From 5cfe9eae8399cb27f870f1e1e184de9afb86e84f Mon Sep 17 00:00:00 2001 From: aaron <> Date: Tue, 3 Mar 2026 19:35:18 +0800 Subject: [PATCH] update --- backend/app/stock_agent/analysis_tools.py | 569 ++++++++++++++++++ .../app/stock_agent/market_signal_analyzer.py | 204 +++---- 2 files changed, 671 insertions(+), 102 deletions(-) create mode 100644 backend/app/stock_agent/analysis_tools.py diff --git a/backend/app/stock_agent/analysis_tools.py b/backend/app/stock_agent/analysis_tools.py new file mode 100644 index 0000000..b125dbc --- /dev/null +++ b/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) + } diff --git a/backend/app/stock_agent/market_signal_analyzer.py b/backend/app/stock_agent/market_signal_analyzer.py index a06357e..f23ff9f 100644 --- a/backend/app/stock_agent/market_signal_analyzer.py +++ b/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 - if df_1d is None or len(df_1d) == 0: - return "" # 没有数据就返回空 + return "" 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 - for tf_name, df in data.items(): - if df is None or len(df) == 0: - continue + # ===== 趋势分析（预计算） ===== + context_parts.append(f"\n**## 趋势分析**") + trend_info = StockAnalysisTools.detect_trend(df_1d) + 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] - context_parts.append(f"\n## {tf_name} 数据") - context_parts.append(f"开: {latest['open']}, 高: {latest['high']}, 低: {latest['low']}, 收: {latest['close']}") - context_parts.append(f"成交量: {latest.get('volume', 'N/A')}") + # 多周期共振（预计算） + context_parts.append(f"\n**## 多周期共振**") + resonance = StockAnalysisTools.calculate_multi_timeframe_resonance(data) + 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 - if 'rsi' in df.columns: - 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}") + # ===== 技术指标（只显示日线关键指标） ===== + context_parts.append(f"\n**## 技术指标（日线）**") + latest = df_1d.iloc[-1] - # 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')}") + # RSI + if 'rsi' in df_1d.columns: + rsi = df_1d['rsi'].iloc[-1] + context_parts.append(f"- RSI(14): {rsi:.1f} {'(超买⚠️)' if rsi > 70 else '(超卖💡)' if rsi < 30 else '(正常)'}") - # EMA 均线（用于趋势判断） - ema20 = latest.get('ema20', None) - ema50 = latest.get('ema50', None) - ema200 = latest.get('ema200', None) - 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}") + # MACD + if 'macd' in df_1d.columns: + macd = df_1d['macd'].iloc[-1] + signal = df_1d['macd_signal'].iloc[-1] + context_parts.append(f"- MACD: {macd:.4f} (信号: {signal:.4f})") - # 判断 MA 排列 - ma5 = latest.get('ma5', 0) - ma10 = latest.get('ma10', 0) - ma20 = latest.get('ma20', 0) - ma50 = latest.get('ma50', 0) + # 布林带 + if 'bb_upper' in df_1d.columns: + bb_upper = df_1d['bb_upper'].iloc[-1] + bb_lower = df_1d['bb_lower'].iloc[-1] + 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}%)") - if all([ma5, ma10, ma20, ma50]): - if ma5 > ma10 > ma20 > ma50: - context_parts.append("MA排列: 多头排列 📈") - elif ma5 < ma10 < ma20 < ma50: - context_parts.append("MA排列: 空头排列 📉") - else: - context_parts.append("MA排列: 交织，方向不明") + # 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'}") - # EMA 排列（更重要的趋势判断） - 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) ➡️") + # 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}%)") - # 价格与 EMA20 的关系（趋势判断关键） - 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") + # ===== 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(f"- 阻力位: 未明确") + if key_levels['support']: + context_parts.append(f"- 支撑位: ${', '.join(f'{s:.2f}' for s in key_levels['support'][:3])}") + else: + context_parts.append(f"- 支撑位: 未明确") - # 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 + # ===== 基本面分析（评分） ===== if fundamental_data: - context_parts.append(f"\n## 基本面分析") - context_parts.append(self._format_fundamental_data(fundamental_data)) + context_parts.append(f"\n**## 基本面分析**") + 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)