增加基本面的分析

backend/app/crypto_agent/llm_signal_analyzer.py

@@ -480,15 +480,64 @@ class LLMSignalAnalyzer:
 - 理想的风险收益比应该在 1:3 以上
 - 即：潜在风险 3%，潜在收益 9% 以上
 
+## 九、基本面分析（重要补充）
+**对于股票交易，基本面分析是重要的参考维度：**
+
+### 估值指标分析
+- **PE（市盈率）**：
+  - PE < 15：估值偏低，价值投资机会
+  - PE 15-25：估值合理
+  - PE 25-40：估值偏高
+  - PE > 40：估值过高，风险较大
+- **PB（市净率）**：PB < 1.5 通常表示被低估
+- **PEG（市盈率相对盈利增长比率）**：PEG < 1 表示被低估
+
+### 盈利能力分析
+- **ROE（净资产收益率）**：
+  - ROE > 20%：优秀公司，盈利能力强
+  - ROE 15-20%：良好
+  - ROE 10-15%：一般
+  - ROE < 10%：盈利能力较弱
+- **净利率**：净利率 > 20% 表示盈利质量高
+- **毛利率**：毛利率 > 40% 表示有竞争优势
+
+### 成长性分析
+- **营收增长率**：
+  - > 30%：高成长
+  - 20-30%：稳定成长
+  - 10-20%：一般成长
+  - < 10%：成长性不足
+- **盈利增长率**：与营收增长同步更健康
+
+### 财务健康分析
+- **债务股本比**：
+  - < 1：财务健康
+  - 1-2：可控范围
+  - > 2：风险较高
+- **流动比率**：> 2 表示偿债能力强
+
+### 基本面与技术面结合
+1. **基本面优秀 + 技术面突破** = 高质量做多机会（可提高置信度）
+2. **基本面差 + 技术面破位** = 高质量做空机会（可提高置信度）
+3. **基本面优秀 + 技术面回调** = 低吸机会（中线/长线）
+4. **基本面差 + 技术面上涨** = 谨慎（可能是诱多）
+
+### 基本面评分参考
+- **80分以上（A级）**：基本面优秀，技术信号确认时可提高置信度
+- **60-80分（B级）**：基本面良好，可作为参考
+- **40-60分（C级）**：基本面一般，主要依赖技术分析
+- **40分以下（D级）**：基本面较差，降低信号置信度
+
 ## 重要原则
 1. **量价优先** - 任何信号都必须有量能配合才可靠
 2. **精选机会** - 股票不需要频繁交易，等待高质量信号
 3. **多周期确认** - 日线决定方向，小周期决定入场
 4. **结构止损** - 止损必须基于关键支撑/阻力位（前低前高、均线）
 5. **合理止盈** - 根据交易周期设置合理的止盈目标
-6. **reason 字段必须包含量价分析**（如"放量突破+RSI=45，量比1.8确认有效"）
+6. **基本面参考** - 结合基本面评分和技术面综合判断，提高信号质量
-7. **entry_type 必须明确**：信号已触发用 market，等待更好价位用 limit
+7. **reason 字段必须包含量价分析**（如"放量突破+RSI=45，量比1.8确认有效"）
-8. **position_size 必须明确**：根据信号质量给出 heavy/medium/light"""
+8. **entry_type 必须明确**：信号已触发用 market，等待更好价位用 limit
+9. **position_size 必须明确**：根据信号质量给出 heavy/medium/light"""
 
     # 兼容旧代码，使用加密货币提示词作为默认值
     SYSTEM_PROMPT = CRYPTO_SYSTEM_PROMPT
@@ -534,7 +583,9 @@ class LLMSignalAnalyzer:
 
     async def analyze(self, symbol: str, data: Dict[str, pd.DataFrame],
                        symbols: List[str] = None,
-                       position_info: Dict[str, Any] = None) -> Dict[str, Any]:
+                       position_info: Dict[str, Any] = None,
+                       fundamental_data: Dict[str, Any] = None,
+                       fundamental_summary: str = "") -> Dict[str, Any]:
         """
         使用 LLM 分析市场数据
 
@@ -547,6 +598,8 @@ class LLMSignalAnalyzer:
                 - total_position_value: 总持仓价值
                 - current_leverage: 当前杠杆倍数
                 - positions: 各交易对持仓列表
+            fundamental_data: 基本面数据（仅股票）
+            fundamental_summary: 基本面摘要文本（仅股票）
 
         Returns:
             分析结果
@@ -576,7 +629,8 @@ class LLMSignalAnalyzer:
                 system_prompt = self.CRYPTO_SYSTEM_PROMPT
 
             # 构建数据提示
-            data_prompt = self._build_data_prompt(symbol, data, news_text, position_info, futures_data)
+            data_prompt = self._build_data_prompt(symbol, data, news_text, position_info, futures_data,
+                                                  fundamental_data, fundamental_summary)
 
             # 调用 LLM（使用异步方法避免阻塞事件循环）
             response = await llm_service.achat([
@@ -681,9 +735,77 @@ class LLMSignalAnalyzer:
 
         return "\n".join(lines)
 
+    def _format_fundamental_data(self, fundamental_data: Dict[str, Any]) -> str:
+        """格式化基本面数据供 LLM 参考"""
+        lines = []
+
+        # 基本信息
+        company_name = fundamental_data.get('company_name', 'N/A')
+        sector = fundamental_data.get('sector', 'N/A')
+        industry = fundamental_data.get('industry', 'N/A')
+        market_cap = fundamental_data.get('market_cap', 0)
+
+        lines.append(f"**公司**: {company_name}")
+        lines.append(f"**行业**: {sector} / {industry}")
+        if market_cap:
+            lines.append(f"**市值**: ${market_cap:,.0f}")
+
+        # 基本面评分
+        score_data = fundamental_data.get('score', {})
+        total_score = score_data.get('total', 0)
+        rating = score_data.get('rating', 'N/A')
+        if total_score > 0:
+            lines.append(f"**基本面评分**: {total_score:.0f}/100 ({rating}级)")
+
+        # 估值指标
+        valuation = fundamental_data.get('valuation', {})
+        if valuation.get('pe_ratio'):
+            pe = valuation['pe_ratio']
+            pb = valuation.get('pb_ratio', 'N/A')
+            ps = valuation.get('ps_ratio', 'N/A')
+            lines.append(f"**估值**: PE={pe:.2f} | PB={pb} | PS={ps}")
+
+        # 盈利能力
+        profitability = fundamental_data.get('profitability', {})
+        if profitability.get('return_on_equity'):
+            roe = profitability['return_on_equity']
+            profit_margin = profitability.get('profit_margin')
+            gross_margin = profitability.get('gross_margin')
+            pm_str = f"{profit_margin:.1f}" if profit_margin is not None else "N/A"
+            gm_str = f"{gross_margin:.1f}" if gross_margin is not None else "N/A"
+            lines.append(f"**盈利**: ROE={roe:.2f}% | 净利率={pm_str}% | 毛利率={gm_str}%")
+
+        # 成长性
+        growth = fundamental_data.get('growth', {})
+        revenue_growth = growth.get('revenue_growth')
+        earnings_growth = growth.get('earnings_growth')
+        if revenue_growth is not None or earnings_growth is not None:
+            rg_str = f"{revenue_growth:.1f}" if revenue_growth is not None else "N/A"
+            eg_str = f"{earnings_growth:.1f}" if earnings_growth is not None else "N/A"
+            lines.append(f"**成长**: 营收增长={rg_str}% | 盈利增长={eg_str}%")
+
+        # 财务健康
+        financial = fundamental_data.get('financial_health', {})
+        if financial.get('debt_to_equity'):
+            debt_to_equity = financial['debt_to_equity']
+            current_ratio = financial.get('current_ratio')
+            cr_str = f"{current_ratio:.2f}" if current_ratio is not None else "N/A"
+            lines.append(f"**财务**: 债务股本比={debt_to_equity:.2f} | 流动比率={cr_str}")
+
+        # 分析师建议
+        analyst = fundamental_data.get('analyst', {})
+        if analyst.get('target_price'):
+            target_price = analyst['target_price']
+            recommendation = analyst.get('recommendation', 'N/A')
+            lines.append(f"**分析师**: 目标价=${target_price:.2f} | 建议={recommendation}")
+
+        return "\n".join(lines)
+
     def _build_data_prompt(self, symbol: str, data: Dict[str, pd.DataFrame],
                            news_text: str = "", position_info: Dict[str, Any] = None,
-                           futures_data: Dict[str, Any] = None) -> str:
+                           futures_data: Dict[str, Any] = None,
+                           fundamental_data: Dict[str, Any] = None,
+                           fundamental_summary: str = "") -> str:
         """构建数据提示词"""
         parts = [f"# {symbol} 市场数据分析\n"]
         parts.append(f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
@@ -694,6 +816,14 @@ class LLMSignalAnalyzer:
             current_price = float(data['5m'].iloc[-1]['close'])
             parts.append(f"**当前价格**: ${current_price:,.2f}\n")
 
+        # === 新增：基本面数据（仅股票） ===
+        if fundamental_data and self.agent_type == 'stock':
+            parts.append("\n## 基本面分析")
+            if fundamental_summary:
+                parts.append(fundamental_summary)
+            else:
+                parts.append(self._format_fundamental_data(fundamental_data))
+
         # === 新增：合约市场数据 ===
         if futures_data and self.agent_type == 'crypto':
             parts.append(self.binance_service.format_futures_data_for_llm(symbol, futures_data))

backend/app/services/fundamental_service.py

@@ -0,0 +1,520 @@
+"""
+基本面因子数据服务
+获取美股和港股的基本面数据，包括估值、盈利能力、成长性等指标
+"""
+from typing import Dict, Any, Optional, List
+from datetime import datetime
+import pandas as pd
+
+try:
+    import yfinance as yf
+    YFINANCE_AVAILABLE = True
+except ImportError:
+    YFINANCE_AVAILABLE = False
+
+from app.utils.logger import logger
+
+
+class FundamentalService:
+    """基本面因子数据服务"""
+
+    def __init__(self):
+        """初始化服务"""
+        if not YFINANCE_AVAILABLE:
+            logger.warning("yfinance 未安装，基本面数据功能将不可用")
+            return
+
+        self._cache = {}  # 数据缓存
+        self._cache_time = {}  # 缓存时间
+        self._cache_ttl = 3600  # 缓存有效期1小时
+
+        logger.info("基本面数据服务初始化成功")
+
+    def get_fundamental_data(self, symbol: str) -> Optional[Dict[str, Any]]:
+        """
+        获取股票的基本面数据
+
+        Args:
+            symbol: 股票代码，如 'AAPL', '0700.HK'
+
+        Returns:
+            基本面数据字典，包含估值、盈利、成长等指标
+        """
+        if not YFINANCE_AVAILABLE:
+            return None
+
+        try:
+            ticker = yf.Ticker(symbol)
+
+            # 获取股票信息
+            info = ticker.info
+
+            if not info:
+                logger.warning(f"无法获取 {symbol} 的基本面数据")
+                return None
+
+            # 提取关键指标
+            fundamental_data = {
+                'symbol': symbol,
+                'timestamp': datetime.now().isoformat(),
+
+                # 基本信息
+                'company_name': info.get('longName', info.get('shortName', 'N/A')),
+                'sector': info.get('sector', 'N/A'),
+                'industry': info.get('industry', 'N/A'),
+                'market_cap': info.get('marketCap'),
+                'shares_outstanding': info.get('sharesOutstanding'),
+
+                # 估值指标
+                'valuation': self._extract_valuation_metrics(info),
+
+                # 盈利能力
+                'profitability': self._extract_profitability_metrics(info),
+
+                # 成长性
+                'growth': self._extract_growth_metrics(info),
+
+                # 财务健康
+                'financial_health': self._extract_financial_health_metrics(info),
+
+                # 股票回报
+                'returns': self._extract_return_metrics(info),
+
+                # 分析师建议
+                'analyst': self._extract_analyst_metrics(info),
+            }
+
+            # 计算综合评分
+            fundamental_data['score'] = self._calculate_fundamental_score(fundamental_data)
+
+            # 输出基本面关键指标
+            score = fundamental_data.get('score', {})
+            logger.info(f"✓ {symbol} 基本面数据获取成功")
+            logger.info(f"  【公司】{fundamental_data.get('company_name', 'N/A')} | {fundamental_data.get('sector', 'N/A')}")
+            logger.info(f"  【评分】总分: {score.get('total', 0):.0f}/100 ({score.get('rating', 'N/A')}级) | "
+                       f"估值:{score.get('valuation', 0)} 盈利:{score.get('profitability', 0)} "
+                       f"成长:{score.get('growth', 0)} 财务:{score.get('financial_health', 0)}")
+
+            # 估值指标
+            val = fundamental_data.get('valuation', {})
+            if val.get('pe_ratio'):
+                pe = val['pe_ratio']
+                pb = val.get('pb_ratio')
+                ps = val.get('ps_ratio')
+                peg = val.get('peg_ratio')
+                pb_str = f"{pb:.2f}" if pb is not None else "N/A"
+                ps_str = f"{ps:.2f}" if ps is not None else "N/A"
+                peg_str = f"{peg:.2f}" if peg is not None else "N/A"
+                logger.info(f"  【估值】PE:{pe:.2f} | PB:{pb_str} | PS:{ps_str} | PEG:{peg_str}")
+
+            # 盈利能力
+            prof = fundamental_data.get('profitability', {})
+            if prof.get('return_on_equity'):
+                roe = prof['return_on_equity']
+                pm = prof.get('profit_margin')
+                gm = prof.get('gross_margin')
+                pm_str = f"{pm:.1f}" if pm is not None else "N/A"
+                gm_str = f"{gm:.1f}" if gm is not None else "N/A"
+                logger.info(f"  【盈利】ROE:{roe:.2f}% | 净利率:{pm_str}% | 毛利率:{gm_str}%")
+
+            # 成长性
+            growth = fundamental_data.get('growth', {})
+            rg = growth.get('revenue_growth')
+            eg = growth.get('earnings_growth')
+            if rg is not None or eg is not None:
+                rg_str = f"{rg:.1f}" if rg is not None else "N/A"
+                eg_str = f"{eg:.1f}" if eg is not None else "N/A"
+                logger.info(f"  【成长】营收增长:{rg_str}% | 盈利增长:{eg_str}%")
+
+            # 财务健康
+            fin = fundamental_data.get('financial_health', {})
+            if fin.get('debt_to_equity'):
+                de = fin['debt_to_equity']
+                cr = fin.get('current_ratio')
+                cr_str = f"{cr:.2f}" if cr is not None else "N/A"
+                logger.info(f"  【财务】债务股本比:{de:.2f} | 流动比率:{cr_str}")
+
+            # 分析师建议
+            analyst = fundamental_data.get('analyst', {})
+            tp = analyst.get('target_price')
+            if tp:
+                rec = analyst.get('recommendation', 'N/A')
+                logger.info(f"  【分析师】目标价:${tp:.2f} | 评级:{rec}")
+
+            return fundamental_data
+
+        except Exception as e:
+            logger.error(f"获取 {symbol} 基本面数据失败: {e}")
+            return None
+
+    def _extract_valuation_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取估值指标"""
+        return {
+            'pe_ratio': info.get('trailingPE'),  # 市盈率
+            'forward_pe': info.get('forwardPE'),  # 远期市盈率
+            'peg_ratio': info.get('pegRatio'),  # PEG
+            'pb_ratio': info.get('priceToBook'),  # 市净率
+            'ps_ratio': info.get('priceToSalesTrailing12M'),  # 市销率
+            'ev_to_ebitda': info.get('enterpriseToEbitda'),  # EV/EBITDA
+            'enterprise_value': info.get('enterpriseValue'),  # 企业价值
+        }
+
+    def _extract_profitability_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取盈利能力指标"""
+        return {
+            'eps': info.get('trailingEps'),  # 每股收益
+            'forward_eps': info.get('forwardEps'),  # 预期每股收益
+            'revenue': info.get('totalRevenue'),  # 总收入
+            'net_income': info.get('netIncomeToCommon'),  # 净收入
+            'profit_margin': info.get('profitMargins'),  # 利润率
+            'operating_margin': info.get('operatingMargins'),  # 营业利润率
+            'gross_margin': info.get('grossMargins'),  # 毛利率
+            'ebitda': info.get('ebitda'),  # EBITDA
+            'ebitda_margins': info.get('ebitdaMargins'),  # EBITDA利润率
+        }
+
+    def _extract_growth_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取成长性指标"""
+        return {
+            'revenue_growth': info.get('revenueGrowth'),  # 营收增长率
+            'earnings_growth': info.get('earningsGrowth'),  # 盈利增长
+            'earnings_quarterly_growth': info.get('earningsQuarterlyGrowth'),  # 季度盈利增长
+            'revenue_quarterly_growth': info.get('revenueQuarterlyGrowth'),  # 季度营收增长
+        }
+
+    def _extract_financial_health_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取财务健康指标"""
+        return {
+            'debt_to_equity': info.get('debtToEquity'),  # 债务股本比
+            'current_ratio': info.get('currentRatio'),  # 流动比率
+            'quick_ratio': info.get('quickRatio'),  # 速动比率
+            'total_cash': info.get('totalCash'),  # 总现金
+            'total_debt': info.get('totalDebt'),  # 总债务
+            'operating_cashflow': info.get('operatingCashflow'),  # 经营现金流
+            'free_cashflow': info.get('freeCashflow'),  # 自由现金流
+        }
+
+    def _extract_return_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取股票回报指标"""
+        return {
+            'dividend_rate': info.get('dividendRate'),  # 股息率
+            'dividend_yield': info.get('dividendYield'),  # 股息收益率
+            'payout_ratio': info.get('payoutRatio'),  # 派息比率
+            'five_year_avg_dividend_yield': info.get('fiveYearAvgDividendYield'),  # 5年平均股息率
+            'return_on_equity': info.get('returnOnEquity'),  # ROE
+            'return_on_assets': info.get('returnOnAssets'),  # ROA
+        }
+
+    def _extract_analyst_metrics(self, info: Dict) -> Dict[str, Any]:
+        """提取分析师建议"""
+        return {
+            'target_price': info.get('targetMeanPrice'),  # 目标价
+            'target_high': info.get('targetHighPrice'),  # 目标价上限
+            'target_low': info.get('targetLowPrice'),  # 目标价下限
+            'recommendation': info.get('recommendationKey'),  # 分析师建议
+            'number_of_analysts': info.get('numberOfAnalystOpinions'),  # 分析师数量
+        }
+
+    def _calculate_fundamental_score(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        计算基本面综合评分（0-100分）
+
+        评分维度：
+        1. 估值合理性 (0-25分)
+        2. 盈利能力 (0-25分)
+        3. 成长性 (0-25分)
+        4. 财务健康 (0-25分)
+        """
+        scores = {
+            'valuation': 0,
+            'profitability': 0,
+            'growth': 0,
+            'financial_health': 0,
+            'total': 0
+        }
+
+        try:
+            # 1. 估值评分 (0-25分)
+            valuation = data.get('valuation', {})
+            if valuation.get('pe_ratio'):
+                pe = valuation['pe_ratio']
+                # PE < 15: 优秀，15-25: 良好，25-40: 一般，>40: 偏高
+                if pe < 15:
+                    scores['valuation'] = 25
+                elif pe < 25:
+                    scores['valuation'] = 20
+                elif pe < 40:
+                    scores['valuation'] = 10
+                else:
+                    scores['valuation'] = 5
+
+            # 2. 盈利能力评分 (0-25分)
+            profitability = data.get('profitability', {})
+            roe = profitability.get('return_on_equity')
+            profit_margin = profitability.get('profit_margin')
+
+            # 处理 None 值
+            if roe is None:
+                roe = 0
+            if profit_margin is None:
+                profit_margin = 0
+
+            if roe > 0:
+                # ROE > 20%: 优秀，15-20%: 良好，10-15%: 一般，< 10%: 较差
+                if roe > 20:
+                    scores['profitability'] += 15
+                elif roe > 15:
+                    scores['profitability'] += 12
+                elif roe > 10:
+                    scores['profitability'] += 8
+                else:
+                    scores['profitability'] += 4
+
+            if profit_margin > 0:
+                # 净利率 > 20%: 优秀，10-20%: 良好，5-10%: 一般
+                if profit_margin > 20:
+                    scores['profitability'] += 10
+                elif profit_margin > 10:
+                    scores['profitability'] += 7
+                else:
+                    scores['profitability'] += 4
+
+            # 3. 成长性评分 (0-25分)
+            growth = data.get('growth', {})
+            revenue_growth = growth.get('revenue_growth')
+            earnings_growth = growth.get('earnings_growth')
+
+            # 处理 None 值
+            if revenue_growth is None:
+                revenue_growth = 0
+            if earnings_growth is None:
+                earnings_growth = 0
+
+            if revenue_growth > 0:
+                # 营收增长 > 30%: 优秀，20-30%: 良好，10-20%: 一般，< 10%: 较差
+                if revenue_growth > 30:
+                    scores['growth'] += 12
+                elif revenue_growth > 20:
+                    scores['growth'] += 10
+                elif revenue_growth > 10:
+                    scores['growth'] += 6
+                else:
+                    scores['growth'] += 3
+
+            if earnings_growth > 0:
+                # 盈利增长 > 30%: 优秀，20-30%: 良好，10-20%: 一般
+                if earnings_growth > 30:
+                    scores['growth'] += 13
+                elif earnings_growth > 20:
+                    scores['growth'] += 10
+                elif earnings_growth > 10:
+                    scores['growth'] += 6
+                else:
+                    scores['growth'] += 3
+
+            # 4. 财务健康评分 (0-25分)
+            financial = data.get('financial_health', {})
+            debt_to_equity = financial.get('debt_to_equity')
+            current_ratio = financial.get('current_ratio')
+
+            # 处理 None 值
+            if debt_to_equity is None:
+                debt_to_equity = 0
+            if current_ratio is None:
+                current_ratio = 0
+
+            # 债务股本比 < 1: 优秀，1-2: 良好，2-3: 一般，> 3: 风险高
+            if debt_to_equity < 1:
+                scores['financial_health'] += 12
+            elif debt_to_equity < 2:
+                scores['financial_health'] += 10
+            elif debt_to_equity < 3:
+                scores['financial_health'] += 5
+            else:
+                scores['financial_health'] += 2
+
+            # 流动比率 > 2: 优秀，1.5-2: 良好，1-1.5: 一般，< 1: 风险
+            if current_ratio > 2:
+                scores['financial_health'] += 13
+            elif current_ratio > 1.5:
+                scores['financial_health'] += 10
+            elif current_ratio > 1:
+                scores['financial_health'] += 5
+            else:
+                scores['financial_health'] += 0
+
+            # 现金流评分
+            fc = financial.get('free_cashflow')
+            if fc is not None and fc > 0:
+                scores['financial_health'] += 0  # 已在盈利能力中考虑
+
+            # 计算总分
+            scores['total'] = sum([scores['valuation'], scores['profitability'],
+                                   scores['growth'], scores['financial_health']])
+
+            # 添加评级
+            if scores['total'] >= 80:
+                scores['rating'] = 'A'
+            elif scores['total'] >= 60:
+                scores['rating'] = 'B'
+            elif scores['total'] >= 40:
+                scores['rating'] = 'C'
+            else:
+                scores['rating'] = 'D'
+
+        except Exception as e:
+            logger.error(f"计算基本面评分失败: {e}")
+
+        return scores
+
+    def get_fundamental_summary(self, symbol: str, data: Dict[str, Any] = None) -> str:
+        """
+        生成基本面数据摘要文本，用于 LLM 分析
+
+        Args:
+            symbol: 股票代码
+            data: 可选，已获取的基本面数据。如果为None，则自动获取
+
+        Returns:
+            基本面摘要文本
+        """
+        if data is None:
+            data = self.get_fundamental_data(symbol)
+        if not data:
+            return f"{symbol}: 暂无基本面数据"
+
+        summary_parts = []
+
+        # 基本信息
+        summary_parts.append(f"【公司信息】{data.get('company_name', 'N/A')} | "
+                          f"行业: {data.get('sector', 'N/A')}")
+
+        # 估值情况
+        val = data.get('valuation', {})
+        if val.get('pe_ratio'):
+            summary_parts.append(f"【估值】PE: {val['pe_ratio']:.2f} | "
+                             f"PB: {val.get('pb_ratio', 'N/A')} | "
+                             f"PS: {val.get('ps_ratio', 'N/A')}")
+
+        # 盈利能力
+        prof = data.get('profitability', {})
+        if prof.get('return_on_equity'):
+            pm = prof.get('profit_margin')
+            gm = prof.get('gross_margin')
+            pm_str = f"{pm:.1f}" if pm is not None else "N/A"
+            gm_str = f"{gm:.1f}" if gm is not None else "N/A"
+            summary_parts.append(f"【盈利】ROE: {prof['return_on_equity']:.2f}% | "
+                               f"净利率: {pm_str}% | "
+                               f"毛利率: {gm_str}%")
+
+        # 成长性
+        growth = data.get('growth', {})
+        rg = growth.get('revenue_growth')
+        eg = growth.get('earnings_growth')
+        if rg is not None or eg is not None:
+            rg_str = f"{rg:.1f}" if rg is not None else "N/A"
+            eg_str = f"{eg:.1f}" if eg is not None else "N/A"
+            summary_parts.append(f"【成长】营收增长: {rg_str}% | "
+                               f"盈利增长: {eg_str}%")
+
+        # 财务健康
+        fin = data.get('financial_health', {})
+        if fin.get('debt_to_equity'):
+            cr = fin.get('current_ratio')
+            cr_str = f"{cr:.2f}" if cr is not None else "N/A"
+            summary_parts.append(f"【财务】债务股本比: {fin['debt_to_equity']:.2f} | "
+                               f"流动比率: {cr_str}")
+
+        # 分析师建议
+        analyst = data.get('analyst', {})
+        if analyst.get('target_price'):
+            summary_parts.append(f"【分析师建议】目标价: ${analyst['target_price']:.2f} | "
+                             f"评级: {analyst.get('recommendation', 'N/A')}")
+
+        # 基本面评分
+        score = data.get('score', {})
+        summary_parts.append(f"【基本面评分】{score.get('total', 0):.0f}/100 ({score.get('rating', 'N/A')}级)")
+
+        return "\n".join(summary_parts)
+
+    def batch_get_fundamentals(self, symbols: List[str]) -> Dict[str, Dict[str, Any]]:
+        """
+        批量获取多只股票的基本面数据
+
+        Args:
+            symbols: 股票代码列表
+
+        Returns:
+            股票代码到基本面数据的映射
+        """
+        results = {}
+        for symbol in symbols:
+            data = self.get_fundamental_data(symbol)
+            if data:
+                results[symbol] = data
+
+        logger.info(f"批量获取基本面数据完成: {len(results)}/{len(symbols)} 只股票")
+        return results
+
+    def compare_stocks(self, symbols: List[str]) -> Dict[str, Any]:
+        """
+        比较多只股票的基本面指标
+
+        Args:
+            symbols: 股票代码列表
+
+        Returns:
+            比较结果
+        """
+        fundamentals = self.batch_get_fundamentals(symbols)
+
+        comparison = {
+            'symbols': symbols,
+            'metrics': {}
+        }
+
+        # 提取可比较的指标
+        metrics_to_compare = [
+            ('valuation', ['pe_ratio', 'pb_ratio']),
+            ('profitability', ['return_on_equity', 'profit_margin']),
+            ('growth', ['revenue_growth', 'earnings_growth']),
+            ('financial_health', ['debt_to_equity', 'current_ratio']),
+        ]
+
+        for category, metric_names in metrics_to_compare:
+            comparison['metrics'][category] = {}
+            for metric in metric_names:
+                values = {}
+                for symbol in symbols:
+                    if symbol in fundamentals:
+                        category_data = fundamentals[symbol].get(category, {})
+                        value = category_data.get(metric)
+                        if value is not None:
+                            values[symbol] = value
+
+                if values:
+                    comparison['metrics'][category][metric] = values
+
+        # 计算排名
+        comparison['rankings'] = {}
+        if 'valuation' in comparison['metrics']:
+            pe_ratios = {s: v.get('valuation', {}).get('pe_ratio')
+                         for s, v in fundamentals.items() if v.get('valuation', {}).get('pe_ratio')}
+            if pe_ratios:
+                # PE 越低越好
+                sorted_pe = sorted(pe_ratios.items(), key=lambda x: x[1])
+                comparison['rankings']['pe_low_to_high'] = [s[0] for s in sorted_pe]
+
+        return comparison
+
+
+# 全局单例
+_fundamental_service: Optional[FundamentalService] = None
+
+
+def get_fundamental_service() -> FundamentalService:
+    """获取基本面数据服务单例"""
+    global _fundamental_service
+    if _fundamental_service is None:
+        _fundamental_service = FundamentalService()
+    return _fundamental_service

backend/app/stock_agent/stock_agent.py

@@ -13,6 +13,7 @@ from app.services.yfinance_service import get_yfinance_service
 from app.services.feishu_service import get_feishu_service
 from app.services.telegram_service import get_telegram_service
 from app.services.signal_database_service import get_signal_db_service
+from app.services.fundamental_service import get_fundamental_service
 from app.crypto_agent.llm_signal_analyzer import LLMSignalAnalyzer
 
 
@@ -87,6 +88,7 @@ class StockAgent:
         self.telegram = get_telegram_service()
         self.llm_analyzer = LLMSignalAnalyzer(agent_type="stock")  # 指定使用 stock 模型配置
         self.signal_db = get_signal_db_service()  # 信号数据库服务
+        self.fundamental = get_fundamental_service()  # 基本面数据服务
 
         # 状态管理
         self.last_signals: Dict[str, Dict[str, Any]] = {}
@@ -366,12 +368,29 @@ class StockAgent:
             logger.info(f"📊 分析 {symbol_display} @ ${current_price:,.2f}")
             logger.info(f"{'='*60}")
 
-            # 4. LLM 分析
+            # 4. 获取基本面数据
+            logger.info(f"\n📈 【基本面分析】")
+            fundamental_data = None
+            fundamental_summary = ""
+            try:
+                fundamental_data = self.fundamental.get_fundamental_data(symbol)
+                if fundamental_data:
+                    # 传递已获取的数据，避免重复调用
+                    fundamental_summary = self.fundamental.get_fundamental_summary(symbol, fundamental_data)
+                    # 基本面评分已经在 fundamental_service 中输出
+                else:
+                    logger.warning(f"   ⚠️  无法获取基本面数据")
+            except Exception as e:
+                logger.warning(f"   ⚠️  获取基本面数据失败: {e}")
+
+            # 5. LLM 分析
             logger.info(f"\n🤖 【LLM 分析中...】")
             analysis = await self.llm_analyzer.analyze(
                 symbol, data,
                 symbols=self.symbols,
-                position_info=None  # 美股不跟踪持仓
+                position_info=None,  # 美股不跟踪持仓
+                fundamental_data=fundamental_data,  # 传递基本面数据
+                fundamental_summary=fundamental_summary  # 传递基本面摘要
             )
 
             # 输出分析摘要
@@ -542,10 +561,23 @@ class StockAgent:
         if not self._validate_data(data):
             return {'error': '数据不完整'}
 
+        # 获取基本面数据
+        fundamental_data = None
+        fundamental_summary = ""
+        try:
+            fundamental_data = self.fundamental.get_fundamental_data(symbol)
+            if fundamental_data:
+                # 传递已获取的数据，避免重复调用
+                fundamental_summary = self.fundamental.get_fundamental_summary(symbol, fundamental_data)
+        except Exception as e:
+            logger.warning(f"获取基本面数据失败: {e}")
+
         result = await self.llm_analyzer.analyze(
             symbol, data,
             symbols=self.symbols,
-            position_info=None
+            position_info=None,
+            fundamental_data=fundamental_data,
+            fundamental_summary=fundamental_summary
         )
         return result
 

scripts/test_stock.py

@@ -19,6 +19,7 @@ import asyncio
 from app.services.yfinance_service import get_yfinance_service
 from app.services.feishu_service import get_feishu_service
 from app.services.telegram_service import get_telegram_service
+from app.services.fundamental_service import get_fundamental_service
 from app.crypto_agent.llm_signal_analyzer import LLMSignalAnalyzer
 from app.config import get_settings
 from app.utils.logger import logger
@@ -60,6 +61,7 @@ async def analyze(symbol: str, send_notification: bool = True):
         # 获取服务
         yf_service = get_yfinance_service()
         llm = LLMSignalAnalyzer(agent_type="stock")  # 指定使用 stock 模型配置
+        fundamental = get_fundamental_service()  # 基本面服务
         feishu = get_feishu_service()
         telegram = get_telegram_service()
 
@@ -86,9 +88,89 @@ async def analyze(symbol: str, send_notification: bool = True):
 
         print(f"时间周期: {', '.join(data.keys())}")
 
+        # 获取基本面数据
+        print(f"\n📈 基本面分析中...")
+        fundamental_data = None
+        fundamental_summary = ""
+        try:
+            fundamental_data = fundamental.get_fundamental_data(symbol)
+            if fundamental_data:
+                # 传递已获取的数据，避免重复调用
+                fundamental_summary = fundamental.get_fundamental_summary(symbol, fundamental_data)
+                # 输出基本面详细信息
+                score = fundamental_data.get('score', {})
+                print(f"   ✓ 基本面数据获取成功")
+
+                # 公司信息
+                company = fundamental_data.get('company_name', 'N/A')
+                sector = fundamental_data.get('sector', 'N/A')
+                print(f"   【公司】{company} | {sector}")
+
+                # 评分
+                print(f"   【评分】总分: {score.get('total', 0):.0f}/100 ({score.get('rating', 'N/A')}级) | "
+                      f"估值:{score.get('valuation', 0)} 盈利:{score.get('profitability', 0)} "
+                      f"成长:{score.get('growth', 0)} 财务:{score.get('financial_health', 0)}")
+
+                # 估值指标
+                val = fundamental_data.get('valuation', {})
+                if val.get('pe_ratio'):
+                    pe = val['pe_ratio']
+                    pb = val.get('pb_ratio')
+                    ps = val.get('ps_ratio')
+                    peg = val.get('peg_ratio')
+                    pb_str = f"{pb:.2f}" if pb is not None else "N/A"
+                    ps_str = f"{ps:.2f}" if ps is not None else "N/A"
+                    peg_str = f"{peg:.2f}" if peg is not None else "N/A"
+                    print(f"   【估值】PE:{pe:.2f} | PB:{pb_str} | PS:{ps_str} | PEG:{peg_str}")
+
+                # 盈利能力
+                prof = fundamental_data.get('profitability', {})
+                if prof.get('return_on_equity'):
+                    roe = prof['return_on_equity']
+                    pm = prof.get('profit_margin')
+                    gm = prof.get('gross_margin')
+                    pm_str = f"{pm:.1f}" if pm is not None else "N/A"
+                    gm_str = f"{gm:.1f}" if gm is not None else "N/A"
+                    print(f"   【盈利】ROE:{roe:.2f}% | 净利率:{pm_str}% | 毛利率:{gm_str}%")
+
+                # 成长性
+                growth = fundamental_data.get('growth', {})
+                rg = growth.get('revenue_growth')
+                eg = growth.get('earnings_growth')
+                if rg is not None or eg is not None:
+                    rg_str = f"{rg:.1f}" if rg is not None else "N/A"
+                    eg_str = f"{eg:.1f}" if eg is not None else "N/A"
+                    print(f"   【成长】营收增长:{rg_str}% | 盈利增长:{eg_str}%")
+
+                # 财务健康
+                fin = fundamental_data.get('financial_health', {})
+                if fin.get('debt_to_equity'):
+                    de = fin['debt_to_equity']
+                    cr = fin.get('current_ratio')
+                    cr_str = f"{cr:.2f}" if cr is not None else "N/A"
+                    print(f"   【财务】债务股本比:{de:.2f} | 流动比率:{cr_str}")
+
+                # 分析师建议
+                analyst = fundamental_data.get('analyst', {})
+                tp = analyst.get('target_price')
+                if tp:
+                    rec = analyst.get('recommendation', 'N/A')
+                    print(f"   【分析师】目标价:${tp:.2f} | 评级:{rec}")
+
+            else:
+                print(f"   ⚠️  无法获取基本面数据")
+        except Exception as e:
+            print(f"   ⚠️  获取基本面数据失败: {e}")
+
         # LLM分析
         print(f"\n🤖 LLM分析中...\n")
-        analysis = await llm.analyze(symbol, data, symbols=[symbol], position_info=None)
+        analysis = await llm.analyze(
+            symbol, data,
+            symbols=[symbol],
+            position_info=None,
+            fundamental_data=fundamental_data,
+            fundamental_summary=fundamental_summary
+        )
 
         # 输出结果
         summary = analysis.get('analysis_summary', '')