Backup before system reinstall

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--- a/jixia_academy/core/debate_system/models/financial_data_models.py
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+# 金融数据抽象层设计
+
+## 概述
+
+在"炼妖壶-稷下学宫AI辩论系统"中，我们需要构建一个统一的金融数据抽象层，以支持多种数据源（包括现有的RapidAPI永动机引擎、新增的OpenBB集成引擎，以及未来可能添加的其他数据提供商）。该抽象层将为上层AI智能体提供一致的数据接口，同时隐藏底层数据源的具体实现细节。
+
+## 设计目标
+
+1. **统一接口**：为所有金融数据访问提供一致的API
+2. **可扩展性**：易于添加新的数据提供商
+3. **容错性**：当主数据源不可用时，能够自动切换到备用数据源
+4. **性能优化**：支持缓存和异步数据获取
+5. **类型安全**：使用Python类型注解确保数据结构的一致性
+
+## 核心组件
+
+### 1. 数据模型 (Data Models)
+
+定义标准化的金融数据结构：
+
+```python
+# src/jixia/models/financial_data_models.py
+
+from dataclasses import dataclass
+from typing import Optional, List
+from datetime import datetime
+
+@dataclass
+class StockQuote:
+    symbol: str
+    price: float
+    change: float
+    change_percent: float
+    volume: int
+    timestamp: datetime
+
+@dataclass
+class HistoricalPrice:
+    date: datetime
+    open: float
+    high: float
+    low: float
+    close: float
+    volume: int
+
+@dataclass
+class CompanyProfile:
+    symbol: str
+    name: str
+    industry: str
+    sector: str
+    market_cap: float
+    pe_ratio: Optional[float]
+    dividend_yield: Optional[float]
+
+@dataclass
+class FinancialNews:
+    title: str
+    summary: str
+    url: str
+    timestamp: datetime
+    sentiment: Optional[float]  # -1 (负面) to 1 (正面)
+```
+
+### 2. 抽象基类 (Abstract Base Class)
+
+定义数据提供商的通用接口：
+
+```python
+# src/jixia/engines/data_abstraction.py
+
+from abc import ABC, abstractmethod
+from typing import List, Optional
+from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
+
+class DataProvider(ABC):
+    """金融数据提供商抽象基类"""
+    
+    @abstractmethod
+    def get_quote(self, symbol: str) -> Optional[StockQuote]:
+        """获取股票报价"""
+        pass
+    
+    @abstractmethod
+    def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
+        """获取历史价格数据"""
+        pass
+    
+    @abstractmethod
+    def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
+        """获取公司概况"""
+        pass
+    
+    @abstractmethod
+    def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
+        """获取相关新闻"""
+        pass
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """数据提供商名称"""
+        pass
+
+    @property
+    @abstractmethod
+    def priority(self) -> int:
+        """优先级（数字越小优先级越高）"""
+        pass
+```
+
+### 3. Provider适配器 (Provider Adapters)
+
+为每个具体的数据源实现适配器：
+
+#### RapidAPI永动机引擎适配器
+
+```python
+# src/jixia/engines/rapidapi_adapter.py
+
+from typing import List, Optional
+from src.jixia.engines.data_abstraction import DataProvider
+from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
+from src.jixia.engines.perpetual_engine import JixiaPerpetualEngine
+from config.settings import get_rapidapi_key
+
+class RapidAPIDataProvider(DataProvider):
+    """RapidAPI永动机引擎适配器"""
+    
+    def __init__(self):
+        self.engine = JixiaPerpetualEngine(get_rapidapi_key())
+        self._name = "RapidAPI"
+        self._priority = 2  # 中等优先级
+    
+    def get_quote(self, symbol: str) -> Optional[StockQuote]:
+        result = self.engine.get_immortal_data("吕洞宾", "quote", symbol)
+        if result.success and result.data:
+            # 解析RapidAPI返回的数据并转换为StockQuote
+            # 这里需要根据实际API返回的数据结构进行调整
+            return StockQuote(
+                symbol=symbol,
+                price=result.data.get("price", 0),
+                change=result.data.get("change", 0),
+                change_percent=result.data.get("change_percent", 0),
+                volume=result.data.get("volume", 0),
+                timestamp=result.data.get("timestamp")
+            )
+        return None
+    
+    def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
+        # 实现历史价格数据获取逻辑
+        pass
+    
+    def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
+        # 实现公司概况获取逻辑
+        pass
+    
+    def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
+        # 实现新闻获取逻辑
+        pass
+    
+    @property
+    def name(self) -> str:
+        return self._name
+    
+    @property
+    def priority(self) -> int:
+        return self._priority
+```
+
+#### OpenBB引擎适配器
+
+```python
+# src/jixia/engines/openbb_adapter.py
+
+from typing import List, Optional
+from src.jixia.engines.data_abstraction import DataProvider
+from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
+from src.jixia.engines.openbb_engine import OpenBBEngine
+
+class OpenBBDataProvider(DataProvider):
+    """OpenBB引擎适配器"""
+    
+    def __init__(self):
+        self.engine = OpenBBEngine()
+        self._name = "OpenBB"
+        self._priority = 1  # 最高优先级
+    
+    def get_quote(self, symbol: str) -> Optional[StockQuote]:
+        result = self.engine.get_immortal_data("吕洞宾", "price", symbol)
+        if result.success and result.data:
+            # 解析OpenBB返回的数据并转换为StockQuote
+            return StockQuote(
+                symbol=symbol,
+                price=result.data.get("close", 0),
+                change=0,  # 需要计算
+                change_percent=0,  # 需要计算
+                volume=result.data.get("volume", 0),
+                timestamp=result.data.get("date")
+            )
+        return None
+    
+    def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
+        # 实现历史价格数据获取逻辑
+        pass
+    
+    def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
+        # 实现公司概况获取逻辑
+        pass
+    
+    def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
+        # 实现新闻获取逻辑
+        pass
+    
+    @property
+    def name(self) -> str:
+        return self._name
+    
+    @property
+    def priority(self) -> int:
+        return self._priority
+```
+
+### 4. 数据抽象层管理器 (Data Abstraction Layer Manager)
+
+管理多个数据提供商并提供统一接口：
+
+```python
+# src/jixia/engines/data_abstraction_layer.py
+
+from typing import List, Optional
+from src.jixia.engines.data_abstraction import DataProvider
+from src.jixia.models.financial_data_models import StockQuote, HistoricalPrice, CompanyProfile, FinancialNews
+import asyncio
+
+class DataAbstractionLayer:
+    """金融数据抽象层管理器"""
+    
+    def __init__(self):
+        self.providers: List[DataProvider] = []
+        self._initialize_providers()
+    
+    def _initialize_providers(self):
+        """初始化所有可用的数据提供商"""
+        # 根据配置和环境动态加载适配器
+        try:
+            from src.jixia.engines.rapidapi_adapter import RapidAPIDataProvider
+            self.providers.append(RapidAPIDataProvider())
+        except ImportError:
+            pass  # RapidAPI引擎不可用
+        
+        try:
+            from src.jixia.engines.openbb_adapter import OpenBBDataProvider
+            self.providers.append(OpenBBDataProvider())
+        except ImportError:
+            pass  # OpenBB引擎不可用
+        
+        # 按优先级排序
+        self.providers.sort(key=lambda p: p.priority)
+    
+    def get_quote(self, symbol: str) -> Optional[StockQuote]:
+        """获取股票报价（带故障转移）"""
+        for provider in self.providers:
+            try:
+                quote = provider.get_quote(symbol)
+                if quote:
+                    return quote
+            except Exception as e:
+                print(f"警告: {provider.name} 获取报价失败: {e}")
+                continue
+        return None
+    
+    async def get_quote_async(self, symbol: str) -> Optional[StockQuote]:
+        """异步获取股票报价（带故障转移）"""
+        for provider in self.providers:
+            try:
+                # 如果提供商支持异步方法，则使用异步方法
+                if hasattr(provider, 'get_quote_async'):
+                    quote = await provider.get_quote_async(symbol)
+                else:
+                    # 否则在执行器中运行同步方法
+                    quote = await asyncio.get_event_loop().run_in_executor(
+                        None, provider.get_quote, symbol
+                    )
+                if quote:
+                    return quote
+            except Exception as e:
+                print(f"警告: {provider.name} 获取报价失败: {e}")
+                continue
+        return None
+    
+    def get_historical_prices(self, symbol: str, days: int = 30) -> List[HistoricalPrice]:
+        """获取历史价格数据（带故障转移）"""
+        for provider in self.providers:
+            try:
+                prices = provider.get_historical_prices(symbol, days)
+                if prices:
+                    return prices
+            except Exception as e:
+                print(f"警告: {provider.name} 获取历史价格失败: {e}")
+                continue
+        return []
+    
+    def get_company_profile(self, symbol: str) -> Optional[CompanyProfile]:
+        """获取公司概况（带故障转移）"""
+        for provider in self.providers:
+            try:
+                profile = provider.get_company_profile(symbol)
+                if profile:
+                    return profile
+            except Exception as e:
+                print(f"警告: {provider.name} 获取公司概况失败: {e}")
+                continue
+        return None
+    
+    def get_news(self, symbol: str, limit: int = 10) -> List[FinancialNews]:
+        """获取相关新闻（带故障转移）"""
+        for provider in self.providers:
+            try:
+                news = provider.get_news(symbol, limit)
+                if news:
+                    return news
+            except Exception as e:
+                print(f"警告: {provider.name} 获取新闻失败: {e}")
+                continue
+        return []
+```
+
+## 八仙与数据源的智能映射
+
+```python
+# src/jixia/engines/baxian_data_mapping.py
+
+# 设计八仙与数据源的智能映射
+immortal_data_mapping = {
+    '吕洞宾': {
+        'specialty': 'technical_analysis',    # 技术分析专家
+        'preferred_data_types': ['historical', 'price'],
+        'data_providers': ['OpenBB', 'RapidAPI']
+    },
+    '何仙姑': {
+        'specialty': 'risk_metrics',         # 风险控制专家  
+        'preferred_data_types': ['price', 'profile'],
+        'data_providers': ['RapidAPI', 'OpenBB']
+    },
+    '张果老': {
+        'specialty': 'historical_data',      # 历史数据分析师
+        'preferred_data_types': ['historical'],
+        'data_providers': ['OpenBB', 'RapidAPI']
+    },
+    '韩湘子': {
+        'specialty': 'sector_analysis',      # 新兴资产专家
+        'preferred_data_types': ['profile', 'news'],
+        'data_providers': ['RapidAPI', 'OpenBB']
+    },
+    '汉钟离': {
+        'specialty': 'market_movers',        # 热点追踪
+        'preferred_data_types': ['news', 'price'],
+        'data_providers': ['RapidAPI', 'OpenBB']
+    },
+    '蓝采和': {
+        'specialty': 'value_discovery',      # 潜力股发现
+        'preferred_data_types': ['screener', 'profile'],
+        'data_providers': ['OpenBB', 'RapidAPI']
+    },
+    '铁拐李': {
+        'specialty': 'contrarian_analysis',  # 逆向思维专家
+        'preferred_data_types': ['profile', 'short_interest'],
+        'data_providers': ['RapidAPI', 'OpenBB']
+    },
+    '曹国舅': {
+        'specialty': 'macro_economics',       # 宏观经济分析师
+        'preferred_data_types': ['profile', 'institutional_holdings'],
+        'data_providers': ['OpenBB', 'RapidAPI']
+    }
+}
+```
+
+## 缓存策略
+
+为了提高性能，我们将实现多级缓存策略：
+
+```python
+# src/jixia/engines/data_cache.py
+
+import time
+from typing import Any, Optional
+from functools import lru_cache
+
+class DataCache:
+    """金融数据缓存"""
+    
+    def __init__(self):
+        self._cache = {}
+        self._cache_times = {}
+        self.default_ttl = 60  # 默认缓存时间（秒）
+    
+    def get(self, key: str) -> Optional[Any]:
+        """获取缓存数据"""
+        if key in self._cache:
+            # 检查是否过期
+            if time.time() - self._cache_times[key] < self.default_ttl:
+                return self._cache[key]
+            else:
+                # 删除过期缓存
+                del self._cache[key]
+                del self._cache_times[key]
+        return None
+    
+    def set(self, key: str, value: Any, ttl: Optional[int] = None):
+        """设置缓存数据"""
+        self._cache[key] = value
+        self._cache_times[key] = time.time()
+        if ttl:
+            # 可以为特定数据设置不同的TTL
+            pass  # 实际实现中需要更复杂的TTL管理机制
+    
+    @lru_cache(maxsize=128)
+    def get_quote_cache(self, symbol: str) -> Optional[Any]:
+        """LRU缓存装饰器示例"""
+        # 这个方法将自动缓存最近128个调用的结果
+        pass
+```
+
+## 数据质量监控机制
+
+为了确保数据的准确性和可靠性，我们将实现数据质量监控：
+
+```python
+# src/jixia/engines/data_quality_monitor.py
+
+from typing import Dict, Any
+from datetime import datetime
+
+class DataQualityMonitor:
+    """数据质量监控"""
+    
+    def __init__(self):
+        self.provider_stats = {}
+    
+    def record_access(self, provider_name: str, success: bool, response_time: float, data_size: int):
+        """记录数据访问统计"""
+        if provider_name not in self.provider_stats:
+            self.provider_stats[provider_name] = {
+                'total_requests': 0,
+                'successful_requests': 0,
+                'failed_requests': 0,
+                'total_response_time': 0,
+                'total_data_size': 0,
+                'last_access': None
+            }
+        
+        stats = self.provider_stats[provider_name]
+        stats['total_requests'] += 1
+        if success:
+            stats['successful_requests'] += 1
+        else:
+            stats['failed_requests'] += 1
+        stats['total_response_time'] += response_time
+        stats['total_data_size'] += data_size
+        stats['last_access'] = datetime.now()
+    
+    def get_provider_health(self, provider_name: str) -> Dict[str, Any]:
+        """获取提供商健康状况"""
+        if provider_name not in self.provider_stats:
+            return {'status': 'unknown'}
+        
+        stats = self.provider_stats[provider_name]
+        success_rate = stats['successful_requests'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
+        avg_response_time = stats['total_response_time'] / stats['total_requests'] if stats['total_requests'] > 0 else 0
+        
+        status = 'healthy' if success_rate > 0.95 and avg_response_time < 2.0 else 'degraded' if success_rate > 0.8 else 'unhealthy'
+        
+        return {
+            'status': status,
+            'success_rate': success_rate,
+            'avg_response_time': avg_response_time,
+            'total_requests': stats['total_requests'],
+            'last_access': stats['last_access']
+        }
+```
+
+## 使用示例
+
+```python
+# 示例：在智能体中使用数据抽象层
+from src.jixia.engines.data_abstraction_layer import DataAbstractionLayer
+from src.jixia.models.financial_data_models import StockQuote
+
+# 初始化数据抽象层
+dal = DataAbstractionLayer()
+
+# 获取股票报价
+quote = dal.get_quote("AAPL")
+if quote:
+    print(f"Apple股价: ${quote.price}")
+else:
+    print("无法获取股价数据")
+
+# 异步获取报价
+import asyncio
+async def async_example():
+    quote = await dal.get_quote_async("GOOGL")
+    if quote:
+        print(f"Google股价: ${quote.price}")
+
+# asyncio.run(async_example())
+```
+
+## 总结
+
+这个金融数据抽象层设计提供了以下优势：
+
+1. **统一接口**：所有智能体都可以通过相同的接口访问任何数据源
+2. **故障转移**：当主数据源不可用时，自动切换到备用数据源
+3. **可扩展性**：可以轻松添加新的数据提供商适配器
+4. **性能优化**：通过缓存机制提高数据访问速度
+5. **质量监控**：实时监控各数据源的健康状况
+6. **文化融合**：通过八仙与数据源的智能映射，保持项目的文化特色
+
+这将为"炼妖壶-稷下学宫AI辩论系统"提供一个强大、可靠且可扩展的金融数据基础。