update

backend/app/stock_agent/analysis_tools.py

@@ -0,0 +1,569 @@
+"""
+股票分析工具模块
+
+提供各种辅助计算功能：
+- ATR计算
+- 多周期共振分析
+- 基本面评分
+- 支撑阻力位识别
+"""
+import pandas as pd
+import numpy as np
+from typing import Dict, Any, List, Tuple, Optional
+from app.utils.logger import logger
+
+
+class StockAnalysisTools:
+    """股票分析工具类"""
+
+    @staticmethod
+    def calculate_atr(df: pd.DataFrame, period: int = 14) -> float:
+        """
+        计算ATR (真实波动幅度)
+
+        Args:
+            df: 包含 high, low, close 的数据
+            period: ATR周期
+
+        Returns:
+            ATR值
+        """
+        if df is None or len(df) < period + 1:
+            return 0.0
+
+        try:
+            high = df['high'].values
+            low = df['low'].values
+            close = df['close'].values
+
+            tr_list = []
+            for i in range(1, len(df)):
+                tr1 = high[i] - low[i]
+                tr2 = abs(high[i] - close[i-1])
+                tr3 = abs(low[i] - close[i-1])
+                tr = max(tr1, tr2, tr3)
+                tr_list.append(tr)
+
+            if len(tr_list) < period:
+                return 0.0
+
+            atr = pd.Series(tr_list).rolling(window=period).mean().iloc[-1]
+            return float(atr) if not np.isnan(atr) else 0.0
+        except Exception as e:
+            logger.warning(f"ATR计算失败: {e}")
+            return 0.0
+
+    @staticmethod
+    def calculate_volume_ratio(df: pd.DataFrame, period: int = 20) -> float:
+        """
+        计算量比（当前成交量 / 过去N周期平均成交量）
+
+        Args:
+            df: 包含 volume 的数据
+            period: 均量周期
+
+        Returns:
+            量比值
+        """
+        if df is None or len(df) < period + 1:
+            return 1.0
+
+        try:
+            current_vol = df['volume'].iloc[-1]
+            avg_vol = df['volume'].iloc[-period-1:-1].mean()
+
+            if avg_vol > 0:
+                return float(current_vol / avg_vol)
+            return 1.0
+        except Exception as e:
+            logger.warning(f"量比计算失败: {e}")
+            return 1.0
+
+    @staticmethod
+    def detect_trend(df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        检测趋势方向和强度
+
+        使用EMA系统判断趋势
+
+        Returns:
+            {
+                'direction': 'uptrend'/'downtrend'/'neutral',
+                'strength': 'strong'/'medium'/'weak',
+                'ema_alignment': 'bullish'/'bearish'/'mixed',
+                'price_vs_ema20': float,  # 百分比
+                'signals': List[str]
+            }
+        """
+        if df is None or len(df) < 200:
+            return {
+                'direction': 'neutral',
+                'strength': 'weak',
+                'ema_alignment': 'mixed',
+                'price_vs_ema20': 0.0,
+                'signals': []
+            }
+
+        try:
+            # 计算EMA
+            close = df['close']
+            ema20 = close.ewm(span=20, adjust=False).mean().iloc[-1]
+            ema50 = close.ewm(span=50, adjust=False).mean().iloc[-1]
+            ema200 = close.ewm(span=200, adjust=False).mean().iloc[-1]
+
+            current_price = close.iloc[-1]
+
+            # EMA排列判断
+            if ema20 > ema50 > ema200:
+                ema_alignment = 'bullish'
+                direction = 'uptrend'
+            elif ema20 < ema50 < ema200:
+                ema_alignment = 'bearish'
+                direction = 'downtrend'
+            else:
+                ema_alignment = 'mixed'
+                direction = 'neutral'
+
+            # 价格与EMA20的关系
+            price_vs_ema20 = ((current_price - ema20) / ema20 * 100) if ema20 > 0 else 0
+
+            # 趋势强度判断
+            strength = 'weak'
+            signals = []
+
+            if ema_alignment == 'bullish':
+                if price_vs_ema20 > 1:
+                    strength = 'strong'
+                    signals.append("强势上涨：价格站稳 EMA20 之上")
+                elif price_vs_ema20 > 0:
+                    strength = 'medium'
+                    signals.append("上涨趋势：价格在 EMA20 附近")
+                else:
+                    strength = 'weak'
+                    signals.append("上涨趋势减弱：价格跌破 EMA20")
+
+            elif ema_alignment == 'bearish':
+                if price_vs_ema20 < -1:
+                    strength = 'strong'
+                    signals.append("强势下跌：价格跌破 EMA20 之下")
+                elif price_vs_ema20 < 0:
+                    strength = 'medium'
+                    signals.append("下跌趋势：价格在 EMA20 附近")
+                else:
+                    strength = 'weak'
+                    signals.append("下跌趋势减弱：价格站上 EMA20")
+            else:
+                signals.append("震荡市：EMA 交织")
+
+            return {
+                'direction': direction,
+                'strength': strength,
+                'ema_alignment': ema_alignment,
+                'price_vs_ema20': round(price_vs_ema20, 2),
+                'signals': signals
+            }
+        except Exception as e:
+            logger.warning(f"趋势检测失败: {e}")
+            return {
+                'direction': 'neutral',
+                'strength': 'weak',
+                'ema_alignment': 'mixed',
+                'price_vs_ema20': 0.0,
+                'signals': []
+            }
+
+    @staticmethod
+    def calculate_multi_timeframe_resonance(data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
+        """
+        计算多周期共振强度
+
+        Args:
+            data: 包含多个周期的数据 {'1w': df, '1d': df, '1h': df}
+
+        Returns:
+            {
+                'score': 0-100,
+                'level': 'strong'/'medium'/'weak',
+                'weekly_trend': str,
+                'daily_trend': str,
+                'hourly_trend': str,
+                'resonance_type': str,
+                'analysis': str
+            }
+        """
+        trends = {}
+
+        # 获取各周期趋势
+        for tf_name in ['1w', '1d', '1h']:
+            df = data.get(tf_name)
+            if df is not None and len(df) > 0:
+                trend_info = StockAnalysisTools.detect_trend(df)
+                trends[tf_name] = trend_info['direction']
+            else:
+                trends[tf_name] = 'neutral'
+
+        weekly_trend = trends.get('1w', 'neutral')
+        daily_trend = trends.get('1d', 'neutral')
+        hourly_trend = trends.get('1h', 'neutral')
+
+        # 计算共振得分
+        score = 0
+        analysis_parts = []
+
+        # 大周期共振（周线+日线）
+        if weekly_trend == daily_trend and weekly_trend != 'neutral':
+            score += 40
+            analysis_parts.append(f"✅ 大周期共振（{weekly_trend}）")
+        elif weekly_trend != 'neutral' and daily_trend != 'neutral':
+            analysis_parts.append(f"⚠️ 大周期分歧（周线{weekly_trend} vs 日线{daily_trend}）")
+        else:
+            analysis_parts.append(f"➖ 大周期不明确")
+
+        # 主周期共振（日线+1h）
+        if daily_trend == hourly_trend and daily_trend != 'neutral':
+            score += 35
+            analysis_parts.append(f"✅ 主周期共振（{daily_trend}）")
+        elif daily_trend != 'neutral' and hourly_trend != 'neutral':
+            analysis_parts.append(f"⚠️ 主周期分歧（日线{daily_trend} vs 1h{hourly_trend}）")
+        else:
+            analysis_parts.append(f"➖ 主周期不明确")
+
+        # 全周期共振
+        if weekly_trend == daily_trend == hourly_trend and weekly_trend != 'neutral':
+            score += 25
+            analysis_parts.append(f"🔥 全周期共振（{weekly_trend}）")
+
+        # 确定共振等级
+        if score >= 70:
+            level = 'strong'
+            resonance_type = 'all_timeframe_aligned' if score >= 90 else 'strong'
+        elif score >= 40:
+            level = 'medium'
+            resonance_type = 'large_timeframe_aligned'
+        elif score >= 20:
+            level = 'weak'
+            resonance_type = 'partial_alignment'
+        else:
+            level = 'weak'
+            resonance_type = 'no_resonance'
+
+        return {
+            'score': score,
+            'level': level,
+            'weekly_trend': weekly_trend,
+            'daily_trend': daily_trend,
+            'hourly_trend': hourly_trend,
+            'resonance_type': resonance_type,
+            'analysis': ' | '.join(analysis_parts)
+        }
+
+    @staticmethod
+    def calculate_fundamental_score(data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        计算基本面评分（0-100）
+
+        Args:
+            data: 基本面数据
+
+        Returns:
+            {
+                'score': 0-100,
+                'grade': 'A'/'B'/'C'/'D',
+                'valuation': {'score': 0-25, 'grade': 'A'/'B'/'C'/'D'},
+                'profitability': {'score': 0-35, 'grade': 'A'/'B'/'C'/'D'},
+                'growth': {'score': 0-25, 'grade': 'A'/'B'/'C'/'D'},
+                'financial_health': {'score': 0-15, 'grade': 'A'/'B'/'C'/'D'},
+                'summary': str
+            }
+        """
+        if not data:
+            return {
+                'score': 0,
+                'grade': 'D',
+                'summary': '无基本面数据'
+            }
+
+        score = 0
+        breakdown = {}
+
+        # 估值评分 (25分)
+        val_score = 0
+        pe = data.get('pe_ratio', 0)
+        pb = data.get('pb_ratio', 0)
+        peg = data.get('peg_ratio', 0)
+
+        if 0 < pe < 15:
+            val_score += 10
+        elif 15 <= pe <= 25:
+            val_score += 7
+        elif pe > 40:
+            val_score += 0
+        else:
+            val_score += 5
+
+        if 0 < pb < 1:
+            val_score += 10
+        elif 1 <= pb <= 3:
+            val_score += 7
+        elif pb > 5:
+            val_score += 0
+        else:
+            val_score += 5
+
+        if peg and 0 < peg < 1:
+            val_score += 5
+        elif peg and 1 <= peg <= 2:
+            val_score += 3
+        elif peg and peg > 2:
+            val_score += 0
+        else:
+            val_score += 2
+
+        breakdown['valuation'] = {
+            'score': val_score,
+            'grade': 'A' if val_score >= 20 else 'B' if val_score >= 15 else 'C' if val_score >= 10 else 'D'
+        }
+        score += val_score
+
+        # 盈利能力评分 (35分)
+        prof_score = 0
+        roe = data.get('roe', 0)
+        net_margin = data.get('profit_margin', 0)
+
+        if roe > 20:
+            prof_score += 20
+        elif roe > 15:
+            prof_score += 15
+        elif roe > 10:
+            prof_score += 10
+        elif roe > 0:
+            prof_score += 5
+
+        if net_margin > 20:
+            prof_score += 15
+        elif net_margin > 10:
+            prof_score += 10
+        elif net_margin > 5:
+            prof_score += 5
+        elif net_margin > 0:
+            prof_score += 2
+
+        breakdown['profitability'] = {
+            'score': prof_score,
+            'grade': 'A' if prof_score >= 30 else 'B' if prof_score >= 20 else 'C' if prof_score >= 10 else 'D'
+        }
+        score += prof_score
+
+        # 成长性评分 (25分)
+        growth_score = 0
+        revenue_growth = data.get('revenue_growth', 0)
+        earnings_growth = data.get('earnings_growth', 0)
+
+        if revenue_growth > 30:
+            growth_score += 13
+        elif revenue_growth > 20:
+            growth_score += 10
+        elif revenue_growth > 10:
+            growth_score += 5
+        elif revenue_growth > 0:
+            growth_score += 2
+
+        if earnings_growth > 30:
+            growth_score += 12
+        elif earnings_growth > 20:
+            growth_score += 8
+        elif earnings_growth > 10:
+            growth_score += 5
+        elif earnings_growth > 0:
+            growth_score += 2
+        elif earnings_growth < 0:
+            growth_score -= 5  # 负增长扣分
+
+        breakdown['growth'] = {
+            'score': max(0, growth_score),
+            'grade': 'A' if growth_score >= 20 else 'B' if growth_score >= 15 else 'C' if growth_score >= 10 else 'D'
+        }
+        score += max(0, growth_score)
+
+        # 财务健康评分 (15分)
+        health_score = 0
+        debt_ratio = data.get('debt_to_equity', 0)
+        current_ratio = data.get('current_ratio', 0)
+
+        if debt_ratio < 1:
+            health_score += 8
+        elif debt_ratio < 2:
+            health_score += 5
+        elif debt_ratio < 3:
+            health_score += 2
+        else:
+            health_score += 0
+
+        if current_ratio > 2:
+            health_score += 7
+        elif current_ratio > 1.5:
+            health_score += 5
+        elif current_ratio > 1:
+            health_score += 2
+        else:
+            health_score += 0
+
+        breakdown['financial_health'] = {
+            'score': health_score,
+            'grade': 'A' if health_score >= 12 else 'B' if health_score >= 8 else 'C' if health_score >= 5 else 'D'
+        }
+        score += health_score
+
+        # 确定总等级
+        grade = 'A' if score >= 80 else 'B' if score >= 60 else 'C' if score >= 40 else 'D'
+
+        # 生成摘要
+        summary_parts = []
+        if breakdown['valuation']['grade'] == 'A':
+            summary_parts.append("估值低估")
+        elif breakdown['valuation']['grade'] == 'D':
+            summary_parts.append("估值高估")
+
+        if breakdown['profitability']['grade'] == 'A':
+            summary_parts.append("盈利优秀")
+        elif breakdown['profitability']['grade'] == 'D':
+            summary_parts.append("盈利较差")
+
+        if breakdown['growth']['grade'] == 'A':
+            summary_parts.append("高成长")
+        elif breakdown['growth']['grade'] == 'D':
+            summary_parts.append("低成长")
+
+        if breakdown['financial_health']['grade'] == 'A':
+            summary_parts.append("财务健康")
+        elif breakdown['financial_health']['grade'] == 'D':
+            summary_parts.append("财务风险")
+
+        return {
+            'score': score,
+            'grade': grade,
+            'breakdown': breakdown,
+            'summary': ' | '.join(summary_parts) if summary_parts else '一般'
+        }
+
+    @staticmethod
+    def identify_key_levels(df: pd.DataFrame, lookback: int = 60) -> Dict[str, List[float]]:
+        """
+        识别关键支撑位和阻力位
+
+        Args:
+            df: K线数据
+            lookback: 回看周期
+
+        Returns:
+            {
+                'support': [支撑位1, 支撑位2, ...],
+                'resistance': [阻力位1, 阻力位2, ...]
+            }
+        """
+        if df is None or len(df) < lookback:
+            return {'support': [], 'resistance': []}
+
+        try:
+            recent = df.tail(lookback)
+            highs = recent['high'].values
+            lows = recent['low'].values
+
+            # 找局部高点和低点
+            from scipy.signal import argrelextrema
+            from numpy import array
+
+            high_indices = argrelextrema(array(highs), np.greater, order=5)
+            low_indices = argrelextrema(array(lows), np.less, order=5)
+
+            resistance_levels = sorted([highs[i] for i in high_indices], reverse=True)[:3]
+            support_levels = sorted([lows[i] for i in low_indices])[:3]
+
+            return {
+                'resistance': resistance_levels,
+                'support': support_levels
+            }
+        except Exception as e:
+            logger.warning(f"关键位识别失败: {e}")
+            return {'support': [], 'resistance': []}
+
+    @staticmethod
+    def calculate_stop_loss_take_profit(
+        entry_price: float,
+        atr: float,
+        direction: str,
+        key_levels: Dict[str, List[float]] = None
+    ) -> Dict[str, Any]:
+        """
+        计算止损止盈价格
+
+        Args:
+            entry_price: 入场价格
+            atr: ATR值
+            direction: 'long'/'short'
+            key_levels: 支撑阻力位
+
+        Returns:
+            {
+                'stop_loss': float,
+                'take_profit': float,
+                'method': str,
+                'risk_reward_ratio': float
+            }
+        """
+        if direction == 'long':
+            # 做多
+            # 止损：入场价 - 1.5×ATR，或设在前支撑位下方
+            sl_atr = entry_price - 1.5 * atr
+            sl_support = None
+
+            if key_levels and key_levels.get('support'):
+                closest_support = [s for s in key_levels['support'] if s < entry_price]
+                if closest_support:
+                    sl_support = min(closest_support) * 0.995  # 支撑位下方0.5%
+
+            # 选择更保守的止损（更远的）
+            if sl_support and sl_support < sl_atr:
+                stop_loss = sl_atr
+            else:
+                stop_loss = sl_atr
+
+            # 止盈：入场价 + 3×ATR（风险收益比1:2）
+            take_profit = entry_price + 3 * atr
+
+        else:
+            # 做空
+            # 止损：入场价 + 1.5×ATR，或设在前阻力位上方
+            sl_atr = entry_price + 1.5 * atr
+            sl_resistance = None
+
+            if key_levels and key_levels.get('resistance'):
+                closest_resistance = [r for r in key_levels['resistance'] if r > entry_price]
+                if closest_resistance:
+                    sl_resistance = min(closest_resistance) * 1.005  # 阻力位上方0.5%
+
+            # 选择更保守的止损
+            if sl_resistance and sl_resistance > sl_atr:
+                stop_loss = sl_atr
+            else:
+                stop_loss = sl_atr
+
+            # 止盈：入场价 - 3×ATR
+            take_profit = entry_price - 3 * atr
+
+        # 计算风险收益比
+        if direction == 'long':
+            risk = entry_price - stop_loss
+            reward = take_profit - entry_price
+        else:
+            risk = stop_loss - entry_price
+            reward = entry_price - take_profit
+
+        risk_reward_ratio = reward / risk if risk > 0 else 0
+
+        return {
+            'stop_loss': round(stop_loss, 2),
+            'take_profit': round(take_profit, 2),
+            'method': 'ATR-based (1:2 risk-reward)',
+            'risk_reward_ratio': round(risk_reward_ratio, 2)
+        }

backend/app/stock_agent/market_signal_analyzer.py

@@ -308,6 +308,45 @@ class StockMarketSignalAnalyzer:
 - 必须同时输出 `entry_price`（建议入场价）和 `entry_type`（入场方式）
 - 入场方式由你的市场分析判断，不是简单的价格距离计算
 
+## 止损止盈计算规则
+使用ATR（真实波动幅度）动态计算止损止盈：
+
+**做多**：
+- 止损 = 入场价 - 1.5 × ATR(14日)
+- 止盈 = 入场价 + 3 × ATR(14日) （风险收益比 1:2）
+- 如果附近有明显支撑位，可将止损设在支撑位下方
+
+**做空**：
+- 止损 = 入场价 + 1.5 × ATR(14日)
+- 止盈 = 入场价 - 3 × ATR(14日) （风险收益比 1:2）
+- 如果附近有明显阻力位，可将止损设在阻力位上方
+
+## 信号输出条件（严格遵守）
+**满足以下条件才输出信号（否则 signals 返回空数组）**：
+
+### 做多信号条件：
+1. ✅ 趋势：EMA20 > EMA50 > EMA200（或处于回调中的上升趋势）
+2. ✅ 价格：站稳 EMA20 之上或回调到 EMA20 附近
+3. ✅ 量价：放量上涨或缩量回调后重新放量
+4. ✅ 共振：多周期共振得分 >= 40（至少大周期一致）
+5. ✅ 基本面：评分 >= C级（40分以上）
+6. ✅ 新闻：无重大负面消息（财报暴雷、监管处罚等）
+
+### 做空信号条件：
+1. ✅ 趋势：EMA20 < EMA50 < EMA200（或处于反弹中的下降趋势）
+2. ✅ 价格：跌破 EMA20 或反弹到 EMA20 附近
+3. ✅ 量价：放量下跌或缩量反弹后继续下跌
+4. ✅ 共振：多周期共振得分 >= 40（至少大周期一致）
+5. ✅ 基本面：评分 >= C级（40分以上）
+6. ✅ 新闻：无重大正面消息
+
+### 禁止输出信号的情况：
+- ❌ 趋势不明确（EMA 交织，震荡市）
+- ❌ 多周期方向完全相反
+- ❌ 基本面差（D级，<40分）
+- ❌ 有重大负面新闻（做多时）或重大正面新闻（做空时）
+- ❌ 技术指标矛盾（如RSI超买但MACD死叉）
+
 ## 输出格式
 请严格按照以下 JSON 格式输出：
 
@@ -416,128 +455,89 @@ class StockMarketSignalAnalyzer:
                                 symbols: List[str] = None,
                                 fundamental_data: Dict[str, Any] = None,
                                 news_data: List[Dict[str, Any]] = None) -> str:
-        """准备市场上下文信息（技术面 + 基本面 + 新闻）"""
+        """准备市场上下文信息（结构化版本，减少token消耗）"""
+        from app.stock_agent.analysis_tools import StockAnalysisTools
+
         context_parts = []
 
-        # 当前价格和24h变化（使用日线数据）
+        # 当前价格和24h变化
         df_1d = data.get('1d')
         if df_1d is None or len(df_1d) == 0:
-            df_1h = data.get('1h')  # 备用：使用1h数据
+            return ""
-        if df_1d is None or len(df_1d) == 0:
-            return ""  # 没有数据就返回空
 
         current_price = float(df_1d.iloc[-1]['close'])
         price_change_24h = self._calculate_price_change_24h(df_1d)
-        context_parts.append(f"当前价格: ${current_price:,.2f} ({price_change_24h})")
+        context_parts.append(f"**当前价格**: ${current_price:,.2f} ({price_change_24h})")
 
-        # 多周期数据
+        # ===== 趋势分析（预计算） =====
-        for tf_name, df in data.items():
+        context_parts.append(f"\n**## 趋势分析**")
-            if df is None or len(df) == 0:
+        trend_info = StockAnalysisTools.detect_trend(df_1d)
-                continue
+        context_parts.append(f"- 方向: {trend_info['direction']} ({trend_info['strength']})")
+        context_parts.append(f"- EMA排列: {trend_info['ema_alignment']}")
+        context_parts.append(f"- 价格相对EMA20: {trend_info['price_vs_ema20']:+.2f}%")
 
-            latest = df.iloc[-1]
+        # 多周期共振（预计算）
-            context_parts.append(f"\n## {tf_name} 数据")
+        context_parts.append(f"\n**## 多周期共振**")
-            context_parts.append(f"开: {latest['open']}, 高: {latest['high']}, 低: {latest['low']}, 收: {latest['close']}")
+        resonance = StockAnalysisTools.calculate_multi_timeframe_resonance(data)
-            context_parts.append(f"成交量: {latest.get('volume', 'N/A')}")
+        context_parts.append(f"- 共振等级: {resonance['level'].upper()} (得分: {resonance['score']}/100)")
+        context_parts.append(f"- 共振分析: {resonance['analysis']}")
+        context_parts.append(f"- 周线: {resonance['weekly_trend']} | 日线: {resonance['daily_trend']} | 1h: {resonance['hourly_trend']}")
 
-            # 技术指标
+        # ===== 技术指标（只显示日线关键指标） =====
-            if 'rsi' in df.columns:
+        context_parts.append(f"\n**## 技术指标（日线）**")
-                rsi = df['rsi'].iloc[-1]
-                context_parts.append(f"RSI: {rsi:.2f}")
-            if 'macd' in df.columns:
-                macd = df['macd'].iloc[-1]
-                signal = df['macd_signal'].iloc[-1]
-                context_parts.append(f"MACD: {macd:.4f}, 信号线: {signal:.4f}")
-            if 'bb_upper' in df.columns:
-                bb_upper = df['bb_upper'].iloc[-1]
-                bb_lower = df['bb_lower'].iloc[-1]
-                context_parts.append(f"布林带: 上轨 {bb_upper:.2f}, 下轨 {bb_lower:.2f}")
-
-        # 均线系统（使用日线数据）
-        context_parts.append(f"\n## 均线系统")
-        df_1d = data.get('1d')
-        if df_1d is not None and len(df_1d) > 0:
         latest = df_1d.iloc[-1]
-            context_parts.append(f"MA5: {latest.get('ma5', 'N/A')}")
-            context_parts.append(f"MA10: {latest.get('ma10', 'N/A')}")
-            context_parts.append(f"MA20: {latest.get('ma20', 'N/A')}")
-            context_parts.append(f"MA50: {latest.get('ma50', 'N/A')}")
 
-            # EMA 均线（用于趋势判断）
+        # RSI
-            ema20 = latest.get('ema20', None)
+        if 'rsi' in df_1d.columns:
-            ema50 = latest.get('ema50', None)
+            rsi = df_1d['rsi'].iloc[-1]
-            ema200 = latest.get('ema200', None)
+            context_parts.append(f"- RSI(14): {rsi:.1f} {'(超买⚠️)' if rsi > 70 else '(超卖💡)' if rsi < 30 else '(正常)'}")
-            if ema20 is not None:
-                context_parts.append(f"EMA20: {ema20:.2f}")
-            if ema50 is not None:
-                context_parts.append(f"EMA50: {ema50:.2f}")
-            if ema200 is not None:
-                context_parts.append(f"EMA200: {ema200:.2f}")
 
-            # 判断 MA 排列
+        # MACD
-            ma5 = latest.get('ma5', 0)
+        if 'macd' in df_1d.columns:
-            ma10 = latest.get('ma10', 0)
+            macd = df_1d['macd'].iloc[-1]
-            ma20 = latest.get('ma20', 0)
+            signal = df_1d['macd_signal'].iloc[-1]
-            ma50 = latest.get('ma50', 0)
+            context_parts.append(f"- MACD: {macd:.4f} (信号: {signal:.4f})")
 
-            if all([ma5, ma10, ma20, ma50]):
+        # 布林带
-                if ma5 > ma10 > ma20 > ma50:
+        if 'bb_upper' in df_1d.columns:
-                    context_parts.append("MA排列: 多头排列 📈")
+            bb_upper = df_1d['bb_upper'].iloc[-1]
-                elif ma5 < ma10 < ma20 < ma50:
+            bb_lower = df_1d['bb_lower'].iloc[-1]
-                    context_parts.append("MA排列: 空头排列 📉")
+            bb_position = (current_price - bb_lower) / (bb_upper - bb_lower) * 100 if bb_upper != bb_lower else 50
+            context_parts.append(f"- 布林带: [{bb_lower:.2f}, {bb_upper:.2f}] (位置: {bb_position:.0f}%)")
+
+        # 量价分析
+        volume_ratio = StockAnalysisTools.calculate_volume_ratio(df_1d, 20)
+        context_parts.append(f"- 量比: {volume_ratio:.2f}x {'放量📊' if volume_ratio > 1.5 else '缩量📉' if volume_ratio < 0.7 else '平量'}")
+
+        # ATR（用于止损止盈）
+        atr = StockAnalysisTools.calculate_atr(df_1d, 14)
+        atr_pct = (atr / current_price * 100) if current_price > 0 else 0
+        context_parts.append(f"- ATR(14): ${atr:.2f} ({atr_pct:.2f}%)")
+
+        # ===== 关键支撑阻力位 =====
+        context_parts.append(f"\n**## 关键位**")
+        key_levels = StockAnalysisTools.identify_key_levels(df_1d, lookback=60)
+        if key_levels['resistance']:
+            context_parts.append(f"- 阻力位: ${', '.join(f'{r:.2f}' for r in key_levels['resistance'][:3])}")
         else:
-                    context_parts.append("MA排列: 交织，方向不明")
+            context_parts.append(f"- 阻力位: 未明确")
-
+        if key_levels['support']:
-            # EMA 排列（更重要的趋势判断）
+            context_parts.append(f"- 支撑位: ${', '.join(f'{s:.2f}' for s in key_levels['support'][:3])}")
-            if all([ema20, ema50, ema200]):
-                if ema20 > ema50 > ema200:
-                    context_parts.append("EMA排列: 多头排列 (长期趋势向上) 📈")
-                elif ema20 < ema50 < ema200:
-                    context_parts.append("EMA排列: 空头排列 (长期趋势向下) 📉")
         else:
-                    context_parts.append("EMA排列: 交织 (震荡市) ➡️")
+            context_parts.append(f"- 支撑位: 未明确")
 
-            # 价格与 EMA20 的关系（趋势判断关键）
+        # ===== 基本面分析（评分） =====
-            if ema20 is not None:
-                if latest['close'] > ema20:
-                    context_parts.append("价格位置: 站稳 EMA20 之上 (多头强势)")
-                elif latest['close'] < ema20:
-                    context_parts.append("价格位置: 跌破 EMA20 (空头强势)")
-                else:
-                    context_parts.append("价格位置: 接近 EMA20")
-
-        # 量比分析（使用日线数据）
-        df_1d = data.get('1d')
-        if df_1d is not None and len(df_1d) >= 20:
-            vol_latest = df_1d['volume'].iloc[-1]
-            vol_ma20 = df_1d['volume'].iloc[-20:-1].mean()
-            volume_ratio = vol_latest / vol_ma20 if vol_ma20 > 0 else 1
-            context_parts.append(f"\n## 量价分析")
-            context_parts.append(f"最新成交量: {vol_latest:.0f}")
-            context_parts.append(f"20周期均量: {vol_ma20:.0f}")
-            context_parts.append(f"量比: {volume_ratio:.2f}")
-
-            if volume_ratio > 1.5:
-                context_parts.append("量价状态: 放量 📊")
-            elif volume_ratio < 0.7:
-                context_parts.append("量价状态: 缩量 📉")
-            else:
-                context_parts.append("量价状态: 平量 ➖")
-
-        # 波动率分析
-        volatility_analysis = self._analyze_volatility(data)
-        if volatility_analysis:
-            context_parts.append(f"\n## 波动率分析")
-            context_parts.append(volatility_analysis)
-
-        # 基本面分析
         if fundamental_data:
-            context_parts.append(f"\n## 基本面分析")
+            context_parts.append(f"\n**## 基本面分析**")
-            context_parts.append(self._format_fundamental_data(fundamental_data))
+            fund_score = StockAnalysisTools.calculate_fundamental_score(fundamental_data)
+            context_parts.append(f"- 综合评分: {fund_score['grade']}级 ({fund_score['score']}/100)")
+            context_parts.append(f"- 估值: {fund_score['breakdown']['valuation']['grade']}级 | 盈利: {fund_score['breakdown']['profitability']['grade']}级")
+            context_parts.append(f"- 成长: {fund_score['breakdown']['growth']['grade']}级 | 财务: {fund_score['breakdown']['financial_health']['grade']}级")
+            context_parts.append(f"- 摘要: {fund_score['summary']}")
 
-        # 新闻舆情
+        # ===== 新闻舆情 =====
         if news_data:
-            context_parts.append(f"\n## 最新新闻")
+            context_parts.append(f"\n**## 最新新闻**")
             context_parts.append(self._format_news_data(news_data))
 
         return "\n".join(context_parts)