feat: 增加专业分析内容 - 市场情绪/资金流向/板块强弱/投资建议

board_monitor.py

@@ -92,6 +92,127 @@ def get_board_data(board_type: str, sort_by: str = "f3", limit: int = 100) -> Op
         return None
 
 
+def analyze_market(all_industry: List, all_concept: List) -> Dict:
+    """
+    专业市场分析
+    
+    返回各种分析指标
+    """
+    analysis = {}
+    
+    # 1. 市场整体统计
+    if all_industry:
+        up_count = len([b for b in all_industry if b['pct_change'] > 0])
+        down_count = len([b for b in all_industry if b['pct_change'] < 0])
+        flat_count = len([b for b in all_industry if b['pct_change'] == 0])
+        
+        avg_pct = sum(b['pct_change'] for b in all_industry) / len(all_industry)
+        max_pct = max(b['pct_change'] for b in all_industry)
+        min_pct = min(b['pct_change'] for b in all_industry)
+        
+        total_flow = sum(b['main_flow'] for b in all_industry)
+        
+        analysis['market_stats'] = {
+            'up_count': up_count,
+            'down_count': down_count,
+            'flat_count': flat_count,
+            'up_ratio': up_count / len(all_industry) * 100,
+            'avg_pct': avg_pct,
+            'max_pct': max_pct,
+            'min_pct': min_pct,
+            'total_flow': total_flow,
+        }
+    
+    # 2. 资金集中度分析
+    if all_industry:
+        sorted_by_flow = sorted(all_industry, key=lambda x: x['main_flow'], reverse=True)
+        top5_flow = sum(b['main_flow'] for b in sorted_by_flow[:5])
+        analysis['fund_concentration'] = {
+            'top5_flow': top5_flow,
+            'top5_ratio': abs(top5_flow) / abs(analysis['market_stats']['total_flow']) * 100 if analysis['market_stats']['total_flow'] != 0 else 0,
+        }
+    
+    # 3. 板块强弱分析
+    if all_industry:
+        strong_boards = [b for b in all_industry if b['pct_change'] > 1 and b['main_flow'] > 5]
+        weak_boards = [b for b in all_industry if b['pct_change'] < -1 and b['main_flow'] < -5]
+        analysis['strength'] = {
+            'strong_count': len(strong_boards),
+            'weak_count': len(weak_boards),
+            'strong_boards': strong_boards[:5],
+            'weak_boards': weak_boards[:5],
+        }
+    
+    # 4. 概念板块热度分析
+    if all_concept:
+        hot_concepts = sorted([b for b in all_concept if b['main_flow'] > 0], 
+                              key=lambda x: x['main_flow'], reverse=True)[:5]
+        cold_concepts = sorted([b for b in all_concept if b['main_flow'] < 0], 
+                               key=lambda x: x['main_flow'])[:5]
+        analysis['concept_heat'] = {
+            'hot': hot_concepts,
+            'cold': cold_concepts,
+        }
+    
+    # 5. 市场情绪判断
+    if analysis.get('market_stats'):
+        stats = analysis['market_stats']
+        
+        # 综合判断
+        sentiment_score = 0
+        
+        # 涨跌比例贡献
+        if stats['up_ratio'] > 70:
+            sentiment_score += 2
+        elif stats['up_ratio'] > 50:
+            sentiment_score += 1
+        elif stats['up_ratio'] < 30:
+            sentiment_score -= 2
+        elif stats['up_ratio'] < 50:
+            sentiment_score -= 1
+        
+        # 平均涨跌幅贡献
+        if stats['avg_pct'] > 0.5:
+            sentiment_score += 1
+        elif stats['avg_pct'] < -0.5:
+            sentiment_score -= 1
+        
+        # 资金流向贡献
+        if stats['total_flow'] > 50:
+            sentiment_score += 2
+        elif stats['total_flow'] > 0:
+            sentiment_score += 1
+        elif stats['total_flow'] < -50:
+            sentiment_score -= 2
+        elif stats['total_flow'] < 0:
+            sentiment_score -= 1
+        
+        # 情绪等级
+        if sentiment_score >= 4:
+            sentiment = '强势上涨'
+            sentiment_desc = '市场情绪高涨，多数板块上涨，资金大幅流入，建议关注强势板块机会。'
+        elif sentiment_score >= 2:
+            sentiment = '偏强'
+            sentiment_desc = '市场整体偏强，资金流向积极，可适度参与热门板块。'
+        elif sentiment_score >= 0:
+            sentiment = '平稳'
+            sentiment_desc = '市场情绪平稳，涨跌均衡，建议观望或轻仓布局。'
+        elif sentiment_score >= -2:
+            sentiment = '偏弱'
+            sentiment_desc = '市场整体偏弱，资金流出明显，建议谨慎操作，关注防御性板块。'
+        else:
+            sentiment = '弱势下跌'
+            sentiment_desc = '市场情绪低迷，多数板块下跌，资金大幅流出，建议规避风险，等待企稳信号。'
+        
+        analysis['sentiment'] = {
+            'score': sentiment_score,
+            'level': sentiment,
+            'description': sentiment_desc,
+        }
+    
+    return analysis
+
+
 def generate_daily_report(boards_data: Dict, to_email: str = "wlq@tphai.com") -> bool:
     """
     生成盘后分析报告并发送邮件
@@ -113,90 +234,176 @@ def generate_daily_report(boards_data: Dict, to_email: str = "wlq@tphai.com") ->
     # 分析总结
     timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     
-    # 计算市场趋势
-    avg_pct = 0
-    if all_industry:
-        avg_pct = sum(b['pct_change'] for b in all_industry) / len(all_industry)
-    
-    market_trend = '平稳'
-    if avg_pct > 0.5:
-        market_trend = '上涨'
-    elif avg_pct < -0.5:
-        market_trend = '下跌'
-    
     # 排序数据
     industry_by_pct = sorted(all_industry, key=lambda x: x['pct_change'], reverse=True)
     industry_by_flow = sorted(all_industry, key=lambda x: x['main_flow'], reverse=True)
     concept_by_pct = sorted(all_concept, key=lambda x: x['pct_change'], reverse=True)
     concept_by_flow = sorted(all_concept, key=lambda x: x['main_flow'], reverse=True)
     
-    # 生成HTML正文（分析总结）
+    # 执行专业分析
+    analysis = analyze_market(all_industry, all_concept)
+    
+    # ========== 生成HTML正文 ==========
     html_lines = [
         "<h2>📊 A股板块盘后分析报告</h2>",
         f"<p>报告时间: {timestamp}</p>",
-        f"<p>市场整体: <strong>{market_trend}</strong> (行业平均涨跌 {avg_pct:+.2f}%)</p>",
         "",
         "<hr>",
-        "",
-        "<h3>🔥 今日热门概念板块 TOP5</h3>",
-        "<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>",
-        "<tr style='background:#f0f0f0'><th>板块</th><th>涨跌幅</th><th>主力资金</th></tr>",
     ]
     
-    for board in concept_by_pct[:5]:
+    # ===== 一、市场情绪分析 =====
-        pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
+    if analysis.get('sentiment'):
-        flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
+        sent = analysis['sentiment']
-        html_lines.append(f"<tr><td>{board['name']}</td><td>{pct_str}</td><td>{flow_str}</td></tr>")
+        stats = analysis.get('market_stats', {})
+        
+        html_lines.append("")
+        html_lines.append("<h3>一、市场情绪分析</h3>")
+        html_lines.append(f"<p><strong>市场评级: {sent['level']}</strong></p>")
+        html_lines.append(f"<p>{sent['description']}</p>")
+        
+        if stats:
+            html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+            html_lines.append("<tr><td>上涨板块</td><td>{}</td><td>占比 {:.1f}%</td></tr>".format(stats['up_count'], stats['up_ratio']))
+            html_lines.append("<tr><td>下跌板块</td><td>{}</td><td>占比 {:.1f}%</td></tr>".format(stats['down_count'], 100 - stats['up_ratio']))
+            html_lines.append("<tr><td>平均涨跌</td><td>{:.2f}%</td><td>最大涨幅 {:.2f}%</td></tr>".format(stats['avg_pct'], stats['max_pct']))
+            html_lines.append("<tr><td>资金净流</td><td>{:.2f}亿</td><td>最大跌幅 {:.2f}%</td></tr>".format(stats['total_flow'], stats['min_pct']))
+            html_lines.append("</table>")
     
-    html_lines.append("</table>")
+    # ===== 二、资金流向分析 =====
+    if analysis.get('fund_concentration'):
+        fund = analysis['fund_concentration']
+        
+        html_lines.append("")
+        html_lines.append("<h3>二、资金流向分析</h3>")
+        html_lines.append(f"<p>TOP5板块资金合计: <strong>{fund['top5_flow']:.2f}亿</strong></p>")
+        html_lines.append(f"<p>资金集中度: <strong>{fund['top5_ratio']:.1f}%</strong>（流入资金集中在少数板块）</p>")
+    
+    # ===== 三、板块强弱分析 =====
+    if analysis.get('strength'):
+        strength = analysis['strength']
+        
+        html_lines.append("")
+        html_lines.append("<h3>三、板块强弱分析</h3>")
+        html_lines.append(f"<p>强势板块（涨幅>1%且资金流入>5亿）: <strong>{strength['strong_count']} 个</strong></p>")
+        html_lines.append(f"<p>弱势板块（跌幅>1%且资金流出>5亿）: <strong>{strength['weak_count']} 个</strong></p>")
+        
+        if strength['strong_boards']:
+            html_lines.append("<p>强势板块示例:</p>")
+            html_lines.append("<ul>")
+            for b in strength['strong_boards'][:3]:
+                html_lines.append("<li>{name}: +{pct:.2f}%, 资金+{flow:.2f}亿</li>".format(
+                    name=b['name'], pct=b['pct_change'], flow=b['main_flow']))
+            html_lines.append("</ul>")
+        
+        if strength['weak_boards']:
+            html_lines.append("<p>弱势板块示例:</p>")
+            html_lines.append("<ul>")
+            for b in strength['weak_boards'][:3]:
+                html_lines.append("<li>{name}: {pct:.2f}%, 资金{flow:.2f}亿</li>".format(
+                    name=b['name'], pct=b['pct_change'], flow=b['main_flow']))
+            html_lines.append("</ul>")
+    
+    # ===== 四、热门概念分析 =====
+    if analysis.get('concept_heat'):
+        heat = analysis['concept_heat']
+        
+        html_lines.append("")
+        html_lines.append("<h3>四、概念板块热度</h3>")
+        
+        if heat['hot']:
+            html_lines.append("<p>🔥 热门概念（资金流入TOP）:</p>")
+            html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+            html_lines.append("<tr style='background:#fff0f0'><th>概念</th><th>资金(亿)</th><th>涨跌</th></tr>")
+            for b in heat['hot'][:5]:
+                pct_str = f"+{b['pct_change']:.2f}%" if b['pct_change'] > 0 else f"{b['pct_change']:.2f}%"
+                html_lines.append(f"<tr><td>{b['name']}</td><td>+{b['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
+            html_lines.append("</table>")
+        
+        if heat['cold']:
+            html_lines.append("<p>❄️ 冷门概念（资金流出TOP）:</p>")
+            html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
+            html_lines.append("<tr style='background:#f0f0ff'><th>概念</th><th>资金(亿)</th><th>涨跌</th></tr>")
+            for b in heat['cold'][:5]:
+                pct_str = f"+{b['pct_change']:.2f}%" if b['pct_change'] > 0 else f"{b['pct_change']:.2f}%"
+                html_lines.append(f"<tr><td>{b['name']}</td><td>{b['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
+            html_lines.append("</table>")
+    
+    # ===== 五、行业板块排行 =====
     html_lines.append("")
+    html_lines.append("<hr>")
+    html_lines.append("")
+    html_lines.append("<h3>五、行业板块涨跌排行</h3>")
     
-    html_lines.append("<h3>📈 行业板块涨幅 TOP5</h3>")
+    html_lines.append("<p>📈 涨幅 TOP5:</p>")
     html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
     html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>涨跌幅</th><th>主力资金</th><th>领涨股</th></tr>")
-    
     for board in industry_by_pct[:5]:
         pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
         flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
         html_lines.append(f"<tr><td>{board['name']}</td><td>{pct_str}</td><td>{flow_str}</td><td>{board['leader_name'] or '-'}</td></tr>")
-    
     html_lines.append("</table>")
-    html_lines.append("")
     
-    html_lines.append("<h3>📉 行业板块跌幅 TOP5</h3>")
+    html_lines.append("<p>📉 跌幅 TOP5:</p>")
     html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
     html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>涨跌幅</th><th>主力资金</th></tr>")
-    
     for board in industry_by_pct[-5:]:
         pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
         flow_str = f"+{board['main_flow']:.2f}亿" if board['main_flow'] > 0 else f"{board['main_flow']:.2f}亿"
         html_lines.append(f"<tr><td>{board['name']}</td><td>{pct_str}</td><td>{flow_str}</td></tr>")
-    
     html_lines.append("</table>")
+    
+    # ===== 六、主力资金排行 =====
     html_lines.append("")
+    html_lines.append("<h3>六、主力资金流向排行</h3>")
     
-    html_lines.append("<h3>💰 主力资金大幅流入 TOP10</h3>")
+    html_lines.append("<p>💰 大幅流入 TOP10 (≥10亿):</p>")
     html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
-    html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金流入(亿)</th><th>涨跌幅</th></tr>")
+    html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金(亿)</th><th>涨跌</th></tr>")
-    
     inflow_boards = [b for b in industry_by_flow if b['main_flow'] > 10][:10]
     for board in inflow_boards:
         pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
         html_lines.append(f"<tr><td>{board['name']}</td><td>+{board['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
-    
     html_lines.append("</table>")
-    html_lines.append("")
     
-    html_lines.append("<h3>💸 主力资金大幅流出 TOP10</h3>")
+    html_lines.append("<p>💸 大幅流出 TOP10 (≤-10亿):</p>")
     html_lines.append("<table border='1' cellpadding='6' cellspacing='0' style='border-collapse: collapse;'>")
-    html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金流出(亿)</th><th>涨跌幅</th></tr>")
+    html_lines.append("<tr style='background:#f0f0f0'><th>板块</th><th>资金(亿)</th><th>涨跌</th></tr>")
-    
     outflow_boards = [b for b in industry_by_flow if b['main_flow'] < -10][:10]
     for board in outflow_boards:
         pct_str = f"+{board['pct_change']:.2f}%" if board['pct_change'] > 0 else f"{board['pct_change']:.2f}%"
         html_lines.append(f"<tr><td>{board['name']}</td><td>{board['main_flow']:.2f}</td><td>{pct_str}</td></tr>")
-    
     html_lines.append("</table>")
+    
+    # ===== 七、投资建议 =====
+    html_lines.append("")
+    html_lines.append("<hr>")
+    html_lines.append("<h3>七、投资建议</h3>")
+    
+    if analysis.get('sentiment'):
+        sent = analysis['sentiment']
+        
+        if sent['score'] >= 2:
+            html_lines.append("<p><strong>策略建议: 积极参与</strong></p>")
+            html_lines.append("<ul>")
+            html_lines.append("<li>关注资金大幅流入的热门板块，如新能源、科技类</li>")
+            html_lines.append("<li>可适当追涨强势板块龙头股</li>")
+            html_lines.append("<li>注意板块轮动节奏，避免追高</li>")
+            html_lines.append("</ul>")
+        elif sent['score'] >= 0:
+            html_lines.append("<p><strong>策略建议: 观望为主</strong></p>")
+            html_lines.append("<ul>")
+            html_lines.append("<li>等待明确的市场方向信号</li>")
+            html_lines.append("<li>可轻仓布局有资金流入的潜力板块</li>")
+            html_lines.append("<li>规避资金流出明显的板块</li>")
+            html_lines.append("</ul>")
+        else:
+            html_lines.append("<p><strong>策略建议: 谨慎防守</strong></p>")
+            html_lines.append("<ul>")
+            html_lines.append("<li>控制仓位，规避风险板块</li>")
+            html_lines.append("<li>关注防御性板块如银行、医药等</li>")
+            html_lines.append("<li>等待市场企稳后再介入</li>")
+            html_lines.append("</ul>")
+    
     html_lines.append("")
     html_lines.append("<hr>")
     html_lines.append("<p><em>📊 详细数据请查看附件 CSV 文件</em></p>")

data/board_detail_20260410.csv

@@ -1,5 +1,5 @@
 # A股板块详细数据
-# 生成时间: 2026-04-10 17:19:21
+# 生成时间: 2026-04-10 17:42:51
 
 === 行业板块涨跌幅排行 ===
 板块名称,涨跌幅(%),主力资金(亿),领涨股