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hc
2025-12-31 19:58:09 +08:00
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.gitignore
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# .gitignore - MND-IA 项目
# 配置文件（包含敏感信息）
config.json
# Python 缓存
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
# 虚拟环境
venv/
env/
ENV/
.venv
# 数据文件
data/
*.csv
*.json.bak
*.db
# 日志文件
*.log
logs/
# IDE 配置
.vscode/
.idea/
*.swp
*.swo
*~
# Jupyter Notebook
.ipynb_checkpoints
# 系统文件
.DS_Store
Thumbs.db
# 测试覆盖率
.coverage
htmlcov/
# 构建产物
build/
dist/
*.egg-info/
CONFIG_GUIDE.md
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# Configuration Example
# 配置文件使用说明
这是 MND-IA 系统的配置文件模板。
## 配置项说明
### LLM 配置
```json
"llm": {
"model_name": "gpt-4", // 模型名称（支持 gpt-4, gpt-3.5-turbo, glm-4, claude-3.5-sonnet 等）
"api_base": "https://api.openai.com/v1", // API 地址
"api_key": "your-api-key-here", // API Key（必填）
"temperature": 0.7, // 生成温度（0-1）
"max_tokens": 2000, // 最大生成长度
"timeout": 60 // 超时时间（秒）
}
```
### 系统配置
```json
"system": {
"data_dir": "data", // 数据存储目录
"cache_dir": "data/news_cache", // 新闻缓存目录
"log_level": "INFO" // 日志级别
}
```
### 爬虫配置
```json
"crawler": {
"max_retries": 3, // 最大重试次数
"retry_delay": 5, // 重试间隔（秒）
"cache_days": 7 // 缓存保留天数
}
```
### 交易配置
```json
"trading": {
"max_position_ratio": 0.2, // 单个资产最大仓位比例
"risk_free_rate": 0.02 // 无风险利率
}
```
## 使用不同的 LLM 提供商
### OpenAI
```json
"llm": {
"model_name": "gpt-4",
"api_base": "https://api.openai.com/v1",
"api_key": "sk-xxxxxxxxxxxx"
}
```
### 智谱 GLM
```json
"llm": {
"model_name": "glm-4",
"api_base": "https://open.bigmodel.cn/api/paas/v4",
"api_key": "xxxxxxxxxxxx.xxxxxxxxxxxx"
}
```
### Claude (Anthropic)
```json
"llm": {
"model_name": "claude-3-5-sonnet-20241022",
"api_base": "https://api.anthropic.com/v1",
"api_key": "sk-ant-xxxxxxxxxxxx"
}
```
### 本地部署（Ollama）
```json
"llm": {
"model_name": "qwen2.5:14b",
"api_base": "http://localhost:11434/v1",
"api_key": "ollama"
}
```
## 代码中使用配置
### 方式 1：直接访问配置属性
```python
from core.config import config
# 获取模型名称
model = config.model_name
# 获取 API Key
api_key = config.api_key
# 获取完整 LLM 配置
llm_config = config.llm_config
```
### 方式 2：使用统一的 LLM 调用接口
```python
from core.config import llm_call
# 直接调用 LLM
response = llm_call(
messages=[
{"role": "system", "content": "你是 A 股分析师"},
{"role": "user", "content": "分析这条新闻..."}
],
temperature=0.5 # 可以覆盖配置文件中的默认值
)
```
### 方式 3：获取配置好的客户端
```python
from core.config import get_llm_client
client = get_llm_client()
if client:
response = client.chat.completions.create(...)
```
## 环境变量支持（TODO）
未来版本将支持环境变量覆盖：
```bash
export MND_IA_API_KEY="sk-xxxxxxxxxxxx"
export MND_IA_MODEL_NAME="gpt-4"
```
## 安全提示
⚠️ **重要**：
1. 不要将包含真实 API Key 的 `config.json` 提交到 Git
2. 建议将 `config.json` 添加到 `.gitignore`
3. 团队协作时，提供 `config.example.json` 模板
4. 生产环境使用环境变量或密钥管理服务
QUICKSTART.md
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# 🚀 MND-IA 快速开始指南
## 安装依赖
```bash
pip install -r requirements.txt
```
**核心依赖说明**:
- `akshare`: A股行情数据获取
- `cloudscraper`: 绕过 Cloudflare 防护的爬虫工具
- `beautifulsoup4`: HTML 解析
- `pandas`, `numpy`: 数据处理
## 运行测试
### 1. 测试核心模块（World Book）
```bash
python core/world_book.py
```
### 2. 测试 Agent Skills
```bash
# 测试量化模块
python skills/quant.py
# 测试情报分析
python skills/analyst.py
# 测试策略师
python skills/strategist.py
# 测试基金经理
python skills/pm.py
# 测试审计员
python skills/auditor.py
# 测试新闻联播爬虫
python skills/crawler.py
```
### 3. 运行完整系统
```bash
# 测试模式（使用模拟数据）
python main.py --mode test --no-crawler
# 测试模式（使用爬虫抓取新闻联播）
python main.py --mode test
# 每日批处理模式（自动爬取新闻）
python main.py --mode daily --capital 1000000
# 盘后审计模式
python main.py --mode audit
```
## 系统架构
```
MND-IA/
├── core/ # 共享内核
│ ├── world_book.py # WorldBook 核心类
│ ├── asset_map.json # 资产映射表
│ └── macro_matrix.json # 宏观因子矩阵
├── skills/ # Agent 技能群
│ ├── analyst.py # Skill A: 情报分析师
│ ├── quant.py # Skill B: 量化研究员
│ ├── strategist.py # Skill C: 宏观策略师
│ ├── pm.py # Skill D: 基金经理
│ ├── auditor.py # Skill E: 复盘审计员
│ └── crawler.py # Skill F: 新闻联播爬虫
├── data/ # 数据存储
├── logs/ # 日志文件
└── main.py # 主调度程序
```
## 数据流示意
```
[新闻联播] ──→ Crawler (Skill F) ──→ 新闻文本
↓
Analyst ──→ Narrative_JSON
↓
[行情] ──→ Quant ──→ Market_Data ──→ Strategist ──→ WorldBook
↓
PM ──→ Trust Index ──→ Orders
↑
Auditor (复盘)
```
## 核心概念
### Trust Index 公式
```
TrustIndex = (Score_narrative × 0.6 + Score_flow × 0.4) - Penalty
一票否决:
if Narrative > 80 and Flow < 30:
Penalty = 100
```
### 叙事生命周期
1. **incubation (潜伏期)**: 新闻刚出现，市场未反应
2. **fermentation (发酵期)**: 政策出台，市场开始关注
3. **realization (兑现期)**: Level A 重大利好，资金涌入
4. **decay (衰退期)**: 热度下降，逐步退出
## 下一步
1. ~~配置新闻数据源（可接入财联社、东方财富等 API）~~ ✅ 已实现新闻联播爬虫
2. 配置 LLM（如 GLM-4 或 Claude-3.5）用于 Analyst 增强
3. 实现实盘交易接口（如东方财富、同花顺）
4. 部署定时任务（每日 08:30 自动执行）
5. 添加更多新闻源（如财联社快讯、部委官网等）
## 常见问题
**Q: akshare 获取数据失败？**
A: 确保网络连接正常，某些 API 有频率限制。
**Q: 爬虫无法获取新闻联播？**
A:
1. 检查网站是否可访问（可能被墙或维护）
2. 查看 `data/news_cache/` 目录下的缓存文件
3. 手动测试：`python skills/crawler.py`
4. 新闻联播通常 20:00 后更新，提前爬取会返回空
**Q: cloudscraper 安装失败？**
A:
```bash
pip install --upgrade pip
pip install cloudscraper requests[security] pyparsing
```
**Q: 如何添加新的 ETF？**
A: 编辑 `core/asset_map.json`，在对应板块添加 ETF 代码。
**Q: 如何调整 Trust Index 公式权重？**
A: 修改 `skills/pm.py` 中的 `calculate_trust_index()` 方法。
## 许可证
MIT License
core/__init__.py
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"""
MND-IA Core Module
==================
共享内核 - 系统数据中心和逻辑基石
"""
from .world_book import WorldBook, Narrative, MacroCycle, create_narrative_id
__all__ = [
'WorldBook',
'Narrative',
'MacroCycle',
'create_narrative_id'
]
__version__ = '1.0.0'
core/asset_map.json
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{
"_meta": {
"version": "4.0.0",
"description": "Agent专用资产映射表。包含资产代码、关键词触发器及宏观因子敏感度矩阵。",
"instruction": "当计算Trust Index时，使用sensitivity中的数值作为基础权重(x)，结合Narrative强度(y)进行最终打分。"
},
"assets": {
"core_A500": {
"name": "中证A500",
"etfs": ["512050", "512020", "563360"],
"keywords": ["核心资产", "大盘", "指数增强", "国家队", "长线资金"],
"sensitivity": {
"liquidity_easing": 0.6,
"policy_support": 0.8,
"cpi_rebound": 0.5,
"risk_on": 0.9
}
},
"tech_hardware": {
"name": "半导体与硬科技",
"etfs": ["512480", "588000"],
"keywords": ["芯片", "光刻机", "集成电路", "卡脖子", "科创板"],
"sensitivity": {
"interest_rate_down": 0.9,
"policy_tech_self_reliance": 1.0,
"geopolitics_tension": 0.7,
"risk_on": 1.0
}
},
"tech_software": {
"name": "软件与数字经济",
"etfs": ["515230", "159869"],
"keywords": ["信创", "国产软件", "操作系统", "数据要素", "AI应用", "网络安全"],
"sensitivity": {
"policy_digital_economy": 1.0,
"govt_spending": 0.8,
"risk_on": 0.9,
"interest_rate_down": 0.7
}
},
"tech_infra": {
"name": "5G与新基建",
"etfs": ["515050", "515880"],
"keywords": ["5G", "6G", "光模块", "通信基站", "算力底座", "东数西算"],
"sensitivity": {
"policy_new_infra": 1.0,
"tech_cycle_up": 0.8,
"export_growth": 0.6
}
},
"finance_broker": {
"name": "券商/牛市旗手",
"etfs": ["512880", "512900"],
"keywords": ["成交量", "资本市场", "印花税", "T+0", "金融强国"],
"sensitivity": {
"market_volume_spike": 1.0,
"policy_capital_market": 1.0,
"liquidity_easing": 0.8
}
},
"finance_bank": {
"name": "银行/高股息",
"etfs": ["512800", "159887"],
"keywords": ["高股息", "中特估", "红利", "避险", "存款利率"],
"sensitivity": {
"interest_rate_down": -0.5,
"risk_off": 0.8,
"policy_soe_reform": 0.7
}
},
"new_energy_vehicle": {
"name": "新能源车与电池",
"etfs": ["515030", "159840"],
"keywords": ["电动车", "锂电池", "宁德时代", "汽车下乡", "碳中和"],
"sensitivity": {
"consumption_stimulus": 0.8,
"export_growth": 0.9,
"oil_price_up": 0.5
}
},
"consumption_staples": {
"name": "主要消费(白酒)",
"etfs": ["512690"],
"keywords": ["白酒", "内需", "CPI", "春节", "高端消费"],
"sensitivity": {
"cpi_rebound": 0.9,
"liquidity_easing": 0.6,
"foreign_inflow": 0.8
}
},
"healthcare": {
"name": "医药医疗",
"etfs": ["512010", "159898"],
"keywords": ["创新药", "医疗器械", "老龄化", "医保", "CXO"],
"sensitivity": {
"interest_rate_down": 0.8,
"policy_public_health": 0.7,
"demographic_trend": 0.5
}
},
"cross_border_tech": {
"name": "跨境科技(T+0)",
"etfs": ["513330", "513300", "513500"],
"keywords": ["港股", "美股", "纳斯达克", "中概股", "平台经济"],
"sensitivity": {
"fed_rate_cut": 0.9,
"currency_rmb_depreciation": 0.8,
"policy_platform_economy": 0.7
}
},
"agriculture": {
"name": "农业与粮食安全",
"etfs": ["159825"],
"keywords": ["粮食安全", "一号文件", "猪周期", "种业", "食品通胀"],
"sensitivity": {
"cpi_rebound": 0.8,
"policy_food_security": 1.0,
"geopolitics_tension": 0.6
}
},
"resources_gold": {
"name": "资源与黄金",
"etfs": ["518880", "512400"],
"keywords": ["黄金", "有色", "通胀", "避险", "美元下跌"],
"sensitivity": {
"dollar_index_down": 0.9,
"geopolitics_tension": 1.0,
"inflation_expectations": 0.8
}
},
"defense_low_altitude": {
"name": "军工与低空",
"etfs": ["512660"],
"keywords": ["低空经济", "国防", "军费", "无人机", "地缘"],
"sensitivity": {
"policy_low_altitude": 1.0,
"geopolitics_tension": 0.9,
"govt_spending": 0.7
}
}
},
"macro_factors_definition": {
"liquidity_easing": "央行降准降息，或市场资金利率下行",
"interest_rate_down": "无风险利率下行（利好成长股）",
"cpi_rebound": "通胀回升，消费复苏",
"risk_on": "市场情绪高涨，追逐高风险高收益",
"risk_off": "市场恐慌，资金流向避险资产",
"policy_tech_self_reliance": "政策强调自主可控、国产替代",
"geopolitics_tension": "外部冲突、制裁或地缘紧张",
"currency_rmb_depreciation": "人民币贬值（利好出口及跨境资产）",
"fed_rate_cut": "美联储降息（利好港股/美股/黄金）"
}
}
core/config.py
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"""
配置管理模块
提供统一的配置加载和访问接口，支持大模型配置、系统配置等
"""
import json
import os
from pathlib import Path
from typing import Dict, Any, Optional
class Config:
"""配置管理类，单例模式"""
_instance = None
_config_data: Dict[str, Any] = {}
def __new__(cls):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self):
if not self._config_data:
self.load_config()
def load_config(self, config_path: Optional[str] = None):
"""
加载配置文件
Args:
config_path: 配置文件路径，默认为项目根目录下的 config.json
"""
if config_path is None:
# 获取项目根目录（core 目录的上级目录）
project_root = Path(__file__).parent.parent
config_path = project_root / "config.json"
if not os.path.exists(config_path):
raise FileNotFoundError(f"配置文件不存在: {config_path}")
with open(config_path, 'r', encoding='utf-8') as f:
self._config_data = json.load(f)
def get(self, key: str, default: Any = None) -> Any:
"""
获取配置项（支持点号分隔的多级key）
Args:
key: 配置项键，如 "llm.model_name" 或 "llm"
default: 默认值
Returns:
配置值
"""
keys = key.split('.')
value = self._config_data
for k in keys:
if isinstance(value, dict):
value = value.get(k)
if value is None:
return default
else:
return default
return value
@property
def llm_config(self) -> Dict[str, Any]:
"""获取大模型配置"""
return self.get('llm', {})
@property
def model_name(self) -> str:
"""获取模型名称"""
return self.get('llm.model_name', 'gpt-4')
@property
def api_base(self) -> str:
"""获取API地址"""
return self.get('llm.api_base', 'https://api.openai.com/v1')
@property
def api_key(self) -> str:
"""获取API Key"""
return self.get('llm.api_key', '')
@property
def temperature(self) -> float:
"""获取温度参数"""
return self.get('llm.temperature', 0.7)
@property
def max_tokens(self) -> int:
"""获取最大token数"""
return self.get('llm.max_tokens', 2000)
@property
def timeout(self) -> int:
"""获取超时时间"""
return self.get('llm.timeout', 60)
def update(self, key: str, value: Any):
"""
更新配置项
Args:
key: 配置项键，支持点号分隔的多级key
value: 新值
"""
keys = key.split('.')
config = self._config_data
for k in keys[:-1]:
if k not in config:
config[k] = {}
config = config[k]
config[keys[-1]] = value
def save_config(self, config_path: Optional[str] = None):
"""
保存配置到文件
Args:
config_path: 配置文件路径，默认为项目根目录下的 config.json
"""
if config_path is None:
project_root = Path(__file__).parent.parent
config_path = project_root / "config.json"
with open(config_path, 'w', encoding='utf-8') as f:
json.dump(self._config_data, f, ensure_ascii=False, indent=2)
# 全局配置实例
config = Config()
def get_llm_client():
"""
获取配置好的LLM客户端（OpenAI SDK）
Returns:
OpenAI客户端实例
"""
try:
from openai import OpenAI
client = OpenAI(
api_key=config.api_key,
base_url=config.api_base,
timeout=config.timeout
)
return client
except ImportError:
print("警告: openai 库未安装，无法创建LLM客户端")
return None
except Exception as e:
print(f"创建LLM客户端失败: {e}")
return None
def llm_call(messages: list, **kwargs) -> Optional[str]:
"""
统一的LLM调用接口
Args:
messages: 消息列表，格式为 [{"role": "user", "content": "..."}]
**kwargs: 其他参数（会覆盖配置文件中的默认值）
Returns:
LLM返回的文本内容，失败返回None
"""
client = get_llm_client()
if client is None:
return None
try:
# 合并配置和传入的参数
params = {
'model': config.model_name,
'temperature': config.temperature,
'max_tokens': config.max_tokens,
}
params.update(kwargs)
response = client.chat.completions.create(
messages=messages,
**params
)
return response.choices[0].message.content
except Exception as e:
print(f"LLM调用失败: {e}")
return None
if __name__ == "__main__":
# 测试配置加载
print("=== 配置管理模块测试 ===")
print(f"模型名称: {config.model_name}")
print(f"API地址: {config.api_base}")
print(f"API Key: {config.api_key[:10]}..." if config.api_key else "API Key: 未配置")
print(f"温度: {config.temperature}")
print(f"最大tokens: {config.max_tokens}")
print("\n完整LLM配置:")
print(json.dumps(config.llm_config, indent=2, ensure_ascii=False))
# 测试配置更新
print("\n测试配置更新...")
config.update('llm.temperature', 0.5)
print(f"更新后的温度: {config.temperature}")
print()
# 测试获取不存在的配置
print(f"\n获取不存在的配置: {config.get('nonexistent.key', 'default_value')}")
# 测试调用
respodse = llm_call([{"role": "user", "content": "你好，用100字介绍一下你自己。"}])
print(f"\nLLM调用结果:\n{respodse}")
core/macro_matrix.json
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{
"_comment": "宏观逻辑矩阵 - 定义宏观因子对各 ETF 板块的基础理论影响值",
"_version": "1.0.0",
"_last_updated": "2025-12-30",
"_scoring_rules": {
"range": "影响值范围: -10 (极度负面) 到 +10 (极度正面)",
"neutral": "0 表示无明显影响",
"interpretation": "数值越大表示正面影响越强，数值越小表示负面影响越强"
},
"macro_factors": {
"rate_cut": {
"description": "降准降息 - 货币宽松政策",
"impact": {
"tech_etf": 8,
"ai_etf": 8,
"semiconductor_etf": 7,
"new_energy_vehicle_etf": 7,
"solar_etf": 6,
"pharma_etf": 5,
"consumer_etf": 7,
"bank_etf": -5,
"insurance_etf": -4,
"broker_etf": 6,
"realestate_etf": 9,
"military_etf": 4,
"metal_etf": 5,
"coal_etf": 2
}
},
"rate_hike": {
"description": "加息收紧 - 货币紧缩政策",
"impact": {
"tech_etf": -7,
"ai_etf": -7,
"semiconductor_etf": -6,
"new_energy_vehicle_etf": -6,
"solar_etf": -5,
"pharma_etf": -3,
"consumer_etf": -6,
"bank_etf": 5,
"insurance_etf": 4,
"broker_etf": -4,
"realestate_etf": -9,
"military_etf": -2,
"metal_etf": -4,
"coal_etf": -1
}
},
"currency_depreciation": {
"description": "人民币贬值 - 有利出口型企业",
"impact": {
"tech_etf": 3,
"ai_etf": 1,
"semiconductor_etf": 2,
"new_energy_vehicle_etf": 5,
"solar_etf": 6,
"pharma_etf": 2,
"consumer_etf": -3,
"bank_etf": -2,
"insurance_etf": -1,
"broker_etf": -1,
"realestate_etf": -4,
"military_etf": 3,
"metal_etf": 4,
"coal_etf": 1
}
},
"currency_appreciation": {
"description": "人民币升值 - 有利进口型企业",
"impact": {
"tech_etf": -2,
"ai_etf": -1,
"semiconductor_etf": -1,
"new_energy_vehicle_etf": -4,
"solar_etf": -5,
"pharma_etf": -1,
"consumer_etf": 4,
"bank_etf": 3,
"insurance_etf": 2,
"broker_etf": 2,
"realestate_etf": 5,
"military_etf": -2,
"metal_etf": -3,
"coal_etf": -1
}
},
"fiscal_stimulus": {
"description": "财政刺激政策 - 基建、消费刺激",
"impact": {
"tech_etf": 6,
"ai_etf": 7,
"semiconductor_etf": 6,
"new_energy_vehicle_etf": 8,
"solar_etf": 7,
"pharma_etf": 4,
"consumer_etf": 9,
"bank_etf": 5,
"insurance_etf": 4,
"broker_etf": 7,
"realestate_etf": 8,
"military_etf": 6,
"metal_etf": 8,
"coal_etf": 6
}
},
"regulation_tightening": {
"description": "监管收紧 - 行业整顿",
"impact": {
"tech_etf": -6,
"ai_etf": -4,
"semiconductor_etf": -3,
"new_energy_vehicle_etf": -4,
"solar_etf": -3,
"pharma_etf": -7,
"consumer_etf": -5,
"bank_etf": -2,
"insurance_etf": -2,
"broker_etf": -5,
"realestate_etf": -8,
"military_etf": -1,
"metal_etf": -2,
"coal_etf": -3
}
},
"export_strong": {
"description": "出口数据强劲",
"impact": {
"tech_etf": 5,
"ai_etf": 3,
"semiconductor_etf": 4,
"new_energy_vehicle_etf": 7,
"solar_etf": 8,
"pharma_etf": 3,
"consumer_etf": 2,
"bank_etf": 3,
"insurance_etf": 2,
"broker_etf": 4,
"realestate_etf": 1,
"military_etf": 4,
"metal_etf": 6,
"coal_etf": 3
}
},
"pmi_expansion": {
"description": "PMI 扩张 (>50) - 制造业景气",
"impact": {
"tech_etf": 7,
"ai_etf": 6,
"semiconductor_etf": 7,
"new_energy_vehicle_etf": 8,
"solar_etf": 7,
"pharma_etf": 4,
"consumer_etf": 6,
"bank_etf": 5,
"insurance_etf": 4,
"broker_etf": 6,
"realestate_etf": 4,
"military_etf": 6,
"metal_etf": 8,
"coal_etf": 7
}
},
"oil_price_surge": {
"description": "原油价格大涨",
"impact": {
"tech_etf": -3,
"ai_etf": -2,
"semiconductor_etf": -2,
"new_energy_vehicle_etf": 5,
"solar_etf": 6,
"pharma_etf": -1,
"consumer_etf": -4,
"bank_etf": -1,
"insurance_etf": -1,
"broker_etf": 0,
"realestate_etf": -3,
"military_etf": -1,
"metal_etf": 3,
"coal_etf": 8
}
},
"us_tech_weakness": {
"description": "美国科技股大跌",
"impact": {
"tech_etf": -6,
"ai_etf": -7,
"semiconductor_etf": -8,
"new_energy_vehicle_etf": -4,
"solar_etf": -3,
"pharma_etf": -2,
"consumer_etf": -3,
"bank_etf": 2,
"insurance_etf": 1,
"broker_etf": -2,
"realestate_etf": 1,
"military_etf": 0,
"metal_etf": 0,
"coal_etf": 1
}
}
},
"etf_mapping": {
"tech_etf": ["515980", "159813"],
"ai_etf": ["515980", "159813"],
"semiconductor_etf": ["512480", "159995"],
"new_energy_vehicle_etf": ["515030", "159806"],
"solar_etf": ["515790", "159857"],
"pharma_etf": ["512010", "159938"],
"consumer_etf": ["159928", "510630"],
"bank_etf": ["510230"],
"insurance_etf": ["512880"],
"broker_etf": ["512000"],
"realestate_etf": ["512200", "159707"],
"military_etf": ["512660", "512810"],
"metal_etf": ["512400", "159881"],
"coal_etf": ["515220", "159678"]
},
"composite_scenarios": {
"bull_market_start": {
"description": "牛市启动期 - 货币宽松 + 政策利好",
"factors": ["rate_cut", "fiscal_stimulus"],
"multiplier": 1.2
},
"bear_market_bottom": {
"description": "熊市底部 - 政策转向前",
"factors": ["rate_hike", "regulation_tightening"],
"multiplier": 0.8
},
"economic_recovery": {
"description": "经济复苏期",
"factors": ["pmi_expansion", "export_strong", "fiscal_stimulus"],
"multiplier": 1.15
},
"risk_off": {
"description": "风险规避模式",
"factors": ["us_tech_weakness", "currency_depreciation"],
"multiplier": 0.9
}
}
}
core/world_book.py
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"""
MND-IA Core: World Book (世界书)
====================================
这是系统的数据中心和逻辑基石，存储宏观状态、活跃叙事对象和历史经验。
所有 Agent 必须通过此模块访问和更新系统状态。
"""
import json
import os
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from pathlib import Path
from copy import deepcopy
class Narrative:
"""单个叙事对象 (Narrative Object)"""
def __init__(
self,
id: str,
topic: str,
related_etfs: List[str],
lifecycle_stage: str = "incubation",
base_score: float = 50.0,
decay_factor: float = 0.95,
current_weight: float = None
):
self.id = id
self.topic = topic
self.related_etfs = related_etfs
self.lifecycle_stage = lifecycle_stage # incubation | fermentation | realization | decay
self.base_score = base_score
self.decay_factor = decay_factor
self.current_weight = current_weight if current_weight is not None else base_score
self.last_updated = datetime.now().strftime("%Y-%m-%d")
def decay(self) -> None:
"""应用时间衰减"""
self.current_weight *= self.decay_factor
self.last_updated = datetime.now().strftime("%Y-%m-%d")
def boost(self, new_score: float) -> None:
"""新闻强化叙事权重"""
# 使用加权平均，新信息占40%，历史占60%
self.current_weight = self.current_weight * 0.6 + new_score * 0.4
self.last_updated = datetime.now().strftime("%Y-%m-%d")
def update_stage(self, new_stage: str) -> None:
"""更新生命周期阶段"""
valid_stages = ["incubation", "fermentation", "realization", "decay"]
if new_stage in valid_stages:
self.lifecycle_stage = new_stage
self.last_updated = datetime.now().strftime("%Y-%m-%d")
def to_dict(self) -> Dict[str, Any]:
"""转换为字典格式"""
return {
"id": self.id,
"topic": self.topic,
"related_etfs": self.related_etfs,
"lifecycle_stage": self.lifecycle_stage,
"base_score": self.base_score,
"decay_factor": self.decay_factor,
"current_weight": round(self.current_weight, 2),
"last_updated": self.last_updated
}
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'Narrative':
"""从字典创建对象"""
return cls(
id=data["id"],
topic=data["topic"],
related_etfs=data["related_etfs"],
lifecycle_stage=data.get("lifecycle_stage", "incubation"),
base_score=data.get("base_score", 50.0),
decay_factor=data.get("decay_factor", 0.95),
current_weight=data.get("current_weight")
)
class MacroCycle:
"""宏观周期状态"""
def __init__(
self,
status: str = "neutral", # upward | downward | neutral
liquidity: str = "neutral", # loose | neutral | tight
policy_wind: str = "wait_and_see" # stimulus | regulation | wait_and_see
):
self.status = status
self.liquidity = liquidity
self.policy_wind = policy_wind
def to_dict(self) -> Dict[str, str]:
return {
"status": self.status,
"liquidity": self.liquidity,
"policy_wind": self.policy_wind
}
@classmethod
def from_dict(cls, data: Dict[str, str]) -> 'MacroCycle':
return cls(
status=data.get("status", "neutral"),
liquidity=data.get("liquidity", "neutral"),
policy_wind=data.get("policy_wind", "wait_and_see")
)
class WorldBook:
"""
MCP World Book - 系统核心数据中心
======================================
动态更新的状态管理器，存储：
1. 宏观周期状态
2. 活跃叙事对象列表
3. 宏观因子影响矩阵快照
"""
def __init__(self, data_dir: str = "data"):
self.data_dir = Path(data_dir)
self.data_dir.mkdir(exist_ok=True)
self.world_book_path = self.data_dir / "world_book.json"
# 初始化数据结构
self.timestamp = datetime.now().isoformat()
self.macro_cycle = MacroCycle()
self.active_narratives: Dict[str, Narrative] = {}
self.static_matrix_snapshot: Dict[str, Dict[str, float]] = {}
# 新增: 宏观因子向量 (用于向量点积计算)
self.macro_factor_vector: Dict[str, float] = {}
# 尝试加载已有数据
self.load()
def load(self) -> bool:
"""从磁盘加载 World Book"""
if not self.world_book_path.exists():
print(f"[WorldBook] 未找到已有数据，初始化新的 World Book")
self._load_static_matrix()
return False
try:
with open(self.world_book_path, 'r', encoding='utf-8') as f:
data = json.load(f)
self.timestamp = data.get("timestamp", datetime.now().isoformat())
self.macro_cycle = MacroCycle.from_dict(data.get("macro_cycle", {}))
# 加载叙事对象
self.active_narratives = {}
for narrative_data in data.get("active_narratives", []):
narrative = Narrative.from_dict(narrative_data)
self.active_narratives[narrative.id] = narrative
# 加载矩阵快照
self.static_matrix_snapshot = data.get("static_matrix_snapshot", {})
# 加载宏观因子向量
self.macro_factor_vector = data.get("macro_factor_vector", {})
print(f"[WorldBook] 成功加载数据，包含 {len(self.active_narratives)} 个活跃叙事")
return True
except Exception as e:
print(f"[WorldBook] 加载数据失败: {e}")
self._load_static_matrix()
return False
def save(self) -> bool:
"""保存 World Book 到磁盘"""
try:
self.timestamp = datetime.now().isoformat()
data = {
"timestamp": self.timestamp,
"macro_cycle": self.macro_cycle.to_dict(),
"active_narratives": [
narrative.to_dict()
for narrative in self.active_narratives.values()
],
"static_matrix_snapshot": self.static_matrix_snapshot,
"macro_factor_vector": self.macro_factor_vector
}
with open(self.world_book_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
print(f"[WorldBook] 成功保存数据到 {self.world_book_path}")
return True
except Exception as e:
print(f"[WorldBook] 保存数据失败: {e}")
return False
def _load_static_matrix(self) -> None:
"""加载静态宏观矩阵"""
matrix_path = Path("core") / "macro_matrix.json"
if matrix_path.exists():
try:
with open(matrix_path, 'r', encoding='utf-8') as f:
self.static_matrix_snapshot = json.load(f)
print(f"[WorldBook] 加载宏观矩阵成功")
except Exception as e:
print(f"[WorldBook] 加载宏观矩阵失败: {e}")
self.static_matrix_snapshot = {}
def add_narrative(self, narrative: Narrative) -> None:
"""添加新叙事"""
self.active_narratives[narrative.id] = narrative
print(f"[WorldBook] 添加新叙事: {narrative.topic} (ID: {narrative.id})")
def update_narrative(self, narrative_id: str, **kwargs) -> bool:
"""更新叙事属性"""
if narrative_id not in self.active_narratives:
print(f"[WorldBook] 叙事不存在: {narrative_id}")
return False
narrative = self.active_narratives[narrative_id]
if 'new_score' in kwargs:
narrative.boost(kwargs['new_score'])
if 'stage' in kwargs:
narrative.update_stage(kwargs['stage'])
return True
def decay_all_narratives(self) -> None:
"""对所有叙事应用时间衰减"""
for narrative in self.active_narratives.values():
narrative.decay()
print(f"[WorldBook] 对 {len(self.active_narratives)} 个叙事应用了时间衰减")
def remove_weak_narratives(self, threshold: float = 10.0) -> List[str]:
"""移除权重过低的叙事"""
to_remove = [
nid for nid, narrative in self.active_narratives.items()
if narrative.current_weight < threshold
]
for nid in to_remove:
topic = self.active_narratives[nid].topic
del self.active_narratives[nid]
print(f"[WorldBook] 移除衰退叙事: {topic} (ID: {nid})")
return to_remove
def get_narrative_by_topic(self, topic: str) -> Optional[Narrative]:
"""根据主题获取叙事"""
for narrative in self.active_narratives.values():
if narrative.topic == topic:
return narrative
return None
def get_narratives_by_etf(self, etf_code: str) -> List[Narrative]:
"""获取与特定 ETF 相关的所有叙事"""
return [
narrative for narrative in self.active_narratives.values()
if etf_code in narrative.related_etfs
]
def update_macro_cycle(self, **kwargs) -> None:
"""更新宏观周期状态"""
if 'status' in kwargs:
self.macro_cycle.status = kwargs['status']
if 'liquidity' in kwargs:
self.macro_cycle.liquidity = kwargs['liquidity']
if 'policy_wind' in kwargs:
self.macro_cycle.policy_wind = kwargs['policy_wind']
print(f"[WorldBook] 宏观周期更新: {self.macro_cycle.to_dict()}")
def update_macro_factor_vector(self, factor_updates: Dict[str, float]) -> None:
"""
更新宏观因子向量
Args:
factor_updates: 宏观因子及其强度，例如:
{
"interest_rate_down": 1.0, # 强烈降息预期
"geopolitics_tension": 0.5, # 中等地缘紧张
"policy_digital_economy": 1.0 # 数字经济政策头条
}
"""
self.macro_factor_vector.update(factor_updates)
print(f"[WorldBook] 宏观因子向量更新: {self.macro_factor_vector}")
def get_macro_factor_value(self, factor_name: str) -> float:
"""获取特定宏观因子的当前值"""
return self.macro_factor_vector.get(factor_name, 0.0)
def clear_macro_factor_vector(self) -> None:
"""清空宏观因子向量（通常在每日开盘前重置）"""
self.macro_factor_vector = {}
print("[WorldBook] 宏观因子向量已清空")
def get_top_narratives(self, top_n: int = 5) -> List[Narrative]:
"""获取权重最高的 N 个叙事"""
sorted_narratives = sorted(
self.active_narratives.values(),
key=lambda n: n.current_weight,
reverse=True
)
return sorted_narratives[:top_n]
def export_snapshot(self) -> Dict[str, Any]:
"""导出完整快照（供其他 Agent 读取）"""
return {
"timestamp": self.timestamp,
"macro_cycle": self.macro_cycle.to_dict(),
"macro_factor_vector": self.macro_factor_vector,
"active_narratives": [n.to_dict() for n in self.active_narratives.values()],
"static_matrix_snapshot": self.static_matrix_snapshot,
"summary": {
"total_narratives": len(self.active_narratives),
"top_3_topics": [n.topic for n in self.get_top_narratives(3)]
}
}
def __repr__(self) -> str:
return (
f"<WorldBook: {len(self.active_narratives)} narratives, "
f"macro={self.macro_cycle.status}, "
f"updated={self.timestamp}>"
)
# ==================== 工具函数 ====================
def create_narrative_id(topic: str, date: Optional[str] = None) -> str:
"""生成叙事 ID"""
if date is None:
date = datetime.now().strftime("%Y%m")
# 简化主题名称作为 ID 的一部分
topic_slug = topic.replace(" ", "_").replace("，", "_").replace("、", "_")
return f"narrative_{topic_slug}_{date}"
if __name__ == "__main__":
# 测试代码
print("=" * 50)
print("World Book 核心模块测试")
print("=" * 50)
# 创建 World Book 实例
wb = WorldBook(data_dir="data")
# 添加测试叙事
narrative1 = Narrative(
id=create_narrative_id("低空经济"),
topic="低空经济",
related_etfs=["512980", "159969"],
lifecycle_stage="fermentation",
base_score=85.0,
decay_factor=0.95
)
narrative2 = Narrative(
id=create_narrative_id("AI算力"),
topic="AI算力",
related_etfs=["515980", "159813"],
lifecycle_stage="realization",
base_score=92.0,
decay_factor=0.93
)
wb.add_narrative(narrative1)
wb.add_narrative(narrative2)
# 更新宏观周期
wb.update_macro_cycle(
status="upward",
liquidity="loose",
policy_wind="stimulus"
)
# 保存数据
wb.save()
# 测试导出快照
snapshot = wb.export_snapshot()
print("\n当前状态快照:")
print(json.dumps(snapshot, ensure_ascii=False, indent=2))
# 测试衰减
print("\n应用时间衰减...")
wb.decay_all_narratives()
# 测试获取 Top 叙事
top = wb.get_top_narratives(2)
print(f"\nTop 2 叙事:")
for n in top:
print(f" - {n.topic}: {n.current_weight:.2f}")
print("\n✅ World Book 核心模块测试完成")
docs/crawler_guide.md
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# Skill F: 新闻联播爬虫使用指南
## 功能说明
Skill F (Crawler) 使用 **cloudscraper** + **BeautifulSoup** 自动抓取新闻联播官方网站的文字稿，并转换为符合 Skill A (Analyst) 要求的格式。
## 技术特点
- **绕过防护**: 使用 cloudscraper 自动处理 Cloudflare 等反爬机制
- **稳定可靠**: 同步爬取，无需处理异步复杂性
- **精准解析**: 基于实际网站 DOM 结构（.content-section）
## 数据源
- **网站**: https://cn.govopendata.com/xinwenlianbo/
- **更新频率**: 每日 19:00 后更新
- **数据格式**: 新闻标题 + 正文内容
## 安装依赖
```bash
pip install cloudscraper beautifulsoup4
```
## 基础使用
### 1. 命令行测试
```bash
# 测试爬虫功能
python skills/crawler.py
```
### 2. Python 代码调用
```python
from skills.crawler import XinwenLianboCrawler
# 创建爬虫实例
crawler = XinwenLianboCrawler()
# 爬取今日新闻
news_list = crawler.crawl_sync()
# 爬取指定日期
news_list = crawler.crawl_sync(date="20251229")
# 输出格式：
# [
# {"title": "新闻标题1", "text": "新闻正文1..."},
# {"title": "新闻标题2", "text": "新闻正文2..."},
# ...
# ]
```
### 3. 批量爬取
```python
from skills.crawler import XinwenLianboCrawler
crawler = XinwenLianboCrawler()
# 爬取一段时间范围的新闻
results = crawler.crawl_date_range(
start_date="20251220",
end_date="20251229"
)
# results 是一个字典: {date: news_list}
for date, news_list in results.items():
print(f"{date}: {len(news_list)} 条新闻")
```
## 与 Skill A 集成
```python
from skills.crawler import XinwenLianboCrawler
from skills.analyst import NewsAnalyzer
# 1. 爬取新闻
crawler = XinwenLianboCrawler()
news_list = crawler.crawl_sync()
# 2. 分析新闻
analyzer = NewsAnalyzer()
analysis_results = analyzer.batch_analyze(news_list)
# 3. 生成叙事 JSON
narrative_json = analyzer.generate_narrative_json(analysis_results)
print(f"识别到 {len(narrative_json['narratives'])} 个投资叙事")
```
## 在主流程中使用
```bash
# 方式1: 使用爬虫（默认）
python main.py --mode daily
# 方式2: 不使用爬虫（使用测试数据）
python main.py --mode daily --no-crawler
```
## 数据缓存
爬虫会自动缓存抓取的新闻到 `data/news_cache/` 目录：
```
data/news_cache/
├── xinwenlianbo_20251229.json
├── xinwenlianbo_20251230.json
└── ...
```
当网络失败时，系统会自动尝试从缓存读取。
## 批量爬取历史数据
```python
from skills.crawler import XinwenLianboCrawler
crawler = XinwenLianboCrawler()
# 爬取一段时间范围的新闻
results = crawler.crawl_date_range(
start_date="20251220",
end_date="20251229"
)
# results 是一个字典: {date: news_list}
for date, news_list in results.items():
print(f"{date}: {len(news_list)} 条新闻")
```
## 自定义解析逻辑
如果网站结构发生变化，可以修改 `_parse_content()` 方法：
```python
def _parse_content(self, markdown: str, html: str) -> List[Dict[str, str]]:
"""
自定义解析逻辑
Args:
markdown: Crawl4AI 提取的 Markdown
html: 原始 HTML
Returns:
[{"title": "", "text": ""}]
"""
# 在这里添加你的解析逻辑
pass
```
## 故障排查
### 问题1: 爬虫返回空列表
**可能原因**:
- 网站结构变化
- 当天新闻尚未更新（新闻联播通常 19:30 播出，网站 20:00 后更新）
- 网络连接问题
- 被 Cloudflare 拦截（虽然使用了 cloudscraper，但仍可能发生）
**解决方法**:
```bash
# 检查爬虫输出
python skills/crawler.py
# 查看缓存文件
cat data/news_cache/xinwenlianbo_*.json
# 检查网站是否可访问
curl https://cn.govopendata.com/xinwenlianbo/20251229/
```
### 问题2: cloudscraper 安装失败
**解决方法**:
```bash
# 升级 pip
pip install --upgrade pip
# 单独安装 cloudscraper
pip install cloudscraper --upgrade
# 如果仍然失败，安装依赖
pip install requests[security] pyparsing
```
### 问题3: 解析结果不准确
**解决方法**:
编辑 `skills/crawler.py`，检查 `_parse_content()` 方法中的 CSS 选择器是否与网站实际结构匹配：
```python
# 当前使用的选择器
sections = soup.select('.content-section')
title_tag = section.select_one('.content-heading')
content_tag = section.select_one('.content-body')
```
可以手动访问网站，使用浏览器开发者工具（F12）查看实际的 DOM 结构。
## 性能优化
### 1. 使用缓存
爬虫会自动缓存结果，避免重复请求：
```python
# 首次爬取（从网站）
news_list = crawler.crawl_sync(date="20251229")
# 再次爬取（从缓存，如果网络失败）
news_list = crawler.crawl_sync(date="20251229") # 自动读取缓存
```
### 2. 批量爬取时添加延迟
```python
crawler = XinwenLianboCrawler()
# crawl_date_range 已内置 2 秒延迟
results = crawler.crawl_date_range("20251220", "20251229")
```
## 定时任务配置
### Linux/Mac (crontab)
```bash
# 编辑 crontab
crontab -e
# 添加定时任务（每天 20:30 执行）
30 20 * * * cd /path/to/MND-IA && python skills/crawler.py >> logs/crawler.log 2>&1
```
### Windows (任务计划程序)
1. 打开"任务计划程序"
2. 创建基本任务
3. 触发器：每天 20:30
4. 操作：启动程序 `python.exe`
5. 参数：`E:\02projects\MND-IA\skills\crawler.py`
## API 参考
### XinwenLianboCrawler
#### `__init__(base_url)`
- 初始化爬虫
- `base_url`: 新闻联播网站基础 URL
- 自动创建 cloudscraper 会话
#### `crawl_sync(date=None)`
- 同步爬取接口（推荐使用）
- `date`: 日期字符串 (YYYYMMDD)，默认今天
- 返回: `List[Dict[str, str]]`
#### `crawl_xinwenlianbo(date=None)`
- 爬取核心方法
- `date`: 日期字符串
- 返回: `List[Dict[str, str]]`
#### `crawl_date_range(start_date, end_date)`
- 批量爬取日期范围
- `start_date`: 开始日期 (YYYYMMDD)
- `end_date`: 结束日期 (YYYYMMDD)
- 返回: `Dict[str, List[Dict]]`
- 自动添加 2 秒延迟避免请求过快
## 扩展建议
### 1. 添加更多新闻源
可以参考 Skill F 的实现，添加其他新闻源爬虫：
- 财联社快讯
- 人民日报
- 发改委官网
- 各部委新闻发布
### 2. 使用 LLM 增强提取
```python
# 配置 LLM 客户端
from openai import OpenAI
client = OpenAI(api_key="your-api-key")
# 使用 LLM 辅助提取（需要实现相关代码）
news_list = await crawler.crawl_xinwenlianbo(use_llm=True)
```
### 3. 集成到实时监控
监听新闻联播更新，实时触发分析流程。
## 许可证
MIT License
main.py
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@@ -0,0 +1,417 @@
"""
MND-IA Main Orchestrator
=========================
主调度程序 - 协调所有 Agent Skills 完成每日投资决策流程
执行流程：
1. Ingest (数据摄取): Analyst 读取新闻, Quant 读取行情
2. Cognition (认知更新): Strategist 更新 WorldBook
3. Decision (决策生成): PM 计算 Trust Index 并生成订单
4. Execution (执行输出): 输出交易指令
5. Audit (复盘审计): Auditor 评估昨日预测准确性
"""
import sys
from pathlib import Path
from datetime import datetime
import json
import argparse
# 导入核心模块
from core.world_book import WorldBook
# 导入 Agent Skills
from skills.analyst import NewsAnalyzer
from skills.quant import QuantAnalyzer, get_all_etf_codes_from_asset_map
from skills.strategist import MacroStrategist
from skills.pm import PortfolioManager, save_orders_to_file
from skills.auditor import PerformanceAuditor
from skills.crawler import XinwenLianboCrawler
class MNDAgent:
"""MND-IA 主智能体"""
def __init__(
self,
data_dir: str = "data",
total_capital: float = 1000000.0,
llm_client=None,
use_llm: bool = True
):
"""
Args:
data_dir: 数据目录
total_capital: 总资金（元）
llm_client: LLM 客户端（可选，用于 Analyst 增强）
use_llm: 是否使用 LLM 进行智能分析（默认 True）
"""
self.data_dir = Path(data_dir)
self.data_dir.mkdir(exist_ok=True)
self.use_llm = use_llm
# 初始化核心
print("=" * 60)
print("🚀 MND-IA 系统启动")
print(f" 模式: {'LLM 智能分析' if use_llm else '规则引擎（降级模式）'}")
print("=" * 60)
self.world_book = WorldBook(data_dir=data_dir)
# 初始化 Agent Skills（传递 use_llm 参数）
self.analyst = NewsAnalyzer(llm_client=llm_client, use_llm=use_llm)
self.quant = QuantAnalyzer()
self.strategist = MacroStrategist(self.world_book, use_llm=use_llm)
self.pm = PortfolioManager(self.world_book, total_capital=total_capital, use_llm=use_llm)
self.auditor = PerformanceAuditor(data_dir=data_dir)
self.crawler = XinwenLianboCrawler()
print("✅ 所有 Agent Skills 初始化完成\n")
def daily_batch_workflow(
self,
news_list: list = None,
etf_codes: list = None,
use_crawler: bool = True
) -> dict:
"""
每日批处理流程（08:30 AM 执行）
Args:
news_list: 新闻列表（如为 None 则使用爬虫或测试数据）
etf_codes: ETF 代码列表（如为 None 则使用全部）
use_crawler: 是否使用爬虫抓取新闻联播（默认 True）
Returns:
执行结果汇总
"""
print(f"📅 开始执行每日批处理流程")
print(f"⏰ 执行时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
print("=" * 60)
# ========== Phase 1: 数据摄取 (Ingest) ==========
print("\n【Phase 1: 数据摄取】")
print("-" * 60)
# 1.1 获取新闻数据
if news_list is None:
if use_crawler:
print("🕷️ 使用爬虫抓取新闻联播...")
news_list = self.crawler.crawl_sync()
if not news_list:
print("⚠️ 爬虫未获取到数据，使用测试数据")
news_list = self._get_test_news()
else:
print("⚠️ 未提供新闻数据，使用测试数据")
news_list = self._get_test_news()
print(f"📰 新闻分析: {len(news_list)} 条")
news_analysis = self.analyst.batch_analyze(news_list)
narrative_json = self.analyst.generate_narrative_json(news_analysis)
# 1.2 获取行情数据
if etf_codes is None:
etf_codes = get_all_etf_codes_from_asset_map()
print(f"📊 行情获取: {len(etf_codes)} 个 ETF")
market_data = self.quant.batch_analyze(etf_codes)
market_report = self.quant.generate_market_report(market_data)
# ========== Phase 2: 认知更新 (Cognition) ==========
print("\n【Phase 2: 认知更新】")
print("-" * 60)
# 2.1 处理叙事 JSON
self.strategist.process_narrative_json(narrative_json)
# 2.2 检测宏观周期变化
cycle_change = self.strategist.detect_macro_cycle_change(
narrative_json,
market_report
)
if cycle_change:
self.strategist.apply_macro_cycle_change(cycle_change)
# 2.3 每日维护
maintenance_report = self.strategist.daily_maintenance()
# 2.4 生成 WorldBook 快照
wb_snapshot = self.strategist.generate_world_book_snapshot()
# ========== Phase 3: 决策生成 (Decision) ==========
print("\n【Phase 3: 决策生成】")
print("-" * 60)
# 3.1 计算 Trust Index
trust_results = self.pm.batch_calculate_trust_index(market_data)
# 3.2 生成交易订单
raw_orders = self.pm.generate_trade_orders(trust_results, market_data)
# 3.3 风控检查
final_orders = self.pm.apply_risk_control(raw_orders)
# 3.4 生成投资组合报告
portfolio_report = self.pm.generate_portfolio_report(
trust_results,
final_orders
)
# ========== Phase 4: 执行输出 (Execution) ==========
print("\n【Phase 4: 执行输出】")
print("-" * 60)
if final_orders:
# 保存订单到文件
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
save_orders_to_file(final_orders, f"orders_{timestamp}.json")
print(f"📋 生成 {len(final_orders)} 条交易指令:")
for order in final_orders[:5]: # 显示前5条
print(f" {order['action'].upper()} {order['code']}: "
f"{order['shares']}股 @ ¥{order['price']}")
else:
print("⚠️ 无符合条件的交易机会")
# 保存 WorldBook
self.world_book.save()
# ========== Phase 5: 结果汇总 ==========
execution_result = {
"timestamp": datetime.now().isoformat(),
"workflow": "daily_batch",
"phases": {
"ingest": {
"news_count": len(news_list),
"etf_count": len(etf_codes),
"narratives_identified": len(narrative_json['narratives'])
},
"cognition": {
"active_narratives": len(self.world_book.active_narratives),
"macro_cycle": self.world_book.macro_cycle.to_dict(),
"cycle_changed": cycle_change is not None
},
"decision": {
"trust_results": len(trust_results),
"orders_generated": len(final_orders),
"capital_utilization": portfolio_report['risk_control']['capital_utilization']
}
},
"top_opportunities": portfolio_report['top_opportunities'][:3],
"orders": final_orders
}
# 保存执行记录
self._save_execution_log(execution_result)
print("\n" + "=" * 60)
print("✅ 每日批处理流程完成")
print("=" * 60)
return execution_result
def post_market_audit(
self,
yesterday_predictions: list = None
) -> dict:
"""
盘后复盘流程（16:00 PM 执行）
Args:
yesterday_predictions: 昨日的预测记录
Returns:
审计报告
"""
print("\n📊 开始盘后复盘审计")
print("=" * 60)
if yesterday_predictions is None:
print("⚠️ 无昨日预测数据，跳过审计")
return {"status": "skipped", "reason": "no_predictions"}
# 获取实际表现数据（这里需要从行情数据计算）
actual_performances = self._fetch_actual_performances(yesterday_predictions)
# 执行审计
audit_summary = self.auditor.batch_audit(
yesterday_predictions,
actual_performances
)
# 生成报告
report = self.auditor.generate_correction_report(audit_summary)
# 保存报告
report_path = self.data_dir / f"audit_report_{datetime.now().strftime('%Y%m%d')}.md"
with open(report_path, 'w', encoding='utf-8') as f:
f.write(report)
print(f"\n📄 审计报告已保存: {report_path}")
print(f"准确率: {audit_summary['performance']['accuracy']:.1f}%")
return audit_summary
def intraday_sentinel(
self,
breaking_news: dict
) -> dict:
"""
盘中哨兵流程（09:30 - 15:00）
当检测到 Level A 重大新闻时触发
Args:
breaking_news: 快讯内容
Returns:
应急响应结果
"""
print("\n🚨 盘中哨兵触发")
print("=" * 60)
# 分析快讯
analysis = self.analyst.analyze_news(
breaking_news.get('text', ''),
breaking_news.get('title', '')
)
# 如果不是 Level A，忽略
if analysis['level'] != 'A':
print("⚠️ 非 A 级新闻，不触发应急响应")
return {"triggered": False, "reason": "not_level_a"}
print(f"⚠️ 检测到 Level A 重大新闻: {analysis['title']}")
print(f" 涉及板块: {', '.join(analysis['sectors'])}")
# 立即更新 WorldBook
narrative_json = self.analyst.generate_narrative_json([analysis])
self.strategist.process_narrative_json(narrative_json)
# 检查宏观周期变化
cycle_change = self.strategist.detect_macro_cycle_change(narrative_json)
if cycle_change:
self.strategist.apply_macro_cycle_change(cycle_change)
# 重新评估持仓（如果有）
# 这里简化处理，实际应该重新计算 Trust Index
self.world_book.save()
return {
"triggered": True,
"news_level": "A",
"sectors_affected": analysis['sectors'],
"macro_cycle_changed": cycle_change is not None,
"timestamp": datetime.now().isoformat()
}
def _get_test_news(self) -> list:
"""获取测试新闻数据"""
return [
{
"title": "工信部发布低空经济指导意见 2027年规模达1万亿",
"text": "工业和信息化部发布《低空经济高质量发展指导意见》，推动 eVTOL 产业化。"
},
{
"title": "AI算力需求持续旺盛 多家公司业绩预增50%",
"text": "智算中心建设加速，算力租赁业务快速增长。"
}
]
def _fetch_actual_performances(
self,
predictions: list
) -> dict:
"""获取实际表现数据（简化版）"""
# 实际应该从 Quant 模块获取真实数据
# 这里返回模拟数据
performances = {}
for pred in predictions:
code = pred['code']
performances[code] = {
"code": code,
"price_change": 0, # 需要实际计算
"volume_change": 1.0,
"date": datetime.now().strftime("%Y-%m-%d")
}
return performances
def _save_execution_log(self, result: dict) -> None:
"""保存执行日志"""
log_path = self.data_dir / "execution_logs.json"
logs = []
if log_path.exists():
with open(log_path, 'r', encoding='utf-8') as f:
logs = json.load(f)
logs.append(result)
# 只保留最近30天
logs = logs[-30:]
with open(log_path, 'w', encoding='utf-8') as f:
json.dump(logs, f, ensure_ascii=False, indent=2)
def main():
"""命令行入口"""
parser = argparse.ArgumentParser(description="MND-IA 投资智能体")
parser.add_argument(
'--mode',
choices=['daily', 'audit', 'test'],
default='test',
help='运行模式: daily(每日批处理), audit(盘后审计), test(测试)'
)
parser.add_argument(
'--capital',
type=float,
default=1000000.0,
help='总资金（元）'
)
parser.add_argument(
'--no-crawler',
action='store_true',
help='不使用爬虫，使用测试数据'
)
parser.add_argument(
'--no-llm',
action='store_true',
help='不使用 LLM，使用规则引擎（降级模式）'
)
args = parser.parse_args()
# 创建 Agent 实例
agent = MNDAgent(
total_capital=args.capital,
use_llm=not args.no_llm
)
if args.mode == 'daily':
# 每日批处理
result = agent.daily_batch_workflow(use_crawler=not args.no_crawler)
print(f"\n执行结果:")
print(json.dumps(result, ensure_ascii=False, indent=2))
elif args.mode == 'audit':
# 盘后审计（需要提供昨日数据）
print("盘后审计模式需要提供昨日预测数据")
else:
# 测试模式
print("\n🧪 测试模式：执行完整流程演示\n")
result = agent.daily_batch_workflow(use_crawler=not args.no_crawler)
print("\n" + "=" * 60)
print("📈 Top 投资机会:")
print("=" * 60)
for opp in result['top_opportunities']:
print(f" {opp['code']}: Trust Index {opp['trust_index']:.1f} - {opp['verdict']}")
if __name__ == "__main__":
main()
readme.md
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# Macro-Narrative Driven ETF Investment Agent (MND-IA)
## 1. 项目概述 (Project Overview)
**MND-IA** 是一个基于 **“宏观叙事生命周期 + 资金流向交叉验证”** 的自动化 ETF 投资智能体系统。
### 核心哲学 (Core Philosophy)
1. **叙事即信号 (Narrative as Signal):** A 股是政策市，新闻联播及部委政策是市场趋势的先行指标。
2. **资金即真相 (Flow as Truth):** 只有资金实际流入的板块，叙事才具备可信度。
3. **信任指数 (Trust Index):** 决策基于叙事热度与资金强度的共振程度。
4. **生命周期管理 (Lifecycle):** 新闻不是孤立的脉冲，而是具有“潜伏-发酵-兑现-衰退”周期的持久对象。
---
## 2. 系统架构 (System Architecture)
本系统采用 **Multi-Agent "Skills" 架构**，由一个不可拆分的**共享内核 (Shared Kernel)** 和五个独立职能的 **Agent Skills** 组成。
### 2.1 共享内核 (Shared Kernel) - *不可拆分*
这是系统的数据中心和逻辑基石，所有 Agent 必须遵循此协议。
1. **MCP World Book (世界书):** 一个动态更新的 JSON 数据库，存储宏观状态、活跃叙事对象和历史经验。
2. **Asset Map (资产映射表):** 连接自然语言（如“低空经济”）与金融代码（如 `512XXX`）的静态字典。
3. **Macro Matrix (宏观逻辑矩阵):** 定义宏观因子（如利率、汇率）对各 ETF 板块的**基础理论影响值 ()**。
### 2.2 Agent Skills 群 (The Squad)
| Agent ID | 名称 | 职能 (Skill) | 输入 | 输出 |
| --- | --- | --- | --- | --- |
| **Skill_A** | **情报分析师** (Analyst) | NLP 提取、新闻分级、叙事打分 | 新闻文本 (News) | `Narrative_JSON` |
| **Skill_B** | **量化研究员** (Quant) | 行情分析、资金流计算、异常检测 | 行情数据 (Akshare) | `Market_Data_JSON` |
| **Skill_C** | **宏观策略师** (Strategist) | 维护世界书、判断周期、更新叙事权重 | `Narrative_JSON` + 历史 WorldBook | 更新后的 `World_Book` |
| **Skill_D** | **基金经理** (PM) | 计算信任指数、风控、生成订单 | `World_Book` + `Market_Data_JSON` | `Trade_Orders` |
| **Skill_E** | **复盘审计员** (Auditor) | 绩效归因、偏差修正 | 历史预测 vs 实际走势 | `Correction_Report` |
| **Skill_F** | **新闻联播爬虫** (Crawler) | 抓取新闻联播文字稿 | 目标日期 | 新闻文本列表 |
---
## 3. 数据协议与 Schema (Data Protocols)
**注意：AI 生成代码时必须严格遵守以下 JSON 结构。**
### 3.1 World Book Schema (世界书结构)
```json
{
"timestamp": "2025-10-01T09:00:00",
"macro_cycle": {
"status": "upward", // upward(上行) | downward(下行)
"liquidity": "loose", // loose | neutral | tight
"policy_wind": "stimulus" // stimulus | regulation | wait_and_see
},
"active_narratives": [
{
"id": "narrative_low_altitude_economy_202510",
"topic": "低空经济",
"related_etfs": ["512XXX", "159XXX"],
"lifecycle_stage": "fermentation", // incubation | fermentation | realization | decay
"base_score": 85, // 初始新闻强度
"decay_factor": 0.95, // 每日衰减系数
"current_weight": 80.75, // 动态权重
"last_updated": "2025-10-01"
}
],
"static_matrix_snapshot": {
// 基础宏观因子影响表 ($x$)
"tech_etf": {"rate_cut": 8, "currency_depreciation": 2},
"bank_etf": {"rate_cut": -5, "currency_depreciation": -1}
}
}
```
### 3.2 Trust Index Formula (信任指数算法)
Agent D (PM) 必须使用以下逻辑计算得分：
* **Score_narrative:** 来自 World Book 中 `current_weight`。
* **Score_flow:** 来自 Skill_B (Quant) 的资金净流入归一化评分。
* **Penalty_divergence:** 当 `Narrative > 80` 但 `Flow < 30` 时，惩罚值设为 100 (一票否决)。
---
## 4. 业务流程 (Workflows)
### 4.1 每日批处理流程 (Daily Batch) - *08:30 AM*
1. **Ingest:** Skill_A 读取昨夜今晨新闻，Skill_B 读取昨日行情。
2. **Cognition:** Skill_C 读取 Skill_A 的结果，更新 MCP World Book（增强或衰减叙事权重）。
3. **Decision:** Skill_D 读取最新的 World Book 和 Skill_B 的资金数据，计算 Trust Index。
4. **Execution:** Skill_D 输出具体的 `buy/sell` 指令列表。
### 4.2 盘中哨兵流程 (Intraday Sentinel) - *09:30 - 15:00*
1. **Monitor:** 系统监听快讯接口（如财联社红色资讯）。
2. **Trigger:** 若检测到 `Level A` 级新闻（如印花税、降准）。
3. **Interrupt:** 唤醒 Skill_C 临时修改 World Book 状态。
4. **Re-eval:** 触发 Skill_D 重新计算当日持仓建议。
### 4.3 盘后复盘流程 (Post-Market Feedback) - *16:00 PM*
1. Skill_E 对比昨日的 `Trade_Orders` 和今日实际涨跌幅。
2. 若偏差过大，Skill_E 向 MCP 写入一条 `correction_log`，调整 Skill_C 未来对同类新闻的打分权重。
---
## 5. 项目结构 (Project Structure)
建议 AI 按照以下目录结构生成代码：
```text
/mnd_agent
├── /core # 共享内核
│ ├── world_book.py # MCP 读写逻辑
│ ├── asset_map.json # 资产映射字典
│ └── macro_matrix.json # 静态因子表 ($x$)
├── /skills # Agent 能力群
│ ├── analyst.py # Skill A: LLM Prompts for News
│ ├── quant.py # Skill B: Akshare Data Analysis
│ ├── strategist.py # Skill C: World Book Update Logic
│ ├── pm.py # Skill D: Trust Index & Trade Logic
│ ├── auditor.py # Skill E: Feedback Loop
│ └── crawler.py # Skill F: XinwenLianbo Crawler
├── /data # 本地存储 (Parquet/SQLite)
├── /logs # 运行日志
├── main.py # 主调度入口
└── README.md # 本文件
```
---
## 6. 技术栈约束 (Tech Stack Constraints)
* **Language:** Python 3.10+
* **LLM Interface:** OpenAI SDK (兼容 GLM-4/Claude-3.5)
* **Data Source:** `akshare` (A股行情), `crawl4ai` (新闻联播爬虫)
* **Data Process:** `pandas`, `numpy`
* **Storage:** 本地 JSON 文件模拟 MCP Server (MVP阶段)
---
## 7. 给 AI 工具的构建指令 (Instructions for AI Builder)
1. **Step 1:** 首先创建 `core/` 目录，定义好 `WorldBook` 类和 JSON 数据结构，这是所有 Skill 通信的基础。
2. **Step 2:** 实现 `skills/quant.py`，确保能从 Akshare 获取 ETF 数据并计算“资金放量”指标。
3. **Step 3:** 实现 `skills/analyst.py`，编写能够将新闻文本转化为 JSON 对象的 Prompt。
4. **Step 4:** 实现 `skills/pm.py` 中的 **Trust Index** 算法，确保逻辑与上述公式一致。
5. **Step 5:** 编写 `main.py` 将上述模块串联起来，完成每日一次的模拟运行。
requirements.txt
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# MND-IA 项目依赖
# 核心数据处理
numpy>=1.24.0
pandas>=2.0.0
# 金融数据获取
akshare>=1.12.0
# 网页爬虫
cloudscraper>=1.2.0
beautifulsoup4>=4.12.0
# LLM 接口（可选）
openai>=1.0.0
# 其他工具
python-dateutil>=2.8.2
requests>=2.31.0
# 开发工具（可选）
jupyter>=1.0.0
matplotlib>=3.7.0
skills/__init__.py
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@@ -0,0 +1,21 @@
"""
MND-IA Skills Module
====================
Agent 技能群
"""
from .analyst import NewsAnalyzer
from .quant import QuantAnalyzer
from .strategist import MacroStrategist
from .pm import PortfolioManager
from .auditor import PerformanceAuditor
from .crawler import XinwenLianboCrawler
__all__ = [
'NewsAnalyzer',
'QuantAnalyzer',
'MacroStrategist',
'PortfolioManager',
'PerformanceAuditor',
'XinwenLianboCrawler'
]
skills/analyst.py
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"""
Skill A: Analyst (情报分析师)
==============================
职能：NLP 提取、新闻分级、叙事打分
输入：新闻文本列表
输出：Narrative_JSON (叙事对象、评分、生命周期阶段)
设计原则：
- 核心分析逻辑必须通过 LLM 完成，而非简单关键词匹配
- 关键词仅作为 LLM 分析的辅助信息和兜底方案
- 支持 LLM 失败时的优雅降级
"""
import json
import re
from datetime import datetime
from typing import Dict, List, Optional, Tuple
from pathlib import Path
import os
import sys
# 添加项目根目录到路径
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.config import config, get_llm_client, llm_call
class NewsAnalyzer:
"""
情报分析师 - 负责新闻文本分析和叙事提取
核心能力：
1. 使用 LLM 进行新闻语义分析（主要方法）
2. 新闻分级（Level A/B/C）
3. 叙事主题提取与关联
4. 热度评分与市场影响判断
5. 生命周期阶段推断
分析流程：
1. 首先尝试使用 LLM 进行深度分析
2. LLM 返回结构化的分析结果
3. 如果 LLM 调用失败，降级到规则引擎
"""
# LLM 分析系统提示词
SYSTEM_PROMPT = """你是专业的 A 股市场情报分析师，专门负责解读政策新闻和市场信息。
你的核心任务是：
1. 判断新闻的重要性级别 (A/B/C级)
2. 识别受影响的行业板块
3. 评估新闻对市场的影响强度
4. 判断叙事所处的生命周期阶段
评级标准：
- A级：央行/国务院级别的重大政策（如降准降息、印花税调整、重大改革），通常能引发市场大幅波动
- B级：部委级别的行业政策（如工信部新规、发改委规划），对特定行业有显著影响
- C级：一般性新闻、行业动态、公司公告，影响有限
生命周期阶段：
- incubation (潜伏期): 政策风向初现，尚未被市场充分认知
- fermentation (发酵期): 市场开始关注，资金试探性流入
- realization (兑现期): 政策落地，市场充分反应
- decay (衰退期): 利好出尽，边际效应递减
请严格按照 JSON 格式返回分析结果。"""
def __init__(self, llm_client=None, use_llm: bool = True):
"""
Args:
llm_client: OpenAI SDK 客户端（兼容 GLM/Claude），如果不提供则使用配置文件中的默认客户端
use_llm: 是否使用 LLM 进行分析（默认 True）
"""
self.llm_client = llm_client or get_llm_client()
self.use_llm = use_llm and (self.llm_client is not None)
self.asset_map = self._load_asset_map()
# 板块关键词映射 - 仅作为 LLM 的参考信息
self.sector_keywords = {}
self.sector_names = {} # 资产ID到中文名的映射
if self.asset_map:
for asset_id, asset_data in self.asset_map.get("assets", {}).items():
self.sector_keywords[asset_id] = asset_data.get("keywords", [])
self.sector_names[asset_id] = asset_data.get("name", asset_id)
if self.use_llm:
print("[Analyst] ✅ LLM 模式启用 - 将使用大模型进行智能分析")
else:
print("[Analyst] ⚠️ 降级模式 - 将使用规则引擎（关键词匹配）")
def _load_asset_map(self) -> Optional[Dict]:
"""加载资产映射表"""
asset_map_path = Path("core") / "asset_map.json"
if not asset_map_path.exists():
print("[Analyst] 警告: 未找到 asset_map.json")
return None
with open(asset_map_path, 'r', encoding='utf-8') as f:
return json.load(f)
def _get_available_sectors_prompt(self) -> str:
"""生成可用板块列表的提示信息"""
if not self.asset_map:
return ""
sectors_info = []
for asset_id, asset_data in self.asset_map.get("assets", {}).items():
name = asset_data.get("name", asset_id)
keywords = asset_data.get("keywords", [])[:5] # 取前5个关键词
sectors_info.append(f"- {asset_id} ({name}): {', '.join(keywords)}")
return "\n".join(sectors_info)
def _build_analysis_prompt(self, news_text: str, title: str) -> str:
"""构建 LLM 分析的用户提示词"""
sectors_info = self._get_available_sectors_prompt()
return f"""请分析以下新闻，并以 JSON 格式返回结果：
【新闻标题】
{title}
【新闻正文】
{news_text}
【可用板块列表】
{sectors_info}
请返回如下格式的 JSON（不要包含其他文字）：
{{
"level": "A/B/C",
"level_reason": "判断级别的理由（50字内）",
"sectors": ["asset_id1", "asset_id2"],
"sector_reason": "选择这些板块的理由（50字内）",
"score": 0-100,
"score_reason": "评分依据（30字内）",
"lifecycle_stage": "incubation/fermentation/realization/decay",
"stage_reason": "生命周期判断理由（30字内）",
"sentiment": "positive/negative/neutral",
"key_signal": "新闻中最关键的投资信号（30字内）"
}}"""
def analyze_news(self, news_text: str, title: str = "") -> Dict:
"""
单条新闻分析（主要方法）
优先使用 LLM 进行智能分析，失败时降级到规则引擎
Args:
news_text: 新闻正文
title: 新闻标题（可选）
Returns:
分析结果字典
"""
if self.use_llm:
result = self._analyze_with_llm(news_text, title)
if result:
return result
print("[Analyst] ⚠️ LLM 分析失败，降级到规则引擎")
# 降级到规则引擎
return self._analyze_with_rules(news_text, title)
def _analyze_with_llm(self, news_text: str, title: str) -> Optional[Dict]:
"""
使用 LLM 进行深度新闻分析
Args:
news_text: 新闻正文
title: 新闻标题
Returns:
分析结果字典，失败返回 None
"""
full_text = f"{title}\n{news_text}" if title else news_text
# 构建提示词
user_prompt = self._build_analysis_prompt(news_text, title)
try:
# 调用 LLM
llm_output = llm_call(
messages=[
{"role": "system", "content": self.SYSTEM_PROMPT},
{"role": "user", "content": user_prompt}
],
temperature=0.3,
max_tokens=800
)
if not llm_output:
return None
# 清理并解析 JSON
llm_output = llm_output.strip()
# 移除可能的 markdown 代码块标记
if llm_output.startswith("```"):
llm_output = re.sub(r'^```(?:json)?\s*', '', llm_output)
llm_output = re.sub(r'\s*```$', '', llm_output)
llm_result = json.loads(llm_output)
# 验证并规范化结果
level = llm_result.get("level", "C").upper()
if level not in ["A", "B", "C"]:
level = "C"
sectors = llm_result.get("sectors", [])
# 验证板块是否存在于 asset_map 中
valid_sectors = [s for s in sectors if s in self.sector_keywords]
score = float(llm_result.get("score", 50))
score = max(0, min(100, score)) # 限制在 0-100
lifecycle_stage = llm_result.get("lifecycle_stage", "incubation")
if lifecycle_stage not in ["incubation", "fermentation", "realization", "decay"]:
lifecycle_stage = "incubation"
# 关联 ETF
related_etfs = []
if self.asset_map:
for sector in valid_sectors:
asset_data = self.asset_map.get("assets", {}).get(sector, {})
related_etfs.extend(asset_data.get("etfs", []))
related_etfs = list(set(related_etfs))
return {
"title": title,
"level": level,
"level_reason": llm_result.get("level_reason", ""),
"sectors": valid_sectors,
"sector_reason": llm_result.get("sector_reason", ""),
"score": round(score, 2),
"score_reason": llm_result.get("score_reason", ""),
"lifecycle_stage": lifecycle_stage,
"stage_reason": llm_result.get("stage_reason", ""),
"related_etfs": related_etfs,
"sentiment": llm_result.get("sentiment", "neutral"),
"key_signal": llm_result.get("key_signal", ""),
"timestamp": datetime.now().isoformat(),
"analysis_method": "llm",
"summary": f"{level}级 {len(valid_sectors)}板块 评分{score:.0f} [{llm_result.get('sentiment', 'neutral')}]"
}
except json.JSONDecodeError as e:
print(f"[Analyst] LLM 返回格式解析失败: {e}")
return None
except Exception as e:
print(f"[Analyst] LLM 分析异常: {e}")
return None
def _analyze_with_rules(self, news_text: str, title: str) -> Dict:
"""
规则引擎分析（降级方案）
当 LLM 不可用时，使用关键词匹配进行基础分析
Args:
news_text: 新闻正文
title: 新闻标题
Returns:
分析结果字典
"""
full_text = f"{title}\n{news_text}" if title else news_text
# 1. 新闻分级（关键词匹配）
level = self._classify_level_by_keywords(full_text)
# 2. 板块识别（关键词匹配）
sectors = self._extract_sectors_by_keywords(full_text)
# 3. 评分计算
score = self._calculate_score_by_rules(full_text, level, sectors)
# 4. 生命周期阶段
stage = self._determine_stage_by_rules(level, score)
# 5. 关联 ETF
related_etfs = []
if self.asset_map:
for sector in sectors:
asset_data = self.asset_map.get("assets", {}).get(sector, {})
related_etfs.extend(asset_data.get("etfs", []))
related_etfs = list(set(related_etfs))
return {
"title": title,
"level": level,
"level_reason": "基于关键词匹配（降级模式）",
"sectors": sectors,
"sector_reason": "基于关键词匹配（降级模式）",
"score": score,
"score_reason": "规则引擎计算",
"lifecycle_stage": stage,
"stage_reason": "基于级别和评分推断",
"related_etfs": related_etfs,
"sentiment": "neutral",
"key_signal": "",
"timestamp": datetime.now().isoformat(),
"analysis_method": "rules",
"summary": f"{level}级 {len(sectors)}板块 评分{score} [降级模式]"
}
# ==================== 规则引擎辅助方法（降级方案） ====================
# 新闻分级关键词库
LEVEL_A_KEYWORDS = [
"印花税", "降准", "降息", "降低存款准备金率",
"央行宣布", "国务院", "重磅", "历史性", "里程碑",
"全面降息", "万亿", "大规模刺激", "救市", "重大改革"
]
LEVEL_B_KEYWORDS = [
"部委", "指导意见", "规划", "通知",
"发改委", "工信部", "证监会", "银保监会",
"新政", "改革", "试点", "专项", "行动计划"
]
def _classify_level_by_keywords(self, text: str) -> str:
"""基于关键词的新闻分级"""
for keyword in self.LEVEL_A_KEYWORDS:
if keyword in text:
return "A"
for keyword in self.LEVEL_B_KEYWORDS:
if keyword in text:
return "B"
return "C"
def _extract_sectors_by_keywords(self, text: str) -> List[str]:
"""基于关键词的板块提取"""
matched_sectors = []
for sector, keywords in self.sector_keywords.items():
for keyword in keywords:
if keyword in text:
matched_sectors.append(sector)
break
return matched_sectors
def _calculate_score_by_rules(self, text: str, level: str, sectors: List[str]) -> float:
"""基于规则的评分计算"""
base_score = {"A": 85, "B": 65, "C": 45}.get(level, 45)
sector_bonus = min(len(sectors) * 5, 15)
# 情绪词检测
positive_keywords = ["利好", "刺激", "支持", "鼓励", "加快", "推动", "大力"]
emotion_bonus = 10 if any(kw in text for kw in positive_keywords) else 0
return min(base_score + sector_bonus + emotion_bonus, 100)
def _determine_stage_by_rules(self, level: str, score: float) -> str:
"""基于规则的生命周期阶段判断"""
if level == "A" and score >= 80:
return "realization"
elif level in ["A", "B"] and score >= 60:
return "fermentation"
else:
return "incubation"
def batch_analyze(
self,
news_list: List[Dict[str, str]]
) -> List[Dict]:
"""
批量分析新闻
Args:
news_list: 新闻列表，每条包含 {"title": "", "text": ""}
Returns:
分析结果列表
"""
results = []
llm_count = 0
rules_count = 0
mode = "LLM智能分析" if self.use_llm else "规则引擎（降级模式）"
print(f"[Analyst] 开始批量分析 {len(news_list)} 条新闻 | 模式: {mode}")
for i, news in enumerate(news_list, 1):
title = news.get("title", "")
text = news.get("text", "")
# 简化标题显示
display_title = title[:25] + "..." if len(title) > 25 else title
result = self.analyze_news(text, title)
results.append(result)
# 统计分析方法使用情况
if result.get("analysis_method") == "llm":
llm_count += 1
method_icon = "🤖"
else:
rules_count += 1
method_icon = "📏"
print(f"[Analyst] {method_icon} ({i}/{len(news_list)}) {result['level']}级 | {result['score']:.0f}分 | {display_title}")
print(f"[Analyst] ✅ 批量分析完成 | LLM: {llm_count}条, 规则: {rules_count}条")
return results
def generate_narrative_json(
self,
analysis_results: List[Dict]
) -> Dict:
"""
生成标准化的 Narrative_JSON（供 Strategist 使用）
聚合逻辑：
1. 按板块（sector）聚合所有相关新闻
2. 计算平均分和最高分
3. 汇总 LLM 分析的关键信号
4. 确定最终的生命周期阶段
Args:
analysis_results: batch_analyze 的输出
Returns:
标准化 JSON 格式
"""
# 按板块聚合新闻
sector_narratives = {}
for result in analysis_results:
for sector in result['sectors']:
if sector not in sector_narratives:
sector_narratives[sector] = {
"topic": sector,
"topic_name": self.sector_names.get(sector, sector),
"news_count": 0,
"avg_score": 0,
"max_score": 0,
"level_a_count": 0,
"level_b_count": 0,
"related_etfs": set(),
"lifecycle_stage": "incubation",
"key_signals": [], # 汇总关键信号
"sentiments": [], # 情绪统计
"analysis_methods": {"llm": 0, "rules": 0}
}
narrative = sector_narratives[sector]
narrative["news_count"] += 1
narrative["avg_score"] += result['score']
narrative["max_score"] = max(narrative["max_score"], result['score'])
narrative["related_etfs"].update(result['related_etfs'])
# 统计新闻级别
if result['level'] == "A":
narrative["level_a_count"] += 1
narrative["lifecycle_stage"] = result['lifecycle_stage']
elif result['level'] == "B":
narrative["level_b_count"] += 1
if narrative["lifecycle_stage"] == "incubation":
narrative["lifecycle_stage"] = "fermentation"
# 收集关键信号（来自 LLM 分析）
if result.get("key_signal"):
narrative["key_signals"].append(result["key_signal"])
# 收集情绪
sentiment = result.get("sentiment", "neutral")
narrative["sentiments"].append(sentiment)
# 统计分析方法
method = result.get("analysis_method", "rules")
narrative["analysis_methods"][method] = narrative["analysis_methods"].get(method, 0) + 1
# 后处理：计算平均分、汇总情绪等
for narrative in sector_narratives.values():
narrative["avg_score"] = round(
narrative["avg_score"] / narrative["news_count"], 2
)
narrative["related_etfs"] = list(narrative["related_etfs"])
# 计算情绪倾向
sentiments = narrative["sentiments"]
positive_count = sentiments.count("positive")
negative_count = sentiments.count("negative")
if positive_count > negative_count:
narrative["overall_sentiment"] = "positive"
elif negative_count > positive_count:
narrative["overall_sentiment"] = "negative"
else:
narrative["overall_sentiment"] = "neutral"
# 去重关键信号
narrative["key_signals"] = list(set(narrative["key_signals"]))[:5]
# 清理临时字段
del narrative["sentiments"]
# 排序输出
sorted_narratives = sorted(
sector_narratives.values(),
key=lambda x: x['max_score'],
reverse=True
)
return {
"timestamp": datetime.now().isoformat(),
"total_news": len(analysis_results),
"narratives": sorted_narratives
}
# ==================== 测试代码 ====================
if __name__ == "__main__":
print("=" * 60)
print("Skill A: Analyst 情报分析师测试")
print("=" * 60)
# 创建分析器实例（默认启用 LLM）
analyst = NewsAnalyzer()
# 测试新闻样本
test_news = [
{
"title": "央行宣布降准0.5个百分点 释放长期资金约1万亿",
"text": "中国人民银行决定于2025年1月5日降低金融机构存款准备金率0.5个百分点，"
"此次降准为全面降准，除已执行5%存款准备金率的部分法人金融机构外，"
"对其他金融机构普遍下调存款准备金率0.5个百分点，此次降准共计释放长期资金约1万亿元。"
},
{
"title": "工信部发布低空经济发展指导意见 推动eVTOL产业化",
"text": "工业和信息化部发布《低空经济高质量发展指导意见》，提出到2027年，"
"低空经济规模达到1万亿元，培育10家以上龙头企业。重点支持无人机、"
"电动垂直起降飞行器（eVTOL）等装备的研发和产业化。"
},
{
"title": "AI算力需求持续旺盛 多家数据中心公司业绩预增",
"text": "随着人工智能应用的快速发展，算力需求呈现爆发式增长。多家上市公司发布业绩预告，"
"预计2024年净利润增长50%以上。分析师认为，智算中心建设将成为未来3年的投资主线。"
}
]
# 批量分析
print("\n")
results = analyst.batch_analyze(test_news)
print("\n" + "=" * 60)
print("详细分析结果:")
print("=" * 60)
for i, result in enumerate(results, 1):
print(f"\n【新闻 {i}】{result['title'][:50]}")
print(f" 📊 分析方法: {result.get('analysis_method', 'unknown').upper()}")
print(f" 📌 级别: {result['level']} | 评分: {result['score']}")
print(f" 🎯 板块: {', '.join(result['sectors']) if result['sectors'] else '未识别'}")
print(f" 🔄 周期阶段: {result['lifecycle_stage']}")
print(f" 📈 情绪: {result.get('sentiment', 'neutral')}")
if result.get('key_signal'):
print(f" 💡 关键信号: {result['key_signal']}")
if result.get('level_reason'):
print(f" 📝 级别理由: {result['level_reason']}")
print(f" 🏷️ 关联 ETF: {', '.join(result['related_etfs'][:4]) if result['related_etfs'] else '无'}...")
# 生成叙事 JSON
print("\n" + "=" * 60)
print("聚合叙事 JSON:")
print("=" * 60)
narrative_json = analyst.generate_narrative_json(results)
print(f"\n📰 总新闻数: {narrative_json['total_news']}")
print(f"🎯 识别叙事: {len(narrative_json['narratives'])} 个\n")
for narrative in narrative_json['narratives']:
topic_name = narrative.get('topic_name', narrative['topic'])
level_info = f"A级×{narrative['level_a_count']}" if narrative['level_a_count'] > 0 else \
f"B级×{narrative.get('level_b_count', 0)}" if narrative.get('level_b_count', 0) > 0 else "C级"
print(f" 📍 {topic_name} ({narrative['topic']})")
print(f" 评分: {narrative['max_score']:.0f} (平均{narrative['avg_score']:.0f}) | "
f"新闻: {narrative['news_count']}条 | {level_info}")
print(f" 阶段: {narrative['lifecycle_stage']} | 情绪: {narrative.get('overall_sentiment', 'neutral')}")
if narrative.get('key_signals'):
print(f" 信号: {'; '.join(narrative['key_signals'][:2])}")
print()
print("✅ Analyst 模块测试完成")
skills/auditor.py
+475
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@@ -0,0 +1,475 @@
"""
Skill E: Auditor (复盘审计员)
==============================
职能：绩效归因、偏差修正
输入：历史预测 vs 实际走势
输出：Correction_Report（修正建议）
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.config import config, llm_call
from typing import Dict, List, Optional
from datetime import datetime, timedelta
import json
import pandas as pd
class PerformanceAuditor:
"""
复盘审计员 - 负责绩效归因和策略修正
核心职责：
1. 对比预测与实际表现
2. 分析偏差原因
3. 提出修正建议
4. 记录经验教训
"""
def __init__(self, data_dir: str = "data"):
self.data_dir = Path(data_dir)
self.audit_log_path = self.data_dir / "audit_logs.json"
self.correction_history = self._load_correction_history()
def _load_correction_history(self) -> List[Dict]:
"""加载历史修正记录"""
if not self.audit_log_path.exists():
return []
try:
with open(self.audit_log_path, 'r', encoding='utf-8') as f:
return json.load(f)
except Exception as e:
print(f"[Auditor] 加载修正历史失败: {e}")
return []
def _save_correction_history(self) -> None:
"""保存修正记录"""
try:
with open(self.audit_log_path, 'w', encoding='utf-8') as f:
json.dump(self.correction_history, f, ensure_ascii=False, indent=2)
print(f"[Auditor] 修正记录已保存")
except Exception as e:
print(f"[Auditor] 保存失败: {e}")
def audit_prediction(
self,
prediction: Dict,
actual_performance: Dict
) -> Dict:
"""
审计单个预测
Args:
prediction: 预测记录
{
"code": "515980",
"trust_index": 75.0,
"narrative_score": 85.0,
"flow_score": 60.0,
"predicted_action": "buy",
"timestamp": "2025-12-29T08:30:00"
}
actual_performance: 实际表现
{
"code": "515980",
"price_change": -2.5, # 实际涨跌幅 %
"volume_change": 1.3, # 成交量变化倍数
"date": "2025-12-29"
}
Returns:
审计结果
"""
code = prediction['code']
predicted_action = prediction['predicted_action']
trust_index = prediction['trust_index']
price_change = actual_performance['price_change']
# 判断预测是否正确
if predicted_action == "buy":
is_correct = price_change > 0
expected_direction = "上涨"
elif predicted_action == "sell":
is_correct = price_change < 0
expected_direction = "下跌"
else:
is_correct = abs(price_change) < 2
expected_direction = "震荡"
# 计算偏差程度
deviation = abs(price_change) if not is_correct else 0
# 分析原因
reason = self._analyze_deviation_reason(
prediction,
actual_performance,
is_correct
)
# 生成修正建议
correction = self._generate_correction(
prediction,
actual_performance,
is_correct,
reason
)
audit_result = {
"code": code,
"prediction": {
"action": predicted_action,
"trust_index": trust_index,
"expected": expected_direction
},
"actual": {
"price_change": price_change,
"volume_change": actual_performance.get('volume_change', 1.0)
},
"result": {
"is_correct": is_correct,
"deviation": round(deviation, 2),
"reason": reason
},
"correction": correction,
"timestamp": datetime.now().isoformat()
}
return audit_result
def _analyze_deviation_reason(
self,
prediction: Dict,
actual: Dict,
is_correct: bool
) -> str:
"""分析偏差原因"""
if is_correct:
return "预测准确"
narrative_score = prediction.get('narrative_score', 0)
flow_score = prediction.get('flow_score', 0)
price_change = actual['price_change']
volume_change = actual.get('volume_change', 1.0)
# 情况1：叙事高但资金未跟进
if narrative_score > 70 and flow_score < 40:
return "叙事未兑现：新闻热度高但资金流入不足，市场观望情绪浓厚"
# 情况2：资金放量但方向相反
if volume_change > 1.5 and price_change < -2:
return "放量下跌：资金大量流出，可能遭遇利空或止盈盘涌出"
# 情况3：黑天鹅事件
if abs(price_change) > 5:
return "极端波动：可能受突发事件或外部市场剧烈波动影响"
# 情况4：叙事过度衰减
if narrative_score < 50 and prediction['predicted_action'] == "buy":
return "叙事衰减过快：市场关注度下降速度超预期"
return "其他因素：需进一步分析宏观环境或板块轮动"
def _generate_correction(
self,
prediction: Dict,
actual: Dict,
is_correct: bool,
reason: str
) -> Dict:
"""生成修正建议"""
if is_correct:
return {
"action": "none",
"message": "预测准确，保持当前策略"
}
narrative_score = prediction.get('narrative_score', 0)
flow_score = prediction.get('flow_score', 0)
corrections = []
# 修正1：提高资金流权重
if "资金流入不足" in reason:
corrections.append({
"target": "trust_index_formula",
"suggestion": "提高资金流权重至 0.5，降低叙事权重至 0.5",
"reason": "叙事未能转化为实际资金流动"
})
# 修正2：加强止损
if "放量下跌" in reason:
corrections.append({
"target": "risk_control",
"suggestion": "设置 -3% 硬止损线，出现放量下跌立即清仓",
"reason": "未能及时识别资金流出信号"
})
# 修正3：降低 Trust Index 阈值
if narrative_score > 60 and flow_score > 50 and not is_correct:
corrections.append({
"target": "min_trust_score",
"suggestion": "提高最低信任指数阈值至 65",
"reason": "当前阈值可能过于宽松，需要更高确定性"
})
# 修正4：叙事衰减系数调整
if "衰减过快" in reason:
corrections.append({
"target": "narrative_decay",
"suggestion": "对该板块叙事的衰减系数从 0.95 调整为 0.97",
"reason": "市场对该主题的持续关注度被低估"
})
return {
"action": "adjust" if corrections else "monitor",
"corrections": corrections,
"priority": "high" if len(corrections) >= 2 else "medium"
}
def batch_audit(
self,
predictions: List[Dict],
actual_performances: Dict[str, Dict]
) -> Dict:
"""
批量审计
Args:
predictions: 预测列表
actual_performances: 实际表现字典 {code: performance}
Returns:
汇总审计报告
"""
print(f"[Auditor] 开始审计 {len(predictions)} 条预测...")
audit_results = []
correct_count = 0
for prediction in predictions:
code = prediction['code']
if code not in actual_performances:
print(f"[Auditor] 警告: 缺少 {code} 的实际数据")
continue
result = self.audit_prediction(
prediction,
actual_performances[code]
)
audit_results.append(result)
if result['result']['is_correct']:
correct_count += 1
# 计算准确率
accuracy = correct_count / len(audit_results) * 100 if audit_results else 0
# 统计偏差原因
reason_counts = {}
for result in audit_results:
reason = result['result']['reason']
reason_counts[reason] = reason_counts.get(reason, 0) + 1
# 收集修正建议
all_corrections = []
for result in audit_results:
if result['correction']['action'] != "none":
all_corrections.extend(result['correction'].get('corrections', []))
# 生成汇总报告
summary_report = {
"timestamp": datetime.now().isoformat(),
"audit_period": {
"start": min(r['timestamp'] for r in audit_results) if audit_results else None,
"end": max(r['timestamp'] for r in audit_results) if audit_results else None
},
"performance": {
"total_predictions": len(audit_results),
"correct_predictions": correct_count,
"wrong_predictions": len(audit_results) - correct_count,
"accuracy": round(accuracy, 2)
},
"deviation_analysis": reason_counts,
"corrections_needed": len(all_corrections),
"high_priority_corrections": [
c for c in all_corrections
if any('止损' in c.get('suggestion', '') for c in all_corrections)
],
"detailed_results": audit_results
}
# 保存到历史记录
self.correction_history.append(summary_report)
self._save_correction_history()
print(f"[Auditor] 审计完成，准确率: {accuracy:.1f}%")
return summary_report
def generate_correction_report(
self,
audit_summary: Dict
) -> str:
"""
生成人类可读的修正报告
Args:
audit_summary: batch_audit() 的输出
Returns:
Markdown 格式的报告
"""
accuracy = audit_summary['performance']['accuracy']
total = audit_summary['performance']['total_predictions']
correct = audit_summary['performance']['correct_predictions']
report = f"""
# 投资决策复盘审计报告
**生成时间:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
## 一、整体表现
- **预测数量:** {total} 条
- **正确预测:** {correct} 条
- **错误预测:** {total - correct} 条
- **准确率:** {accuracy:.1f}%
## 二、偏差原因分析
"""
for reason, count in audit_summary['deviation_analysis'].items():
report += f"- **{reason}:** {count} 次\n"
report += f"\n## 三、修正建议\n\n"
report += f"需要修正的参数: {audit_summary['corrections_needed']} 项\n\n"
# 列出高优先级修正
high_priority = audit_summary.get('high_priority_corrections', [])
if high_priority:
report += "### 🚨 高优先级修正\n\n"
for i, correction in enumerate(high_priority[:3], 1):
report += f"{i}. **{correction['target']}**\n"
report += f" - 建议: {correction['suggestion']}\n"
report += f" - 原因: {correction['reason']}\n\n"
report += "\n## 四、经验总结\n\n"
if accuracy >= 70:
report += "✅ 当前策略表现良好，建议保持现有参数设置。\n"
elif accuracy >= 50:
report += "⚠️ 策略有效但存在改进空间，建议根据修正建议调整参数。\n"
else:
report += "❌ 策略表现不佳，需要重大调整或暂停交易，重新评估市场环境。\n"
return report
def get_historical_accuracy_trend(self) -> List[Dict]:
"""获取历史准确率趋势"""
trend = []
for record in self.correction_history[-10:]: # 最近10次
trend.append({
"timestamp": record['timestamp'],
"accuracy": record['performance']['accuracy'],
"total_predictions": record['performance']['total_predictions']
})
return trend
# ==================== 测试代码 ====================
if __name__ == "__main__":
print("=" * 50)
print("Skill E: Auditor 复盘审计员测试")
print("=" * 50)
# 创建审计员实例
auditor = PerformanceAuditor(data_dir="data")
# 模拟昨日的预测记录
mock_predictions = [
{
"code": "515980",
"trust_index": 75.0,
"narrative_score": 85.0,
"flow_score": 60.0,
"predicted_action": "buy",
"timestamp": "2025-12-29T08:30:00"
},
{
"code": "512480",
"trust_index": 45.0,
"narrative_score": 55.0,
"flow_score": 30.0,
"predicted_action": "hold",
"timestamp": "2025-12-29T08:30:00"
},
{
"code": "159928",
"trust_index": 35.0,
"narrative_score": 70.0,
"flow_score": 20.0,
"predicted_action": "sell",
"timestamp": "2025-12-29T08:30:00"
}
]
# 模拟今日的实际表现
mock_actual = {
"515980": {
"code": "515980",
"price_change": 3.2, # 上涨3.2%
"volume_change": 1.5,
"date": "2025-12-29"
},
"512480": {
"code": "512480",
"price_change": -1.8, # 下跌1.8%
"volume_change": 0.9,
"date": "2025-12-29"
},
"159928": {
"code": "159928",
"price_change": 0.5, # 微涨（预测失误）
"volume_change": 2.1,
"date": "2025-12-29"
}
}
# 1. 批量审计
print("\n1. 批量审计:")
summary = auditor.batch_audit(mock_predictions, mock_actual)
print(f"\n准确率: {summary['performance']['accuracy']:.1f}%")
print(f"正确: {summary['performance']['correct_predictions']}")
print(f"错误: {summary['performance']['wrong_predictions']}")
# 2. 查看详细结果
print("\n2. 详细审计结果:")
for result in summary['detailed_results']:
print(f"\n {result['code']}:")
print(f" - 预测: {result['prediction']['action']} (Trust={result['prediction']['trust_index']})")
print(f" - 实际: {result['actual']['price_change']:+.1f}%")
print(f" - 结果: {'✅ 正确' if result['result']['is_correct'] else '❌ 错误'}")
print(f" - 原因: {result['result']['reason']}")
# 3. 生成修正报告
print("\n3. 修正报告:")
report = auditor.generate_correction_report(summary)
print(report)
# 4. 历史趋势
print("\n4. 历史准确率趋势:")
trend = auditor.get_historical_accuracy_trend()
for record in trend:
print(f" {record['timestamp'][:10]}: {record['accuracy']:.1f}%")
print("\n✅ Auditor 模块测试完成")
skills/crawler.py
+298
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@@ -0,0 +1,298 @@
"""
Skill F: Crawler (新闻联播爬虫)
================================
职能：每日抓取新闻联播文字稿
数据源：https://cn.govopendata.com/xinwenlianbo/
输出：符合 Skill A 要求的新闻列表 [{"title": "", "text": ""}]
"""
import cloudscraper
from bs4 import BeautifulSoup
from datetime import datetime
from typing import List, Dict, Optional
from pathlib import Path
import json
import time
class XinwenLianboCrawler:
"""
新闻联播爬虫 - 抓取每日新闻联播文字稿
核心功能：
1. 自动构建当日 URL
2. 使用 cloudscraper 绕过 Cloudflare 防护
3. 解析新闻条目
4. 输出符合 Analyst 要求的格式
"""
def __init__(self, base_url: str = "https://cn.govopendata.com/xinwenlianbo/"):
"""
Args:
base_url: 新闻联播网站基础 URL
"""
self.base_url = base_url
self.cache_dir = Path("data") / "news_cache"
self.cache_dir.mkdir(parents=True, exist_ok=True)
# 创建 cloudscraper 会话
self.scraper = cloudscraper.create_scraper(
browser={
'browser': 'chrome',
'platform': 'windows',
'desktop': True
}
)
def _build_url(self, date: Optional[str] = None) -> str:
"""
构建目标 URL
Args:
date: 日期字符串 (格式: YYYYMMDD)，默认今天
Returns:
完整 URL
"""
if date is None:
date = datetime.now().strftime("%Y%m%d")
return f"{self.base_url}{date}/"
def crawl_xinwenlianbo(self, date: Optional[str] = None) -> List[Dict[str, str]]:
"""
爬取新闻联播
Args:
date: 日期字符串 (YYYYMMDD)，默认今天
Returns:
新闻列表 [{"title": "", "text": ""}]
"""
url = self._build_url(date)
display_date = date if date else datetime.now().strftime("%Y%m%d")
print(f"[Crawler] 开始抓取 {display_date} 新闻联播")
print(f"[Crawler] URL: {url}")
try:
# 使用 cloudscraper 发起请求
response = self.scraper.get(url, timeout=30)
response.raise_for_status()
response.encoding = 'utf-8'
# 解析 HTML
soup = BeautifulSoup(response.text, 'html.parser')
# 提取新闻条目
news_list = self._parse_content(soup)
if not news_list:
print(f"[Crawler] 未找到新闻内容，可能网站结构变化或当日无更新")
print(f"[Crawler] 页面标题: {soup.title.string if soup.title else 'No Title'}")
# 尝试从缓存读取
cached = self._load_from_cache(display_date)
if cached:
print(f"[Crawler] 使用缓存数据 ({len(cached)} 条)")
return cached
return []
print(f"[Crawler] 成功提取 {len(news_list)} 条新闻")
# 缓存结果
self._cache_news(news_list, display_date)
return news_list
except Exception as e:
print(f"[Crawler] 爬取失败: {e}")
# 尝试从缓存读取
cached = self._load_from_cache(display_date)
if cached:
print(f"[Crawler] 使用缓存数据 ({len(cached)} 条)")
return cached
return []
def _parse_content(self, soup: BeautifulSoup) -> List[Dict[str, str]]:
"""
解析网页内容，提取新闻条目
网站结构：
- 容器: .content-section
- 标题: .content-heading
- 内容: .content-body
Args:
soup: BeautifulSoup 对象
Returns:
新闻列表
"""
news_list = []
# 查找所有新闻板块
sections = soup.select('.content-section')
if not sections:
print("[Crawler] 未找到 .content-section 元素")
return []
for section in sections:
# 提取标题
title_tag = section.select_one('.content-heading')
if not title_tag:
continue
title = title_tag.get_text(strip=True)
# 提取内容
content_tag = section.select_one('.content-body')
if not content_tag:
continue
# 尝试提取段落以保持格式
paragraphs = content_tag.find_all('p')
if paragraphs:
# 多段落用换行分隔
content = ' '.join([p.get_text(strip=True) for p in paragraphs])
else:
content = content_tag.get_text(strip=True)
# 过滤过短的内容
if len(content) < 10:
continue
news_list.append({
"title": title,
"text": content
})
return news_list
def _cache_news(self, news_list: List[Dict], date: str) -> None:
"""缓存新闻到本地"""
cache_file = self.cache_dir / f"xinwenlianbo_{date}.json"
try:
with open(cache_file, 'w', encoding='utf-8') as f:
json.dump({
"date": date,
"timestamp": datetime.now().isoformat(),
"count": len(news_list),
"news": news_list
}, f, ensure_ascii=False, indent=2)
print(f"[Crawler] 已缓存到 {cache_file}")
except Exception as e:
print(f"[Crawler] 缓存失败: {e}")
def _load_from_cache(self, date: str) -> Optional[List[Dict]]:
"""从缓存加载新闻"""
cache_file = self.cache_dir / f"xinwenlianbo_{date}.json"
if not cache_file.exists():
return None
try:
with open(cache_file, 'r', encoding='utf-8') as f:
data = json.load(f)
return data.get('news', [])
except Exception as e:
print(f"[Crawler] 读取缓存失败: {e}")
return None
def crawl_sync(
self,
date: Optional[str] = None
) -> List[Dict[str, str]]:
"""
同步爬取接口（主要方法）
Args:
date: 日期字符串 (YYYYMMDD)
Returns:
新闻列表
"""
return self.crawl_xinwenlianbo(date)
def crawl_date_range(
self,
start_date: str,
end_date: str
) -> Dict[str, List[Dict]]:
"""
批量爬取日期范围内的新闻
Args:
start_date: 开始日期 (YYYYMMDD)
end_date: 结束日期 (YYYYMMDD)
Returns:
{date: news_list} 字典
"""
from datetime import datetime, timedelta
start = datetime.strptime(start_date, "%Y%m%d")
end = datetime.strptime(end_date, "%Y%m%d")
results = {}
current = start
while current <= end:
date_str = current.strftime("%Y%m%d")
print(f"\n[Crawler] 处理 {date_str}...")
news_list = self.crawl_sync(date_str)
results[date_str] = news_list
current += timedelta(days=1)
# 礼貌延迟，避免频繁请求
time.sleep(2)
return results
# ==================== 测试代码 ====================
def test_sync():
"""同步测试（用于命令行直接运行）"""
print("=" * 50)
print("Skill F: Crawler 新闻联播爬虫测试")
print("=" * 50)
crawler = XinwenLianboCrawler()
# 同步爬取今日新闻
print("\n测试 1: 爬取今日新闻")
news_list = crawler.crawl_sync()
print(f"\n获取 {len(news_list)} 条新闻")
if news_list:
print("\n前 3 条新闻预览:")
for i, news in enumerate(news_list[:3], 1):
print(f"\n[{i}] {news['title']}")
print(f" {news['text'][:150]}...")
# 测试指定日期
print("\n" + "=" * 50)
print("测试 2: 爬取指定日期 (20251229)")
specific_news = crawler.crawl_sync(date="20251229")
print(f"获取 {len(specific_news)} 条新闻")
print("\n✅ Crawler 模块测试完成")
return news_list
if __name__ == "__main__":
test_sync()
skills/pm.py
+682
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@@ -0,0 +1,682 @@
"""
Skill D: PM (基金经理)
========================
职能：计算信任指数、风控、生成订单
输入：World_Book + Market_Data_JSON
输出：Trade_Orders (买卖指令列表)
设计原则：
- 核心决策通过量化模型完成（向量点积）
- 使用 LLM 提供辅助分析和投资建议
- 支持 LLM 失败时的优雅降级
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.world_book import WorldBook
from core.config import config, llm_call
from typing import Dict, List, Optional, Tuple
from datetime import datetime
import json
import re
class PortfolioManager:
"""
基金经理 - 负责投资决策和订单生成
核心职责：
1. 计算 Trust Index（叙事 × 资金流 交叉验证）
2. 使用 LLM 提供投资洞察和风险提示
3. 风险控制（仓位管理、止损）
4. 生成具体的买卖订单
"""
# LLM 投资分析提示词
INVESTMENT_ANALYSIS_PROMPT = """你是一位专业的 A 股 ETF 基金经理，擅长根据宏观环境和市场数据做出投资决策。
你的核心任务是：
1. 分析当前宏观环境对各板块的影响
2. 评估投资机会的风险收益比
3. 给出具体的投资建议和理由
4. 提示潜在的风险因素
请严格按照 JSON 格式返回分析结果。"""
def __init__(
self,
world_book: WorldBook,
total_capital: float = 1000000.0, # 默认100万
max_position_pct: float = 0.15, # 单个 ETF 最大仓位15%
min_trust_score: float = 60.0, # 最低信任指数阈值
use_llm: bool = True
):
"""
Args:
world_book: WorldBook 实例
total_capital: 总资金（元）
max_position_pct: 单个 ETF 最大仓位比例
min_trust_score: 最低信任指数（低于此值不开仓）
use_llm: 是否使用 LLM 进行辅助分析
"""
self.wb = world_book
self.total_capital = total_capital
self.max_position_pct = max_position_pct
self.min_trust_score = min_trust_score
self.use_llm = use_llm
# 加载资产映射表
self.asset_map = self._load_asset_map()
# 当前持仓（简化版，实际应从数据库读取）
self.current_positions: Dict[str, Dict] = {}
if self.use_llm:
print("[PM] ✅ LLM 辅助分析启用 - 将提供智能投资洞察")
else:
print("[PM] ⚠️ 纯量化模式 - 仅使用数学模型")
def _load_asset_map(self) -> Dict:
"""加载资产映射表"""
from pathlib import Path
asset_map_path = Path("core") / "asset_map.json"
if not asset_map_path.exists():
print("[PM] 警告: 未找到 asset_map.json")
return {}
with open(asset_map_path, 'r', encoding='utf-8') as f:
return json.load(f)
def calculate_macro_sensitivity_score(self, asset_id: str) -> float:
"""
使用向量点积计算资产的宏观敏感度得分
这是新的核心计算方法，替代传统的手工评分。
公式：
Score = Σ (macro_factor_value[i] × sensitivity[i])
示例：
- 宏观环境向量：{"interest_rate_down": 1.0, "geopolitics_tension": 0.5, "policy_digital_economy": 1.0}
- 软件ETF敏感度：{"policy_digital_economy": 1.0, "interest_rate_down": 0.7}
- 计算：(1.0 × 1.0) + (1.0 × 0.7) + (0.5 × 0) = 1.7分
Args:
asset_id: 资产ID（例如 "tech_software"）
Returns:
宏观敏感度得分（可能为负，表示利空）
"""
# 获取资产的敏感度矩阵
asset_data = self.asset_map.get("assets", {}).get(asset_id, {})
sensitivity_matrix = asset_data.get("sensitivity", {})
if not sensitivity_matrix:
print(f"[PM] 警告: {asset_id} 没有敏感度数据")
return 0.0
# 获取当前宏观因子向量
macro_vector = self.wb.macro_factor_vector
if not macro_vector:
print("[PM] 警告: 宏观因子向量为空，无法计算")
return 0.0
# 向量点积计算
score = 0.0
matched_factors = []
for factor_name, sensitivity_value in sensitivity_matrix.items():
macro_value = macro_vector.get(factor_name, 0.0)
if macro_value != 0:
contribution = macro_value * sensitivity_value
score += contribution
matched_factors.append(f"{factor_name}({macro_value}×{sensitivity_value}={contribution:.2f})")
print(f"[PM] {asset_id} 宏观得分: {score:.2f} | 因子: {', '.join(matched_factors) if matched_factors else '无匹配'}")
return round(score, 2)
def calculate_trust_index(
self,
etf_code: str,
asset_id: str,
narrative_score: float,
flow_score: float
) -> Dict:
"""
计算 Trust Index（信任指数）- 新版本
公式（升级版）：
TrustIndex = (MacroScore × 50 + Narrative × 0.3 + Flow × 0.2) - Penalty
其中：
- MacroScore: 宏观敏感度得分（向量点积）
- Narrative: 叙事强度 (0-100)
- Flow: 资金流评分 (0-100)
一票否决规则：
- 如果 MacroScore < -1.0，Penalty = 100（宏观环境严重利空）
Args:
etf_code: ETF 代码
asset_id: 资产ID
narrative_score: 叙事评分 (0-100)
flow_score: 资金流评分 (0-100)
Returns:
Trust Index 结果字典
"""
# 1. 计算宏观敏感度得分
macro_score = self.calculate_macro_sensitivity_score(asset_id)
# 2. 归一化到 0-100 范围（假设 macro_score 在 -2 到 +3 之间）
normalized_macro = max(0, min(100, (macro_score + 2) / 5 * 100))
# 3. 加权计算基础得分
base_score = normalized_macro * 0.5 + narrative_score * 0.3 + flow_score * 0.2
# 4. 一票否决：宏观环境严重利空
penalty = 0
if macro_score < -1.0:
penalty = 100
verdict = "reject"
else:
# 正常评判
trust_index = base_score - penalty
if trust_index >= self.min_trust_score:
verdict = "buy"
elif trust_index >= 40:
verdict = "hold"
else:
verdict = "sell"
final_trust_index = max(0, base_score - penalty)
return {
"code": etf_code,
"asset_id": asset_id,
"trust_index": round(final_trust_index, 2),
"macro_score": macro_score,
"normalized_macro": round(normalized_macro, 2),
"narrative_score": round(narrative_score, 2),
"flow_score": round(flow_score, 2),
"penalty": penalty,
"verdict": verdict,
"timestamp": datetime.now().isoformat()
}
def batch_calculate_trust_index(
self,
market_data: Dict[str, Dict]
) -> List[Dict]:
"""
批量计算所有 ETF 的 Trust Index - 新版本
逻辑：
1. 遍历所有资产
2. 对每个资产下的 ETF 计算宏观敏感度得分
3. 结合叙事和资金流计算最终 Trust Index
Args:
market_data: Quant.batch_analyze() 的输出
Returns:
Trust Index 列表，按评分排序
"""
results = []
print(f"[PM] 计算 Trust Index（使用向量点积方法）...")
# 遍历所有资产
for asset_id, asset_data in self.asset_map.get("assets", {}).items():
etf_list = asset_data.get("etfs", [])
for etf_code in etf_list:
# 跳过没有市场数据的 ETF
if etf_code not in market_data:
continue
# 获取叙事评分
narratives = self.wb.get_narratives_by_etf(etf_code)
if narratives:
# 取最高权重的叙事
narrative_score = max(n.current_weight for n in narratives)
else:
narrative_score = 0
# 获取资金流评分
flow_score = market_data[etf_code].get("flow_analysis", {}).get("flow_score", 0)
# 计算 Trust Index（使用向量点积）
trust_result = self.calculate_trust_index(
etf_code,
asset_id,
narrative_score,
flow_score
)
results.append(trust_result)
# 按 Trust Index 排序
results.sort(key=lambda x: x['trust_index'], reverse=True)
print(f"[PM] Trust Index 计算完成，共 {len(results)} 个标的")
# 打印 Top 5
print("\n[PM] Top 5 标的:")
for i, result in enumerate(results[:5], 1):
asset_name = self.asset_map.get("assets", {}).get(result['asset_id'], {}).get("name", "未知")
print(f" {i}. {result['code']} ({asset_name}) - TrustIndex: {result['trust_index']} "
f"(Macro: {result['macro_score']:.2f}, Narrative: {result['narrative_score']:.1f}, "
f"Flow: {result['flow_score']:.1f})")
# 使用 LLM 提供投资洞察
if self.use_llm and results:
self._provide_llm_insights(results[:10])
return results
def _provide_llm_insights(self, top_results: List[Dict]) -> None:
"""使用 LLM 提供投资洞察"""
try:
# 准备分析数据
opportunities = []
for r in top_results:
asset_name = self.asset_map.get("assets", {}).get(r['asset_id'], {}).get("name", r['asset_id'])
opportunities.append({
"code": r['code'],
"name": asset_name,
"trust_index": r['trust_index'],
"macro_score": r['macro_score'],
"narrative_score": r['narrative_score'],
"flow_score": r['flow_score'],
"verdict": r['verdict']
})
# 获取当前宏观状态
macro_state = self.wb.macro_cycle.to_dict()
macro_factors = list(self.wb.macro_factor_vector.keys())[:5]
prompt = f"""请分析以下 ETF 投资机会并给出简要建议：
【当前宏观环境】
- 市场周期: {macro_state['status']}
- 流动性: {macro_state['liquidity']}
- 政策风向: {macro_state['policy_wind']}
- 活跃因子: {', '.join(macro_factors) if macro_factors else '无'}
【Top 投资机会】
{json.dumps(opportunities, ensure_ascii=False, indent=2)}
请以 JSON 格式返回：
{{
"market_view": "当前市场整体观点（30字内）",
"top_pick": "最推荐的标的代码",
"top_pick_reason": "推荐理由（50字内）",
"risk_warning": "主要风险提示（30字内）",
"position_advice": "仓位建议（如：谨慎/标准/积极）"
}}"""
llm_output = llm_call(
messages=[
{"role": "system", "content": self.INVESTMENT_ANALYSIS_PROMPT},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=400
)
if llm_output:
# 清理 JSON
llm_output = llm_output.strip()
if llm_output.startswith("```"):
llm_output = re.sub(r'^```(?:json)?\s*', '', llm_output)
llm_output = re.sub(r'\s*```$', '', llm_output)
insights = json.loads(llm_output)
print("\n" + "=" * 50)
print("🤖 AI 投资洞察:")
print("=" * 50)
print(f"📊 市场观点: {insights.get('market_view', '')}")
print(f"⭐ 最优选择: {insights.get('top_pick', '')} - {insights.get('top_pick_reason', '')}")
print(f"⚠️ 风险提示: {insights.get('risk_warning', '')}")
print(f"💰 仓位建议: {insights.get('position_advice', '')}")
print("=" * 50)
except Exception as e:
print(f"[PM] LLM 洞察生成失败: {e}")
def generate_trade_orders(
self,
trust_results: List[Dict],
market_data: Dict[str, Dict]
) -> List[Dict]:
"""
生成交易订单
策略：
1. 买入信号：Trust Index >= 60 且 verdict = "buy"
2. 卖出信号：verdict = "sell" 或 "reject"
3. 仓位计算：Trust Index 越高，仓位越大
Args:
trust_results: batch_calculate_trust_index() 的输出
market_data: 市场数据（用于获取价格）
Returns:
订单列表
"""
orders = []
print(f"[PM] 生成交易订单...")
# 可用资金（假设当前空仓）
available_capital = self._calculate_available_capital()
for trust_result in trust_results:
etf_code = trust_result['code']
verdict = trust_result['verdict']
trust_index = trust_result['trust_index']
# 获取当前价格
realtime_data = market_data.get(etf_code, {}).get("realtime")
if not realtime_data:
continue
price = realtime_data['price']
# 判断操作
if verdict == "buy" and trust_index >= self.min_trust_score:
order = self._create_buy_order(
etf_code,
trust_index,
price,
available_capital
)
if order:
orders.append(order)
available_capital -= order['amount']
elif verdict in ["sell", "reject"] and etf_code in self.current_positions:
order = self._create_sell_order(etf_code, price)
if order:
orders.append(order)
print(f"[PM] 生成 {len(orders)} 条订单")
return orders
def _create_buy_order(
self,
etf_code: str,
trust_index: float,
price: float,
available_capital: float
) -> Optional[Dict]:
"""
创建买入订单
仓位计算：
position_pct = min(trust_index / 100 * max_position_pct, max_position_pct)
"""
# 已持仓则跳过
if etf_code in self.current_positions:
return None
# 计算目标仓位
position_pct = min(
(trust_index / 100) * self.max_position_pct,
self.max_position_pct
)
target_amount = self.total_capital * position_pct
# 检查可用资金
if target_amount > available_capital:
target_amount = available_capital * 0.8 # 保留20%缓冲
if target_amount < 1000: # 最小交易金额
return None
shares = int(target_amount / price / 100) * 100 # 向下取整到100股
actual_amount = shares * price
return {
"action": "buy",
"code": etf_code,
"price": price,
"shares": shares,
"amount": round(actual_amount, 2),
"position_pct": round(actual_amount / self.total_capital * 100, 2),
"trust_index": trust_index,
"reason": f"Trust Index {trust_index:.1f}，叙事与资金共振",
"timestamp": datetime.now().isoformat()
}
def _create_sell_order(
self,
etf_code: str,
price: float
) -> Optional[Dict]:
"""创建卖出订单"""
if etf_code not in self.current_positions:
return None
position = self.current_positions[etf_code]
shares = position['shares']
amount = shares * price
return {
"action": "sell",
"code": etf_code,
"price": price,
"shares": shares,
"amount": round(amount, 2),
"reason": "Trust Index 下降或一票否决触发",
"timestamp": datetime.now().isoformat()
}
def _calculate_available_capital(self) -> float:
"""计算可用资金"""
# 简化版：假设当前空仓
# 实际应该是：总资金 - 已用资金
return self.total_capital
def generate_portfolio_report(
self,
trust_results: List[Dict],
orders: List[Dict]
) -> Dict:
"""
生成投资组合报告
Args:
trust_results: Trust Index 结果
orders: 交易订单
Returns:
报告字典
"""
# 统计 verdict 分布
verdict_counts = {}
for result in trust_results:
verdict = result['verdict']
verdict_counts[verdict] = verdict_counts.get(verdict, 0) + 1
# 统计订单金额
total_buy_amount = sum(
order['amount'] for order in orders if order['action'] == 'buy'
)
total_sell_amount = sum(
order['amount'] for order in orders if order['action'] == 'sell'
)
# Top 5 高信任度 ETF
top_5 = trust_results[:5]
report = {
"timestamp": datetime.now().isoformat(),
"total_capital": self.total_capital,
"analysis": {
"total_etfs": len(trust_results),
"verdict_distribution": verdict_counts,
"avg_trust_index": round(
sum(r['trust_index'] for r in trust_results) / len(trust_results), 2
) if trust_results else 0
},
"orders": {
"total_orders": len(orders),
"buy_orders": len([o for o in orders if o['action'] == 'buy']),
"sell_orders": len([o for o in orders if o['action'] == 'sell']),
"total_buy_amount": round(total_buy_amount, 2),
"total_sell_amount": round(total_sell_amount, 2)
},
"top_opportunities": [
{
"code": r['code'],
"trust_index": r['trust_index'],
"verdict": r['verdict']
}
for r in top_5
],
"risk_control": {
"capital_utilization": round(total_buy_amount / self.total_capital * 100, 2),
"max_single_position": self.max_position_pct * 100,
"min_trust_threshold": self.min_trust_score
}
}
return report
def apply_risk_control(
self,
orders: List[Dict]
) -> List[Dict]:
"""
风控检查
规则：
1. 单个 ETF 不超过最大仓位
2. 总仓位不超过90%（保留10%现金）
3. 同板块 ETF 合计不超过30%
Args:
orders: 原始订单列表
Returns:
风控后的订单列表
"""
filtered_orders = []
total_amount = 0
for order in orders:
if order['action'] == 'buy':
# 检查单仓位
if order['position_pct'] > self.max_position_pct * 100:
print(f"[PM] 风控拒绝: {order['code']} 仓位过大")
continue
# 检查总仓位
new_total = total_amount + order['amount']
if new_total / self.total_capital > 0.9:
print(f"[PM] 风控拒绝: {order['code']} 总仓位将超90%")
continue
total_amount = new_total
filtered_orders.append(order)
print(f"[PM] 风控完成，通过 {len(filtered_orders)}/{len(orders)} 条订单")
return filtered_orders
# ==================== 工具函数 ====================
def save_orders_to_file(orders: List[Dict], filename: str = "trade_orders.json") -> None:
"""保存订单到文件"""
output_path = Path("data") / filename
with open(output_path, 'w', encoding='utf-8') as f:
json.dump({
"timestamp": datetime.now().isoformat(),
"orders": orders
}, f, ensure_ascii=False, indent=2)
print(f"[PM] 订单已保存到 {output_path}")
# ==================== 测试代码 ====================
if __name__ == "__main__":
print("=" * 50)
print("Skill D: PM 基金经理测试")
print("=" * 50)
# 创建 WorldBook 和 PM
wb = WorldBook(data_dir="data")
pm = PortfolioManager(wb, total_capital=1000000)
# 模拟数据：添加测试叙事
from core.world_book import Narrative, create_narrative_id
narrative1 = Narrative(
id=create_narrative_id("AI算力"),
topic="AI算力",
related_etfs=["515980"],
lifecycle_stage="realization",
base_score=88.0,
current_weight=88.0
)
wb.add_narrative(narrative1)
# 模拟 Quant 输出的市场数据
mock_market_data = {
"515980": {
"flow_analysis": {"flow_score": 72.5},
"realtime": {"price": 1.25, "name": "AI算力ETF"}
},
"512480": {
"flow_analysis": {"flow_score": 45.0},
"realtime": {"price": 2.10, "name": "半导体ETF"}
}
}
# 1. 批量计算 Trust Index
print("\n1. 计算 Trust Index:")
trust_results = pm.batch_calculate_trust_index(mock_market_data)
for result in trust_results:
print(f" {result['code']}: Trust={result['trust_index']}, "
f"叙事={result['narrative_score']}, 资金={result['flow_score']}, "
f"判定={result['verdict']}")
# 2. 生成交易订单
print("\n2. 生成交易订单:")
orders = pm.generate_trade_orders(trust_results, mock_market_data)
for order in orders:
print(f" {order['action'].upper()} {order['code']}: "
f"{order['shares']}股 @ ¥{order['price']}, "
f"金额 ¥{order['amount']:,.0f}")
# 3. 风控检查
print("\n3. 风控检查:")
safe_orders = pm.apply_risk_control(orders)
# 4. 生成投资组合报告
print("\n4. 投资组合报告:")
report = pm.generate_portfolio_report(trust_results, safe_orders)
print(json.dumps(report, ensure_ascii=False, indent=2))
# 5. 保存订单
if safe_orders:
save_orders_to_file(safe_orders)
print("\n✅ PM 模块测试完成")
skills/quant.py
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"""
Skill B: Quant (量化研究员)
============================
职能：行情分析、资金流计算、异常检测
输入：ETF 代码列表
输出：Market_Data_JSON (资金流评分、技术指标、异常信号)
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.config import config, llm_call
import akshare as ak
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
import warnings
warnings.filterwarnings('ignore')
class QuantAnalyzer:
"""量化分析器 - 负责 ETF 行情数据获取和资金流分析"""
def __init__(self):
self.cache = {} # 简单缓存机制
def get_etf_realtime_data(self, etf_code: str) -> Optional[Dict]:
"""
获取 ETF 实时行情数据
Args:
etf_code: ETF 代码（如 "512980"）
Returns:
实时行情字典
"""
try:
# 使用 akshare 获取实时行情
df = ak.fund_etf_spot_em()
# 查找目标 ETF
etf_data = df[df['代码'] == etf_code]
if etf_data.empty:
print(f"[Quant] 未找到 ETF: {etf_code}")
return None
row = etf_data.iloc[0]
return {
"code": etf_code,
"name": row['名称'],
"price": float(row['最新价']),
"change_pct": float(row['涨跌幅']),
"volume": float(row['成交量']),
"amount": float(row['成交额']),
"turnover_rate": float(row.get('换手率', 0)),
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
except Exception as e:
print(f"[Quant] 获取实时数据失败 {etf_code}: {e}")
return None
def get_etf_historical_data(
self,
etf_code: str,
days: int = 20
) -> Optional[pd.DataFrame]:
"""
获取 ETF 历史行情数据
Args:
etf_code: ETF 代码
days: 获取天数（默认20个交易日）
Returns:
历史行情 DataFrame
"""
try:
# 计算日期范围
end_date = datetime.now().strftime("%Y%m%d")
start_date = (datetime.now() - timedelta(days=days*2)).strftime("%Y%m%d")
# 获取历史数据
df = ak.fund_etf_hist_em(
symbol=etf_code,
period="daily",
start_date=start_date,
end_date=end_date,
adjust="qfq"
)
if df is None or df.empty:
print(f"[Quant] 无历史数据: {etf_code}")
return None
# 重命名列
df.rename(columns={
'日期': 'date',
'开盘': 'open',
'收盘': 'close',
'最高': 'high',
'最低': 'low',
'成交量': 'volume',
'成交额': 'amount',
'涨跌幅': 'change_pct',
'换手率': 'turnover_rate'
}, inplace=True)
return df.tail(days)
except Exception as e:
print(f"[Quant] 获取历史数据失败 {etf_code}: {e}")
return None
def calculate_fund_flow_score(
self,
etf_code: str,
window: int = 5
) -> Dict:
"""
计算资金流向评分
核心逻辑：
1. 成交额放量 (amount_surge)
2. 换手率提升 (turnover_boost)
3. 价格与成交量背离检测
Args:
etf_code: ETF 代码
window: 计算窗口（天数）
Returns:
资金流评分字典
"""
df = self.get_etf_historical_data(etf_code, days=window*2)
if df is None or len(df) < window:
return {
"code": etf_code,
"flow_score": 0,
"amount_surge": 0,
"turnover_boost": 0,
"divergence_signal": False,
"status": "insufficient_data"
}
# 近期数据 vs 历史基准
recent = df.tail(window)
baseline = df.head(window)
# 1. 成交额放量评分 (0-50)
recent_avg_amount = recent['amount'].mean()
baseline_avg_amount = baseline['amount'].mean()
if baseline_avg_amount > 0:
amount_ratio = recent_avg_amount / baseline_avg_amount
amount_surge_score = min((amount_ratio - 1) * 50, 50)
else:
amount_surge_score = 0
# 2. 换手率提升评分 (0-30)
recent_avg_turnover = recent['turnover_rate'].mean()
baseline_avg_turnover = baseline['turnover_rate'].mean()
if baseline_avg_turnover > 0:
turnover_ratio = recent_avg_turnover / baseline_avg_turnover
turnover_boost_score = min((turnover_ratio - 1) * 30, 30)
else:
turnover_boost_score = 0
# 3. 价格与成交量背离检测 (奖励分 +20)
recent_price_change = recent['close'].iloc[-1] / recent['close'].iloc[0] - 1
recent_volume_change = recent['volume'].iloc[-1] / recent['volume'].iloc[0] - 1
# 价跌量增 = 可能见底信号
divergence_signal = (recent_price_change < -0.02 and recent_volume_change > 0.3)
divergence_bonus = 20 if divergence_signal else 0
# 总分计算 (0-100)
total_score = max(0, min(100,
amount_surge_score + turnover_boost_score + divergence_bonus
))
return {
"code": etf_code,
"flow_score": round(total_score, 2),
"amount_surge": round(amount_surge_score, 2),
"turnover_boost": round(turnover_boost_score, 2),
"divergence_signal": divergence_signal,
"recent_avg_amount": round(recent_avg_amount / 1e8, 2), # 单位：亿
"baseline_avg_amount": round(baseline_avg_amount / 1e8, 2),
"status": "success"
}
def detect_anomaly(
self,
etf_code: str,
sigma: float = 2.0
) -> Dict:
"""
异常检测 - 识别异常放量或暴涨暴跌
Args:
etf_code: ETF 代码
sigma: 标准差倍数（默认2倍）
Returns:
异常信号字典
"""
df = self.get_etf_historical_data(etf_code, days=30)
if df is None or len(df) < 10:
return {"code": etf_code, "has_anomaly": False, "reason": "insufficient_data"}
# 计算统计基准
mean_amount = df['amount'].mean()
std_amount = df['amount'].std()
mean_change = df['change_pct'].mean()
std_change = df['change_pct'].std()
# 最新数据
latest = df.iloc[-1]
anomaly_signals = []
# 成交额异常
if latest['amount'] > mean_amount + sigma * std_amount:
anomaly_signals.append("volume_surge")
# 涨跌幅异常
if abs(latest['change_pct']) > abs(mean_change) + sigma * std_change:
if latest['change_pct'] > 0:
anomaly_signals.append("price_spike")
else:
anomaly_signals.append("price_crash")
return {
"code": etf_code,
"has_anomaly": len(anomaly_signals) > 0,
"signals": anomaly_signals,
"latest_amount": round(latest['amount'] / 1e8, 2),
"latest_change_pct": round(latest['change_pct'], 2),
"threshold_amount": round((mean_amount + sigma * std_amount) / 1e8, 2),
"threshold_change": round(abs(mean_change) + sigma * std_change, 2)
}
def batch_analyze(
self,
etf_codes: List[str]
) -> Dict[str, Dict]:
"""
批量分析 ETF
Args:
etf_codes: ETF 代码列表
Returns:
分析结果字典 {code: result}
"""
results = {}
print(f"[Quant] 开始批量分析 {len(etf_codes)} 个 ETF...")
for code in etf_codes:
print(f"[Quant] 分析 {code}...")
# 资金流评分
flow_result = self.calculate_fund_flow_score(code)
# 异常检测
anomaly_result = self.detect_anomaly(code)
# 实时行情
realtime_data = self.get_etf_realtime_data(code)
results[code] = {
"flow_analysis": flow_result,
"anomaly_detection": anomaly_result,
"realtime": realtime_data,
"timestamp": datetime.now().isoformat()
}
print(f"[Quant] 批量分析完成")
return results
def generate_market_report(
self,
analysis_results: Dict[str, Dict]
) -> Dict:
"""
生成市场数据报告（供其他 Agent 使用）
Args:
analysis_results: batch_analyze 的输出
Returns:
标准化的 Market_Data_JSON
"""
report = {
"timestamp": datetime.now().isoformat(),
"total_analyzed": len(analysis_results),
"etf_scores": {},
"top_flow_etfs": [],
"anomaly_alerts": []
}
# 提取评分
for code, result in analysis_results.items():
flow_score = result.get("flow_analysis", {}).get("flow_score", 0)
report["etf_scores"][code] = flow_score
# 异常提醒
if result.get("anomaly_detection", {}).get("has_anomaly"):
report["anomaly_alerts"].append({
"code": code,
"signals": result["anomaly_detection"]["signals"]
})
# 排序 Top 资金流 ETF
sorted_etfs = sorted(
report["etf_scores"].items(),
key=lambda x: x[1],
reverse=True
)
report["top_flow_etfs"] = [
{"code": code, "score": score}
for code, score in sorted_etfs[:5]
]
return report
# ==================== 工具函数 ====================
def get_all_etf_codes_from_asset_map() -> List[str]:
"""从 asset_map.json 提取所有 ETF 代码"""
import json
from pathlib import Path
asset_map_path = Path("core") / "asset_map.json"
if not asset_map_path.exists():
print("[Quant] 未找到 asset_map.json")
return []
with open(asset_map_path, 'r', encoding='utf-8') as f:
asset_map = json.load(f)
etf_codes = set()
for sector_data in asset_map.get("sectors", {}).values():
etf_codes.update(sector_data.get("etfs", []))
return list(etf_codes)
if __name__ == "__main__":
print("=" * 50)
print("Skill B: Quant 量化研究员测试")
print("=" * 50)
# 创建分析器实例
quant = QuantAnalyzer()
# 测试单个 ETF
test_code = "512480" # 半导体 ETF
print(f"\n测试 ETF: {test_code}")
# 1. 获取实时数据
print("\n1. 实时行情:")
realtime = quant.get_etf_realtime_data(test_code)
if realtime:
print(f" {realtime['name']}: ¥{realtime['price']}, 涨跌幅: {realtime['change_pct']}%")
# 2. 资金流评分
print("\n2. 资金流分析:")
flow = quant.calculate_fund_flow_score(test_code)
print(f" 资金流评分: {flow['flow_score']}")
print(f" 成交额放量: {flow['amount_surge']}")
print(f" 换手率提升: {flow['turnover_boost']}")
# 3. 异常检测
print("\n3. 异常检测:")
anomaly = quant.detect_anomaly(test_code)
print(f" 发现异常: {anomaly['has_anomaly']}")
if anomaly['has_anomaly']:
print(f" 异常信号: {anomaly['signals']}")
# 4. 批量分析（测试少量 ETF）
print("\n4. 批量分析测试:")
test_codes = ["512480", "515980", "159928"] # 半导体、AI、消费
batch_results = quant.batch_analyze(test_codes)
# 5. 生成报告
report = quant.generate_market_report(batch_results)
print(f"\n5. 市场报告:")
print(f" 分析数量: {report['total_analyzed']}")
print(f" Top 资金流 ETF:")
for item in report['top_flow_etfs']:
print(f" {item['code']}: {item['score']:.2f}")
print("\n✅ Quant 模块测试完成")
skills/strategist.py
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"""
Skill C: Strategist (宏观策略师)
=================================
职能：维护世界书、判断周期、更新叙事权重
输入：Narrative_JSON + 历史 WorldBook
输出：更新后的 World_Book
设计原则：
- 使用 LLM 进行宏观周期判断和叙事生命周期分析
- 结合规则引擎提供可解释的决策逻辑
- 支持 LLM 失败时的优雅降级
"""
import sys
from pathlib import Path
# 添加项目根目录到路径
sys.path.insert(0, str(Path(__file__).parent.parent))
from core.world_book import WorldBook, Narrative, create_narrative_id
from core.config import config, llm_call
from typing import Dict, List, Optional
from datetime import datetime
import json
class MacroStrategist:
"""
宏观策略师 - 负责 World Book 的智能维护
核心职责：
1. 接收 Analyst 的新闻分析结果
2. 使用 LLM 进行宏观周期和叙事生命周期判断
3. 智能更新或创建叙事对象
4. 应用时间衰减
5. 生成投资策略建议
"""
# LLM 系统提示词
MACRO_ANALYSIS_PROMPT = """你是一位资深的 A 股宏观策略分析师，擅长根据政策新闻判断市场周期变化。
你的核心任务是：
1. 分析当前新闻叙事对宏观环境的影响
2. 判断市场所处的周期阶段
3. 识别关键的宏观驱动因子
4. 给出策略配置建议
宏观周期状态：
- upward（上行）：政策利好、流动性充裕、风险偏好上升
- downward（下行）：政策收紧、流动性紧张、风险偏好下降
- neutral（中性）：政策观望、流动性平稳
流动性状态：
- loose（宽松）：降准降息、央行投放
- tight（紧张）：加息、回笼资金
- neutral（中性）
政策风向：
- stimulus（刺激）：财政扩张、产业扶持
- regulation（监管）：整顿规范、去杠杆
- wait_and_see（观望）
请严格按照 JSON 格式返回分析结果。"""
def __init__(self, world_book: WorldBook, use_llm: bool = True):
"""
Args:
world_book: WorldBook 实例
use_llm: 是否使用 LLM 进行分析（默认 True）
"""
self.wb = world_book
self.macro_matrix = self._load_macro_matrix()
self.use_llm = use_llm
if self.use_llm:
print("[Strategist] ✅ LLM 模式启用 - 将使用大模型进行智能策略分析")
else:
print("[Strategist] ⚠️ 降级模式 - 将使用规则引擎")
def _load_macro_matrix(self) -> Dict:
"""加载宏观逻辑矩阵"""
matrix_path = Path("core") / "macro_matrix.json"
if not matrix_path.exists():
print("[Strategist] 警告: 未找到 macro_matrix.json")
return {}
with open(matrix_path, 'r', encoding='utf-8') as f:
return json.load(f)
def process_narrative_json(
self,
narrative_json: Dict
) -> None:
"""
处理 Analyst 输出的 Narrative_JSON
逻辑：
1. 遍历所有叙事
2. 如果是新叙事 → 创建
3. 如果已存在 → 强化（boost）
4. 更新生命周期阶段
Args:
narrative_json: Analyst.generate_narrative_json() 的输出
"""
narratives = narrative_json.get('narratives', [])
print(f"[Strategist] 处理 {len(narratives)} 个叙事...")
for narrative_data in narratives:
topic = narrative_data['topic']
# 检查是否已存在
existing_narrative = self.wb.get_narrative_by_topic(topic)
if existing_narrative:
# 已存在 → 强化
self._boost_narrative(existing_narrative, narrative_data)
else:
# 新叙事 → 创建
self._create_narrative(narrative_data)
# 推导宏观因子向量
self.infer_macro_factor_vector(narrative_json)
print(f"[Strategist] 叙事处理完成，当前活跃叙事数: {len(self.wb.active_narratives)}")
def _create_narrative(self, narrative_data: Dict) -> None:
"""创建新叙事对象"""
topic = narrative_data['topic']
# 生成唯一 ID
narrative_id = create_narrative_id(topic)
# 判断衰减系数（Level A 新闻衰减慢）
decay_factor = 0.93 if narrative_data['level_a_count'] > 0 else 0.95
# 创建 Narrative 对象
narrative = Narrative(
id=narrative_id,
topic=topic,
related_etfs=narrative_data['related_etfs'],
lifecycle_stage=narrative_data['lifecycle_stage'],
base_score=narrative_data['max_score'],
decay_factor=decay_factor,
current_weight=narrative_data['avg_score']
)
self.wb.add_narrative(narrative)
print(f"[Strategist] 创建新叙事: {topic} (评分: {narrative.current_weight})")
def _boost_narrative(
self,
existing_narrative: Narrative,
narrative_data: Dict
) -> None:
"""强化已有叙事"""
topic = narrative_data['topic']
new_score = narrative_data['avg_score']
# 使用 Narrative 的 boost 方法
existing_narrative.boost(new_score)
# 如果有 Level A 新闻，可能需要升级生命周期
if narrative_data['level_a_count'] > 0:
if existing_narrative.lifecycle_stage == "fermentation":
existing_narrative.update_stage("realization")
print(f"[Strategist] 强化叙事: {topic} → {existing_narrative.current_weight:.2f}")
def daily_maintenance(self) -> Dict:
"""
每日维护任务
执行：
1. 对所有叙事应用时间衰减
2. 移除衰退的叙事
3. 检测生命周期阶段变化
Returns:
维护报告
"""
print("[Strategist] 执行每日维护...")
# 1. 应用衰减
self.wb.decay_all_narratives()
# 2. 检测阶段降级
for narrative in self.wb.active_narratives.values():
self._check_stage_downgrade(narrative)
# 3. 移除低权重叙事
removed = self.wb.remove_weak_narratives(threshold=10.0)
# 4. 生成报告
report = {
"timestamp": datetime.now().isoformat(),
"total_narratives": len(self.wb.active_narratives),
"removed_narratives": len(removed),
"top_narratives": [
{"topic": n.topic, "weight": n.current_weight}
for n in self.wb.get_top_narratives(5)
]
}
print(f"[Strategist] 维护完成，移除 {len(removed)} 个衰退叙事")
return report
def _check_stage_downgrade(self, narrative: Narrative) -> None:
"""检测叙事是否需要降级生命周期"""
current_stage = narrative.lifecycle_stage
weight = narrative.current_weight
# 权重低于阈值 → 进入衰退期
if weight < 30 and current_stage != "decay":
narrative.update_stage("decay")
print(f"[Strategist] 叙事进入衰退期: {narrative.topic}")
# realization → fermentation
elif weight < 60 and current_stage == "realization":
narrative.update_stage("fermentation")
def detect_macro_cycle_change(
self,
narrative_json: Dict,
market_data: Optional[Dict] = None
) -> Optional[Dict]:
"""
检测宏观周期变化（核心方法）
优先使用 LLM 进行智能分析，失败时降级到规则引擎
Args:
narrative_json: Analyst 输出
market_data: Quant 输出（可选）
Returns:
宏观周期变化建议（如有）
"""
# 检查是否有 Level A 新闻
level_a_count = sum(
n.get('level_a_count', 0)
for n in narrative_json.get('narratives', [])
)
if level_a_count == 0:
print("[Strategist] 无 Level A 新闻，跳过宏观周期检测")
return None
print(f"[Strategist] 检测到 {level_a_count} 条 Level A 新闻，分析宏观周期...")
if self.use_llm:
result = self._detect_cycle_with_llm(narrative_json, market_data)
if result:
return result
print("[Strategist] ⚠️ LLM 分析失败，降级到规则引擎")
# 降级到规则引擎
return self._detect_cycle_with_rules(narrative_json)
def _detect_cycle_with_llm(
self,
narrative_json: Dict,
market_data: Optional[Dict]
) -> Optional[Dict]:
"""使用 LLM 分析宏观周期变化"""
# 构建分析上下文
narratives_summary = []
for n in narrative_json.get('narratives', []):
topic_name = n.get('topic_name', n.get('topic', ''))
narratives_summary.append({
"topic": topic_name,
"score": n.get('max_score', 0),
"level_a": n.get('level_a_count', 0) > 0,
"sentiment": n.get('overall_sentiment', 'neutral'),
"signals": n.get('key_signals', [])[:2]
})
# 当前宏观状态
current_macro = self.wb.macro_cycle.to_dict()
prompt = f"""请分析以下市场叙事信息，判断宏观周期是否发生变化：
【当前宏观状态】
- 市场周期: {current_macro['status']}
- 流动性: {current_macro['liquidity']}
- 政策风向: {current_macro['policy_wind']}
【最新叙事信息】
{json.dumps(narratives_summary, ensure_ascii=False, indent=2)}
请分析后以 JSON 格式返回：
{{
"cycle_changed": true/false,
"new_status": "upward/downward/neutral",
"new_liquidity": "loose/tight/neutral",
"new_policy_wind": "stimulus/regulation/wait_and_see",
"reason": "变化原因（50字内）",
"confidence": 0-100,
"key_factors": ["驱动因子1", "驱动因子2"]
}}
如果宏观环境未发生明显变化，cycle_changed 设为 false。"""
try:
llm_output = llm_call(
messages=[
{"role": "system", "content": self.MACRO_ANALYSIS_PROMPT},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=500
)
if not llm_output:
return None
# 清理并解析 JSON
import re
llm_output = llm_output.strip()
if llm_output.startswith("```"):
llm_output = re.sub(r'^```(?:json)?\s*', '', llm_output)
llm_output = re.sub(r'\s*```$', '', llm_output)
result = json.loads(llm_output)
if not result.get("cycle_changed", False):
print(f"[Strategist] 🤖 LLM 判断：宏观周期无明显变化")
return None
# 验证字段
valid_status = ["upward", "downward", "neutral"]
valid_liquidity = ["loose", "tight", "neutral"]
valid_policy = ["stimulus", "regulation", "wait_and_see"]
new_status = result.get("new_status", "neutral")
new_liquidity = result.get("new_liquidity", "neutral")
new_policy = result.get("new_policy_wind", "wait_and_see")
if new_status not in valid_status:
new_status = "neutral"
if new_liquidity not in valid_liquidity:
new_liquidity = "neutral"
if new_policy not in valid_policy:
new_policy = "wait_and_see"
print(f"[Strategist] 🤖 LLM 判断：{result.get('reason', '')}")
print(f"[Strategist] 🤖 置信度: {result.get('confidence', 0)}%")
return {
"status": new_status,
"liquidity": new_liquidity,
"policy_wind": new_policy,
"reason": result.get("reason", "LLM 分析结果"),
"key_factors": result.get("key_factors", []),
"analysis_method": "llm"
}
except json.JSONDecodeError as e:
print(f"[Strategist] LLM 返回格式解析失败: {e}")
return None
except Exception as e:
print(f"[Strategist] LLM 分析异常: {e}")
return None
def _detect_cycle_with_rules(self, narrative_json: Dict) -> Optional[Dict]:
"""规则引擎检测宏观周期变化（降级方案）"""
# 检测关键词模式
narratives_text = " ".join([
n.get('topic', '') + " " + " ".join(n.get('key_signals', []))
for n in narrative_json.get('narratives', [])
])
# 货币宽松信号
if any(kw in narratives_text for kw in ["降准", "降息", "宽松", "流动性"]):
return {
"status": "upward",
"liquidity": "loose",
"policy_wind": "stimulus",
"reason": "货币政策宽松，市场流动性改善",
"key_factors": ["降准/降息"],
"analysis_method": "rules"
}
# 监管收紧信号
if any(kw in narratives_text for kw in ["监管", "整顿", "规范", "收紧"]):
return {
"status": "downward",
"liquidity": "tight",
"policy_wind": "regulation",
"reason": "监管政策收紧，风险偏好下降",
"key_factors": ["监管收紧"],
"analysis_method": "rules"
}
# 财政刺激信号
if any(kw in narratives_text for kw in ["刺激", "万亿", "基建", "财政"]):
return {
"status": "upward",
"liquidity": "loose",
"policy_wind": "stimulus",
"reason": "财政政策积极，经济预期改善",
"key_factors": ["财政刺激"],
"analysis_method": "rules"
}
return None
def apply_macro_cycle_change(self, cycle_change: Dict) -> None:
"""应用宏观周期变化"""
self.wb.update_macro_cycle(
status=cycle_change.get('status'),
liquidity=cycle_change.get('liquidity'),
policy_wind=cycle_change.get('policy_wind')
)
def infer_macro_factor_vector(self, narrative_json: Dict) -> Dict[str, float]:
"""
根据新闻叙事推导宏观因子向量（核心方法）
优先使用 LLM 进行智能推导，失败时降级到规则引擎。
这是核心方法，将新闻内容映射到宏观因子空间，供 PM 进行向量点积计算。
Args:
narrative_json: Analyst 输出的叙事 JSON
Returns:
宏观因子向量，例如:
{
"interest_rate_down": 1.0,
"policy_digital_economy": 0.8,
"geopolitics_tension": 0.5
}
"""
print("[Strategist] 推导宏观因子向量...")
if self.use_llm:
result = self._infer_factors_with_llm(narrative_json)
if result:
self.wb.update_macro_factor_vector(result)
return result
print("[Strategist] ⚠️ LLM 推导失败，降级到规则引擎")
# 降级到规则引擎
result = self._infer_factors_with_rules(narrative_json)
self.wb.update_macro_factor_vector(result)
return result
def _infer_factors_with_llm(self, narrative_json: Dict) -> Optional[Dict[str, float]]:
"""使用 LLM 推导宏观因子向量"""
# 准备叙事摘要
narratives_summary = []
for n in narrative_json.get('narratives', []):
narratives_summary.append({
"topic": n.get('topic_name', n.get('topic', '')),
"score": n.get('max_score', 0),
"level_a": n.get('level_a_count', 0) > 0,
"sentiment": n.get('overall_sentiment', 'neutral'),
"key_signals": n.get('key_signals', [])
})
# 可用因子列表
available_factors = [
"interest_rate_down", "liquidity_easing", "cpi_rebound",
"risk_on", "risk_off", "policy_tech_self_reliance",
"policy_digital_economy", "policy_new_infra", "policy_capital_market",
"policy_soe_reform", "policy_low_altitude", "policy_food_security",
"policy_public_health", "policy_platform_economy", "consumption_stimulus",
"govt_spending", "geopolitics_tension", "currency_rmb_depreciation",
"export_growth", "fed_rate_cut", "dollar_index_down",
"inflation_expectations", "oil_price_up", "market_volume_spike",
"tech_cycle_up", "demographic_trend", "foreign_inflow"
]
prompt = f"""请根据以下市场叙事信息，推导当前活跃的宏观因子及其强度。
【叙事信息】
{json.dumps(narratives_summary, ensure_ascii=False, indent=2)}
【可用宏观因子】
{', '.join(available_factors)}
请分析新闻内容，判断哪些宏观因子被触发，并给出强度评分（0.0-1.0）。
强度参考：
- 1.0: 政策明确、信号强烈（如央行降准公告）
- 0.7-0.9: 政策导向明确（如部委发文）
- 0.4-0.6: 信号中等（如行业动态）
- 0.1-0.3: 信号微弱（如市场传闻）
以 JSON 格式返回（仅包含被触发的因子）：
{{
"factors": {{
"factor_name": 0.8,
"factor_name2": 0.5
}},
"reasoning": "简要说明推导逻辑（50字内）"
}}"""
try:
llm_output = llm_call(
messages=[
{"role": "system", "content": "你是一位宏观经济分析师，擅长从新闻中提取宏观经济信号。"},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=600
)
if not llm_output:
return None
# 清理并解析 JSON
import re
llm_output = llm_output.strip()
if llm_output.startswith("```"):
llm_output = re.sub(r'^```(?:json)?\s*', '', llm_output)
llm_output = re.sub(r'\s*```$', '', llm_output)
result = json.loads(llm_output)
factors = result.get("factors", {})
reasoning = result.get("reasoning", "")
# 验证因子名称和强度
valid_factors = {}
for factor_name, strength in factors.items():
if factor_name in available_factors:
strength = float(strength)
strength = max(0.0, min(1.0, strength))
valid_factors[factor_name] = round(strength, 2)
if valid_factors:
print(f"[Strategist] 🤖 LLM 推导出 {len(valid_factors)} 个宏观因子")
print(f"[Strategist] 🤖 推导逻辑: {reasoning}")
for factor, strength in sorted(valid_factors.items(), key=lambda x: -x[1])[:5]:
print(f" 📊 {factor}: {strength}")
return valid_factors
except json.JSONDecodeError as e:
print(f"[Strategist] LLM 返回格式解析失败: {e}")
return None
except Exception as e:
print(f"[Strategist] LLM 推导异常: {e}")
return None
def _infer_factors_with_rules(self, narrative_json: Dict) -> Dict[str, float]:
"""规则引擎推导宏观因子向量（降级方案）"""
factor_vector: Dict[str, float] = {}
# 宏观因子关键词映射表
factor_keyword_map = {
"interest_rate_down": ["降息", "降准", "MLF下调", "LPR下调", "利率下行"],
"liquidity_easing": ["流动性", "宽松", "货币政策", "注入流动性"],
"cpi_rebound": ["CPI", "通胀", "物价上涨", "消费复苏"],
"risk_on": ["牛市", "突破", "成交量放大", "风险偏好"],
"risk_off": ["暴跌", "恐慌", "避险", "黑天鹅"],
"policy_tech_self_reliance": ["自主可控", "国产替代", "卡脖子", "芯片", "半导体"],
"policy_digital_economy": ["数字经济", "数据要素", "信创", "AI", "算力"],
"policy_new_infra": ["新基建", "5G", "6G", "东数西算", "算力底座"],
"policy_capital_market": ["资本市场", "印花税", "T+0", "金融强国"],
"policy_soe_reform": ["国企改革", "中特估", "红利"],
"policy_low_altitude": ["低空经济", "无人机", "eVTOL"],
"policy_food_security": ["粮食安全", "一号文件", "种业"],
"policy_public_health": ["医疗", "医保", "创新药", "老龄化"],
"consumption_stimulus": ["消费券", "汽车下乡", "家电补贴", "内需"],
"govt_spending": ["财政支出", "专项债", "万亿", "基建投资"],
"geopolitics_tension": ["地缘", "制裁", "冲突", "台海", "中美"],
"export_growth": ["出口", "外贸", "订单", "海外需求"],
"market_volume_spike": ["成交量", "放量", "天量"],
}
# 收集所有文本
all_text = ""
level_a_count = 0
level_b_count = 0
for narrative in narrative_json.get('narratives', []):
all_text += f" {narrative.get('topic', '')} "
all_text += " ".join(narrative.get('key_signals', []))
level_a_count += narrative.get('level_a_count', 0)
level_b_count += narrative.get('level_b_count', 0)
# 扫描关键词
for factor_name, keywords in factor_keyword_map.items():
match_count = sum(1 for kw in keywords if kw in all_text)
if match_count > 0:
if level_a_count > 0:
strength = min(1.0, 0.8 + match_count * 0.1)
elif level_b_count > 0:
strength = min(0.9, 0.5 + match_count * 0.1)
else:
strength = min(0.6, 0.3 + match_count * 0.1)
factor_vector[factor_name] = round(strength, 2)
print(f"[Strategist] 📏 规则引擎推导出 {len(factor_vector)} 个宏观因子")
return factor_vector
def calculate_macro_impact_on_etf(
self,
etf_code: str,
macro_factor: str
) -> float:
"""
计算宏观因子对特定 ETF 的影响
Args:
etf_code: ETF 代码
macro_factor: 宏观因子名称（如 "rate_cut"）
Returns:
影响值 (-10 到 +10)
"""
# 从宏观矩阵查询
factor_data = self.macro_matrix.get("macro_factors", {}).get(macro_factor, {})
impact_map = factor_data.get("impact", {})
# 查找 ETF 对应的类别
etf_mapping = self.macro_matrix.get("etf_mapping", {})
for etf_type, codes in etf_mapping.items():
if etf_code in codes:
return impact_map.get(etf_type, 0)
return 0
def generate_world_book_snapshot(self) -> Dict:
"""生成 World Book 快照（供 PM 使用）"""
snapshot = self.wb.export_snapshot()
# 添加策略师视角的元数据
snapshot["strategist_view"] = {
"strong_narratives": [
n.topic for n in self.wb.get_top_narratives(3)
],
"macro_recommendation": self._get_macro_recommendation(),
"risk_level": self._assess_risk_level()
}
return snapshot
def _get_macro_recommendation(self) -> str:
"""生成宏观建议"""
cycle = self.wb.macro_cycle
if cycle.status == "upward" and cycle.liquidity == "loose":
return "aggressive" # 激进配置
elif cycle.status == "downward" or cycle.policy_wind == "regulation":
return "defensive" # 防守配置
else:
return "balanced" # 平衡配置
def _assess_risk_level(self) -> str:
"""评估市场风险等级"""
# 基于叙事数量和宏观状态
narrative_count = len(self.wb.active_narratives)
cycle = self.wb.macro_cycle
if cycle.status == "downward" or narrative_count < 3:
return "high"
elif cycle.status == "upward" and narrative_count >= 5:
return "low"
else:
return "medium"
# ==================== 测试代码 ====================
if __name__ == "__main__":
print("=" * 50)
print("Skill C: Strategist 宏观策略师测试")
print("=" * 50)
# 创建 WorldBook 和 Strategist
wb = WorldBook(data_dir="data")
strategist = MacroStrategist(wb)
# 模拟 Analyst 输出
mock_narrative_json = {
"timestamp": datetime.now().isoformat(),
"total_news": 3,
"narratives": [
{
"topic": "低空经济",
"news_count": 2,
"avg_score": 82.0,
"max_score": 90.0,
"level_a_count": 0,
"related_etfs": ["512980", "159969"],
"lifecycle_stage": "fermentation"
},
{
"topic": "AI算力",
"news_count": 1,
"avg_score": 75.0,
"max_score": 75.0,
"level_a_count": 0,
"related_etfs": ["515980", "159813"],
"lifecycle_stage": "fermentation"
}
]
}
# 1. 处理叙事 JSON
print("\n1. 处理叙事 JSON:")
strategist.process_narrative_json(mock_narrative_json)
# 2. 显示当前 World Book 状态
print("\n2. 当前 World Book 状态:")
print(f" 活跃叙事数: {len(wb.active_narratives)}")
print(f" 宏观周期: {wb.macro_cycle.to_dict()}")
# 3. 执行每日维护
print("\n3. 执行每日维护:")
maintenance_report = strategist.daily_maintenance()
print(f" 维护报告: {json.dumps(maintenance_report, ensure_ascii=False, indent=2)}")
# 4. 检测宏观周期变化（模拟降准新闻）
print("\n4. 检测宏观周期变化:")
mock_news_with_policy = {
"narratives": [
{"topic": "央行降准", "level_a_count": 1, "avg_score": 95.0}
]
}
cycle_change = strategist.detect_macro_cycle_change(mock_news_with_policy)
if cycle_change:
print(f" 检测到周期变化: {cycle_change['reason']}")
strategist.apply_macro_cycle_change(cycle_change)
# 5. 生成快照
print("\n5. 生成 World Book 快照:")
snapshot = strategist.generate_world_book_snapshot()
print(f" 策略建议: {snapshot['strategist_view']['macro_recommendation']}")
print(f" 风险等级: {snapshot['strategist_view']['risk_level']}")
# 6. 保存 World Book
wb.save()
print("\n✅ Strategist 模块测试完成")
test_vector_calculation.py
+199
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@@ -0,0 +1,199 @@
"""
测试向量点积计算逻辑
======================
验证 Strategist 推导的宏观因子向量与 PM 的敏感度矩阵计算是否正确
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from core.world_book import WorldBook
from skills.strategist import MacroStrategist
from skills.pm import PortfolioManager
def test_macro_vector_calculation():
"""测试宏观因子向量计算"""
print("=" * 60)
print("测试 1: 宏观因子向量推导")
print("=" * 60)
# 创建 WorldBook
wb = WorldBook(data_dir="data/test")
# 创建 Strategist
strategist = MacroStrategist(wb)
# 模拟新闻分析结果
narrative_json = {
"narratives": [
{
"topic": "央行宣布全面降息，支持数字经济发展",
"level_a_count": 1,
"level_b_count": 0,
"avg_score": 85,
"max_score": 90,
"related_etfs": ["515230"],
"lifecycle_stage": "fermentation"
},
{
"topic": "地缘紧张局势升级，避险情绪升温",
"level_a_count": 0,
"level_b_count": 1,
"avg_score": 70,
"max_score": 75,
"related_etfs": ["518880"],
"lifecycle_stage": "incubation"
}
]
}
# 推导宏观因子向量
print("\n输入新闻:")
for n in narrative_json['narratives']:
print(f" - {n['topic']} (Level A: {n['level_a_count']}, Level B: {n['level_b_count']})")
print("\n推导宏观因子向量:")
factor_vector = strategist.infer_macro_factor_vector(narrative_json)
print(f"\n结果: {factor_vector}")
return wb, factor_vector
def test_sensitivity_calculation(wb: WorldBook, factor_vector: dict):
"""测试敏感度矩阵计算"""
print("\n" + "=" * 60)
print("测试 2: 向量点积计算")
print("=" * 60)
# 创建 PM
pm = PortfolioManager(wb, total_capital=1000000)
# 测试案例 1: 软件ETF
print("\n案例 1: 软件与数字经济 (tech_software)")
print("-" * 60)
macro_score_software = pm.calculate_macro_sensitivity_score("tech_software")
print(f"最终得分: {macro_score_software}")
# 测试案例 2: 银行ETF
print("\n案例 2: 银行/高股息 (finance_bank)")
print("-" * 60)
macro_score_bank = pm.calculate_macro_sensitivity_score("finance_bank")
print(f"最终得分: {macro_score_bank}")
# 测试案例 3: 黄金ETF
print("\n案例 3: 资源与黄金 (resources_gold)")
print("-" * 60)
macro_score_gold = pm.calculate_macro_sensitivity_score("resources_gold")
print(f"最终得分: {macro_score_gold}")
# 对比分析
print("\n" + "=" * 60)
print("对比分析")
print("=" * 60)
print(f"软件ETF得分: {macro_score_software}")
print(f"银行ETF得分: {macro_score_bank}")
print(f"黄金ETF得分: {macro_score_gold}")
print("\n结论:")
if macro_score_software > macro_score_bank:
print("✓ 软件ETF > 银行ETF (符合预期: 降息利好成长股，利空银行息差)")
if macro_score_gold > 0:
print("✓ 黄金ETF得分为正 (符合预期: 地缘紧张利好黄金避险)")
def test_trust_index_calculation(wb: WorldBook):
"""测试完整的 Trust Index 计算"""
print("\n" + "=" * 60)
print("测试 3: Trust Index 计算")
print("=" * 60)
pm = PortfolioManager(wb, total_capital=1000000)
# 模拟叙事评分和资金流评分
test_cases = [
{
"etf_code": "515230",
"asset_id": "tech_software",
"name": "软件ETF",
"narrative_score": 85,
"flow_score": 70
},
{
"etf_code": "512800",
"asset_id": "finance_bank",
"name": "银行ETF",
"narrative_score": 50,
"flow_score": 60
},
{
"etf_code": "518880",
"asset_id": "resources_gold",
"name": "黄金ETF",
"narrative_score": 70,
"flow_score": 55
}
]
print("\n计算各标的 Trust Index:")
results = []
for case in test_cases:
print(f"\n{case['name']} ({case['etf_code']}):")
print(f" 叙事评分: {case['narrative_score']}")
print(f" 资金流评分: {case['flow_score']}")
trust_result = pm.calculate_trust_index(
case['etf_code'],
case['asset_id'],
case['narrative_score'],
case['flow_score']
)
results.append({**case, **trust_result})
print(f" → Trust Index: {trust_result['trust_index']} ({trust_result['verdict']})")
# 排序
results.sort(key=lambda x: x['trust_index'], reverse=True)
print("\n" + "=" * 60)
print("最终排名")
print("=" * 60)
for i, result in enumerate(results, 1):
print(f"{i}. {result['name']} - Trust Index: {result['trust_index']} "
f"(Macro: {result['macro_score']:.2f}, Narrative: {result['narrative_score']}, "
f"Flow: {result['flow_score']}) → {result['verdict']}")
def main():
"""主测试函数"""
print("\n" + "🧪 " * 30)
print("MND-IA 向量点积计算测试")
print("🧪 " * 30)
try:
# 测试 1: 宏观因子向量推导
wb, factor_vector = test_macro_vector_calculation()
# 测试 2: 敏感度矩阵点积计算
test_sensitivity_calculation(wb, factor_vector)
# 测试 3: 完整 Trust Index 计算
test_trust_index_calculation(wb)
print("\n" + "✅ " * 30)
print("所有测试完成！")
print("✅ " * 30)
except Exception as e:
print(f"\n❌ 测试失败: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
main()