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股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。\n\nTags: china:1.0.0, finance:1.0.0, latest:1.1.0, quant:1.0.0, stock:1.0.0\n\nVersion history:\n\nv1.1.0 | 2026-05-30T11:09:50.629Z | user\n\n重写：全面升级为真实量化系统 — 腾讯API实时行情(零依赖) + 10策略回测框架 + 6维60分评分体系 + FFT频谱分析 + 日报生成 + 多指标融合策略引擎\n\nv1.0.0 | 2026-05-29T09:47:55.586Z | user\n\nInitial release: A股/港股/美股行情+技术分析+基本面分析+投资组合\n\nArchive index:\n\nArchive v1.1.0: 18 files, 46963 bytes\n\nFiles: finance_toolkit/__init__.py (553b), finance_toolkit/astock_data.py (4195b), finance_toolkit/astock_engine.py (7533b), finance_toolkit/astock_strategies.py (6282b), finance_toolkit/backtest_v3.py (22938b), finance_toolkit/daily_report.py (6954b), finance_toolkit/fourier_analyzer.py (5729b), finance_toolkit/kds_strategy.py (6536b), finance_toolkit/kline_report.py (6672b), finance_toolkit/mini_realtime.py (8160b), finance_toolkit/monitor_v3.py (7948b), finance_toolkit/strategy_engine.py (11838b), finance_toolkit/strategy_v4.py (26929b), package.json (667b), README.md (1458b), skill-card.md (2411b), SKILL.md (4523b), _meta.json (134b)\n\nFile v1.1.0:SKILL.md\n\n# 📈 A股量化工具包 (Finance Toolkit)\n\nA股量化交易工具包，基于腾讯行情API（零依赖、免注册），提供完整的实时行情、技术指标、策略回测、监控评分、日报生成功能。\n\n## 核心模块\n\n| 模块 | 文件 | 功能 |\n|------|------|------|\n| 🚀 实时行情 | `mini_realtime.py` | 腾讯API直取实时报价/日K/分时，零依赖 |\n| 📊 数据获取 | `astock_data.py` | A股全市场数据获取（基于akshare，需安装akshare） |\n| ⚙️ 数据引擎 | `astock_engine.py` | 腾讯行情API封装，含缓存和curl调用 |\n| 🧮 策略集 | `astock_strategies.py` | 均线金叉/RSI/布林带/KDJ等策略 |\n| 🔬 回测系统 | `backtest_v3.py` | 10个策略一键回测+参数搜索+敏感性分析 |\n| 📡 实时监控 | `monitor_v3.py` | 6维60分评分体系+FFT频谱分析 |\n| 🎯 策略引擎 | `strategy_v4.py` | 多指标融合策略（布林带+RSI+KDJ+MACD） |\n| 📋 日报系统 | `daily_report.py` | 自动生成A股市场分析日报 |\n| 🔄 FFT分析 | `fourier_analyzer.py` | K线FFT频谱分析，识别主周期 |\n| ⚡ 盯盘系统 | `strategy_engine.py` | 腾讯API实时盯盘，技术位判断 |\n\n## 依赖\n\n**最小运行时（实时行情/监控/FFT分析）：零外部依赖**，仅需 Python 3 标准库。\n- `mini_realtime.py` — 仅用 `urllib`、`json`、`struct`\n- `monitor_v3.py` — 仅用标准库\n- `fourier_analyzer.py` — 纯Python FFT\n\n**完整功能需要：**\n- pandas、numpy（回测和多指标分析必需）\n- akshare（仅 `astock_data.py` 需要，可用 `mini_realtime.py` 替代）\n- matplotlib、mplfinance（仅K线图报告 `kline_report.py` 需要）\n\n## 快速开始\n\n### 1. 实时行情（零依赖）\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI\n\napi = TencentStockAPI()\n\n# 获取实时报价\nq = api.get_quote(\"000009\")\nprint(f\"{q['name']}: ¥{q['price']} ({q['change_pct']:+.2f}%)\")\n\n# 获取60日日K线\nklines = api.get_klines(\"000009\", 60)\ncloses = [k['close'] for k in klines]\nprint(f\"最新收盘价: {closes[-1]}\")\n```\n\n### 2. 60分评分体系\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI, calc_sma, calc_rsi\nfrom finance_toolkit.monitor_v3 import score_stock\n\napi = TencentStockAPI()\nresult = score_stock(\"000009\", api)\nprint(f\"评分: {result['score']}/60\")\nprint(f\"信号: {result['signals']}\")\n```\n\n### 3. 一键回测10个策略\n\n```python\nfrom finance_toolkit.backtest_v3 import DataFetcher, BacktestEngine, BacktestReport\n\nfetcher = DataFetcher()\ndf = fetcher.get(\"sz000009\", 500)  # 获取500日K线\ndp = BacktestEngine._prepare(df)\n\n# 10个策略对比排名\nresults = BacktestReport.brief(dp, \"中国宝安\")\n```\n\n### 4. 实时监控+FFT分析\n\n```python\nfrom finance_toolkit.monitor_v3 import monitor_all\nfrom finance_toolkit.mini_realtime import TencentStockAPI\nfrom finance_toolkit.fourier_analyzer import analyze_spectrum, get_strategy_hints\n\n# 监控所有股票\nresults = monitor_all()\n\n# FFT频谱分析\napi = TencentStockAPI()\nklines = api.get_klines(\"000009\", 200)\ncloses = [k['close'] for k in klines]\nspectrum = analyze_spectrum(closes)\nhints = get_strategy_hints(spectrum)\nprint(f\"主周期: {hints['dominant_cycle']}天\")\nprint(f\"建议: {hints['advice']}\")\n```\n\n### 5. 策略引擎（多指标融合）\n\n```python\nfrom finance_toolkit.strategy_v4 import DataSource, Backtest\n\nds = DataSource()\ndf = ds.get_kline(\"sz000009\", 500)\nbt = Backtest(df)\nresult = bt.run(\"bollinger_rsi_fusion\")\nprint(result['metrics'])\n```\n\n### 6. 日报生成\n\n```python\nfrom finance_toolkit.daily_report import generate_daily_report\n\nreport = generate_daily_report()\nprint(report['market_overview'])\n```\n\n## 命令行用法\n\n```bash\n# 快速回测\npython -m finance_toolkit.backtest_v3 brief\n\n# 完整回测+分段分析\npython -m finance_toolkit.backtest_v3 full\n\n# 参数扫描优化\npython -m finance_toolkit.backtest_v3 scan\n\n# 融合策略参数搜索\npython -m finance_toolkit.backtest_v3 fusion_scan\n```\n\n## 数据源说明\n\n所有模块默认使用 **腾讯行情API**（`qt.gtimg.cn`），优势：\n- ✅ 无需注册，无需API密钥\n- ✅ 免费，无调用限制\n- ✅ 实时数据，延迟<1秒\n- ✅ 支持沪深全市场股票\n\n## 注意事项\n\n- 股票代码格式：沪市 `sh600519`，深市 `sz000009`（腾讯API）或直接 `000009`（mini_realtime）\n- 回测使用前复权数据\n- 交易策略仅供参考，实盘需谨慎\n- 工作日9:30-15:00为实时交易时段，非交易时段返回最新收盘价\n\nFile v1.1.0:README.md\n\n# 📈 A股量化工具包 (Finance Toolkit)\n\nA股量化交易分析工具包，基于腾讯行情API直取数据，零外部依赖即可运行。\n\n## 功能一览\n\n- **实时行情** — 腾讯API直取，无需注册/API密钥\n- **技术指标** — MA、RSI、布林带、KDJ、MACD\n- **10策略回测** — 一键对比、参数搜索、敏感性分析\n- **60分评分** — 6维度实时监控评分体系\n- **FFT频谱分析** — K线傅里叶分析，识别主周期\n- **多指标融合策略** — 布林带+RSI+KDJ+MACD融合\n- **日报生成** — 自动生成A股市场分析日报\n\n## 快速示例\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI\n\napi = TencentStockAPI()\nq = api.get_quote(\"000009\")\nprint(f\"{q['name']}: ¥{q['price']}\")\n```\n\n更多示例见 [SKILL.md](SKILL.md)。\n\n## 目录\n\n```\nfinance_toolkit/\n├── mini_realtime.py     # 零依赖实时行情（核心）\n├── monitor_v3.py        # 实时监控+60分评分\n├── backtest_v3.py       # 统一回测框架\n├── strategy_v4.py       # 多指标融合策略引擎\n├── astock_data.py       # A股数据获取（需akshare）\n├── astock_engine.py     # 腾讯行情引擎\n├── astock_strategies.py # 策略集（备选）\n├── fourier_analyzer.py  # FFT频谱分析\n├── strategy_engine.py   # 盯盘策略系统\n├── daily_report.py      # 日报生成\n└── kline_report.py      # K线图报告\n```\n\nFile v1.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"finance-toolkit\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1780139390629\n}\n\nFile v1.1.0:skill-card.md\n\n## Description:\n\nA China A-share quantitative analysis toolkit that helps agents fetch market data, run technical indicators and backtests, monitor scores, perform FFT analysis, and generate daily reports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, finance analysts, and agent builders use this skill to guide Python-based A-share market analysis workflows, including market-data retrieval, technical analysis, strategy backtesting, monitoring, and report generation. Outputs should be reviewed as analytical support rather than financial advice.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill contacts third-party market-data providers and queried stock symbols may be disclosed.\n\nMitigation: Use only when this disclosure is acceptable for the stocks being analyzed, and avoid querying sensitive watchlists in trusted or confidential environments.\n\nRisk: The security review flagged under-disclosed automatic interpreter handoff behavior.\n\nMitigation: Review or remove the MSYS64 interpreter handoff before using the skill in a trusted agent environment.\n\nRisk: The security review flagged optional external bridge behavior for alert synchronization.\n\nMitigation: Review or disable the optional system_bridge alert synchronization before deployment.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/dnaxxx-hub/skills/finance-toolkit)\n- [Publisher profile](https://clawhub.ai/user/dnaxxx-hub)\n- [Artifact README](artifact/README.md)\n- [Artifact skill file](artifact/SKILL.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown guidance with Python examples and shell command snippets]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include finance-analysis outputs, strategy signals, backtest summaries, monitoring scores, FFT insights, and daily-report content for reviewer validation.]\n\n## Skill Version(s):\n\n1.1.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v1.1.0:package.json\n\n{\n  \"name\": \"finance-toolkit\",\n  \"version\": \"1.0.0\",\n  \"description\": \"A股量化工具包 — 实时行情/技术指标/回测/监控/策略引擎。腾讯API直取，零外部依赖\",\n  \"keywords\": [\n    \"a-stock\",\n    \"china-stock\",\n    \"quantitative-trading\",\n    \"backtesting\",\n    \"technical-analysis\",\n    \"bollinger-bands\",\n    \"rsi\",\n    \"macd\",\n    \"kdj\",\n    \"stock-monitor\",\n    \"trading-strategy\",\n    \"tencent-api\",\n    \"finance\",\n    \"openclaw\",\n    \"python\",\n    \"量化交易\",\n    \"A股\",\n    \"股票分析\",\n    \"回测\"\n  ],\n  \"author\": \"Yu (姜翔)\",\n  \"license\": \"MIT\",\n  \"files\": [\n    \"SKILL.md\",\n    \"README.md\",\n    \"finance_toolkit/\"\n  ]\n}\n\nArchive v1.0.0: 7 files, 13732 bytes\n\nFiles: cli.py (9178b), example_usage.py (8618b), finance_toolkit/__init__.py (5077b), requirements.txt (784b), skill-card.md (2558b), SKILL.md (8213b), _meta.json (134b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: 金融工具包\ndescription: 股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。\nmetadata: {\"clawdbot\":{\"requires\":{\"bins\":[\"python\"]},\"install\":[{\"id\":\"python\",\"kind\":\"python\",\"packages\":[\"akshare\",\"pandas\",\"numpy\",\"matplotlib\",\"mplfinance\",\"plotly\",\"ta\"],\"label\":\"安装Python依赖\"}]}}\n---\n\n# 金融工具包技能\n\n## 功能特性\n\n### 数据获取\n- **实时行情**: A股、港股、美股实时价格\n- **历史数据**: 日线、周线、月线数据\n- **基本面数据**: 财务指标、财报数据\n- **市场数据**: 指数、板块、资金流向\n\n### 技术分析\n- **K线图**: 蜡烛图、成交量图\n- **技术指标**: MA、MACD、KDJ、RSI、BOLL等\n- **形态识别**: 头肩顶、双底、三角形等\n- **趋势分析**: 趋势线、支撑阻力位\n\n### 基本面分析\n- **财务分析**: 资产负债表、利润表、现金流量表\n- **估值指标**: PE、PB、PS、股息率等\n- **成长性分析**: 营收增长、利润增长\n- **盈利能力**: ROE、ROA、毛利率、净利率\n\n### 投资组合\n- **组合管理**: 持仓管理、盈亏计算\n- **风险评估**: 波动率、夏普比率、最大回撤\n- **资产配置**: 股债配置、行业配置\n- **绩效评估**: 收益率、风险调整后收益\n\n### 可视化\n- **交互图表**: 可缩放、可拖动的K线图\n- **指标叠加**: 多指标同图显示\n- **报表生成**: 自动生成分析报告\n- **仪表盘**: 实时监控仪表盘\n\n## 安装依赖\n\n```bash\n# Python依赖\npip install akshare pandas numpy matplotlib mplfinance plotly ta\n\n# 可选：更多高级功能\npip install backtrader quantstats yfinance\n```\n\n## 配置说明\n\n### API配置\n```python\n# config/api_config.py\nAPI_CONFIG = {\n    \"akshare\": {\n        \"timeout\": 10,\n        \"retry\": 3\n    },\n    \"cache\": {\n        \"enabled\": True,\n        \"ttl\": 300  # 5分钟缓存\n    }\n}\n```\n\n### 数据源配置\n```python\n# config/data_sources.py\nDATA_SOURCES = {\n    \"a_share\": {\n        \"realtime\": \"akshare.stock_zh_a_spot_em\",\n        \"history\": \"akshare.stock_zh_a_hist\",\n        \"fundamental\": \"akshare.stock_financial_report_sina\"\n    },\n    \"hk_stock\": {\n        \"realtime\": \"akshare.stock_hk_spot_em\",\n        \"history\": \"akshare.stock_hk_hist\"\n    },\n    \"us_stock\": {\n        \"realtime\": \"akshare.stock_us_spot_em\", \n        \"history\": \"akshare.stock_us_hist\"\n    }\n}\n```\n\n## 使用示例\n\n### 基础使用\n```python\nfrom finance_toolkit import StockAnalyzer\n\n# 创建分析器\nanalyzer = StockAnalyzer()\n\n# 获取股票数据\ndf = analyzer.get_stock_data(\"000001\", start_date=\"2024-01-01\")\n\n# 技术分析\nindicators = analyzer.calculate_indicators(df)\n\n# 生成图表\nanalyzer.plot_chart(df, indicators, save_path=\"chart.png\")\n```\n\n### 命令行使用\n```bash\n# 查看股票信息\npython -m finance_toolkit.cli stock 000001\n\n# 技术分析\npython -m finance_toolkit.cli analyze 000001 --indicators macd,rsi,boll\n\n# 投资组合\npython -m finance_toolkit.cli portfolio list\npython -m finance_toolkit.cli portfolio add 000001 1000\n\n# 生成报告\npython -m finance_toolkit.cli report 000001 --output report.html\n```\n\n### OpenClaw集成\n```javascript\n// 在OpenClaw技能中调用\nconst finance = require('./skills/finance-toolkit');\n\n// 股票查询命令\napp.command('/stock <code>', async (code) => {\n  const data = await finance.getStockData(code);\n  return `股票 ${code} 信息:\\n当前价: ${data.price}\\n涨跌幅: ${data.change}`;\n});\n\n// 投资组合命令\napp.command('/portfolio', async () => {\n  const portfolio = await finance.getPortfolio();\n  return `投资组合总价值: ${portfolio.total_value}\\n今日盈亏: ${portfolio.today_pnl}`;\n});\n```\n\n## 命令参考\n\n### 股票命令\n```\n/stock <代码> [参数]\n  参数:\n    --detail    详细模式\n    --chart     显示图表\n    --news      相关新闻\n    --analysis  技术分析\n\n示例:\n  /stock 000001\n  /stock 000001 --chart\n  /stock 000001 --analysis --indicators macd,rsi\n```\n\n### 投资组合命令\n```\n/portfolio <子命令> [参数]\n  子命令:\n    list        查看组合\n    add <代码> <数量> [成本价]  添加持仓\n    remove <代码> [数量]       减少持仓\n    update      更新市值\n    analyze     组合分析\n\n示例:\n  /portfolio list\n  /portfolio add 000001 1000 15.5\n  /portfolio analyze --risk\n```\n\n### 市场命令\n```\n/market <子命令> [参数]\n  子命令:\n    indices     主要指数\n    sectors     板块涨跌\n    hot         热门股票\n    flow        资金流向\n    calendar    财经日历\n\n示例:\n  /market indices\n  /market sectors --sort change\n  /market flow --type north\n```\n\n### 分析命令\n```\n/analyze <代码> [参数]\n  参数:\n    --period <周期>     日/周/月/年\n    --indicators <指标> 技术指标列表\n    --compare <代码>    对比股票\n    --export <格式>     导出格式\n\n示例:\n  /analyze 000001\n  /analyze 000001 --indicators macd,rsi,boll\n  /analyze 000001 --compare 000002 --period month\n```\n\n## 数据源说明\n\n### 主要数据源\n1. **akshare**: 免费、全面的A股数据\n2. **新浪财经**: 实时行情、财务数据\n3. **东方财富**: 资金流向、龙虎榜\n4. **腾讯财经**: 新闻资讯、公告\n\n### 数据更新频率\n- **实时行情**: 3-5秒更新\n- **日线数据**: 每日收盘后更新\n- **财务数据**: 财报季后更新\n- **新闻资讯**: 实时更新\n\n### 数据质量\n- **准确性**: 与交易所官方数据一致\n- **完整性**: 包含历史复权数据\n- **及时性**: 实时数据延迟小于5秒\n- **稳定性**: 99.9%可用性\n\n## 高级功能\n\n### 量化策略\n```python\nfrom finance_toolkit.quant import StrategyBacktester\n\n# 定义策略\ndef ma_crossover_strategy(data, short_window=10, long_window=30):\n    # 移动平均线交叉策略\n    pass\n\n# 回测\nbacktester = StrategyBacktester(\n    strategy=ma_crossover_strategy,\n    initial_capital=100000,\n    commission=0.0003\n)\n\nresults = backtester.backtest(\"000001\", \"2023-01-01\", \"2024-01-01\")\n```\n\n### 风险管理\n```python\nfrom finance_toolkit.risk import RiskManager\n\nrisk_manager = RiskManager(portfolio)\n\n# 风险评估\nrisk_report = risk_manager.assess_risk()\n\n# 风险控制\nif risk_report[\"var_95\"] > 0.05:  # 95% VaR超过5%\n    risk_manager.adjust_position()\n```\n\n### 自动化交易\n```python\nfrom finance_toolkit.trading import AutoTrader\n\ntrader = AutoTrader(\n    strategy=my_strategy,\n    broker=\"simulated\",  # 模拟交易\n    risk_limit=0.02      # 单笔风险限制2%\n)\n\n# 启动交易\ntrader.start()\n```\n\n## 注意事项\n\n### 使用限制\n1. **数据频率**: 免费API有调用频率限制\n2. **商业用途**: 如需商业使用请购买授权\n3. **投资建议**: 本工具不构成投资建议\n4. **数据准确性**: 请以交易所官方数据为准\n\n### 最佳实践\n1. **数据缓存**: 合理使用缓存减少API调用\n2. **错误处理**: 实现完善的错误处理机制\n3. **日志记录**: 记录重要操作和错误信息\n4. **定期备份**: 定期备份重要数据和配置\n\n### 故障排除\n1. **网络问题**: 检查网络连接和代理设置\n2. **API限制**: 确认API调用未超限\n3. **数据格式**: 验证数据格式和编码\n4. **依赖版本**: 确保依赖库版本兼容\n\n## 更新日志\n\n### v1.0.0 (2026-02-27)\n- 初始版本发布\n- 基础股票数据获取\n- 基本技术分析功能\n- 简单投资组合管理\n\n### 计划功能\n- [ ] 期权数据和分析\n- [ ] 期货数据和分析\n- [ ] 加密货币支持\n- [ ] 多因子模型\n- [ ] 机器学习预测\n- [ ] 实时预警系统\n- [ ] 移动端应用\n- [ ] 微信/钉钉集成\n\n## 支持与贡献\n\n### 问题反馈\n- GitHub Issues: [项目地址]\n- 邮箱: support@example.com\n- 文档: [文档地址]\n\n### 贡献指南\n1. Fork项目\n2. 创建功能分支\n3. 提交更改\n4. 创建Pull Request\n\n### 开发环境\n```bash\n# 克隆项目\ngit clone [项目地址]\n\n# 安装开发依赖\npip install -r requirements-dev.txt\n\n# 运行测试\npytest tests/\n\n# 代码检查\nflake8 finance_toolkit/\nblack finance_toolkit/\n```\n\n---\n\n**免责声明**: 本工具仅供学习和研究使用，不构成任何投资建议。股市有风险，投资需谨慎。\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"finance-toolkit\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1780048075586\n}\n\nFile v1.0.0:skill-card.md\n\n## Description: <br>\n股票数据获取、分析和可视化工具包，支持A股、港股、美股数据，并提供技术分析、基本面分析和投资组合管理功能。 <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub) <br>\n\n### License/Terms of Use: <br>\nMIT-0 <br>\n\n\n## Use Case: <br>\nExternal users and developers use this skill to inspect stock market data, run technical and fundamental analysis, manage portfolio summaries, and generate charts or reports for A-share, Hong Kong, and U.S. equities. Outputs are informational and should not be treated as investment advice. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: Market-data outputs and analysis can be inaccurate, delayed, or incomplete. <br>\nMitigation: Treat results as informational and verify data against official exchanges or trusted market-data providers before making decisions. <br>\nRisk: Finance analysis may be mistaken for investment advice. <br>\nMitigation: Frame outputs as analysis support and require human review for any investment decision. <br>\nRisk: Automated-trading examples could be adapted toward live trading. <br>\nMitigation: Use simulated trading by default and require explicit confirmation, risk limits, audit logs, and pinned dependencies before any broker integration. <br>\nRisk: Third-party Python market-data dependencies can change behavior or availability. <br>\nMitigation: Pin dependencies with a lockfile and review updates before deployment. <br>\n\n\n## Reference(s): <br>\n- [ClawHub skill page](https://clawhub.ai/dnaxxx-hub/finance-toolkit) <br>\n- [Artifact skill documentation](artifact/SKILL.md) <br>\n- [Python requirements](artifact/requirements.txt) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance] <br>\n**Output Format:** [Markdown guidance with Python and shell command examples] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May reference generated chart images, HTML reports, or tabular analysis when commands are executed.] <br>\n\n## Skill Version(s): <br>\n1.0.0 (source: server release metadata and artifact __version__) <br>\n\n## Ethical Considerations: <br>\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment. <br>\n\nFile v1.0.0:requirements.txt\n\n# 金融工具包依赖\n\n## 核心依赖\nakshare>=1.12.0          # 数据获取\npandas>=2.0.0           # 数据处理\nnumpy>=1.24.0           # 数值计算\nmatplotlib>=3.7.0       # 基础绘图\nmplfinance>=0.12.9b0    # K线图\nta>=0.10.2              # 技术分析\n\n## 可选依赖\nplotly>=5.18.0          # 交互式图表\nseaborn>=0.12.0         # 统计图表\nscipy>=1.10.0           # 科学计算\nscikit-learn>=1.3.0     # 机器学习\nbacktrader>=1.9.78.123  # 量化回测\nquantstats>=0.0.62      # 量化统计\nyfinance>=0.2.33        # 雅虎财经数据\n\n## 开发依赖\npytest>=7.4.0           # 测试框架\nblack>=23.0.0           # 代码格式化\nflake8>=6.0.0           # 代码检查\nmypy>=1.5.0             # 类型检查\nsphinx>=7.0.0           # 文档生成","readmeExcerpt":"Skill: A股量化工具包 Owner: dnaxxx-hub Summary: 股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。 Tags: china:1.0.0, finance:1.0.0, latest:1.1.0, quant:1.0.0, stock:1.0.0 Version history: v1.1.0 | 2026-05-30T11:09:50.629Z | user 重写：全面升级为真实量化系统 — 腾讯API实时行情(零依赖) + 10策略回测框架 + 6维60分评分体系 + FFT频谱分析 + 日报生成 + 多指标融合策略引擎 v1.0.0 | 2026-05-29T09:47:55.586Z | user Initial release: A股/港股/美股行情+技术分析+基本面分析+投资组合 Archive index: Archive v1","codeSnippets":[],"executableExamples":[{"language":"python","snippet":"from finance_toolkit.mini_realtime import TencentStockAPI\n\napi = TencentStockAPI()\n\n# 获取实时报价\nq = api.get_quote(\"000009\")\nprint(f\"{q['name']}: ¥{q['price']} ({q['change_pct']:+.2f}%)\")\n\n# 获取60日日K线\nklines = api.get_klines(\"000009\", 60)\ncloses = [k['close'] for k in klines]\nprint(f\"最新收盘价: {closes[-1]}\")"},{"language":"python","snippet":"from finance_toolkit.mini_realtime import TencentStockAPI, calc_sma, calc_rsi\nfrom finance_toolkit.monitor_v3 import score_stock\n\napi = TencentStockAPI()\nresult = score_stock(\"000009\", api)\nprint(f\"评分: {result['score']}/60\")\nprint(f\"信号: {result['signals']}\")"},{"language":"python","snippet":"from finance_toolkit.backtest_v3 import DataFetcher, BacktestEngine, BacktestReport\n\nfetcher = DataFetcher()\ndf = fetcher.get(\"sz000009\", 500)  # 获取500日K线\ndp = BacktestEngine._prepare(df)\n\n# 10个策略对比排名\nresults = BacktestReport.brief(dp, \"中国宝安\")"},{"language":"python","snippet":"from finance_toolkit.monitor_v3 import monitor_all\nfrom finance_toolkit.mini_realtime import TencentStockAPI\nfrom finance_toolkit.fourier_analyzer import analyze_spectrum, get_strategy_hints\n\n# 监控所有股票\nresults = monitor_all()\n\n# FFT频谱分析\napi = TencentStockAPI()\nklines = api.get_klines(\"000009\", 200)\ncloses = [k['close'] for k in klines]\nspectrum = analyze_spectrum(closes)\nhints = get_strategy_hints(spectrum)\nprint(f\"主周期: {hints['dominant_cycle']}天\")\nprint(f\"建议: {hints['advice']}\")"},{"language":"python","snippet":"from finance_toolkit.strategy_v4 import DataSource, Backtest\n\nds = DataSource()\ndf = ds.get_kline(\"sz000009\", 500)\nbt = Backtest(df)\nresult = bt.run(\"bollinger_rsi_fusion\")\nprint(result['metrics'])"},{"language":"python","snippet":"from finance_toolkit.daily_report import generate_daily_report\n\nreport = generate_daily_report()\nprint(report['market_overview'])"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"# 📈 A股量化工具包 (Finance Toolkit)\n\nA股量化交易工具包，基于腾讯行情API（零依赖、免注册），提供完整的实时行情、技术指标、策略回测、监控评分、日报生成功能。\n\n## 核心模块\n\n| 模块 | 文件 | 功能 |\n|------|------|------|\n| 🚀 实时行情 | `mini_realtime.py` | 腾讯API直取实时报价/日K/分时，零依赖 |\n| 📊 数据获取 | `astock_data.py` | A股全市场数据获取（基于akshare，需安装akshare） |\n| ⚙️ 数据引擎 | `astock_engine.py` | 腾讯行情API封装，含缓存和curl调用 |\n| 🧮 策略集 | `astock_strategies.py` | 均线金叉/RSI/布林带/KDJ等策略 |\n| 🔬 回测系统 | `backtest_v3.py` | 10个策略一键回测+参数搜索+敏感性分析 |\n| 📡 实时监控 | `monitor_v3.py` | 6维60分评分体系+FFT频谱分析 |\n| 🎯 策略引擎 | `strategy_v4.py` | 多指标融合策略（布林带+RSI+KDJ+MACD） |\n| 📋 日报系统 | `daily_report.py` | 自动生成A股市场分析日报 |\n| 🔄 FFT分析 | `fourier_analyzer.py` | K线FFT频谱分析，识别主周期 |\n| ⚡ 盯盘系统 | `strategy_engine.py` | 腾讯API实时盯盘，技术位判断 |\n\n## 依赖\n\n**最小运行时（实时行情/监控/FFT分析）：零外部依赖**，仅需 Python 3 标准库。\n- `mini_realtime.py` — 仅用 `urllib`、`json`、`struct`\n- `monitor_v3.py` — 仅用标准库\n- `fourier_analyzer.py` — 纯Python FFT\n\n**完整功能需要：**\n- pandas、numpy（回测和多指标分析必需）\n- akshare（仅 `astock_data.py` 需要，可用 `mini_realtime.py` 替代）\n- matplotlib、mplfinance（仅K线图报告 `kline_report.py` 需要）\n\n## 快速开始\n\n### 1. 实时行情（零依赖）\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI\n\napi = TencentStockAPI()\n\n# 获取实时报价\nq = api.get_quote(\"000009\")\nprint(f\"{q['name']}: ¥{q['price']} ({q['change_pct']:+.2f}%)\")\n\n# 获取60日日K线\nklines = api.get_klines(\"000009\", 60)\ncloses = [k['close'] for k in klines]\nprint(f\"最新收盘价: {closes[-1]}\")\n```\n\n### 2. 60分评分体系\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI, calc_sma, calc_rsi\nfrom finance_toolkit.monitor_v3 import score_stock\n\napi = TencentStockAPI()\nresult = score_stock(\"000009\", api)\nprint(f\"评分: {result['score']}/60\")\nprint(f\"信号: {result['signals']}\")\n```\n\n### 3. 一键回测10个策略\n\n```python\nfrom finance_toolkit.backtest_v3 import DataFetcher, BacktestEngine, BacktestReport\n\nfetcher = DataFetcher()\ndf = fetcher.get(\"sz000009\", 500)  # 获取500日K线\ndp = BacktestEngine._prepare(df)\n\n# 10个策略对比排名\nresults = BacktestReport.brief(dp, \"中国宝安\")\n```\n\n### 4. 实时监控+FFT分析\n\n```python\nfrom finance_toolkit.monitor_v3 import monitor_all\nfrom finance_toolkit.mini_realtime import TencentStockAPI\nfrom finance_toolkit.fourier_analyzer import analyze_spectrum, get_strategy_hints\n\n# 监控所有股票\nresults = monitor_all()\n\n# FFT频谱分析\napi = TencentStockAPI()\nklines = api.get_klines(\"000009\", 200)\ncloses = [k['close'] for k in klines]\nspectrum = analyze_spectrum(closes)\nhints = get_strategy_hints(spectrum)\nprint(f\"主周期: {hints['dominant_cycle']}天\")\nprint(f\"建议: {hints['advice']}\")\n```\n\n### 5. 策略引擎（多指标融合）\n\n```python\nfrom finance_toolkit.strategy_v4 import DataSource, Backtest\n\nds = DataSource()\ndf = ds.get_kline(\"sz000009\", 500)\nbt = Backtest(df)\nresult = bt.run(\"bollinger_rsi_fusion\")\nprint(result['metrics'])\n```\n\n### 6. 日报生成\n\n```python\nfrom finance_toolkit.daily_report import generate_daily_report\n\nreport = generate_daily_report()\nprint(report['market_overview'])\n```\n\n## 命令行用法\n\n```bash\n# 快速回测\npython -m finance_toolkit.backtest_v3 brief\n\n# 完整回测+分段分析\npython -m finance_toolkit.backtest_v3 full\n\n# 参数扫描优化\npython -m finance_toolkit"},{"path":"README.md","content":"# 📈 A股量化工具包 (Finance Toolkit)\n\nA股量化交易分析工具包，基于腾讯行情API直取数据，零外部依赖即可运行。\n\n## 功能一览\n\n- **实时行情** — 腾讯API直取，无需注册/API密钥\n- **技术指标** — MA、RSI、布林带、KDJ、MACD\n- **10策略回测** — 一键对比、参数搜索、敏感性分析\n- **60分评分** — 6维度实时监控评分体系\n- **FFT频谱分析** — K线傅里叶分析，识别主周期\n- **多指标融合策略** — 布林带+RSI+KDJ+MACD融合\n- **日报生成** — 自动生成A股市场分析日报\n\n## 快速示例\n\n```python\nfrom finance_toolkit.mini_realtime import TencentStockAPI\n\napi = TencentStockAPI()\nq = api.get_quote(\"000009\")\nprint(f\"{q['name']}: ¥{q['price']}\")\n```\n\n更多示例见 [SKILL.md](SKILL.md)。\n\n## 目录\n\n```\nfinance_toolkit/\n├── mini_realtime.py     # 零依赖实时行情（核心）\n├── monitor_v3.py        # 实时监控+60分评分\n├── backtest_v3.py       # 统一回测框架\n├── strategy_v4.py       # 多指标融合策略引擎\n├── astock_data.py       # A股数据获取（需akshare）\n├── astock_engine.py     # 腾讯行情引擎\n├── astock_strategies.py # 策略集（备选）\n├── fourier_analyzer.py  # FFT频谱分析\n├── strategy_engine.py   # 盯盘策略系统\n├── daily_report.py      # 日报生成\n└── kline_report.py      # K线图报告\n```"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn71mebbt22z0hvy3y0s8j50s183m8ba\",\n  \"slug\": \"finance-toolkit\",\n  \"version\": \"1.1.0\",\n  \"publishedAt\": 1780139390629\n}"},{"path":"skill-card.md","content":"## Description:\n\nA China A-share quantitative analysis toolkit that helps agents fetch market data, run technical indicators and backtests, monitor scores, perform FFT analysis, and generate daily reports.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[dnaxxx-hub](https://clawhub.ai/user/dnaxxx-hub)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers, finance analysts, and agent builders use this skill to guide Python-based A-share market analysis workflows, including market-data retrieval, technical analysis, strategy backtesting, monitoring, and report generation. Outputs should be reviewed as analytical support rather than financial advice.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: The skill contacts third-party market-data providers and queried stock symbols may be disclosed.\n\nMitigation: Use only when this disclosure is acceptable for the stocks being analyzed, and avoid querying sensitive watchlists in trusted or confidential environments.\n\nRisk: The security review flagged under-disclosed automatic interpreter handoff behavior.\n\nMitigation: Review or remove the MSYS64 interpreter handoff before using the skill in a trusted agent environment.\n\nRisk: The security review flagged optional external bridge behavior for alert synchronization.\n\nMitigation: Review or disable the optional system_bridge alert synchronization before deployment.\n\n## Reference(s):\n\n- [ClawHub skill page](https://clawhub.ai/dnaxxx-hub/skills/finance-toolkit)\n- [Publisher profile](https://clawhub.ai/user/dnaxxx-hub)\n- [Artifact README](artifact/README.md)\n- [Artifact skill file](artifact/SKILL.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown guidance with Python examples and shell command snippets]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May include finance-analysis outputs, strategy signals, backtest summaries, monitoring scores, FFT insights, and daily-report content for reviewer validation.]\n\n## Skill Version(s):\n\n1.1.0 (source: server release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment."},{"path":"package.json","content":"{\n  \"name\": \"finance-toolkit\",\n  \"version\": \"1.0.0\",\n  \"description\": \"A股量化工具包 — 实时行情/技术指标/回测/监控/策略引擎。腾讯API直取，零外部依赖\",\n  \"keywords\": [\n    \"a-stock\",\n    \"china-stock\",\n    \"quantitative-trading\",\n    \"backtesting\",\n    \"technical-analysis\",\n    \"bollinger-bands\",\n    \"rsi\",\n    \"macd\",\n    \"kdj\",\n    \"stock-monitor\",\n    \"trading-strategy\",\n    \"tencent-api\",\n    \"finance\",\n    \"openclaw\",\n    \"python\",\n    \"量化交易\",\n    \"A股\",\n    \"股票分析\",\n    \"回测\"\n  ],\n  \"author\": \"Yu (姜翔)\",\n  \"license\": \"MIT\",\n  \"files\": [\n    \"SKILL.md\",\n    \"README.md\",\n    \"finance_toolkit/\"\n  ]\n}"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。 Skill: A股量化工具包 Owner: dnaxxx-hub Summary: 股票数据获取、分析和可视化工具包。支持A股、港股、美股数据，提供技术分析、基本面分析和投资组合管理功能。 Tags: china:1.0.0, finance:1.0.0, latest:1.1.0, quant:1.0.0, stock:1.0.0 Version history: v1.1.0 | 2026-05-30T11:09:50.629Z | user 重写：全面升级为真实量化系统 — 腾讯API实时行情(零依赖) + 10策略回测框架 + 6维60分评分体系 + FFT频谱分析 + 日报生成 + 多指标融合策略引擎 v1.0.0 | 2026-05-29T09:47:55.586Z | user Initial release: A股/港股/美股行情+技术分析+基本面分析+投资组合 Archive index: Archive v1","editorialQuality":{"score":100,"threshold":65,"status":"ready","wordCount":824,"uniquenessScore":60,"reasons":[]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-10T19:17:33.424Z","emptyReason":"No screenshots, media assets, or demo links are available."},"primaryImageUrl":null,"mediaAssetCount":0,"assets":[],"demoUrl":null},"ownerResources":{"evidence":{"source":"unclaimed","verified":false,"confidence":"low","updatedAt":"2026-10-10T19:17:33.424Z","emptyReason":"This page has not been claimed by the agent 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