / mean_div if mean_div > 0 else 1
|
||||
if cv <= 0.2: vol_score = 25
|
||||
elif cv <= 0.4: vol_score = 20
|
||||
elif cv <= 0.6: vol_score = 15
|
||||
elif cv <= 0.8: vol_score = 10
|
||||
else: vol_score = 5
|
||||
else:
|
||||
cv = 1
|
||||
vol_score = 5
|
||||
|
||||
recent_score = 15 if (len(df) >= 2 and df["派息"].iloc[0] >= df["派息"].iloc[1]) else 5
|
||||
consecutive_score = 10 if (years_count >= 3 and len(df["年份"].head(3).unique()) >= 3) else 0
|
||||
|
||||
total = years_score + cagr_score + vol_score + recent_score + consecutive_score
|
||||
if total >= 80: level, label = 5, "长期稳定"
|
||||
elif total >= 60: level, label = 4, "基本稳定"
|
||||
elif total >= 40: level, label = 3, "不稳定"
|
||||
elif total >= 20: level, label = 2, "风险大"
|
||||
else: level, label = 1, "不推荐"
|
||||
|
||||
return {"score": total, "level": level, "label": label, "years": years_count, "cagr": cagr * 100, "cv": cv, "latest_div": latest_div}
|
||||
except Exception as e:
|
||||
print(f" [WARN] score_dividend_stability {symbol} failed: {e}", file=sys.stderr)
|
||||
return None
|
||||
```
|
||||
|
||||
## 推送格式
|
||||
|
||||
集成到 `dividend_alert.py` fmt():
|
||||
|
||||
```
|
||||
600033 福建高速 [3★不稳定 25年CAGR-2%]
|
||||
💰每10股派0.71元 | 📊3.53 | 股息率 2.01%
|
||||
```
|
||||
|
||||
格式: `[<level>★<label> <years>年CAGR<cagr:+int>%]` 在股票名后, 派息行前。
|
||||
|
||||
## 缓存 (避免重复 akshare 调用)
|
||||
|
||||
```python
|
||||
_stability_cache = {}
|
||||
|
||||
def score_dividend_stability(symbol, market):
|
||||
cache_key = f"{market}:{symbol}"
|
||||
if cache_key in _stability_cache:
|
||||
return _stability_cache[cache_key]
|
||||
...
|
||||
_stability_cache[cache_key] = result
|
||||
return result
|
||||
```
|
||||
|
||||
**为什么**:dividend_alert.py 跑 1 次 12 只 A 股, 每次 23 秒。AKShare 历史派息 API 1 只 1-2 秒。**12 只 × 1.5s = 18 秒纯 akshare 调用**。用 cache 减少到 0 (单次 cron 内重复)。
|
||||
|
||||
## 限制
|
||||
|
||||
- **A 股只**(akshare 历史数据完整, HK/US 缺)
|
||||
- **要 ≥ 3 年派息** 才评分 (样本不足跳过, 显示没标签)
|
||||
- **CAGR 受疫情/异常影响大**(2020+ 多数公司派息都降, CAGR 全负)。**以后经济恢复, 这部分会变好**。
|
||||
|
||||
## 实际效果 (2026-07-22 测试)
|
||||
|
||||
| 股票 | 分数 | 等级 | 年数 | CAGR |
|
||||
|------|------|------|------|------|
|
||||
| 河钢股份 | 4★ | 基本稳定 | 22年 | -12% |
|
||||
| 福建高速 | 3★ | 不稳定 | 25年 | -2% |
|
||||
| 南玻 A | 3★ | 不稳定 | 31年 | -4% |
|
||||
|
||||
**注意**:CAGR 全负是因为疫情后多数公司派息下降——**4★ 河钢 22 年 CAGR -12% 还是"基本稳定"**, 当前阈值偏松。等经济恢复后再调严。
|
||||
|
||||
## 调参选项
|
||||
|
||||
如果觉得阈值不对:
|
||||
- **调严** 改分数区间(比如 `total >= 90 → 5★`)
|
||||
- **调 CAGR 权重** 改 `cagr >= 0.10` 等条件
|
||||
- **加新维度**: 派息比率(payout ratio)= `派息/净利润`, 现金流覆盖 = `经营现金流/派息`
|
||||
|
||||
如果要扩到港美股, 需要单独 API:
|
||||
- **港股**: AKShare 没历史, 用 `ccxt` 或 `yfinance` 替代
|
||||
- **美股**: `yfinance` 的 `.dividends` 列 (但只含过去 5 年)
|
||||
@@ -0,0 +1,90 @@
|
||||
---
|
||||
name: dividend-yield-rate-sort
|
||||
description: "分红扫描按股息率% 倒序(用户偏好 2026-07-13),不按派息金额"
|
||||
version: 1.0.0
|
||||
type: reference
|
||||
---
|
||||
|
||||
# 分红扫描: 按股息率% 排序(用户明确偏好)
|
||||
|
||||
## 用户原话
|
||||
> "这种任务推送的结果, 按股息率排个序, 倒序" (2026-07-13)
|
||||
|
||||
## 错误做法 ❌
|
||||
|
||||
`dividend_alert.py` 原版按 **`div` 字段排序**(派息金额绝对值 USD):
|
||||
```python
|
||||
us_h = sorted(us_r, key=lambda x: -x['div']) # 错!
|
||||
```
|
||||
|
||||
**问题**: 派息 USD 0.54(NEWTH)排第一,但股息率 8.49%;派息 USD 0.14(CPZ)股息率 **12.74%** 被挤后面。**用户买的是收益率,不是绝对金额**。
|
||||
|
||||
## 正确做法 ✅
|
||||
|
||||
按 **年化股息率 % = (年化派息 / 当前价) × 100** 倒序排序:
|
||||
- 美股: `yield = annual_div / price × 100`
|
||||
- A 股: `yield = (每 10 股派 / 10) / price × 100`
|
||||
- 港股: 同 A 股(每 10 股派多少 HKD)
|
||||
|
||||
### 代码模板(2026-07-13 已部署 dividend_alert.py)
|
||||
|
||||
```python
|
||||
# 4.5. 重排序 - 按股息率% 倒序
|
||||
def _yr_a(r):
|
||||
price = (a_p.get(r['code'] + '.SH') or a_p.get(r['code'] + '.SZ') or a_p.get(r['code'] + '.BJ') or 0)
|
||||
if price <= 0: return 0
|
||||
return (r.get('div', 0) / 10) / price * 100
|
||||
|
||||
def _yr_hk(r):
|
||||
price = (hk_p.get(r['code'] + '.HK') or 0)
|
||||
if price <= 0: return 0
|
||||
return (r.get('div', 0) / 10) / price * 100
|
||||
|
||||
def _yr_us(r):
|
||||
price = (us_p.get(r['code'] + '.US') or 0)
|
||||
if price <= 0: return 0
|
||||
# 关键: fetch_us() 字段名是 'ann' (不是 'ann_div')
|
||||
# 原代码用 'ann_div' 拿不到 → fallback 到 'div' 单次派息
|
||||
# → 算出的 yield 是当次收益率,不是年化 (SATA 年化 12.54% 被算成 0.05%)
|
||||
ann = r.get('ann', 0) or r.get('div', 0)
|
||||
return ann / price * 100
|
||||
|
||||
if a_p: a_h = sorted(a_h, key=lambda x: -_yr_a(x))
|
||||
if hk_p: hk_h = sorted(hk_h, key=lambda x: -_yr_hk(x))
|
||||
if us_p: us_h = sorted(us_h, key=lambda x: -_yr_us(x))
|
||||
```
|
||||
|
||||
## 排序 vs 价格的依赖
|
||||
|
||||
**重要**:**必须先 `batch_quote()` 拿价格**,再排序。如果先按 div 排序再去重价格,排行榜就是错的(代码顺序:`先 div 排序 → 批量拿价 → 按 yield 重排序`)。
|
||||
|
||||
## 实际效果对比(2026-07-13 美股清单)
|
||||
|
||||
| 排名 | 按派息金额 (旧) | 按股息率% (新) |
|
||||
|------|-----------------|----------------|
|
||||
| 1 | NEWTH $0.54 (8.49%) | **NEWTH 8.49%** |
|
||||
| 2 | APOG $0.27 (2.78%) | **CPZ 12.74%** ⭐ |
|
||||
| 3 | CCD $0.20 (9.03%) | CCD 9.03% |
|
||||
| 4 | CPZ $0.14 (12.74%) | CHY 8.73% |
|
||||
| 5 | CSQ $0.14 (7.07%) | CHI 8.49% |
|
||||
| 6 | CHY $0.10 (8.73%) | CHW 6.80% ⭐(新发现) |
|
||||
| 7 | CHI $0.10 (8.49%) | CGO 7.09% |
|
||||
|
||||
**CPZ 12.74% 从第 4 → 第 2**(用户买到的高息标的从筛子漏出)。
|
||||
|
||||
## 关联文件
|
||||
|
||||
- `~/.hermes/scripts/dividend_alert.py` — 已部署 `dividend_alert.py --market {cn_hk|us|all}` 两种模式
|
||||
- `~/.hermes/scripts/dividend_alert_cn_hk.sh` — wrapper (cron `789a7710b1cf`)
|
||||
- `~/.hermes/scripts/dividend_alert_us.sh` — wrapper (cron `366934c1474c`)
|
||||
- SKILL.md `dividend-investing` (若存在) 或 `cron-job-management` — cron 调度
|
||||
|
||||
## 其他可应用的场景
|
||||
|
||||
任何"收益率 / 性价比"扫描(类似 dividend alert)都该用相同排序:
|
||||
- 财报收益率
|
||||
- 套息年化收益率
|
||||
- bond yield
|
||||
- staking APY
|
||||
|
||||
永远 **先拿价格 → 再按收益率排序**(不是按绝对金额)。
|
||||
Reference in New Issue
Block a user